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The Latest Data Engineering Topics

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A cluster management framework, Apache Helix
What is Helix? It is used for the automatic management of partitioned, replicated and distributed resources hosted on a cluster of nodes. Helix automates reassignment of resources in the face of node failure and recovery, cluster expansion, and reconfiguration. Modeling a distributed system as a state machine with constraints on states and transitions. Terminologies Node : A single machine Cluster: Set of Nodes Resource : A logical entry (e.g. database, index, task) Partition: Subset of the resource (Each subtask is referred to as a partition) Replica: Copy of a Partition State (e.g Master, Slave). It increase the availability of the system State: Describes the role of a replica (Each node in the cluster has its own Current State) State Machine and Transitions: An action that allows a replica to move from one state to another, thus changing its role. ( e.g Slave --> Master ) spectators: the external clients. Helix provides an External View that is an aggregated view of the current state across all nodes. Current State: represents resource's actual state at a participating node. - INSTANCE_NAME: Unique name representing the process - SESSION_ID: ID that is automatically assigned every time a process joins the cluster Rebalancer: The core component of Helix is the Controller which runs the Rebalance algorithm on every cluster event. Dynamic Ideal State: Helix powerful is that Ideal State can be changed dynamically. It is adjusting the ideal state. Whenever a cluster event occurs, Helix can operate in one of three modes FULL_AUTO SEMI_AUTO CUSTOMIZED Cluster events can be one of the following: Nodes start and/or stop Nodes experience soft and/or hard failures New nodes are added/removed [1] http://helix.apache.org/Concepts.html
April 13, 2015
by Madhuka Udantha
· 7,876 Views
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Adopting Microservices at Netflix: Lessons for Team and Process Design
[this article was written by tony mauro .] in a previous blog post , we shared best practices for designing a microservices architecture, based on adrian cockcroft’s presentation at nginx.conf2014 about his experience as director of web engineering and then cloud architect at netflix . in this follow-up post, we’ll review his recommendations for retooling your development team and processes for a smooth transition to microservices. optimize for speed, not efficiency source: [email protected] the top lesson that cockcroft learned at netflix is that speed wins in the marketplace. if you ask any developer whether a slower development process is better, no one ever says yes. nor do management or customers ever complain that your development cycle is too fast for them. the need for speed doesn’t just apply to tech companies, either: as software becomes increasingly ubiquitous on the internet of things – in cars, appliances, and sensors as well as mobile devices – companies that didn’t used to do software development at all now find that their success depends on being good at it. netflix made an early decision to optimize for speed. this refers specifically to tooling your software development process so that you can react quickly to what your customers want, or even better, can create innovative web experiences that attract customers. speed means learning about your customers and giving them what they want at a faster pace than your competitors. by the time competitors are ready to challenge you in a specific way, you’ve moved on to the next set of improvements. this approach turns the usual paradigm of optimizing for efficiency on its head. efficiency generally means trying to control the overall flow of the development process to eliminate duplication of effort and avoid mistakes, with an eye to keeping costs down. the common result is that you end up focusing on savings instead of looking for opportunities that increase revenue. in cockcroft’s experience, if you say “i’m doing this because it’s more efficient,” the unintended result is that you’re slowing someone else down. this is not an encouragement to be wasteful, but you should optimize for speed first. efficiency becomes secondary as you satisfy the constraint that you’re not slowing things down. the way you grow the business to be more efficient is to go faster. make sure your assumptions are still true many large companies that have enjoyed success in their market (we can call them incumbents ) are finding themselves overtaken by nimbler, usually smaller, organizations ( disruptors ) that react much more quickly to changing consumer behavior. their large size isn’t necessarily the root of the problem – netflix is no longer a small company, for example. as cockcroft sees it, the main cause of difficulty for industry incumbents is that they’re operating under business assumptions that are no longer true. or, as will rogers put it, it’s not what we don’t know that hurts. it’s what we know that ain’t so.” of course, you have to make assumptions as you formulate a business model, and then it makes sense to optimize your business practices around them. the danger comes from sticking with assumptions after they’re no longer true, which means you’re optimizing on the wrong thing. that’s when you become vulnerable to industry disruptors who are making the right assumptions and optimizations for the current business climate. as examples, consider the following assumptions that hold sway at many incumbents. we’ll examine them further in the indicated sections and describe the approach netflix adopted. computing power is expensive. this was true when increasing your computing capacity required capital expenditure on computer hardware. see put your infrastructure in the cloud . process prevents problems. at many companies, the standard response to something going wrong is to add a preventative step to the relevant procedure. see create a high freedom, high responsibility culture with less process . here are some ways to avoid holding onto assumptions that have passed their expiration date: as obvious as it might seem, you need to make your assumptions explicit, then periodically review them to make sure they still hold true. keep aware of technological trends. as an example, the cost of solid state storage drive (ssds) storage continues to go down. it’s still more expensive than regular disks, but the cost difference is becoming small enough that many companies are deciding the superior performance is worth paying a bit more for. [ed: in this entertaining video , fastly founder and ceo artur bergman explains why he believes ssds are always the right choice.] talk to people who aren’t your customers. this is especially necessary for incumbents, who need to make sure that potential new customers are interested in their product. otherwise, they don’t hear about the fact that they’re not being used. as an example, some vendors in the storage space are building hyper-converged systems even as more and more companies are storing their data in the cloud and using open source storage management software. netflix, for example, stores data on amazon web services (aws) servers with ssds and manages it with apache cassandra . a single specialist in java distributed systems is managing the entire configuration without any commercial storage tools or help from engineers specializing in storage, san, or backup. don’t base your future strategy on current it spending, but instead on level of adoption by developers. suppose that your company accounts for nearly all spending in the market for proprietary virtualization software, but then a competitor starts offering an open source-based product at only 1% the cost of yours. if people start choosing it instead of your product, than at the point that your share of total spending is still 90%, your market share has declined to only 10%. if you’re only attending to your revenue, it seems like you’re still in good shape, but 10% of market share can collapse really quickly. put your infrastructure in the cloud source: [email protected] in make sure your assumptions are still true , we mentioned that in the past it was valid to base your business plan on the assumption that computing power was expensive, because it was: the only way to increase your computing capacity was to buy computer hardware. you could then make money by using this expensive resource in the right way to solve customer problems. the advent of cloud computing has pretty much completely invalidated this assumption. it is now possible to buy the amount of capacity you need when you need it, and to pay for only the time you actually use it. the new assumption you need to make is that (virtual) machines are ephemeral. you can create and destroy them at the touch of a button or a call to an api, without any need to negotiate with other departments in your company. one way to think of this change is that the self-service cloud makes formerly impossible things instantaneous. all of netflix’s engineers are in california, but they manage a worldwide infrastructure. the cloud enables them to experiment and determine whether (for example) adding servers in particular location improves performance. suppose they notice problems with video delivery in brazil. they can easily set up 100 cloud server instances in são paulo within a couple hours. if after a week they determine that the difference in delivery speed and reliability isn’t large enought to justify the cost of the additional server instances, they can shut them down just as quickly and easily as they created them. this kind of experiment would be so expensive with a traditional infrastructure that you would never attempt it. you would have to hire an agent in são paulo to coordinate the project, find a data center, satisfy brazilian government regulations, ship machines to brazil, and so on. it would be six months before you could even run the test and find out that increased local capacity didn’t improve your delivery speed. create a high freedom, high responsibility culture with less process in make sure your assumptions are still true , we observed that many companies create rules and processes to prevent problems. when someone makes a mistake, they add a rule to the hr manual that says “well, don’t do that again.” if you read some hr manuals from this perspective, you can extract a historical record of everything that went wrong at the company. when something goes wrong in the development process, the corresponding reaction is to add a new step to the procedure. the major problem with creating process to prevent problems is that over time you build up complex “scar tissue” processes that slow you down. netflix doesn’t have an hr manual. there is a single guideline: “act in netflix’s best interest.” the idea is that if an employee can’t figure out how to interpret the guideline in a given situation, he or she doesn’t have enough judgment to work there. if you don’t trust the judgment of the people on your team, you have to ask why you’re employing them. it’s true that you’ll have to fire people occasionally for violating the guideline. overall, the high level of mutual trust among members of a team, and across the company as a whole, becomes a strong binding force. the following books outline new ways of thinking about process if you’re looking to transform your organization: the goal: a process of ongoing improvement by eliyahu m. goldratt and jeff cox. this book has become a standard management text at business schools since its original publication in 1984. written as a novel about a manager who has only 90 days to improve performance at his factory or have it closed down, it embodies goldratt’s theory of constraints in the context of process control and automation. the phoenix project: a novel about it, devops, and helping your business win by gene kim and kevin behr. as the title indicates, it’s also a novel, about an it manager who has 90 days to save a project that’s late and over budget, or his entire department will be outsourced. he discovers devops as the solution to his problem. replace silos with microservice teams most software development groups are separated into silos, with no overlap of personnel between them. the standard process for a software development project starts with the product manager meeting with the user experience and development groups to discuss ideas for new features. after the idea is implemented in code, the code is passed to the quality assurance (qa) and database administration teams and discussed in more meetings. communication with the system, network, and san administrators is often via tickets. the whole process tends to be slow and loaded with overhead. source: adrian cockcroft some companies try to speed up by creating small “start-up”-style teams that handle the development process from end to end, or sometimes such teams are the result of acquisitions where the acquired company continues to run independently as a separate division. but if the small teams are still doing monolithic delivery, there are usually still handoffs between individuals or groups with responsibility for different functions. the process suffers from the same problems as monolithic delivery in larger companies – it’s simply not very efficient or agile. source: adrian cockcroft conway’s law says that the interface structure of a software system will reflect the social structure of the organization that produced it. so if you want to switch to a microservices architecture, you need to organize your staff into product teams and use devops methodology. there are no longer distinct product managers, ux managers, development managers, and so on, managing downward in their silos. there is a manager for each product feature (implemented as a microservice), who supervises a team that handles all aspects of software development for the microservice, from conception through deployment. the platform team provides infrastructure support that the product teams access via apis. at netflix, the platform team was mostly aws in seattle, with some netflix-managed infrastructure layers built on top. but it doesn’t matter whether your cloud platform is in-house or public; the important thing is that it’s api-driven, self-service, and automatable. source: adrian cockcroft adopt continuous delivery, guided by the ooda loop a siloed team organization is usually paired with monolithic delivery model, in which an integrated, multi-function application is released as a unit (often version-numbered) on a regular schedule. most software development teams use this model initially because it is relatively simple and works well enough with a small number of developers (say, 50 or fewer). however, as the team grows it becomes a real issue when you discover a bug in one developer’s code during qa or production testing and the work of 99 other developers is blocked from release until the bug is fixed. in 2009 netflix adopted a continuous delivery model, which meshes perfectly with a microservices architecture. each microservice represents a single product feature that can be updated independently of the other microservices and on its own schedule. discovering a bug in a microservice has no effect on the release schedule of any other microservice. continuous delivery relies on packaging microservices in standard containers. netflix initially used aws machine images (amis) and it was possible to deploy an update into a test or production environment in about 10 minutes. with docker, that time is reduced even further, to mere seconds in some cases. at netflix, the conceptual framework for continuous development and delivery is an observe-orient-decide-act (ooda) loop . source: adrian cockcroft (http://www.slideshare.net/adrianco) observe refers to examining your current status to look for places where you can innovate. you want your company culture to implicitly authorize anyone who notices an opportunity to start a project to exploit it. for example, you might notice what the diagram calls a “customer pain point”: a lot of people abandoning the registration process on your website when they reach a certain step. you can undertake a project to investigate why and fix the problem. orient refers to analyzing metrics to understand the reasons for the phenomena you’ve observed at the observe point. often this involves analyzing large amounts of unstructured data, such as log files; this is often referred to as big data analysis. the answers you’re looking for are not already in your business intelligence database. you’re examining data that no one has previously looked at and asking questions that haven’t been asked before. decide refers to developing and executing a project plan. company culture is a big factor at this point. as previously discussed, in a high-freedom, high-responsibility culture you don’t need to get management approval before starting to make changes. you share your plan, but you don’t have to ask for permission. act refers to testing your solution and putting it into production. you deploy a microservice that includes your incremental feature to a cloud environment, where it’s automatically put into an ab test to compare it to the previous solution, side by side, for as long as it takes to collect the data that shows whether your approach is better. cooperating microservices aren’t disrupted, and customers don’t see your changes unless they’re selected for the test. if your solution is better, you deploy it into production. it doesn’t have to be a big improvement, either. if the number of clients for your microservice is large enough, then even a fraction of a percent improvement (in response time, say) can be shown to be statistically valid, and the cumulative effect over time of many small changes can be significant. now you’re back at the observe point. you don’t always have to perform all the steps or do them in strict order, either. the important characteristic of the process is that it enables you quickly to determine what your customers want and to create it for them. cockcroft says “it’s hard not to win” if you’re basing your moves on enough data points and your competitors are making guesses that take months to be proven or disproven. the state of art is to circle the loop every one to two weeks, but every microservice team can do it independently. with microservices you can go much faster because you’re not trying to get entire company going around the loop in lockstep. how nginx plus can help at nginx we believe it’s crucial to your future success that you adopt a 4-tier application architecture in which applications are developed and deployed as sets of microservices . we hope the information we’ve shared in this post and its predecessor, adopting microservices at netflix: lessons for architectural design , are helpful as you plan your transition to today’s state-of-the-art architecture for application development. when it’s time to deliver your apps, nginx plus offers an application delivery platform that provides the superior performance, reliability, and scalability your users expect. fully adopting a microservices-based architecture is easier and more likely to succeed when you move to a single software tool for web serving, load balancing, and content caching. nginx plus combines those functions and more in one easy to deploy and manage package. our approach empowers developers to define and control the flawless delivery of their microservices, while respecting the standards and best practices put into place by a platform team. click here to learn more about how nginx plus can help your applications succeed. video recordings fast delivery nginx.conf2014, october 2014 migrating to microservices, part 1 silicon valley microservices meetup, august 2014 migrating to microservices, part 2 silicon valley microservices meetup, august 2014
April 13, 2015
by Patrick Nommensen
· 9,800 Views
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max_allowed_packet and Binary Log Corruption in MySQL
[This article was written by Miguel Angel Nieto] The combination of max_allowed_packet variable and replication in MySQL is a common source of headaches. In a nutshell, max_allowed_packet is the maximum size of a MySQL network protocol packet that the server can create or read. It has a default value of 1MB (<= 5.6.5) or 4MB (>= 5.6.6) and a maximum size of 1GB. This adds some constraints in our replication environment: The master server shouldn’t write events to the binary log larger than max_allowed_packet All the slaves in the replication chain should have the same max_allowed_packet as the master server Sometimes, even following those two basic rules we can have problems. For example, there are situations (also called bugs) where the master writes more data than the max_allowed_packet limit causing the slaves to stop working. In order to fix this Oracle created a new variable called slave_max_allowed_packet. This new configuration variable available from 5.1.64, 5.5.26 and 5.6.6 overrides the max_allowed_packet value for slave threads. Therefore, regardless of the max_allowed_packet value the slaves’ threads will have 1GB limit, the default value of slave_max_allowed_packet. Nice trick that works as expected. Sometimes even with that workaround we can get the max_allowed_packet error in the slave servers. That means that there is a packet larger than 1GB, something that shouldn’t happen in a normal situation. Why? Usually it is caused by a binary log corruption. Let’s see the following example: Slave stops working with the following message: Last_IO_Error: Got fatal error 1236 from master when reading data from binary log: 'log event entry exceeded max_allowed_packet; Increase max_allowed_packet on master' The important part is “got fatal error 1236 from master”. The master cannot read the event it wrote to the binary log seconds ago. To check the problem we can: Use mysqlbinlog to read the binary log from the position it failed with –start-position. This is an example taken from our Percona Forums: #121003 5:22:26 server id 1 end_log_pos 398528 # Unknown event # at 398528 #960218 6:48:44 server id 1813111337 end_log_pos 1835008 # Unknown event ERROR: Error in Log_event::read_log_event(): 'Event too big', data_len: 1953066613, event_type: 8 DELIMITER ; # End of log file Check the size of the event, 1953066613 bytes. Or the “Unknown event” messages. Something is clearly wrong there. Another usual thing to check is the server id that sometimes doesn’t correspond with the real value. In this example the person who posted the binary log event confirmed that the server id was wrong. Check master’s error log. [ERROR] Error in Log_event::read_log_event(): 'Event too big', data_len: 1953066613, event_type: 8 Again, the event is bigger than expected. There is no way the master and slave can read/write it, so the solution is to skip that event in the slave and rotate the logs on the master. Then, use pt-table-checksum to check data consistency. MySQL 5.6 includes replication checksums to avoid problems with log corruptions. You can read more about it in Stephan’s blog post. Conclusion Errors on slave servers about max_allowed_packet can be caused by very different reasons. Although binary log corruption is not a common one, it is something worth checking when you have run out of ideas.
April 13, 2015
by Peter Zaitsev
· 15,127 Views · 1 Like
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How to Batch DELETE Statements with Hibernate
Introduction In my , I explained the Hibernate configurations required for batching INSERT and UPDATE statements. This post will continue this topic with DELETE statements batching. Domain model entities We’ll start with the following entity model: The Post entity has a one-to-many association to a Comment and a one-to-one relationship with the PostDetails entity: @OneToMany(cascade = CascadeType.ALL, mappedBy = "post", orphanRemoval = true) private List comments = new ArrayList<>(); @OneToOne(cascade = CascadeType.ALL, mappedBy = "post", orphanRemoval = true, fetch = FetchType.LAZY) private PostDetails details; The up-coming tests will be run against the following data: doInTransaction(session -> { int batchSize = batchSize(); for(int i = 0; i < itemsCount(); i++) { int j = 0; Post post = new Post(String.format( "Post no. %d", i)); post.addComment(new Comment( String.format( "Post comment %d:%d", i, j++))); post.addComment(new Comment(String.format( "Post comment %d:%d", i, j++))); post.addDetails(new PostDetails()); session.persist(post); if(i % batchSize == 0 && i > 0) { session.flush(); session.clear(); } } }); Hibernate Configuration As , the following properties are required for batching INSERT and UPDATE statements: properties.put("hibernate.jdbc.batch_size", String.valueOf(batchSize())); properties.put("hibernate.order_inserts", "true"); properties.put("hibernate.order_updates", "true"); properties.put("hibernate.jdbc.batch_versioned_data", "true"); Next, we are going to check if DELETE statements are batched as well. JPA Cascade Delete Because is convenient, I’m going to prove that CascadeType.DELETE and JDBC batching don’t mix well. The following tests is going to: Select some Posts along with Comments and PostDetails Delete the Posts, while propagating the delete event to Comments and PostDetails as well @Test public void testCascadeDelete() { LOGGER.info("Test batch delete with cascade"); final AtomicReference startNanos = new AtomicReference<>(); addDeleteBatchingRows(); doInTransaction(session -> { List posts = session.createQuery( "select distinct p " + "from Post p " + "join fetch p.details d " + "join fetch p.comments c") .list(); startNanos.set(System.nanoTime()); for (Post post : posts) { session.delete(post); } }); LOGGER.info("{}.testCascadeDelete took {} millis", getClass().getSimpleName(), TimeUnit.NANOSECONDS.toMillis( System.nanoTime() - startNanos.get() )); } Running this test gives the following output: Query:{[delete from Comment where id=? and version=?][55,0]} {[delete from Comment where id=? and version=?][56,0]} Query:{[delete from PostDetails where id=?][3]} Query:{[delete from Post where id=? and version=?][3,0]} Query:{[delete from Comment where id=? and version=?][54,0]} {[delete from Comment where id=? and version=?][53,0]} Query:{[delete from PostDetails where id=?][2]} Query:{[delete from Post where id=? and version=?][2,0]} Query:{[delete from Comment where id=? and version=?][52,0]} {[delete from Comment where id=? and version=?][51,0]} Query:{[delete from PostDetails where id=?][1]} Query:{[delete from Post where id=? and version=?][1,0]} Only the Comment DELETE statements were batched, the other entities being deleted in separate database round-trips. The reason for this behaviour is given by the ActionQueue sorting implementation: if ( session.getFactory().getSettings().isOrderUpdatesEnabled() ) { // sort the updates by pk updates.sort(); } if ( session.getFactory().getSettings().isOrderInsertsEnabled() ) { insertions.sort(); } While INSERTS and UPDATES are covered, DELETE statements are not sorted at all. A JDBC batch can only be reused when all statements belong to the same database table. When an incoming statement targets a different database table, the current batch has to be released, so that the new batch matches the current statement database table: public Batch getBatch(BatchKey key) { if ( currentBatch != null ) { if ( currentBatch.getKey().equals( key ) ) { return currentBatch; } else { currentBatch.execute(); currentBatch.release(); } } currentBatch = batchBuilder().buildBatch(key, this); return currentBatch; } If you enjoy reading this article, you might want to subscribe to my newsletter and get a discount for my book as well. Orphan removal and manual flushing A work-around is to dissociate all Child entities while manually flushing the HibernateSession before advancing to a new Child association: @Test public void testOrphanRemoval() { LOGGER.info("Test batch delete with orphan removal"); final AtomicReference startNanos = new AtomicReference<>(); addDeleteBatchingRows(); doInTransaction(session -> { List posts = session.createQuery( "select distinct p " + "from Post p " + "join fetch p.details d " + "join fetch p.comments c") .list(); startNanos.set(System.nanoTime()); posts.forEach(Post::removeDetails); session.flush(); posts.forEach(post -> { for (Iterator commentIterator = post.getComments().iterator(); commentIterator.hasNext(); ) { Comment comment = commentIterator.next(); comment.post = null; commentIterator.remove(); } }); session.flush(); posts.forEach(session::delete); }); LOGGER.info("{}.testOrphanRemoval took {} millis", getClass().getSimpleName(), TimeUnit.NANOSECONDS.toMillis( System.nanoTime() - startNanos.get() )); } This time all DELETE statements are properly batched: Query:{[delete from PostDetails where id=?][2]} {[delete from PostDetails where id=?][3]} {[delete from PostDetails where id=?][1]} Query:{[delete from Comment where id=? and version=?][53,0]} {[delete from Comment where id=? and version=?][54,0]} {[delete from Comment where id=? and version=?][56,0]} {[delete from Comment where id=? and version=?][55,0]} {[delete from Comment where id=? and version=?][52,0]} {[delete from Comment where id=? and version=?][51, Query:{[delete from Post where id=? and version=?][2,0]} {[delete from Post where id=? and version=?][3,0]} {[delete from Post where id=? and version=?][1,0]} SQL Cascade Delete A better solution is to use SQL cascade deletion, instead of JPA entity state propagation mechanism. This way, we can also reduce the DML statements count. Because Hibernate Session acts as a , we must be extra cautious when mixing entity state transitions with database-side automatic actions, as the Persistence Context might not reflect the latest database changes. The Post entity one-to-manyComment association is marked with the Hibernate specific @OnDelete annotation, so that the auto-generated database schema includes the ON DELETE CASCADE directive: @OneToMany(cascade = { CascadeType.PERSIST, CascadeType.MERGE}, mappedBy = "post") @OnDelete(action = OnDeleteAction.CASCADE) private List comments = new ArrayList<>(); Generating the following DDL: alter table Comment add constraint FK_apirq8ka64iidc18f3k6x5tc5 foreign key (post_id) references Post on delete cascade The same is done with the PostDetails entity one-to-one Post association: @OneToOne(fetch = FetchType.LAZY) @JoinColumn(name = "id") @MapsId @OnDelete(action = OnDeleteAction.CASCADE) private Post post; And the associated DDL: alter table PostDetails add constraint FK_h14un5v94coafqonc6medfpv8 foreign key (id) references Post on delete cascade The CascadeType.ALL and orphanRemoval were replaced with CascadeType.PERSIST and CascadeType.MERGE, because we no longer want Hibernate to propagate the entity removal event. The test only deletes the Post entities. doInTransaction(session -> { List posts = session.createQuery( "select p from Post p") .list(); startNanos.set(System.nanoTime()); for (Post post : posts) { session.delete(post); } }); The DELETE statements are properly batched as there’s only one target table. Query:{[delete from Post where id=? and version=?][1,0]} {[delete from Post where id=? and version=?][2,0]} {[delete from Post where id=? and version=?][3,0]} If you enjoyed this article, I bet you are going to love my book as well. Conclusion If INSERT and UPDATE statements batching is just a matter of configuration, DELETE statements require some additional steps, which may increase the data access layer complexity. Code available on GitHub. If you have enjoyed reading my article and you’re looking forward to getting instant email notifications of my latest posts, consider .
April 11, 2015
by Vlad Mihalcea
· 22,623 Views · 1 Like
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Patterns of API Virtualization
[This article was written by Matthew Heusser.] When Christopher Alexander wrote A Pattern Language in 1977, he was looking for a more powerful way to describe how towns and buildings were laid out. These patterns would allow architects, builders and planners to work together, to use the same words, mean the same thing, and create systems that were beautiful and worked, instead of more urban sprawl. Twenty years later, Gamma, Helms, Johnson and Vlissdes took the pattern idea and applied it to object-oriented software, which at the time was struggling to figure out how to create windows-based applications. Today the struggle is figuring out how to break software into small components that can be tested independently, and then having those components interact, typically over internet protocols. Raw SQL commands are giving way to service oriented systems that interact through APIs, sometimes all within one company, sometimes outside with Microsoft, Google, Amazon, or other APIs like a manufacturing company or supplier. While I do not claim to be Christopher Alexander or the Gang of Four, I am seeing some patterns emerge – a set of solutions to a defined problem – and would like to share a few of those today. What do you mean API? Alistair Cockburn’s Hexagonal Architecture (below) presents a way to think about APIs. The application we want to develop is in the middle and has a set of adapters to the external world. Those adapters might be an API we expose, like a ‘search’ interface to an online catalog, or the API’s we call, including the database, an email gateway, or the ‘permissions’ service, to see what types of search results we should show to this user. Cockburn’s Hexagonal Architecture gives us two ways to think about APIs: Our own, and the services we call. (Source: http://alistair.cockburn.us/Hexagonal+architecture) That’s a lot of APIs. Let’s explore about some ways to virtualize these services – and why. Automated Build and Continuous Integration Say, for example, you are working on a piece of software to analyze trending terms on social media – such as a customer complaint that is being liked and tweeted. You want companies to find these problems when they start to trend up, then reach out to the customer and solve it, or, perhaps, reach out to say “thank you” and amplify it. Modern build systems, like Jenkins, TFS, and TeamCity can compile, deploy, and even run the system to check for known scenarios. The trouble is those pesky adapters to external systems, like Twitter and Facebook. The software could do its job, but there is no way to know if the application is correct in its guesses about trends and importance. Getting the data from the providers can turn a quick build into a slow process that uses a lot of network traffic. By recording and storing known answers to predictable requests, then simulating the service and playing back known (“canned”) data, API Virtualization allows build systems to do more, with faster, more predictable results. This does not remove the need for end-to-end testing, but it does allow the team to have more confidence with each build. Performance Testing Your Application Like build/deploy systems, performance testing the application (the inside of the hexagon) with live, external services can cause problems. All that extra traffic can cause problems with the actual company network infrastructure; it could cause bandwidth problems at the point of the ISP. Some 3rd Party APIs charge a micro-fee per transaction, or limit bandwidth. Many of them lack a ‘test’ sandbox to develop in, so performance testing could interact with real, production work. Standing up a virtual server to return pre-planned data means you can performance test your application – not the third party – prevent bandwidth throttles, not step on production data, and avoid paying fees intended for real (production) use that is actually being used to test our environment. Avoid Integration Environment Inconsistency A few years ago I worked at a large organization that was wrapping old code in proxy services, so they could be consumed by other teams. Login, add-to-cart, search catalog, create custom catalog, permissions, all of it was possible to access through API calls, most of it as simple as a web URL that returned some text. The problem was the “System Integration Test” environment, or SIT. Every team tested its services in SIT, which meant about a third of the time, something was broken. After finding a bug in the current build, we would track it back to the catalog service, walk over to that team, bring up the issue, and they would say “thanks, we are testing a new build of catalog.” We expected catalog to work in SIT. Anything else meant a waste of someone’s time. Automated tools reporting false errors were even worse. When teams performance tested their services, everything calling the service got slow, if it worked at all. By virtualizing services we could test our application end-to-end against known data, without the troubles of SIT, or having to build additional expensive test-lab-like copies of production. Best of all, creating the virtual services is a snap – just record the live service with a tool and instruct it to play back similar requests. Flip Integration Tests from Virtual To Real for Final Checking All this API virtualization creates a risk that the team will move from test to production and something will be different between the Virtual API and the live one. If the Virtual API server is just returning the same thing product did when we recorded it and we have automated checks in place, we can change our test server to point to the real service and re-run all the automated checks. As long as the source data hasn’t changed and we are reading, not writing, from production, the checks should all pass. If the production API has changed, we will get failures, and they will be easy enough to fix and retest. Simulate Slow or Unresponsive Service In The Middle Of A Long Running Transaction Sometimes you want to test if a server is overloaded or down. Calling Facebook and asking them to turn off their servers is unlikely to work; even just coordinating with the team down the hall could create a lot of overhead. You also might want to test this often – every day or every hour – and manually pulling a plug or coordinating with the Login team every hour might not be realistic. The trick is to bring the service down once and record the exact behavior of the system, then use a virtual server to simulate that behavior, over and over again, every day. That means you’ll get the exact behavior, not a guess, and know exactly how the application under test can deal with it. Early Development of System against an Undeployed API Sometimes the API you are testing against does not exist, even in test. It’s still possible to create a Virt (virtual API) which returns some roughly equivalent data, and makes it possible to move forward on the core application without introducing new risks. Avoid Configuration and Copying Hassles Many companies use a test system that is a copy of production, and then refresh the system periodically. Sometimes, you want test scenarios that do not exist in production, so you have to create them … and lose them during a refresh. The same problem happens with 3rd party APIs, when, for example, a part is discontinued, and you are testing ordering that part, or the sample person you check for insurance coverage leaves the company. If the request for the part of the coverage goes through an API, you can record known good results that don’t change, even after a database refresh – then leave the real, end-to-end testing for an exploratory step that will be lighter, quicker, more accurate, and have more confidence. A Fistful of Techniques Today we discussed a half-dozen common patterns to API virtualization, mostly around testing systems in isolation that consume data through an API, like a 3rd party or an internal service. These ideas are new, and evolving. What are a few of your favorites?
April 9, 2015
by Denis Goodwin
· 4,137 Views
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Adopting Microservices at Netflix: Lessons for Architectural Design
[This article was written by Tony Mauro.] In some recent blog posts, we’ve explained why we believe it’s crucial to adopt a four-tier application architecture in which applications are developed and deployed as sets of microservices. It’s becoming increasingly clear that if you keep using development processes and application architectures that worked just fine ten years ago, you simply can’t move fast enough to capture and hold the interest of mobile users who can choose from an ever-growing number of apps. Switching to a microservices architecture creates exciting opportunities in the marketplace for companies. For system architects and developers, it promises an unprecedented level of control and speed as they deliver innovative new web experiences to customers. But at such a breathless pace, it can feel like there’s not a lot of room for error. In the real world, you can’t stop developing and deploying your apps as you retool the processes for doing so. You know that your future success depends on transitioning to a microservices architecture, but how do you actually do it? Fortunately for us, several early adopters of microservices are now generously sharing their expertise in the spirit of open source, not only in the form of published code but in conference presentations and blog posts. Netflix is a leading example. As the Director of Web Engineering and then Cloud Architect, Adrian Cockcroft oversaw the company’s transition from a traditional development model with 100 engineers producing a monolithic DVD-rental application to a microservices architecture with many small teams responsible for the end-to-end development of hundreds of microservices that work together to stream digital entertainment to millions of Netflix customers every day. Now a Technology Fellow at Battery Ventures, Cockcroft is a prominent evangelist for microservices and cloud-native architectures, and serves on the NGINX Technical Advisory Board. In a two-part series of blog posts, we’ll present top takeaways from two talks that Cockcroft delivered last year, at the first annual NGINX conference in October and at a Silicon Valley Microservices Meetup a couple months earlier. (The complete video recordings are also well worth watching.) This post defines microservices architecture and outlines some best practices for designing one. Adopting Microservices at Netflix: Lessons for Team and Process Design discusses why and how to adopt a new mindset for software development and reorganize your teams around it. What is a Microservices Architecture? Cockcroft defines a microservices architecture as a service-oriented architecture composed of loosely coupled elements that have bounded contexts. Loosely coupled means that you can update the services independently; updating one service doesn’t require changing any other services. If you have a bunch of small, specialized services but still have to update them together, they’re not microservices because they’re not loosely coupled. One kind of coupling that people tend to overlook as they transition to a microservices architecture is database coupling, where all services talk to the same database and updating a service means changing the schema. You need to split the database up and denormalize it. The concept of bounded contexts comes from the book Domain Driven Design by Eric Evans. A microservice with correctly bounded context is self-contained for the purposes of software development. You can understand and update the microservice’s code without knowing anything about the internals of its peers, because the microservices and its peers interact strictly through APIs and so don’t share data structures, database schemata, or other internal representations of objects. If you’ve developed applications for the Internet, you’re already familiar with these concepts, in practice if not by name. Most mobile apps talk to quite a few back-end services, to enable its users to do things like share on Facebook, get directions from Google Maps, and find restaurants on Foursquare, all within the context of the app. If your mobile app were tightly coupled with those services, then before you could release an update you would have to talk to all of their development teams to make sure that your changes aren’t going to break anything. When working with a microservices architecture, you think of other internal development teams like those Internet back ends: as external services that your microservice interacts with through APIs. The commonly understood “contract” between microservices is that their APIs are stable and forward compatible. Just as it’s unacceptable for the Google Maps API to change without warning and in such a way that it breaks its users, your API can evolve but must remain compatible with previous versions. Best Practices for Designing a Microservices Architecture Cockcroft describes his role as Cloud Architect at Netflix not in terms of controlling the architecture, but as discovering and formalizing the architecture that emerged as the Netflix engineers built it. The Netflix development team established several best practices for designing and implementing a microservices architecture. Create a Separate Data Store for Each Microservice Do not use the the same back-end data store across microservices. You want the team for each microservice to choose the database that best suits the service. Moreover, with a single data store it’s too easy for microservices written by different teams to share database structures, perhaps in the name of reducing duplication of work. You end up with the situation where if one team updates a database structure, other services that also use that structure have to be changed too. Breaking apart the data can make data management more complicated, because the separate storage systems can more easily get out sync or become inconsistent, and foreign keys can change unexpectedly. You need to add a tool that performs master data management (MDM) by operating in the background to find and fix inconsistencies. For example, it might examine every database that stores subscriber IDs, to verify that the same IDs exist in all of them (there aren’t missing or extra IDs in any one database). You can write your own tool or buy one. Many commercial relational database management systems (RDBMSs) do these kinds of checks, but they usually impose too many requirements for coupling, and so don’t scale. Keep Code at a Similar Level of Maturity Keep all code in a microservice at a similar level of maturity and stability. In other words, if you need to add or rewrite some of the code in a deployed microservice that’s working well, the best approach is usually to create a new microservice for the new or changed code, leaving the existing microservice in place. [Editor’s note: This is sometimes referred to as the immutable infrastructure principle.] This way you can iteratively deploy and test the new code until it is bug free and maximally efficient, without risking failure or performance degradation in the existing microservice. Once the new microservice is as stable as the original, you can merge them back together if they really perform a single function together, or there are other efficiencies from combining them. However, in Cockcroft’s experience it is much more common to realize you should split up a microservice because it’s gotten too big. Do a Separate Build for Each Microservice Do a separate build for each microservice, so that it can pull in component files from the repository at the revision levels appropriate to it. This sometimes leads to the situation where various microservices pull in a similar set of files, but at different revision levels. That can make it more difficult to clean up your codebase by decommissioning old file versions (because you have to verify more carefully that a revision is no longer being used), but that’s an acceptable trade-off for how easy it is to add new files as you build new microservices. The asymmetry is intentional: you want introducing a new microservice, file, or function easy, not dangerous. Deploy in Containers Deploying microservices in containers is important because it means you just need just one tool to deploy everything. As long as the microservice is in a container, the tool knows how to deploy it. It doesn’t matter what the container is. That said, Docker seems very quickly to have become the de facto standard for containers. Treat Servers as Stateless Treat servers, particularly those that run customer-facing code, as interchangeable members of a group. They all perform the same functions, so you don’t need to be concerned about them individually. Your only concern is that there are enough of them to produce the amount of work you need, and you can use auto scaling to adjust the numbers up and down. If one stops working, it’s automatically replaced by another one. Avoid “snowflake” systems in which you depend on individual servers to perform specialized functions. Cockcroft’s analogy is that you want to think of servers like cattle, not pets. If you have a machine in production that performs a specialized function, and you know it by name, and everyone gets sad when it goes down, it’s a pet. Instead you should think of your servers like a herd of cows. What you care about is how many gallons of milk you get. If one day you notice you’re getting less milk than usual, you find out which cows aren’t producing well and replace them. Netflix Delivery Architecture is Built on nginx Netflix is a longtime nginx user and became the first customer of NGINX, Inc. after it incorporated in 2011. Indeed, Netflix chose nginx as the heart of their delivery infrastructure, the Netflix Open Connect Content Delivery Network (CDN), one of the largest CDNs in the world. With the ability to serve thousands, and sometimes millions, of requests per second, nginx is an optimal solution for high-performance HTTP delivery and enables companies like Netflix to offer high-quality digital experiences to millions of customers every day. Video Recordings Fast Delivery nginx.conf2014, October 2014 Migrating to Microservices, Part 1 Silicon Valley Microservices Meetup, August 2014 Migrating to Microservices, Part 2 Silicon Valley Microservices Meetup, August 2014
April 7, 2015
by Patrick Nommensen
· 33,764 Views · 1 Like
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Introduction to Apache Cassandra's Architecture
Some key concepts for Apache's popular Cassandra Architecture include partitioning, replication, consistency, bootstrapping, and write paths.
April 6, 2015
by Akhil Mehra
· 118,117 Views · 38 Likes
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Package by Component and Architecturally-aligned Testing
i've seen and had lots of discussion about "package by layer" vs "package by feature" over the past couple of weeks. they both have their benefits but there's a hybrid approach i now use that i call "package by component". to recap... package by layer let's assume that we're building a web application based upon the web-mvc pattern. packaging code by layer is typically the default approach because, after all, that's what the books, tutorials and framework samples tell us to do. here we're organising code by grouping things of the same type. there's one top-level package for controllers, one for services (e.g. "business logic") and one for data access. layers are the primary organisation mechanism for the code. terms such as "separation of concerns" are thrown around to justify this approach and generally layered architectures are thought of as a "good thing". need to switch out the data access mechanism? no problem, everything is in one place. each layer can also be tested in isolation to the others around it, using appropriate mocking techniques, etc. the problem with layered architectures is that they often turn into a big ball of mud because, in java anyway, you need to mark your classes as public for much of this to work. package by feature instead of organising code by horizontal slice, package by feature seeks to do the opposite by organising code by vertical slice. now everything related to a single feature (or feature set) resides in a single place. you can still have a layered architecture, but the layers reside inside the feature packages. in other words, layering is the secondary organisation mechanism. the often cited benefit is that it's "easier to navigate the codebase when you want to make a change to a feature", but this is a minor thing given the power of modern ides. what you can do now though is hide feature specific classes and keep them out of sight from the rest of the codebase. for example, if you need any feature specific view models, you can create these as package-protected classes. the big question though is what happens when that new feature set c needs to access data from features a and b? again, in java, you'll need to start making classes publicly accessible from outside of the packages and the big ball of mud will again emerge. package by layer and package by feature both have their advantages and disadvantages. to quote jason gorman from schools of package architecture - an illustration , which was written seven years ago. to round off, then, i would urge you to be mindful of leaning to far towards either school of package architecture. don't just mindlessly put socks in the sock draw and pants in the pants draw, but don't be 100% driven by package coupling and cohesion to make those decisions, either. the real skill is finding the right balance, and creating packages that make stuff easier to find but are as cohesive and loosely coupled as you can make them at the same time. package by component this is a hybrid approach with increased modularity and an architecturally-evident coding style as the primary goals. the basic premise here is that i want my codebase to be made up of a number of coarse-grained components, with some sort of presentation layer (web ui, desktop ui, api, standalone app, etc) built on top. a "component" in this sense is a combination of the business and data access logic related to a specific thing (e.g. domain concept, bounded context, etc). as i've described before , i give these components a public interface and package-protected implementation details, which includes the data access code. if that new feature set c needs to access data related to a and b, it is forced to go through the public interface of components a and b. no direct access to the data access layer is allowed, and you can enforce this if you use java's access modifiers properly. again, "architectural layering" is a secondary organisation mechanism. for this to work, you have to stop using the public keyword by default . this structure raises some interesting questions about testing, not least about how we mock-out the data access code to create quick-running "unit tests". architecturally-aligned testing the short answer is don't bother, unless you really need to. i've spoken about and written about this before, but architecture and testing are related. instead of the typical testing triangle (lots of "unit" tests, fewer slower running "integration" tests and even fewer slower ui tests), consider this. i'm trying to make a conscious effort to not use the term "unit testing" because everybody has a different view of how big a "unit" is. instead, i've adopted a strategy where some classes can and should be tested in isolation. this includes things like domain classes, utility classes, web controllers (with mocked components), etc. then there are some things that are easiest to test as components, through the public interface. if i have a component that stores data in a mysql database, i want to test everything from the public interface right back to the mysql database. these are typically called "integration tests", but again, this term means different things to different people. of course, treating the component as a black box is easier if i have control over everything it touches. if you have a component that is sending asynchronous messages or using an external, third-party service, you'll probably still need to consider adding dependency injection points (e.g. ports and adapters) to adequately test the component, but this is the exception not the rule. all of this still applies if you are building a microservices style of architecture. you'll probably have some low-level class tests, hopefully a bunch of service tests where you're testing your microservices though their public interface, and some system tests that run scenarios end-to-end. oh, and you can still write all of this in a test-first, tdd style if that's how you work. i'm using this strategy for some systems that i'm building and it seems to work really well. i have a relatively simple, clean and (to be honest) boring codebase with understandable dependencies, minimal test-induced design damage and a manageable quantity of test code. this strategy also bridges the model-code gap , where the resulting code actually reflects the architectural intent. in other words, we often draw "components" on a whiteboard when having architecture discussions, but those components are hard to find in the resulting codebase. packaging code by layer is a major reason why this mismatch between the diagram and the code exists. those of you who are familiar with my c4 model will probably have noticed the use of the terms "class" and "component". this is no coincidence. architecture and testing are more related than perhaps we've admitted in the past. p.s. i'll be speaking about this topic over the next few months at events across europe, the us and (hopefully) australia
April 4, 2015
by Simon Brown
· 11,086 Views
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How to Configure a Simple JBoss Cluster in Domain Mode
Clustering is a very important thing to master for any serious user of an application server. Clustering allows for high availability by making your application available on secondary servers when the primary instance is down or it lets you scale up or out by increasing the server density on the host, or by adding servers on other hosts. It can even help to increase performance with effective load balancing between servers based on their respective hardware. Andy Overton has already covered how to set up a cluster of servers in standalone mode fronted by mod_cluster for load balancing, so in this post I'll cover clustering in domain mode. I won't rehash mod_cluster settings, so this will just cover the set up of a doman controller on one host, and the host controller and server instances on another host. To follow along with this blog, you'll need to download either JBoss EAP 6.x or WildFly. I'll be using WildFly 8.2 on Xubuntu 14.04. I'll be using $WF_HOME to refer to your WildFly home directory. Configuring the Domain Controller The domain controller needs both the domain.xml and host.xml configured. In the $WF_HOME/domain/configuration directory, you'll see that those two files are joined by a host-master.xml and a host-slave.xml. These are preconfigured host.xml files which you can use to give you a head start in making a host.xml for the domain controller (master) and host controller (slave) to use. You can either change the name of the file to be host.xml, so it will get picked up and used by default, or you can specify the host configuration you want to use on the command line by adding the --host-config argument: domain.sh --host-config=host-master.xml Whether you choose to modify the host.xml or the host-master.xml, you need to make sure that the empty element has been added to the section. This is so that when WildFly looks to see which server is the domain controller, it knows to become the domain controller itself. The other change is optional, but recommended. We need to tell the domain controller to bind its management interface to the correct IP address because, by default, it will bind to localhost, so the management communication it needs to do with the remote hosts won't be able to reach the domain controller at all! We can set this address permanently in the host.xml by making sure the inet-address value is set to the right IP, by changing the 127.0.0.1 in the example below to the correct IP: The result of that is that the default bind IP of the management interface is no longer localhost, although you can still override this value by starting JBoss with the variable left of the colon as a -D argument: domain.sh -Djboss.bind.address.management=10.0.0.1 Next, we need to modify the domain.xml file, where we need to define our server groups; essentially just defining the cluster. Each server group is named, so we can reference it later, and references a particular profile which needs to be one of the profiles named and defined in the same XML file. As I mentioned in my previous blog, domain mode has several profiles in the same file (domain.xml) rather than multiple files for each, like standalone mode (standalone.xml, standalone-ha.xml etc.). In the screenshot, there are two server groups defined - "main-server-group" which references the "full" profile, and "other-server-group" which references the "full-ha" profile. These are just the defaults which come with WildFly, so you're free to use them and modify the settings or create your own from scratch. Whichever you choose, it's a good idea to rename your server group to something meaningful, like a description of the workload, or the application name. Configuring the Host Controllers Every host server which you want to be part of the cluster must have the host.xml file configured. We've already configured the host.xml on the domain controller, so now we'll focus on the host controller. Remember, this process can be repeated on any number of hosts, depending on how many servers you want in your server group and their topology. First, we need to make sure that the domain controller and the host controller can communicate, and to do that we need a valid management user. On the domain controller, run the add-user.sh or add-user.bat script. You will need to make sure to: Choose a management user Make sure the user is different than the one you would use to log in to the web console Confirm that the new user will connect one AS process to another AS process Make a note of the secret value (this is very important!) You will find that you get prompts similar to the following: mike@mike-C2B2:~$ /opt/wildfly/wildfly-8.2.0.Final/bin/add-user.sh What type of user do you wish to add? a) Management User (mgmt-users.properties) b) Application User (application-users.properties) (a): a Enter the details of the new user to add. Using realm 'ManagementRealm' as discovered from the existing property files. Username : mgmt Password recommendations are listed below. To modify these restrictions edit the add-user.properties configuration file. - The password should not be one of the following restricted values {root, admin, administrator} - The password should contain at least 8 characters, 1 alphabetic character(s), 1 digit(s), 1 non-alphanumeric symbol(s) - The password should be different from the username Password : Re-enter Password : What groups do you want this user to belong to? (Please enter a comma separated list, or leave blank for none)[ ]: About to add user 'mgmt' for realm 'ManagementRealm' Is this correct yes/no? yes Added user 'mgmt' to file '/opt/wildfly/wildfly-8.2.0.Final/standalone/configuration/mgmt-users.properties' Added user 'mgmt' to file '/opt/wildfly/wildfly-8.2.0.Final/domain/configuration/mgmt-users.properties' Added user 'mgmt' with groups to file '/opt/wildfly/wildfly-8.2.0.Final/standalone/configuration/mgmt-groups.properties' Added user 'mgmt' with groups to file '/opt/wildfly/wildfly-8.2.0.Final/domain/configuration/mgmt-groups.properties' Is this new user going to be used for one AS process to connect to another AS process? e.g. for a slave host controller connecting to the master or for a Remoting connection for server to server EJB calls. yes/no? yes To represent the user add the following to the server-identities definition Once we have the secret value for our management user, we can add it to the host.xml file. I'm choosing to modify the host-slave.xml file, since much of the configuration I need is done for me: Next, we need to tell the host controller where to look for the domain controller. We set this to for the domain controller's host.xml file, but in the host-slave.xml we have an example tag filled out for us. All we need to do is add the domain controller's IP or hostname exactly as we did for the management bind address earlier. So our host-slave.xml should go from this: to this: This way, like with the management interface on the domain controller, the default address will be 10.0.0.1, but it can also be overridden on the command line if needed. Once we've sorted the communication out, we need to tell the host controller to actually start some server instances! At the bottom of the host-slave.xml file, there are two predefined servers to use: These are already configured to become members of the two server groups configured in the domain.xml. Note that the second server has to have a port offset. Despite it being in a different server group, it's still going to run on the same host and will attempt to bind to the same ports as the first server unless we tell it not to! We would also need to do the same thing if we added other server instances. Optionally, we can make things a little easier for ourselves when managing a lot of servers on a lot of hosts. We can give each server instance its own unique name, but we can also name the host by adding a name attribute to the parent tag, changing it from: to So both in the logs and in the admin console, you should see this host controller referred to as "host1". Now, if you wanted to name your server instances the same across hosts, you'll be able to tell which is which! If all you wanted was to configure a single domain controller and a single host controller, then that's all we need to do to get them speaking to each other. You can then carry on and configure mod_cluster and Apache to forward requests on to the right server, or just deploy your applications and connect to them directly.
April 3, 2015
by Mike Croft
· 23,577 Views
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To Shard, or Not to Shard
When I talk with customers about sharding decisions I often start by telling the following true story… A couple of years ago, a customer came to me looking for advice on how to shard his system. He told me he was already convinced he needed to do that since he read that some smart people at MySQL giants like Facebook and Twitter were sharding—so naturally this was something he should be doing, too. I paused for a moment and then I asked him what the size of his database was. “10GB,” he said. I nodded and asked if he handles many queries or if they were very complicated. “No,” he said. “Just a few hundred queries per second, and they have not been loading down the system by more than a few percent.” I asked him whether he was expecting exponential growth in the near future—looking to double every week or something like that. “No, our load and data size grew about 7 percent last year and we expect about the same growth this year and for the foreseeable future.” My recommendation to him was not to waste time and effort on sharding because it is just not needed in his company’s case. Before you decide how to shard, you’d best understand whether or not you really need to shard to begin with. Yes, on the extremely large-scale side of database demands, sharding is the only game in town. And not just for MySQL, but for pretty much any technology out there. Yet thanks to emerging technologies there is an increasing amount of applications that can run databases without sharding. Today we can easily run with terabytes of data per MySQL instance and serve tens of thousands of queries in many OLTP environments. This allows organizations to build very large applications without needing to shard. And keep this in mind: Sharding is a pain under all circumstances. Even if you have sharding provided out of the box by the database system, it is a pain because it introduces more components and complexity. Creating good distributed query execution plans is a very complicated task that needs to take network topology and load into account in addition to the data distribution and load of individual nodes. Before you decide if you need to shard, you should look at alternatives to scale your application. In the MySQL world, the solutions are typically as follows: Alternatives to Sharding Functional Partitioning: In many environments a single MySQL instance becomes a dumping ground for all kinds of databases—you might end up having your main application share a database instance with Drupal, which powers your website, with WordPress, which powers your blog, and with vBulletin, which powers your forums. Splitting those pieces into different database instances is something you should consider before you look into sharding. Custom-made systems will often have many applications using different data sets that can be easily split out. Replication: Many applications are read-heavy, so scaling reads becomes the issue earlier than it does with scaling writes. Replication is a great solution for this. MySQL’s built-in replication is very robust, though due to its asynchronous nature it adds complexity to the application. The developer must decide which of the reads can be done from the replica servers and which can’t, because you must be absolutely certain that you’re reading the most recent, actual data. This is the reason that alternative, synchronous replication technologies for MySQL like Percona XtraDB Cluster, are gaining popularity: They provide single database-like behavior from the cluster in most cases. Caching and Queueing: Caching is a great technology for reducing the amount of reads that hit the database. There are many applications that have reduced read load on the database by 80-95% using this technology. Queueing, in contrast, optimizes writes. It does this by merging multiple write operations together so they hit the database efficiently. Most large-scale applications should rely heavily on both of these technologies. Memcached and Redis are two popular caching technologies in the MySQL space. For queueing, the most popular technologies are ActiveMQ and RabbitMQ [1]. Supplemental Technologies: MySQL is great at many things but not at everything. If you’re looking for high-performance full-text search, consider ElasticSearch, Sphinx, or Lucene. If you’re looking at large-scale data analytics, a Hadoop-based infrastructure or Vertica might work well for you. You should let MySQL handle the things it is good at, and leave the rest to supporting tools. Optimizations to Make Before Sharding Scaling isn’t just about architecture either. You also need to make sure your system is reasonably optimized. Many people decide sharding is inevitable for them even though there are much easier and more cost-effective ways to get the performance and scale they are looking for. All of which, I might add, are also going to be valuable if sharding is indeed eventually needed. Hardware: Are you using the right hardware? I’ve seen many people looking into sharding when in fact simply purchasing decent hardware would solve their problems for years to come. Make sure you have plenty of memory and high-performance flash storage if you’re working with a large database. In many cases it can transform your system so much it will look like magic. MySQL version and Configuration: Use a recent MySQL version. By that I mean the latest GA version (MySQL 5.6 at the time of this article’s publication). Percona Server, which is free, often offers additional performance improvements for demanding workloads. Use the most recent operating system too, especially if you’re using modern hardware. Finally, make sure MySQL is configured properly. The difference in MySQL performance between poorly configured MySQL and well-tuned MySQL can be 10x or more. Schema and Queries: The same application logic can be expressed using a variety of schema and queries. I’ve seen a lot of similar applications approaching things differently, and the difference in the performance between an optimal approach and a poor one (but still used in production) can be 100x or more. Many of the changes can be retrofitted to existing schema—such as minor query changes and changes to the index structure—however, if your schema doesn’t fit your application needs well, then you might be looking at a complete redesign. So it is a good idea to think things through early. When to Shard So when should you start thinking about sharding? Basically, if none of the measures listed above have given you the performance you need, it might be time to consider sharding. Sharding does have the advantage of allowing you to potentially use lower-cost hardware or cheaper cloud instances. Most developers are using agile development methods these days and there is a common term, “Architectural Runway,” which defines how far the application can go with its current architecture. If you’ve already found success using replication in particular, it might be a bad decision to add sharding because it will force your developers to deal with the complexity of sharding and asynchronous replication. However, replication is still typically used to achieve high availability even if you’re already sharding, but in this case it’s not for scaling reads. If you’ve come to the point where you’re sure you need to shard, here are some of the questions you need to ask about how you’ll implement your sharding strategy: Shard Level: At which level should we shard? It does not have to be at the database level. Many applications, SaaS in particular, often “shard” on higher levels, deploying multiple copies of their full stack to offer complete isolation for availability, performance, security etc. In many large scale applications you will see multiple copies of a full stack deployed, each having its own sharded MySQL environment. Shard Key: How do we shard? In many cases the choice depends on whether you’re authenticating for user accounts or your organization, but in other cases it is not so obvious. When making a sharding choice, you need to think about two things: 1) as many data access points as possible should go into a single shard, because cross-shard access is expensive if supported at all, and 2) making sure such sharding does not produce a shard that is too large to handle either in terms of data size or traffic. For example, sharding by country is a poor idea because the requirements to handle Belgium traffic won’t be the same for the United States or China, which require a lot more resources. Shard by Schema or Instance: What is the unit of your shard? The typical choices are MySQL instance or database (schema). I like the shard = database approach, which doesn’t limit you to a single MySQL instance per physical box. That way you do not have to run too many MySQL instances, but you can run more than one if the application works better that way. Shard Unit: If you shard by a single MySQL server, you will run into a problem with high availability very soon. When you have 100 MySQL servers there are roughly 100 more chances for one of them to crash compared with having only one, so ensuring there is a high availability solution becomes critical. Instead of sharing across MySQL servers you will usually be sharding across “Replication Clusters,” such as one MySQL primary node and one or several replica or PXC (Percona XtraDB Cluster) nodes. Shard Technology: What technology can you use to assist you with sharding? Within the MySQL world there is no standard sharding technology as of yet that everyone uses. Most of the large web properties have implemented something in-house for their sharding needs, and some have released their solutions as open source projects. One example is Vitess, contributed by Google, and another is JetPants, contributed by Tumblr. Rolling out your own simple sharding framework might look easy for some developers until you have to deal with operational issues like balancing the shards, resharding, etc., on a large scale. There are a number of purpose-built technologies that can help you with sharding if this doesn’t sound like something your team can manage. Sharding Technologies Here are technologies that you should consider: MySQL Fabric: This is the sharding technology being developed by the MySQL team at Oracle. MySQL Fabric is GA, but its functionality right now is rather limited, especially in terms of their support for multi-sharded queries. Given more time however, it has the potential to become the standard sharding technology for MySQL. Tesora: Tesora has a proxy-based solution for MySQL sharding that became open source some time ago. I would be especially looking at Tesora if you’re also looking at deploying OpenStack, as they’ve invested a lot into the integration. ScaleArc: ScaleArc is a commercial database proxy solution that can do caching, filtering, routing, and sharding. It is a pretty mature solution that handles multiple database technologies and not just MySQL. ScaleBase: ScaleBase is a sharding solution designed specifically for MySQL and the cloud, which similarly to MySQL, operates at the proxy level. There are many technologies in the MySQL space that can help you scale your application without sharding. If you’re going to build the next “Facebook,” however, you will surely need to shard, and there are a number of technologies that can help you do it as painlessly as possible. Large-scale applications on large-scale databases will always introduce complexity, which makes them more complicated to develop against and manage. Success comes with cost. [1] http://dzone.com/research/guide-to-enterprise-integration Peter Zaitsev co-founded Percona in 2006, assuming the role of CEO. Percona helps companies of all sizes maximize their success with MySQL. Peter enjoys mixing business leadership with hands on technical expertise. Peter is also the co-author of O’Reilly’s High Performance MySQL, one of the most popular books on MySQL performance.
April 2, 2015
by Peter Zaitsev
· 20,937 Views · 1 Like
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Dismantling invokedynamic
Many Java developers regarded the JDK's version seven release as somewhat a disappointment. On the surface, merely a few language and library extensions made it into the release, namely Project Coin and NIO2. But under the covers, the seventh version of the platform shipped the single biggest extension to the JVM's type system ever introduced after its initial release. Adding the invokedynamic instruction did not only lay the foundation for implementing lambda expressions in Java 8, it also was a game changer for translating dynamic languages into the Java byte code format. While the invokedynamic instruction is an implementation detail for executing a language on the Java virtual machine, understanding the functioning of this instruction gives true insights into the inner workings of executing a Java program. This article gives a beginner's view on what problem the invokedynamic instruction solves and how it solves it. Method handles Method handles are often described as a retrofitted version of Java's reflection API, but this is not what they are meant to represent. While method handles can represent a method, constructor or field, they are not intended to describe properties of these class members. It is for example not possible to directly extract metadata from a method handle such as modifiers or annotation values of the represented method. And while method handles allow for the invocation of a referenced method, their main purpose is to be used together with an invokedynamic call site. For gaining a better understanding of method handles, looking at them as an imperfect replacement for the reflection API is however a reasonable starting point. Method handles cannot be instantiated. Instead, method handles are created by using a designated lookup object. These objects are themselves created by using a factory method that is provided by the MethodHandles class. Whenever this factory is invoked, it first creates a security context which ensures that the resulting lookup object can only locate methods that are also visible to the class from which the factory method was invoked. A lookup object can then be created as follows: class Example { void doSomething() { MethodHandles.Lookup lookup = MethodHandles.lookup(); } private void foo() { /* ... */ } } As argued before, the above lookup object could only be used to locate methods that are also visible to the Example class such asfoo. It would for example be impossible to look up a private method of another class. This is a first major difference to using the reflection API where private methods of outside classes can be located just as any other method and where these methods can even be invoked after marking such a method as accessible. Method handles are therefore sensible of their creation context which is a first major difference to the reflection API. Apart from that, a method handle is more specific than the reflection API by describing a specific type of method rather than representing just any method. In a Java program, a method's type is a composite of both the method's return type and the types of its parameters. For example, the only method of the following Counter class returns an int representing the number of characters of the only String-typed argument: class Counter { static int count(String name) { return name.length(); } } A representation of this method's type can be created by using another factory. This factory is found in the MethodType class which also represents instances of created method types. Using this factory, the method type for Counter::count can be created by handing over the method's return type and its parameter types bundled as an array: MethodType methodType = MethodType.methodType(int.class, new Class[] {String.class}); By using the lookup object that was created before and the above method type, it is now possible to locate a method handle that represents the Counter::count method as depicted in the following code: MethodType methodType = MethodType.methodType(int.class, new Class[] {String.class}); MethodHandles.Lookup lookup = MethodHandles.lookup(); MethodHandle methodHandle = lookup.findStatic(Counter.class, "count", methodType); int count = methodHandle.invokeExact("foo"); assertThat(count, is(3)); At first glance, using a method handle might seem like an overly complex version of using the reflection API. However, keep in mind that the direct invocation of a method using a handle is not the main intent of its use. The main difference of the above example code and of invoking a method via the reflection API is only revealed when looking into the differences of how the Java compiler translates both invocations into Java byte code. When a Java program invokes a method, this method is uniquely identified by its name and by its (non-generic) parameter types and even by its return type. It is for this reason that it is possible to overload methods in Java. And even though the Java programming language does not allow it, the JVM does in theory allow to overload a method by its return type. Following this principle, a reflective method call is executed as a common method call of the Method::invoke method. This method is identified by its two parameters which are of the types Object and Object[]. In addition to this, the method is identified by its Object return type. Because of this signature, all arguments to this method need to always be boxed and enclosed in an array. Similarly, the return value needs to be boxed if it was primitive or null is returned if the method was void. Method handles are the exception to this rule. Instead of invoking a method handle by referring to the signature ofMethodHandle::invokeExact signature which takes an Object[] as its single argument and returns Object, method handles are invoked by using a so-called polymorphic signature. A polymorphic signature is created by the Java compiler dependant on the types of the actual arguments and the expected return type at a call site. For example, when invoking the method handle as above with int count = methodHandle.invokeExact("foo"); the Java compiler translates this invocation as if the invokeExact method was defined to accept a single single argument of typeString and returning an int type. Obviously, such a method does not exist and for (almost) any other method, this would result in a linkage error at runtime. For method handles, the Java Virtual Machine does however recognize this signature to be polymorphic and treats the invocation of the method handle as if the Counter::count method that the handle refers to was inset directly into the call site. Thus, the method can be invoked without the overhead of boxing primitive values or the return type and without placing the argument values inside an array. At the same time, when using the invokeExact invocation, it is guaranteed to the Java virtual machine that the method handle always references a method at runtime that is compatible to the polymorphic signature. For the example, the JVM expected that the referenced method actually accepts a String as its only argument and that it returns a primitive int. If this constraint was not fulfilled, the execution would instead result in a runtime error. However, any other method that accepts a single String and that returns a primitive int could be successfully filled into the method handle's call site to replace Counter::count. In contrast, using the Counter::count method handle at the following three invocations would result in runtime errors, even though the code compiles successfully: int count1 = methodHandle.invokeExact((Object) "foo"); int count2 = (Integer) methodHandle.invokeExact("foo"); methodHandle.invokeExact("foo"); The first statement results in an error because the argument that is handed to the handle is too general. While the JVM expected a String as an argument to the method, the Java compiler suggested that the argument would be an Object type. It is important to understand that the Java compiler took the casting as a hint for creating a different polymorphic signature with anObject type as a single parameter type while the JVM expected a String at runtime. Note that this restriction also holds for handing too specific arguments, for example when casting an argument to an Integer where the method handle required aNumber type as its argument. In the second statement, the Java compiler suggested to the runtime that the handle's method would return an Integer wrapper type instead of the primitive int. And without suggesting a return type at all in the third statement, the Java compiler implicitly translated the invocation into a void method call. Hence, invokeExact really does mean exact. This restriction can sometimes be too harsh. For this reason, instead of requiring an exact invocation, the method handle also allows for a more forgiving invocation where conversions such as type castings and boxings are applied. This sort of invocation can be applied by using the MethodHandle::invoke method. Using this method, the Java compiler still creates a polymorphic signature. This time, the Java virtual machine does however test the actual arguments and the return type for compatibility at run time and converts them by applying boxings or castings, if appropriate. Obviously, these transformations can sometimes add a runtime overhead. Fields, methods and constructors: handles as a unified interface Other than Method instances of the reflection API, method handles can equally reference fields or constructors. The name of theMethodHandle type could therefore be seen as too narrow. Effectively, it does not matter what class member is referenced via a method handle at runtime as long as its MethodType, another type with a misleading name, matches the arguments that are passed at the associated call site. Using the appropriate factories of a MethodHandles.Lookup object, a field can be looked up to represent a getter or a setter. Using getters or setters in this context does not refer to invoking an actual method that follows the Java bean specification. Instead, the field-based method handle directly reads from or writes to the field but in shape of a method call via invoking the method handle. By representing such field access via method handles, field access or method invocations can be used interchangeably. As an example for such interchange, take the following class: class Bean { String value; void print(String x) { System.out.println(x); } } For the above Bean class, the following method handles can be used for either writing a string to the value field or for invoking the print method with the same string as an argument: MethodHandle fieldHandle = lookup.findSetter(Bean.class, "value", String.class); MethodType methodType = MethodType.methodType(void.class, new Class[] {String.class}); MethodHandle methodHandle = lookup.findVirtual(Bean.class, "print", methodType); As long as the method handle call site is handed an instance of Bean together with a String while returning void, both method handles could be used interchangeably as shown here: anyHandle.invokeExact((Bean) mybean, (String) myString); Note that the polymorphic signature of the above call site does not match the method type of the above handle. However, within Java byte code, non-static methods are invoked as if they were static methods with where the this reference is handed as a first, implicit argument. A non-static method's nominal type does therefore diverge from its actual runtime type. Similarly, access to a non-static field requires an instance to be access. Similarly to fields and methods, it is possible to locate and invoke constructors which are considered as methods with a voidreturn value for their nominal type. Furthermore, one can not only invoke a method directly but even invoke a super method as long as this super method is reachable for the class from which the lookup factory was created. In contrast, invoking a super method is not possible at all when relying on the reflection API. If required, it is even possible to return a constant value from a handle. Performance metrics Method handles are often described as being a more performant as the Java reflection API. At least for recent releases of the HotSpot virtual machine, this is not true. The simplest way of proving this is writing an appropriate benchmark. Then again, is not all too simple to write a benchmark for a Java program which is optimized while it is executed. The de facto standard for writing a benchmark has become using JMH, a harness that ships under the OpenJDK umbrella. The full benchmark can be found as a gist in my GitHub profile. In this article, only the most important aspects of this benchmark are covered. From the benchmark, it becomes obvious that reflection is already implemented quite efficiently. Modern JVMs know a concept named inflation where a frequently invoked reflective method call is replaced with runtime generated Java byte code. What remains is the overhead of applying the boxing for passing arguments and receiving a return values. These boxings can sometimes be eliminated by the JVM's Just-in-time compiler but this is not always possible. For this reason, using method handles can be more performant than using the reflection API if method calls involve a significant amount of primitive values. This does however require that the exact method signatures are already known at compile time such that the appropriate polymorphic signature can be created. For most use cases of the reflection API, this guarantee can however not be given because the invoked method's types are not known at compile time. In this case, using method handles does not offer any performance benefits and should not be used to replace it. Creating an invokedynamic call site Normally, invokedynamic call sites are created by the Java compiler only when it needs to translate a lambda expression into byte code. It is worthwhile to note that lambda expressions could have been implemented without invokedynamic call sites altogether, for example by converting them into anonymous inner classes. As a main difference to the suggested approach, using invokedynamic delays the creation of a similar class to runtime. We are looking into class creation in the next section. For now, bear however in mind that invokedynamic does not have anything to do with class creation, it only allows to delay the decision of how to dispatch a method until runtime. For a better understanding of invokedynamic call sites, it helps to create such call sites explicitly in order to look at the mechanic in isolation. To do so, the following example makes use of my code generation framework Byte Buddy which provides explicit byte code generation of invokedynamic call sites without requiring a any knowledge of the byte code format. Any invokedynamic call site eventually yields a MethodHandle that references the method to be invoked. Instead of invoking this method handle manually, it is however up to the Java runtime to do so. Because method handles have become a known concept to the Java virtual machine, these invocations are then optimized similarly to a common method call. Any such method handle is received from a so-called bootstrap method which is nothing more than a plain Java method that fulfills a specific signature. For a trivial example of a bootstrap method, look at the following code: class Bootstrapper { public static CallSite bootstrap(Object... args) throws Throwable { MethodType methodType = MethodType.methodType(int.class, new Class[] {String.class}) MethodHandles.Lookup lookup = MethodHandles.lookup(); MethodHandle methodHandle = lookup.findStatic(Counter.class, "count", methodType); return new ConstantCallSite(methodHandle); } } For now, we do not care much about the arguments of the method. Instead, notice that the method is static what is as a matter of fact a requirement. Within Java byte code, an invokedynamic call site references the full signature of a bootstrap method but not a specific object which could have a state and a life cycle. Once the invokedynamic call site is invoked, control flow is handed to the referenced bootstrap method which is now responsible for identifying a method handle. Once this method handle is returned from the bootstrap method, it is invoked by the Java runtime. As obvious from the above example, a MethodHandle is not returned directly from a bootstrap method. Instead, the handle is wrapped inside of a CallSite object. Whenever a bootstrap method is invoked, the invokedynamic call site is later permanently bound to the CallSite object that is returned from this method. Consequently, a bootstrap method is only invoked a single time for any call site. Thanks to this intermediate CallSite object, it is however possible to exchange the referenced MethodHandle at a later point. For this purpose, the Java class library already offers different implementations of CallSite. We have already seen a ConstantCallSite in the example code above. As the name suggests, a ConstantCallSite always references the same method handle without a possibility of a later exchange. Alternatively, it is however also possible to for example use aMutableCallSite which allows to change the referenced MethodHandle at a later point in time or it is even possible to implement a custom CallSite class. With the above bootstrap method and Byte Buddy, we can now implement a custom invokedynamic instruction. For this, Byte Buddy offers the InvokeDynamic instrumentation that accepts a bootstrap method as its only mandatory argument. Such instrumentations are then fed to Byte Buddy. Assuming the following class: abstract class Example { abstract int method(); } we can use Byte Buddy to subclass Example in order to override method. We are then going to implement this method to contain a single invokedynamic call site. Without any further configuration, Byte Buddy creates a polymorphic signature that resembles the method type of the overridden method. However, for non-static methods, the this reference is set as a first, implicit argument. Assuming that we want to bind the Counter::count method which expects a String as a single argument, we could not bind this handle to Example::method because of this type mismatch. Therefore, we need to create a different call site without the implicit argument but with an String in its place. This can be achieved by using Byte Buddy's domain specific language: Instrumentation invokeDynamic = InvokeDynamic .bootstrap(Bootstrapper.class.getDeclaredMethod(“bootstrap”, Object[].class)) .withoutImplicitArguments() .withValue("foo"); With this instrumentation in place, we can finally extend the Example class and override method to implement the invokedynamic call site as in the following code snippet: Example example = new ByteBuddy() .subclass(Example.class) .method(named(“method”)).intercept(invokeDynamic) .make() .load(Example.class.getClassLoader(), ClassLoadingStrategy.Default.INJECTION) .getLoaded() .newInstance(); int result = example.method(); assertThat(result, is(3)); As obvious from the above assertion, the characters of the "foo" string were counted correctly. By setting appropriate break points in the code, it is further possible to validate that the bootstrap method is called and that control flow further reaches theCounter::count method. So far, we did not gain much from using an invokedynamic call site. The above bootstrap method would always bindCounter::count and can therefore only produce a valid result if the invokedynamic call site really wanted to transform a Stringinto an int. Obviously, bootstrap methods can however be more flexible thanks to the arguments they receive from the invokedynamic call site. Any bootstrap method receives at least three arguments: As a first argument, the bootstrap method receives a MethodHandles.Lookup object. The security context of this object is that of the class that contains the invokedynamic call site that triggered the bootstrapping. As discussed before, this implies that private methods of the defining class could be bound to the invokedynamic call site using this lookup instance. The second argument is a String representing a method name. This string serves as a hint to indicate from the call site which method should be bound to it. Strictly speaking, this argument is not required as it is perfectly legal to bind a method with another name. Byte Buddy simply serves the the name of the overridden method as this argument, if not specified differently. Finally, the MethodType of the method handle that is expected to be returned is served as a third argument. For the example above, we specified explicitly that we expect a String as a single parameter. At the same time, Byte Buddy derived that we require an int as a return value from looking at the overridden method, as we again did not specify any explicit return type. It is up to the implementor of a bootstrap method what exact signature this method should portray as long as it can at least accept these three arguments. If the last parameter of a bootstrap method represents an Object array, this last parameter is treated as a varargs and can therefore accept any excess arguments. This is also the reason why the above example bootstrap method is valid. Additionally, a bootstrap method can receive several arguments from an invokedynamic call site as long as these arguments can be stored in a class's constant pool. For any Java class, a constant pool stores values that are used inside of a class, largely numbers or string values. As of today, such constants can be primitive values of at least 32 bit size, Strings, Classes,MethodHandles and MethodTypes. This allows bootstrap methods to be used more flexible, if locating a suitable method handle requires additional information in form of such arguments. Lambda expressions Whenever the Java compiler translates a lambda expression into byte code, it copies the lambda's body into a private method inside of the class in which the expression is defined. These methods are named lambda$X$Y with X being the name of the method that contains the lambda expression and with Y being a zero-based sequence number. The parameters of such a method are those of the functional interface that the lambda expression implements. Given that the lambda expression makes no use of non-static fields or methods of the enclosing class, the method is also defined to be static. For compensation, the lambda expression is itself substituted by an invokedynamic call site. On its invocation, this call site requests the binding of a factory for an instance of the functional interface. As arguments to this factory, the call site supplies any values of the lambda expression's enclosing method which are used inside of the expression and a reference to the enclosing instance, if required. As a return type, the factory is required to provide an instance of the functional interface. For bootstrapping a call site, any invokedynamic instruction currently delegates to the LambdaMetafactory class which is included in the Java class library. This factory is then responsible for creating a class that implements the functional interface and which invokes the appropriate method that contains the lambda's body which, as described before, is stored in the original class. In the future, this bootstrapping process might however change which is one of the major advantages of using invokedynamic for implementing lambda expressions. If one day, a better suited language feature was available for implementing lambda expressions, the current implementation could simply be swapped out. In order to being able to create a class that implements the functional interface, any call site representing a lambda expression provides additional arguments to the bootstrap method. For the obligatory arguments, it already provides the name of the functional interface's method. Also, it provides a MethodType of the factory method that the bootstrapping is supposed to yield as a result. Additionally, the bootstrap method is supplied another MethodType that describes the signature of the functional interface's method. To that, it receives a MethodHandle referencing the method that contains the lambda's method body. Finally, the call site provides a MethodType of the generic signature of the functional interface's method, i.e. the signature of the method at the call site before type-erasure was applied. When invoked, the bootstrap method looks at these arguments and creates an appropriate implementation of a class that implements the functional interface. This class is created using the ASM library, a low-level byte code parser and writer that has become the de facto standard for direct Java byte code manipulation. Besides implementing the functional interface's method, the bootstrap method also adds an appropriate constructor and a static factory method for creating instances of the class. It is this factory method that is later bound to the invokedyanmic call site. As arguments, the factory receives an instance to the lambda method's enclosing instance, in case it is accessed and also any values that are read from the enclosing method. As an example, consider the following lambda expression: class Foo { int i; void bar(int j) { Consumer consumer = k -> System.out.println(i + j + k); } } In order to be executed, the lambda expression requires access to both the enclosing instance of Foo and to the value j of its enclosing method. Therefore, the desugared version of the above class looks something like the following where the invokedynamic instruction is represented by some pseudo-code: class Foo { int i; void bar(int j) { Consumer consumer = ; } private /* non-static */ void lambda$foo$0(int j, int k) { System.out.println(this.i + j + k); } } In order to being able to invoke lambda$foo$0, both the enclosing Foo instance and the j variable are handed to the factory that is bound by the invokedyanmic instruction. This factory then receives the variables it requires in order to create an instance of the generated class. This generated class would then look something like the following: class Foo$$Lambda$0 implements Consumer { private final Foo _this; private final int j; private Foo$$Lambda$0(Foo _this, int j) { this._this = _this; this.j = j; } private static Consumer get$Lambda(Foo _this, int j) { return new Foo$$Lambda$0(_this, j); } public void accept(Object value) { // type erasure _this.lambda$foo$0(_this, j, (Integer) value); } } Eventually, the factory method of the generated class is bound to the invokedynamic call site via a method handle that is contained by a ConstantCallSite. However, if the lambda expression is fully stateless, i.e. it does not require access to the instance or method in which it is enclosed, the LambdaMetafactory returns a so-called constant method handle that references an eagerly created instance of the generated class. Hence, this instance serves as a singleton to be used for every time that the lambda expression's call site is reached. Obviously, this optimization decision affects your application's memory footprint and is something to keep in mind when writing lambda expressions. Also, no factory method is added to a class of a stateless lambda expression. You might have noticed that the lambda expression's method body is contained in a private method which is now invoked from another class. Normally, this would result in an illegal access error. To overcome this limitation, the generated classes are loaded using so-called anonymous class loading. Anonymous class loading can only be applied when a class is loaded explicitly by handing a byte array. Also, it is not normally possible to apply anonymous class loading in user code as it is hidden away in the internal classes of the Java class library. When a class is loaded using anonymous class loading, it receives a host class of which it inherits its full security context. This involves both method and field access rights and the protection domain such that a lambda expression can also be generated for signed jar files. Using this approch, lambda expression can be considered more secure than anonymous inner classes because private methods are never reachable from outside of a class. Under the covers: lambda forms Lambda forms are an implementation detail of how MethodHandles are executed by the virtual machine. Because of their name, lambda forms are however often confused with lambda expressions. Instead, lambda forms are inspired by lambda calculus and received their name for that reason, not for their actual usage to implement lambda expressions in the OpenJDK. In earlier versions of the OpenJDK 7, method handles could be executed in one of two modes. Method handles were either directly rendered as byte code or they were dispatched using explicit assembly code that was supplied by the Java runtime. The byte code rendering was applied to any method handle that was considered to be fully constant throughout the lifetime of a Java class. If the JVM could however not prove this property, the method handle was instead executed by dispatching it to the supplied assembly code. Unfortunately, because assembly code cannot be optimized by Java's JIT-compiler, this lead to non-constant method handle invocations to "fall off the performance cliff". As this also affected the lazily bound lambda expressions, this was obviously not a satisfactory solution. LambdaForms were introduced to solve this problem. Roughly speaking, lambda forms represent byte code instructions which, as stated before, can be optimized by a JIT-compiler. In the OpenJDK, a MethodHandle's invocation semantics are today represented by a LambdaForm to which the handle carries a reference. With this optimizable intermediate representation, the use of non-constant MethodHandles has become significantly more performant. As a matter of fact, it is even possible to see a byte-code compiled LambdaForm in action. Simply place a break point inside of a bootstrap method or inside of a method that is invoked via a MethodHandle. Once the break point kicks it, the byte code-translated LambdaForms can be found on the call stack. Why this matters for dynamic languages Any language that should be executed on the Java virtual machine needs to be translated to Java byte code. And as the name suggests, Java byte code aligns rather close to the Java programming language. This includes the requirement to define a strict type for any value and before invokedynamic was introduced, a method call required to specify an explicit target class for dispatching a method. Looking at the following JavaScript code, specifying either information is however not possible when translating the method into byte code: 1 2 3 function (foo) { foo.bar(); } Using an invokedynamic call site, it has become possible to delay the identification of the method's dispatcher until runtime and furthermore, to rebind the invocation target, in case that a previous decision needs to be corrected. Before, using the reflection API with all of its performance drawbacks was the only real alternative to implementing a dynamic language. The real profiteer of the invokedynamic instruction are therefore dynamic programming languages. Adding the instruction was a first step away from aligning the byte code format to the Java programming language, making the JVM a powerful runtime even for dynamic languages. And as lambda expressions proved, this stronger focus on hosting dynamic languages on the JVM does neither interfere with evolving the Java language. In contrast, the Java programming languages gained from these efforts.
April 2, 2015
by Rafael Winterhalter
· 13,748 Views · 7 Likes
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Fluent Nhibernate: Create Table from Code(Class- CodeFirst)
With Fluent Nhibernate we don’t have to write and maintain mapping in xml. You can write classes to map your domain objects to your database tables. If you want to learn how we can do this with an existing database and Fluent Nhibernate, I have also written a blog post about CRUD operation with Fluent Nhibernate and ASP.NET MVC .After writing this blog post I was getting a lot of emails about how we can create database from the Fluent Nhibernate, as we can do the same with Entity Framework Code First. So I thought it was a good idea to write a blog post about it instead of writing individual emails. How to create tables from class via Fluent Nhibernate: To Demonstrate how we can create tables based mapping classes create we’re going to create a console application via adding new project like below. Once you are done with creating application. It’s time to add reference for Fluent Nhibernate. You can do this via your library package manager like following. Now once, done with adding Fluent Nhibernate code, I have created a simple class “Customer” like below. namespace FluentNhibernateCodeFirst { public class Customer { public virtual int CustomerId { get; set; } public virtual string FirstName { get; set; } public virtual string LastName { get; set; } } } Here you can see I have created three properties which will be also column of our database table. Now as we know we need to write a mapping class for customer so below is my customer map class. using FluentNHibernate.Mapping; namespace FluentNhibernateCodeFirst { public class CustomerMap : ClassMap { public CustomerMap() { Id(c => c.CustomerId); Map(c => c.FirstName); Map(c => c.LastName); } } } Here, I map ID Customer Id as customer Id will be primary key. Now it’s time to write code creating database and saving some data into customer table created. using System; using System.Configuration; using FluentNHibernate.Cfg; using FluentNHibernate.Cfg.Db; using NHibernate; using NHibernate.Tool.hbm2ddl; namespace FluentNhibernateCodeFirst { class Program { private static ISessionFactory _sessionFactory; static void Main(string[] args) { //creating database string connectionString = ConfigurationManager.ConnectionStrings["DefaultConnectionString"].ConnectionString; CreateDatabase(connectionString); Console.WriteLine("Database Created sucessfully"); //creating a object of customer Customer customer=new Customer { CustomerId = 1, FirstName = "Jalpesh", LastName = "Vadgama" }; //saving customer in database. using(ISession session = _sessionFactory.OpenSession()) session.Save(customer); Console.WriteLine("Customer Saved"); } static void CreateDatabase(string connectionString) { var configuration = Fluently.Configure() .Database(MsSqlConfiguration.MsSql2012.ConnectionString(connectionString).ShowSql) .Mappings(m => m.FluentMappings.AddFromAssemblyOf()) .BuildConfiguration(); var exporter = new SchemaExport(configuration); exporter.Execute(true, true, false); _sessionFactory = configuration.BuildSessionFactory(); } } } Here in the above code, If you above code care fully then, I have created function called CreateDatabase. In this function First it will create a mapping and then that schema mapping will be executed against database to create table. After creating table, I have initialize the customer object and saved it into database. Now when you run this application. You will get output like below as expected. And now if you see database in the SQL Management Studio, Customer table has created like below. And If you see that table, Data is also inserted like below. That’s it. Hope you like it. Stay tuned for more!. You can find complete sourcecode of this example at github on -https://github.com/dotnetjalps/FluentHinbernateCodeFirst
March 30, 2015
by Jalpesh Vadgama
· 21,669 Views · 3 Likes
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Spark and ZooKeeper: Fault-Tolerant Job Manager out of the Box
Apache Spark, Solr, and Zookeeper work together to create a fault-tolerant, distributed ETL system that converts RDBMS data into Solr documents.
March 28, 2015
by Konstantin Smirnov
· 12,809 Views
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Using Google Protocol Buffers with Spring MVC-based REST Services
Written by Josh Long on the Spring blog This week I’m in São Paulo, Brazil presenting at QCon SP. I had an interesting discussion with someone who loves Spring’s REST stack, but wondered if there was something more efficient than plain-ol’ JSON. Indeed, there is! I often get asked about Spring’s support for high-speed binary based encoding of messages. Spring’s long supported RPC encoding with the likes of Hessian, Burlap, etc., and Spring Framework 4.1 introduced support for Google Protocol Buffers which can be used with REST services as well. From the Google Protocol Buffer website: Protocol buffers are Google’s language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages… Google uses Protocol Buffers extensively in their own, internal, service-centric architecture. A .proto document describes the types (_messages_) to be encoded and contains a definition language that should be familiar to anyone who’s used C structs. In the document, you define types, fields in those types, and their ordering (memory offsets!) in the type relative to each other. The .proto files aren’t implementations - they’re declarative descriptions of messages that may be conveyed over the wire. They can prescribe and validate constraints - the type of a given field, or the cardinatlity of that field - on the messages that are encoded and decoded. You must use the Protobuf compiler to generate the appropriate client for your language of choice. You can use Google Protocol Buffers anyway you like, but in this post we’ll look at using it as a way to encode REST service payloads. This approach is powerful: you can use content-negotiation to serve high speed Protocol Buffer payloads to the clients (in any number of languages) that accept it, and something more conventional like JSON for those that don’t. Protocol Buffer messages offer a number of improvements over typical JSON-encoded messages, particularly in a polyglot system where microservices are implemented in various technologies but need to be able to reason about communication between services in a consistant, long-term manner. Protocol Buffers are several nice features that promote stable APIs: Protocol Buffers offer backward compatibility for free. Each field is numbered in a Protocol Buffer, so you don’t have to change the behavior of the code going forward to maintain backward compatability with older clients. Clients that don’t know about new fields won’t bother trying to parse them. Protocol Buffers provide a natural place to specify validation using the required,optional, and repeated keywords. Each client enforces these constraints in their own way. Protocol Buffers are polyglot, and work with all manner of technologies. In the example code for this blog alone there is a Ruby, Python and Java client for the Java service demonstrated. It’s just a matter of using one of the numerous supported compilers. You might think that you could just use Java’s inbuilt serialization mechanism in a homogeneous service environment but, as the Protocol Buffers team were quick to point out whent hey first introduced the technology, there are some problems even with that. Java language luminary Josh Bloch’s epic tome, Effective Java, on page 213, provides further details. Let’s first look at our .proto document: package demo; option java_package = "demo"; option java_outer_classname = "CustomerProtos"; message Customer { required int32 id = 1; required string firstName = 2; required string lastName = 3; enum EmailType { PRIVATE = 1; PROFESSIONAL = 2; } message EmailAddress { required string email = 1; optional EmailType type = 2 [default = PROFESSIONAL]; } repeated EmailAddress email = 5; } message Organization { required string name = 1; repeated Customer customer = 2; } You then pass this definition to the protoc compiler and specify the output type, like this: protoc -I=$IN_DIR --java_out=$OUT_DIR $IN_DIR/customer.proto Here’s the little Bash script I put together to code-generate my various clients: #!/usr/bin/env bash SRC_DIR=`pwd` DST_DIR=`pwd`/../src/main/ echo source: $SRC_DIR echo destination root: $DST_DIR function ensure_implementations(){ # Ruby and Go aren't natively supported it seems # Java and Python are gem list | grep ruby-protocol-buffers || sudo gem install ruby-protocol-buffers go get -u github.com/golang/protobuf/{proto,protoc-gen-go} } function gen(){ D=$1 echo $D OUT=$DST_DIR/$D mkdir -p $OUT protoc -I=$SRC_DIR --${D}_out=$OUT $SRC_DIR/customer.proto } ensure_implementations gen java gen python gen ruby This will generate the appropriate client classes in the src/main/{java,ruby,python}folders. Let’s first look at the Spring MVC REST service itself. A Spring MVC REST Service In our example, we’ll register an instance of Spring framework 4.1’s org.springframework.http.converter.protobuf.ProtobufHttpMessageConverter. This type is an HttpMessageConverter. HttpMessageConverters encode and decode the requests and responses in REST service calls. They’re usually activated after some sort of content negotiation has occurred: if the client specifies Accept: application/x-protobuf, for example, then our REST service will send back the Protocol Buffer-encoded response. package demo; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.context.annotation.Bean; import org.springframework.http.converter.protobuf.ProtobufHttpMessageConverter; import org.springframework.web.bind.annotation.PathVariable; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RestController; import java.util.Arrays; import java.util.Collection; import java.util.Map; import java.util.concurrent.ConcurrentHashMap; import java.util.stream.Collectors; @SpringBootApplication public class DemoApplication { public static void main(String[] args) { SpringApplication.run(DemoApplication.class, args); } @Bean ProtobufHttpMessageConverter protobufHttpMessageConverter() { return new ProtobufHttpMessageConverter(); } private CustomerProtos.Customer customer(int id, String f, String l, Collection emails) { Collection emailAddresses = emails.stream().map(e -> CustomerProtos.Customer.EmailAddress.newBuilder() .setType(CustomerProtos.Customer.EmailType.PROFESSIONAL) .setEmail(e).build()) .collect(Collectors.toList()); return CustomerProtos.Customer.newBuilder() .setFirstName(f) .setLastName(l) .setId(id) .addAllEmail(emailAddresses) .build(); } @Bean CustomerRepository customerRepository() { Map customers = new ConcurrentHashMap<>(); // populate with some dummy data Arrays.asList( customer(1, "Chris", "Richardson", Arrays.asList("[email protected]")), customer(2, "Josh", "Long", Arrays.asList("[email protected]")), customer(3, "Matt", "Stine", Arrays.asList("[email protected]")), customer(4, "Russ", "Miles", Arrays.asList("[email protected]")) ).forEach(c -> customers.put(c.getId(), c)); // our lambda just gets forwarded to Map#get(Integer) return customers::get; } } interface CustomerRepository { CustomerProtos.Customer findById(int id); } @RestController class CustomerRestController { @Autowired private CustomerRepository customerRepository; @RequestMapping("/customers/{id}") CustomerProtos.Customer customer(@PathVariable Integer id) { return this.customerRepository.findById(id); } } Most of this code is pretty straightforward. It’s a Spring Boot application. Spring Boot automatically registers HttpMessageConverter beans so we need only define the ProtobufHttpMessageConverter bean and it gets configured appropriately. The @Configuration class seeds some dummy date and a mock CustomerRepository object. I won’t reproduce the Java type for our Protocol Buffer, demo/CustomerProtos.java, here as it is code-generated bit twiddling and parsing code; not all that interesting to read. One convenience is that the Java implementation automatically provides builder methods for quickly creating instances of these types in Java. The code-generated types are dumb struct like objects. They’re suitable for use as DTOs, but should not be used as the basis for your API. Do not extend them using Java inheritance to introduce new functionality; it’ll break the implementation and it’s bad OOP practice, anyway. If you want to keep things cleaner, simply wrapt and adapt them as appropriate, perhaps handling conversion from an ORM entity to the Protocol Buffer client type as appropriate in that wrapper. HttpMessageConverters may also be used with Spring’s REST client, the RestTemplate. Here’s the appropriate Java-language unit test: package demo; import org.junit.Test; import org.junit.runner.RunWith; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.boot.test.IntegrationTest; import org.springframework.boot.test.SpringApplicationConfiguration; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.http.ResponseEntity; import org.springframework.http.converter.protobuf.ProtobufHttpMessageConverter; import org.springframework.test.context.junit4.SpringJUnit4ClassRunner; import org.springframework.test.context.web.WebAppConfiguration; import org.springframework.web.client.RestTemplate; import java.util.Arrays; @RunWith(SpringJUnit4ClassRunner.class) @SpringApplicationConfiguration(classes = DemoApplication.class) @WebAppConfiguration @IntegrationTest public class DemoApplicationTests { @Configuration public static class RestClientConfiguration { @Bean RestTemplate restTemplate(ProtobufHttpMessageConverter hmc) { return new RestTemplate(Arrays.asList(hmc)); } @Bean ProtobufHttpMessageConverter protobufHttpMessageConverter() { return new ProtobufHttpMessageConverter(); } } @Autowired private RestTemplate restTemplate; private int port = 8080; @Test public void contextLoaded() { ResponseEntity customer = restTemplate.getForEntity( "http://127.0.0.1:" + port + "/customers/2", CustomerProtos.Customer.class); System.out.println("customer retrieved: " + customer.toString()); } } Things just work as you’d expect, not only in Java and Spring, but also in Ruby and Python. For completeness, here is a simple client using Ruby (client types omitted): #!/usr/bin/env ruby require './customer.pb' require 'net/http' require 'uri' uri = URI.parse('http://localhost:8080/customers/3') body = Net::HTTP.get(uri) puts Demo::Customer.parse(body) ..and here’s a client in Python (client types omitted): #!/usr/bin/env python import urllib import customer_pb2 if __name__ == '__main__': customer = customer_pb2.Customer() customers_read = urllib.urlopen('http://localhost:8080/customers/1').read() customer.ParseFromString(customers_read) print customer Where to go from Here If you want very high speed message encoding that works with multiple languages, Protocol Buffers are a compelling option. There are other encoding technologies like Avro or Thrift, but none nearly so mature and entrenched as Protocol Buffers. You don’t necessarily need to use Protocol Buffers with REST, either. You could plug it into some sort of RPC service, if that’s your style. There are almost as many client implementations as there are buildpacks for Cloud Foundry - so you could run almost anything on Cloud Foundry and enjoy the same high speed, consistent messaging across all your services! The code for this example is available online, as well, so don’t hesitate to check it out! Also.. Hi gang, in 2015, I’ve been trying to do a random tech-tip style post every week based on things that I see garnering interest in the community, either here or on the Pivotal blog. I use these weekly-_ish_ (OK! OK! - it’s not been easy doing them as regularly as This Week in Spring, but so far I haven’t missed a week! :-) ) posts as a chance to focus not on a specific new release, per se, but on the application of Spring in service to some community use case that might be cross-cutting or just might benefit from having a spotlight shined on it. So far we’ve looked at all manner of things - Vaadin, Activiti, 12-Factor App Style Configuration, Smarter Service to Service Invocations, Couchbase, and much more, etc. - and we’ve got some interesting stuff lined up, too. I wondered what else you want to see talked about, however. If you’ve got some ideas about what you’d like to see covered, or a community post of your own to contribute, reach out to me on Twitter (@starbuxman) or via email (jlong [at] pivotal [dot] io). I remain, as always, at your service.
March 27, 2015
by Pieter Humphrey
· 15,172 Views
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How to Read Call Logs Programmatically From Android
It’s fairly easy. You need to add the following uses-permission in the Android manifest to get call history programmatically. interface in your activity. It has three methods. abstract Loader onCreateLoader(int id, Bundle args) //Instantiate and return a new Loader for the given ID. abstract void onLoadFinished(Loader loader, D data) //Called when a previously created loader has finished its load. abstract void onLoaderReset(Loader loader) //Called when a previously created loader is being reset, and thus making its data unavailable. To initialize a query, we need to call LoaderManager.initLoader() at the very first place. We are going to add a button and call this in that button events here and after this background framework will be initialized. As soon as the background framework is initialized, it calls your implementation of onCreateLoader(). To start the query, we have to return a CursorLoader from this method. @Override public Loader onCreateLoader(int loaderID, Bundle args) { Log.d(TAG, "onCreateLoader() >> loaderID : " + loaderID); switch (loaderID) { case URL_LOADER: // Returns a new CursorLoader return new CursorLoader( this, // Parent activity context CallLog.Calls.CONTENT_URI, // Table to query null, // Projection to return null, // No selection clause null, // No selection arguments null // Default sort order ); default: return null; } } We are going access our expected data from a Cursor. And we will get this in theonLoadFinished() method. @Override public void onLoadFinished(Loader loader, Cursor managedCursor) { Log.d(TAG, "onLoadFinished()"); StringBuilder sb = new StringBuilder(); int number = managedCursor.getColumnIndex(CallLog.Calls.NUMBER); int type = managedCursor.getColumnIndex(CallLog.Calls.TYPE); int date = managedCursor.getColumnIndex(CallLog.Calls.DATE); int duration = managedCursor.getColumnIndex(CallLog.Calls.DURATION); sb.append("Call Log Details "); sb.append("\n"); sb.append("\n"); sb.append(""); while (managedCursor.moveToNext()) { String phNumber = managedCursor.getString(number); String callType = managedCursor.getString(type); String callDate = managedCursor.getString(date); Date callDayTime = new Date(Long.valueOf(callDate)); String callDuration = managedCursor.getString(duration); String dir = null; int callTypeCode = Integer.parseInt(callType); switch (callTypeCode) { case CallLog.Calls.OUTGOING_TYPE: dir = "Outgoing"; break; case CallLog.Calls.INCOMING_TYPE: dir = "Incoming"; break; case CallLog.Calls.MISSED_TYPE: dir = "Missed"; break; } sb.append("") .append("Phone Number: ") .append("") .append(phNumber) .append(""); sb.append(""); sb.append(""); sb.append("") .append("Call Type:") .append("") .append(dir) .append(""); sb.append(""); sb.append(""); sb.append("") .append("Date & Time:") .append("") .append(callDayTime) .append(""); sb.append(""); sb.append(""); sb.append("") .append("Call Duration (Seconds):") .append("") .append(callDuration) .append(""); sb.append(""); sb.append(""); sb.append(""); } sb.append(""); managedCursor.close(); callLogsTextView.setText(Html.fromHtml(sb.toString())); } Output: Full Source code: https://github.com/rokon12/call-log
March 27, 2015
by A N M Bazlur Rahman DZone Core CORE
· 40,430 Views · 2 Likes
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Getting Started with Couchbase and Spring Data Couchbase
Written by Josh Long on the Spring blog. This blog was inspired by a talk that Laurent Doguin, a developer advocate over at Couchbase, and I gave at Couchbase Connect last year. Merci Laurent! This is a demo of the Spring Data Couchbase integration. From the project page, Spring Data Couchbase is: The Spring Data Couchbase project provides integration with the Couchbase Server database. Key functional areas of Spring Data Couchbase are a POJO centric model for interacting with Couchbase Buckets and easily writing a Repository style data access layer. What is Couchbase? Couchbase is a distributed data-store that enjoys true horizontal scaling. I like to think of it as a mix of Redis and MongoDB: you work with documents that are accessed through their keys. There are numerous client APIs for all languages. If you’re using Couchbase for your backend and using the JVM, you’ll love Spring Data Couchbase. The bullets on the project home page best enumerate its many features: Spring configuration support using Java based @Configuration classes or an XML namespace for the Couchbase driver. CouchbaseTemplate helper class that increases productivity performing common Couchbase operations. Includes integrated object mapping between documents and POJOs. Exception translation into Spring’s portable Data Access Exception hierarchy. Feature Rich Object Mapping integrated with Spring’s Conversion Service. Annotation based mapping metadata but extensible to support other metadata formats. Automatic implementation of Repository interfaces including support for custom finder methods (backed by Couchbase Views). JMX administration and monitoring Transparent @Cacheable support to cache any objects you need for high performance access. Running Couchbase Use Vagrant to Run Couchbase Locally You will need to have Couchbase installed if you don’t already (naturally). Michael Nitschinger (@daschl, also lead of the Spring Data Couchbase project), blogged about how to get a simple4-node Vagrant cluster up and running here. I’ve reproduced his example here in the vagrantdirectory. To use it, you’ll need to install Virtual Box and Vagrant, of course, but then simply run vagrant up in the vagrant directory. To get the most up-to-date version of this configuration script, I went to Michael’s GitHub vagrants project and found that, beyond this example, there are numerous other Vagrant scripts available. I have a submodule in this code’s project directory that points to that, but be sure to consult that for the latest-and-greatest. To get everything running on my machine, I chose the Ubuntu 12 installation of Couchbase 3.0.2. You can change how many nodes are started by configuring the VAGRANT_NODES environment variable before startup: VAGRANT_NODES=2 vagrant up You’ll need to administer and configure Couchbase on initial setup. Point your browser to the right IP for each node. The rules for determining that IP are well described in the README. The admin interface, in my case, was available at 192.168.105.101:8091 and192.168.105.102:8091. For more on this process, I recommend that you follow theguidelines here for the details. Here’s how I did it. I hit the admin interface on the first node and created a new cluster. I usedadmin for the username and password for the password. On all subsequent management pages, I simply joined the existing cluster by pointing the nodes to 192.168.105.101 and using the aforementioned admin credential. Once you’ve joined all nodes, look for theRebalance button in the Server Nodes panel and trigger a cluster rebalance. If you are done with your Vagrant cluster, you can use the vagrant halt command to shut it down cleanly. Very handy is also vagrant suspend, which will save the state of the nodes instead of shutting them down completely. If you want to administer the Couchbase cluster from the command line there is the handycouchbase-cli. You can simply use the vagrant ssh command to get into each of the nodes (by their node-names: node1, node2, etc..). Once there, you can run cluster configuration commands. For example the server-list command will enumerate cluster nodes. /opt/couchbase/bin/couchbase-cli server-list -c 192.168.56.101-u admin -p password It’s easy to trigger a rebalance using: /opt/couchbase/bin/couchbase-cli rebalance -c 192.168.56.101-u admin -p password Couchbase In the Cloud and on Cloud Foundry Couchbase lends itself to use in the cloud. It’s horizontally scalable (like Gemfire or Cassandra) in that there’s no single point of failure. It does not employ a master-slave or active/passive system. There are a few ways to get it up and running where your applications are running. If you’re running a Cloud Foundry installation, then you can install the the Cumulogic Service Broker which then lets your Cloud Foundry installation talk to the Cumulogic platform which itself can manage Couchbase instances. Service brokers are the bit of integration code that teach Cloud Foundry how to provision, destroy and generally interact with a managed service, like Couchbase, in this case. Using Spring Data Couchbase to Store Facebook Places Let’s look at a simple example that reads data (in this case from the Facebook Places API using Spring Social Facebook’s FacebookTemplate API) and then loads it into the Couchbase server. Get a Facebook Access Token You’ll also need a Facebook access token. The easiest way to do this is to go to the Facebook Developer Portal and create a new application and then get an application ID and an application secret. Take these two values and concatenate them with a pike character (|). Thus, you’ll have something of the form: appID|appSecret. The sample application uses Spring’s Environment mechanism to resolve the facebook.accessToken key. You can provide a value for it in the src/main/resources/application.properties file or using any of the other supported Spring Boot property resolution mechanisms. You could even provide the value as a -D argument: -Dfacebook.accessToken=...|... Telling Spring Data Couchbase About our Cluster Data in Couchbase is stored in buckets. It’s logically the same as a database in a SQL RDBMS. It is typically replicated across nodes and has its own configuration. We’ll be using the defaultbucket, but it’s a snap to create more buckets. Let’s look at the basic configuration required to use Spring Data Couchbase (in this case, in terms of a Spring Boot application): @SpringBootApplication @EnableScheduling @EnableCaching public class Application { @EnableCouchbaseRepositories @Configuration static class CouchbaseConfiguration extends AbstractCouchbaseConfiguration { @Value("${couchbase.cluster.bucket}") private String bucketName; @Value("${couchbase.cluster.password}") private String password; @Value("${couchbase.cluster.ip}") private String ip; @Override protected List bootstrapHosts() { return Arrays.asList(this.ip); } @Override protected String getBucketName() { return this.bucketName; } @Override protected String getBucketPassword() { return this.password; } } // more beans } A Spring Data Couchbase Repository Spring Data provides the notion of repositories - objects that handle typical data-access logic and provide convention-based queries. They can be used to map POJOs to data in the backing data store. Our example simply stores the information on businesses it reads from Facebook’s Places API. To acheive this we’ve created a simple Place entity that Spring Data Couchbase repositories will know how to persist: @Document(expiry = 0) class Place { @Id private String id; @Field private Location location; @Field @NotNull private String name; @Field private String affilitation, category, description, about; @Field private Date insertionDate; // .. getters, constructors, toString, etc } The Place entity references another entity, Location, which is basically the same. In the case of Spring Data Couchbase, repository finder methods map to views - queries written in JavaScript - in a Couchbase server. You’ll need to setup views on the Couchbase servers. Go to any Couchbase server’s admin console and visit the Views screen, then clickCreate Development View and name it place, as our entity will be demo.Place (the development view name is adapted from the entity’s class name by default). We’ll create two views, the generic all, which is required for any Spring Data Couchbase POJO, and the byName view, which will be used to drive the repository’s findByName finder method. This mapping is by convention, though you can override which view is employed with the @View annotation on the finder method’s declaration. First, all: Now, byName: When you’re done, be sure to Publish each view! Now you can use Spring Data repositories as you’d expect. The only thing that’s a bit different about these repositories is that we’re declaring a Spring Data Couchbase Query type for the argument to the findByName finder method, not a String. Using the @Query is straightforward: Query query = new Query(); query.setKey("Philz Coffee"); Collection places = placeRepository.findByName(query); places.forEach(System.out::println); Where to go from Here We’ve only covered some of the basics here. Spring Data Couchbase supports the Java bean validation API, and can be configured to honor validation constraints on its entities. Spring Data Couchbase also provides lower-level access to the CouchbaseClient API, if you want it. Spring Data Couchbase also implements the Spring CacheManager abstraction - you can use@Cacheable and friends with data on service methods and it’ll be transparently persisted to Couchbase for you. The code for this example is in my Github repository, co-developed with my pal Laurent Doguin (@ldoguin) over at Couchbase.
March 24, 2015
by Pieter Humphrey
· 19,305 Views
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Getting Started With Activiti and Spring Boot
This post is a guest post by Activiti co-founder and community member Joram Barrez (@jbarrez) who works for Alfresco. Thanks Joram! I’d like to see more of these community guest posts, so - as usual - don’t hesitate to ping me (@starbuxman) with ideas and contributions! -Josh Introduction Activiti is an Apache-licensed business process management (BPM) engine. Such an engine has as core goal to take a process definition comprised of human tasks and service calls and execute those in a certain order, while exposing various API’s to start, manage and query data about process instances for that definition. Contrary to many of its competitors, Activiti is lightweight and integrates easily with any Java technology or project. All that, and it works at any scale - from just a few dozen to many thousands or even millions of process executions. The source code of Activiti can be found on Github. The project was founded and is sponsored by Alfresco, but enjoys contributions from all across the globe and industries. A process definition is typically visualized as a flow-chart-like diagram. In recent years, the BPMN 2.0 standard (an OMG standard, like UML) has become the de-facto ‘language’ of these diagrams. This standard defines how a certain shape on the diagram should be interpreted, both technically and business-wise and how it is stored, as a not-so-hipster XML file.. but luckily most of the tooling hides this for you. This is a standard, and you can use any number of compliant tools to design (and even run) your BPMN processes. That said, if you’re asking me, there is no better choice than Activiti! Spring Boot integration Activiti and Spring play nicely together. The convention-over-configuration approach in Spring Boot works nicely with Activiti’s process engine is setup and use. Out of the box, you only need a database, as process executions can span anywhere from a few seconds to a couple of years. Obviously, as an intrinsic part of a process definition is calling and consuming data to and from various systems with all kinds of technologies. The simplicity of adding the needed dependencies and integrating various pieces of (boiler-plate) logic with Spring Boot really makes this child’s play. Using Spring Boot and Activiti in a microservice approach also makes a lot of sense. Spring Boot makes it easy to get a production-ready service up and running in no time and - in a distributed microservice architecture - Activiti processes can glue together various microservices while also weaving in human workflow (tasks and forms) to achieve a certain goal. The Spring Boot integration in Activiti was created by Spring expert Josh Long. Josh and I did a webinar a couple of months ago that should give you a good insight into the basics of the Activiti integration for Spring Boot. The Activiti user guide section on Spring Boot is also a great starting place to get more information. Getting Started The code for this example can be found in my Github repository. The process we’ll implement here is a hiring process for a developer. It’s simplified of course (as it needs to fit on this web page), but you should get the core concepts. Here’s the diagram: As said in the introduction, all shapes here have a very specific interpretation thanks to the BPMN 2.0 standard. But even without knowledge of BPMN, the process is pretty easy to understand: When the process starts, the resume of the job applicant is stored in an external system. The process then waits until a telephone interview has been conducted. This is done by a user (see the little icon of a person in the corner). If the telephone interview wasn’t all that, a polite rejection email is sent. Otherwise, both a tech interview and financial negotiation should happen. Note that at any point, the applicant can cancel. That’s shown in the diagram as the event on the boundary of the big rectangle. When the event happens, everything inside will be killed and the process halts. If all goes well, a welcome email is sent. This is the BPMN for this process Let’s create a new Maven project, and add the dependencies needed to get Spring Boot, Activiti and a database. We’ll use an in memory database to keep things simple. org.activiti spring-boot-starter-basic ${activiti.version} com.h2database h2 1.4.185 So only two dependencies is what is needed to create a very first Spring Boot + Activiti application: @SpringBootApplication public class MyApp { public static void main(String[] args) { SpringApplication.run(MyApp.class, args); } } You could already run this application, it won’t do anything functionally but behind the scenes it already creates an in-memory H2 database creates an Activiti process engine using that database exposes all Activiti services as Spring Beans configures tidbits here and there such as the Activiti async job executor, mail server, etc. Let’s get something running. Drop the BPMN 2.0 process definition into thesrc/main/resources/processes folder. All processes placed here will automatically be deployed (ie. parsed and made to be executable) to the Activiti engine. Let’s keep things simple to start, and create a CommandLineRunner that will be executed when the app boots up: @Bean CommandLineRunner init( final RepositoryService repositoryService, final RuntimeService runtimeService, final TaskService taskService) { return new CommandLineRunner() { public void run(String... strings) throws Exception { Map variables = new HashMap(); variables.put("applicantName", "John Doe"); variables.put("email", "[email protected]"); variables.put("phoneNumber", "123456789"); runtimeService.startProcessInstanceByKey("hireProcess", variables); } }; } So what’s happening here is that we create a map of all the variables needed to run the process and pass it when starting process. If you’d check the process definition you’ll see we reference those variables using ${variableName} in many places (such as the task description). The first step of the process is an automatic step (see the little cogwheel icon), implemented using an expression that uses a Spring Bean: which is implemented with activiti:expression="${resumeService.storeResume()}" Of course, we need that bean or the process would not start. So let’s create it: @Component public class ResumeService { public void storeResume() { System.out.println("Storing resume ..."); } } When running the application now, you’ll see that the bean is called: . ____ _ __ _ _ /\\ / ___'_ __ _ _(_)_ __ __ _ \ \ \ \ ( ( )\___ | '_ | '_| | '_ \/ _` | \ \ \ \ \\/ ___)| |_)| | | | | || (_| | ) ) ) ) ' |____| .__|_| |_|_| |_\__, | / / / / =========|_|==============|___/=/_/_/_/ :: Spring Boot :: (v1.2.0.RELEASE) 2015-02-16 11:55:11.129 INFO 304 --- [ main] MyApp : Starting MyApp on The-Activiti-Machine.local with PID 304 ... Storing resume ... 2015-02-16 11:55:13.662 INFO 304 --- [ main] MyApp : Started MyApp in 2.788 seconds (JVM running for 3.067) And that’s it! Congrats with running your first process instance using Activiti in Spring Boot! Let’s spice things up a bit, and add following dependency to our pom.xml: org.activiti spring-boot-starter-rest-api ${activiti.version} Having this on the classpath does a nifty thing: it takes the Activiti REST API (which is written in Spring MVC) and exposes this fully in your application. The REST API of Activiti is fully documented in the Activiti User Guide. The REST API is secured by basic auth, and won’t have any users by default. Let’s add an admin user to the system as shown below (add this to the MyApp class). Don’t do this in a production system of course, there you’ll want to hook in the authentication to LDAP or something else. @Bean InitializingBean usersAndGroupsInitializer(final IdentityService identityService) { return new InitializingBean() { public void afterPropertiesSet() throws Exception { Group group = identityService.newGroup("user"); group.setName("users"); group.setType("security-role"); identityService.saveGroup(group); User admin = identityService.newUser("admin"); admin.setPassword("admin"); identityService.saveUser(admin); } }; } Start the application. We can now start a process instance as we did in the CommandLineRunner, but now using REST: curl -u admin:admin -H "Content-Type: application/json" -d '{"processDefinitionKey":"hireProcess", "variables": [ {"name":"applicantName", "value":"John Doe"}, {"name":"email", "value":"[email protected]"}, {"name":"phoneNumber", "value":"1234567"} ]}' http://localhost:8080/runtime/process-instances Which returns us the json representation of the process instance: { "tenantId": "", "url": "http://localhost:8080/runtime/process-instances/5", "activityId": "sid-42BAE58A-8FFB-4B02-AAED-E0D8EA5A7E39", "id": "5", "processDefinitionUrl": "http://localhost:8080/repository/process-definitions/hireProcess:1:4", "suspended": false, "completed": false, "ended": false, "businessKey": null, "variables": [], "processDefinitionId": "hireProcess:1:4" } I just want to stand still for a moment how cool this is. Just by adding one dependency, you’re getting the whole Activiti REST API embedded in your application! Let’s make it even cooler, and add following dependency org.activiti spring-boot-starter-actuator ${activiti.version} This adds a Spring Boot actuator endpoint for Activiti. If we restart the application, and hithttp://localhost:8080/activiti/, we get some basic stats about our processes. With some imagination that in a live system you’ve got many more process definitions deployed and executing, you can see how this is useful. The same actuator is also registered as a JMX bean exposing similar information. { completedTaskCountToday: 0, deployedProcessDefinitions: [ "hireProcess (v1)" ], processDefinitionCount: 1, cachedProcessDefinitionCount: 1, runningProcessInstanceCount: { hireProcess (v1): 0 }, completedTaskCount: 0, completedActivities: 0, completedProcessInstanceCount: { hireProcess (v1): 0 }, openTaskCount: 0 } To finish our coding, let’s create a dedicated REST endpoint for our hire process, that could be consumed by for example a javascript web application (out of scope for this article). So most likely, we’ll have a form for the applicant to fill in the details we’ve been passing programmatically above. And while we’re at it, let’s store the applicant information as a JPA entity. In that case, the data won’t be stored in Activiti anymore, but in a separate table and referenced by Activiti when needed. You probably guessed it by now, JPA support is enabled by adding a dependency: org.activiti spring-boot-starter-jpa ${activiti.version} and add the entity to the MyApp class: @Entity class Applicant { @Id @GeneratedValue private Long id; private String name; private String email; private String phoneNumber; // Getters and setters We’ll also need a Repository for this Entity (put this in a separate file or also in MyApp). No need for any methods, the Repository magic from Spring will generate the methods we need for us. public interface ApplicantRepository extends JpaRepository { // .. } And now we can create the dedicated REST endpoint: @RestController public class MyRestController { @Autowired private RuntimeService runtimeService; @Autowired private ApplicantRepository applicantRepository; @RequestMapping(value="/start-hire-process", method= RequestMethod.POST, produces= MediaType.APPLICATION_JSON_VALUE) public void startHireProcess(@RequestBody Map data) { Applicant applicant = new Applicant(data.get("name"), data.get("email"), data.get("phoneNumber")); applicantRepository.save(applicant); Map variables = new HashMap(); variables.put("applicant", applicant); runtimeService.startProcessInstanceByKey("hireProcessWithJpa", variables); } } Note we’re now using a slightly different process called ‘hireProcessWithJpa’, which has a few tweaks in it to cope with the fact the data is now in a JPA entity. So for example, we can’t use ${applicantName} anymore, but we now have to use ${applicant.name}. Let’s restart the application and start a new process instance: curl -u admin:admin -H "Content-Type: application/json" -d '{"name":"John Doe", "email": "[email protected]", "phoneNumber":"123456789"}' http://localhost:8080/start-hire-process We can now go through our process. You could create a custom endpoints for this too, exposing different task queries with different forms … but I’ll leave this to your imagination and use the default Activiti REST end points to walk through the process. Let’s see which task the process instance currently is at (you could pass in more detailed parameters here, for example the ‘processInstanceId’ for better filtering): curl -u admin:admin -H "Content-Type: application/json" http://localhost:8080/runtime/tasks which returns { "order": "asc", "size": 1, "sort": "id", "total": 1, "data": [{ "id": "14", "processInstanceId": "8", "createTime": "2015-02-16T13:11:26.078+01:00", "description": "Conduct a telephone interview with John Doe. Phone number = 123456789", "name": "Telephone interview" ... }], "start": 0 } So, our process is now at the Telephone interview. In a realistic application, there would be a task list and a form that could be filled in to complete this task. Let’s complete this task (we have to set the telephoneInterviewOutcome variable as the exclusive gateway uses it to route the execution): curl -u admin:admin -H "Content-Type: application/json" -d '{"action" : "complete", "variables": [ {"name":"telephoneInterviewOutcome", "value":true} ]}' http://localhost:8080/runtime/tasks/14 When we get the tasks again now, the process instance will have moved on to the two tasks in parallel in the subprocess (big rectangle): { "order": "asc", "size": 2, "sort": "id", "total": 2, "data": [ { ... "name": "Tech interview" }, { ... "name": "Financial negotiation" } ], "start": 0 } We can now continue the rest of the process in a similar fashion, but I’ll leave that to you to play around with. Testing One of the strengths of using Activiti for creating business processes is that everything is simply Java. As a consequence, processes can be tested as regular Java code with unit tests. Spring Boot makes writing such test a breeze. Here’s how the unit test for the “happy path” looks like (while omitting @Autowired fields and test e-mail server setup). The code also shows the use of the Activiti API’s for querying tasks for a given group and process instance. @RunWith(SpringJUnit4ClassRunner.class) @SpringApplicationConfiguration(classes = {MyApp.class}) @WebAppConfiguration @IntegrationTest public class HireProcessTest { @Test public void testHappyPath() { // Create test applicant Applicant applicant = new Applicant("John Doe", "[email protected]", "12344"); applicantRepository.save(applicant); // Start process instance Map variables = new HashMap(); variables.put("applicant", applicant); ProcessInstance processInstance = runtimeService.startProcessInstanceByKey("hireProcessWithJpa", variables); // First, the 'phone interview' should be active Task task = taskService.createTaskQuery() .processInstanceId(processInstance.getId()) .taskCandidateGroup("dev-managers") .singleResult(); Assert.assertEquals("Telephone interview", task.getName()); // Completing the phone interview with success should trigger two new tasks Map taskVariables = new HashMap(); taskVariables.put("telephoneInterviewOutcome", true); taskService.complete(task.getId(), taskVariables); List tasks = taskService.createTaskQuery() .processInstanceId(processInstance.getId()) .orderByTaskName().asc() .list(); Assert.assertEquals(2, tasks.size()); Assert.assertEquals("Financial negotiation", tasks.get(0).getName()); Assert.assertEquals("Tech interview", tasks.get(1).getName()); // Completing both should wrap up the subprocess, send out the 'welcome mail' and end the process instance taskVariables = new HashMap(); taskVariables.put("techOk", true); taskService.complete(tasks.get(0).getId(), taskVariables); taskVariables = new HashMap(); taskVariables.put("financialOk", true); taskService.complete(tasks.get(1).getId(), taskVariables); // Verify email Assert.assertEquals(1, wiser.getMessages().size()); // Verify process completed Assert.assertEquals(1, historyService.createHistoricProcessInstanceQuery().finished().count()); } Next steps We haven’t touched any of the tooling around Activiti. There is a bunch more than just the engine, like the Eclipse plugin to design processes, a free web editor in the cloud (also included in the .zip download you can get from Activiti's site, a web application that showcases many of the features of the engine, … The current release of Activiti (version 5.17.0) has integration with Spring Boot 1.1.6. However, the current master version is compatible with 1.2.1. Using Spring Boot 1.2.0 brings us sweet stuff like support for XA transactions with JTA. This means you can hook up your processes easily with JMS, JPA and Activiti logic all in the same transaction! ..Which brings us to the next point … In this example, we’ve focussed heavily on human interactions (and barely touched it). But there’s many things you can do around orchestrating systems too. The Spring Boot integration also has Spring Integration support you could leverage to do just that in a very neat way! And of course there is much much more about the BPMN 2.0 standard. Read more about itin the Activiti docs.
March 20, 2015
by Pieter Humphrey
· 50,992 Views · 4 Likes
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Walking Recursive Data Structures Using Java 8 Streams
The Streams API is a real gem in Java 8, and I keep finding more or less unexpected uses for them. I recently wrote about using them as ForkJoinPool facade. Here’s another interesting example: Walking recursive data structures. Without much ado, have a look at the code: class Tree { private int value; private List children = new LinkedList<>(); public Tree(int value, List children) { super(); this.value = value; this.children.addAll(children); } public Tree(int value, Tree... children) { this(value, asList(children)); } public int getValue() { return value; } public List getChildren() { return Collections.unmodifiableList(children); } public Stream flattened() { return Stream.concat( Stream.of(this), children.stream().flatMap(Tree::flattened)); } } It’s pretty boring, except for the few highlighted lines. Let’s say we want to be able to find elements matching some criteria in the tree or find particular element. One typical way to do it is a recursive function – but that has some complexity and is likely to need a mutable argument (e.g. a set where you can append matching elements). Another approach is iteration with a stack or a queue. They work fine, but take a few lines of code and aren’t so easy to generalize. Here’s what we can do with this flattened function: // Get all values in the tree: t.flattened().map(Tree::getValue).collect(toList()); // Get even values: t.flattened().map(Tree::getValue).filter(v -> v % 2 == 0).collect(toList()); // Sum of even values: t.flattened().map(Tree::getValue).filter(v -> v % 2 == 0).reduce((a, b) -> a + b); // Does it contain 13? t.flattened().anyMatch(t -> t.getValue() == 13); I think this solution is pretty slick and versatile. One line of code (here split to 3 for readability on blog) is enough to flatten the tree to a straightforward stream that can be searched, filtered and whatnot. It’s not perfect though: It is not lazy and flattened is called for each and every node in the tree every time. It probably could be improved using a Supplier. Anyway, it doesn’t matter for typical, reasonably small trees, especially in a business application on a very tall stack of libraries. But for very large trees, very frequent execution and tight time constraints the overhead might cause some trouble.
March 18, 2015
by Konrad Garus
· 25,137 Views · 1 Like
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The State of the Storage Engine
This article by Baron Schwartz comes to you from the DZone Guide to Database and Persistence Management.
March 16, 2015
by B Jones
· 16,573 Views · 1 Like
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A Beginner's Guide to JPA and Hibernate Cascade Types
Introduction JPA translates entity state transitions to database DML statements. Because it’s common to operate on entity graphs, JPA allows us to propagate entity state changes from Parents to Child entities. This behavior is configured through the CascadeType mappings. JPA vs Hibernate Cascade Types Hibernate supports all JPA Cascade Types and some additional legacy cascading styles. The following table draws an association between JPA Cascade Types and their Hibernate native API equivalent: JPA EntityManager action JPA CascadeType Hibernate native Session action Hibernate native CascadeType Event Listener detach(entity) DETACH evict(entity) DETACH or EVICT Default Evict Event Listener merge(entity) MERGE merge(entity) MERGE Default Merge Event Listener persist(entity) PERSIST persist(entity) PERSIST Default Persist Event Listener refresh(entity) REFRESH refresh(entity) REFRESH Default Refresh Event Listener remove(entity) REMOVE delete(entity) REMOVE orDELETE Default Delete Event Listener saveOrUpdate(entity) SAVE_UPDATE Default Save Or Update Event Listener replicate(entity, replicationMode) REPLICATE Default Replicate Event Listener lock(entity, lockModeType) buildLockRequest(entity, lockOptions) LOCK Default Lock Event Listener All the above EntityManager methods ALL All the above Hibernate Session methods ALL From this table we can conclude that: There’s no difference between calling persist, merge or refresh on the JPAEntityManager or the Hibernate Session. The JPA remove and detach calls are delegated to Hibernate delete and evict native operations. Only Hibernate supports replicate and saveOrUpdate. While replicate is useful for some very specific scenarios (when the exact entity state needs to be mirrored between two distinct DataSources), the persist and merge combo is always a better alternative than the native saveOrUpdate operation. As a rule of thumb, you should always use persist for TRANSIENT entities and merge for DETACHED ones.The saveOrUpdate shortcomings (when passing a detached entity snapshot to aSession already managing this entity) had lead to the merge operation predecessor: the now extinct saveOrUpdateCopy operation. The JPA lock method shares the same behavior with Hibernate lock request method. The JPA CascadeType.ALL doesn’t only apply to EntityManager state change operations, but to all Hibernate CascadeTypes as well. So if you mapped your associations with CascadeType.ALL, you can still cascade Hibernate specific events. For example, you can cascade the JPA lock operation (although it behaves as reattaching, instead of an actual lock request propagation), even if JPA doesn’t define a LOCK CascadeType. Cascading best practices Cascading only makes sense only for Parent – Child associations (the Parent entity state transition being cascaded to its Child entities). Cascading from Child to Parent is not very useful and usually, it’s a mapping code smell. Next, I’m going to take analyse the cascading behaviour of all JPA Parent – Childassociations. One-To-One The most common One-To-One bidirectional association looks like this: @Entity public class Post { @Id @GeneratedValue(strategy = GenerationType.AUTO) private Long id; private String name; @OneToOne(mappedBy = "post", cascade = CascadeType.ALL, orphanRemoval = true) private PostDetails details; public Long getId() { return id; } public PostDetails getDetails() { return details; } public String getName() { return name; } public void setName(String name) { this.name = name; } public void addDetails(PostDetails details) { this.details = details; details.setPost(this); } public void removeDetails() { if (details != null) { details.setPost(null); } this.details = null; } } @Entity public class PostDetails { @Id @GeneratedValue(strategy = GenerationType.AUTO) private Long id; @Column(name = "created_on") @Temporal(TemporalType.TIMESTAMP) private Date createdOn = new Date(); private boolean visible; @OneToOne @PrimaryKeyJoinColumn private Post post; public Long getId() { return id; } public void setVisible(boolean visible) { this.visible = visible; } public void setPost(Post post) { this.post = post; } } The Post entity plays the Parent role and the PostDetails is the Child. The bidirectional associations should always be updated on both sides, therefore the Parent side should contain the addChild andremoveChild combo. These methods ensure we always synchronize both sides of the association, to avoid Object or Relational data corruption issues. In this particular case, the CascadeType.ALL and orphan removal make sense because the PostDetails life-cycle is bound to that of its Post Parent entity. Cascading the one-to-one persist operation The CascadeType.PERSIST comes along with the CascadeType.ALL configuration, so we only have to persist the Post entity, and the associated PostDetails entity is persisted as well: Post post = new Post(); post.setName("Hibernate Master Class"); PostDetails details = new PostDetails(); post.addDetails(details); session.persist(post); Generating the following output: INSERT INTO post(id, NAME) VALUES (DEFAULT, Hibernate Master Class'') insert into PostDetails (id, created_on, visible) values (default, '2015-03-03 10:17:19.14', false) Cascading the one-to-one merge operation The CascadeType.MERGE is inherited from the CascadeType.ALL setting, so we only have to merge the Post entity and the associated PostDetails is merged as well: Post post = newPost(); post.setName("Hibernate Master Class Training Material"); post.getDetails().setVisible(true); doInTransaction(session -> { session.merge(post); }); The merge operation generates the following output: SELECT onetooneca0_.id AS id1_3_1_, onetooneca0_.NAME AS name2_3_1_, onetooneca1_.id AS id1_4_0_, onetooneca1_.created_on AS created_2_4_0_, onetooneca1_.visible AS visible3_4_0_ FROM post onetooneca0_ LEFT OUTER JOIN postdetails onetooneca1_ ON onetooneca0_.id = onetooneca1_.id WHERE onetooneca0_.id = 1 UPDATE postdetails SET created_on = '2015-03-03 10:20:53.874', visible = true WHERE id = 1 UPDATE post SET NAME = 'Hibernate Master Class Training Material' WHERE id = 1 Cascading the one-to-one delete operation The CascadeType.REMOVE is also inherited from the CascadeType.ALL configuration, so the Post entity deletion triggers a PostDetails entity removal too: Post post = newPost(); doInTransaction(session -> { session.delete(post); }); Generating the following output: delete from PostDetails where id = 1 delete from Post where id = 1 The one-to-one delete orphan cascading operation If a Child entity is dissociated from its Parent, the Child Foreign Key is set to NULL. If we want to have the Child row deleted as well, we have to use the orphan removalsupport. doInTransaction(session -> { Post post = (Post) session.get(Post.class, 1L); post.removeDetails(); }); The orphan removal generates this output: SELECT onetooneca0_.id AS id1_3_0_, onetooneca0_.NAME AS name2_3_0_, onetooneca1_.id AS id1_4_1_, onetooneca1_.created_on AS created_2_4_1_, onetooneca1_.visible AS visible3_4_1_ FROM post onetooneca0_ LEFT OUTER JOIN postdetails onetooneca1_ ON onetooneca0_.id = onetooneca1_.id WHERE onetooneca0_.id = 1 delete from PostDetails where id = 1 Unidirectional one-to-one association Most often, the Parent entity is the inverse side (e.g. mappedBy), the Child controling the association through its Foreign Key. But the cascade is not limited to bidirectional associations, we can also use it for unidirectional relationships: @Entity public class Commit { @Id @GeneratedValue(strategy = GenerationType.AUTO) private Long id; private String comment; @OneToOne(cascade = CascadeType.ALL) @JoinTable( name = "Branch_Merge_Commit", joinColumns = @JoinColumn( name = "commit_id", referencedColumnName = "id"), inverseJoinColumns = @JoinColumn( name = "branch_merge_id", referencedColumnName = "id") ) private BranchMerge branchMerge; public Commit() { } public Commit(String comment) { this.comment = comment; } public Long getId() { return id; } public void addBranchMerge( String fromBranch, String toBranch) { this.branchMerge = new BranchMerge( fromBranch, toBranch); } public void removeBranchMerge() { this.branchMerge = null; } } @Entity public class BranchMerge { @Id @GeneratedValue(strategy = GenerationType.AUTO) private Long id; private String fromBranch; private String toBranch; public BranchMerge() { } public BranchMerge( String fromBranch, String toBranch) { this.fromBranch = fromBranch; this.toBranch = toBranch; } public Long getId() { return id; } } Cascading consists in propagating the Parent entity state transition to one or more Child entities, and it can be used for both unidirectional and bidirectional associations. One-To-Many The most common Parent – Child association consists of a one-to-many and a many-to-one relationship, where the cascade being useful for the one-to-many side only: @Entity public class Post { @Id @GeneratedValue(strategy = GenerationType.AUTO) private Long id; private String name; @OneToMany(cascade = CascadeType.ALL, mappedBy = "post", orphanRemoval = true) private List comments = new ArrayList<>(); public void setName(String name) { this.name = name; } public List getComments() { return comments; } public void addComment(Comment comment) { comments.add(comment); comment.setPost(this); } public void removeComment(Comment comment) { comment.setPost(null); this.comments.remove(comment); } } @Entity public class Comment { @Id @GeneratedValue(strategy = GenerationType.AUTO) private Long id; @ManyToOne private Post post; private String review; public void setPost(Post post) { this.post = post; } public String getReview() { return review; } public void setReview(String review) { this.review = review; } } Like in the one-to-one example, the CascadeType.ALL and orphan removal are suitable because the Comment life-cycle is bound to that of its Post Parent entity. Cascading the one-to-many persist operation We only have to persist the Post entity and all the associated Comment entities are persisted as well: Post post = new Post(); post.setName("Hibernate Master Class"); Comment comment1 = new Comment(); comment1.setReview("Good post!"); Comment comment2 = new Comment(); comment2.setReview("Nice post!"); post.addComment(comment1); post.addComment(comment2); session.persist(post); The persist operation generates the following output: insert into Post (id, name) values (default, 'Hibernate Master Class') insert into Comment (id, post_id, review) values (default, 1, 'Good post!') insert into Comment (id, post_id, review) values (default, 1, 'Nice post!') Cascading the one-to-many merge operation Merging the Post entity is going to merge all Comment entities as well: Post post = newPost(); post.setName("Hibernate Master Class Training Material"); post.getComments() .stream() .filter(comment -> comment.getReview().toLowerCase() .contains("nice")) .findAny() .ifPresent(comment -> comment.setReview("Keep up the good work!") ); doInTransaction(session -> { session.merge(post); }); Generating the following output: SELECT onetomanyc0_.id AS id1_1_1_, onetomanyc0_.NAME AS name2_1_1_, comments1_.post_id AS post_id3_1_3_, comments1_.id AS id1_0_3_, comments1_.id AS id1_0_0_, comments1_.post_id AS post_id3_0_0_, comments1_.review AS review2_0_0_ FROM post onetomanyc0_ LEFT OUTER JOIN comment comments1_ ON onetomanyc0_.id = comments1_.post_id WHERE onetomanyc0_.id = 1 update Post set name = 'Hibernate Master Class Training Material' where id = 1 update Comment set post_id = 1, review='Keep up the good work!' where id = 2 Cascading the one-to-many delete operation When the Post entity is deleted, the associated Comment entities are deleted as well: Post post = newPost(); doInTransaction(session -> { session.delete(post); }); Generating the following output: delete from Comment where id = 1 delete from Comment where id = 2 delete from Post where id = 1 The one-to-many delete orphan cascading operation The orphan-removal allows us to remove the Child entity whenever it’s no longer referenced by its Parent: newPost(); doInTransaction(session -> { Post post = (Post) session.createQuery( "select p " + "from Post p " + "join fetch p.comments " + "where p.id = :id") .setParameter("id", 1L) .uniqueResult(); post.removeComment(post.getComments().get(0)); }); The Comment is deleted, as we can see in the following output: SELECT onetomanyc0_.id AS id1_1_0_, comments1_.id AS id1_0_1_, onetomanyc0_.NAME AS name2_1_0_, comments1_.post_id AS post_id3_0_1_, comments1_.review AS review2_0_1_, comments1_.post_id AS post_id3_1_0__, comments1_.id AS id1_0_0__ FROM post onetomanyc0_ INNER JOIN comment comments1_ ON onetomanyc0_.id = comments1_.post_id WHERE onetomanyc0_.id = 1 delete from Comment where id = 1 If you enjoy reading this article, you might want to subscribe to my newsletter and get a discount for my book as well. Many-To-Many The many-to-many relationship is tricky because each side of this association plays both the Parent and the Child role. Still, we can identify one side from where we’d like to propagate the entity state changes. We shouldn’t default to CascadeType.ALL, because the CascadeTpe.REMOVE might end-up deleting more than we’re expecting (as you’ll soon find out): @Entity public class Author { @Id @GeneratedValue(strategy=GenerationType.AUTO) private Long id; @Column(name = "full_name", nullable = false) private String fullName; @ManyToMany(mappedBy = "authors", cascade = {CascadeType.PERSIST, CascadeType.MERGE}) private List books = new ArrayList<>(); private Author() {} public Author(String fullName) { this.fullName = fullName; } public Long getId() { return id; } public void addBook(Book book) { books.add(book); book.authors.add(this); } public void removeBook(Book book) { books.remove(book); book.authors.remove(this); } public void remove() { for(Book book : new ArrayList<>(books)) { removeBook(book); } } } @Entity public class Book { @Id @GeneratedValue(strategy=GenerationType.AUTO) private Long id; @Column(name = "title", nullable = false) private String title; @ManyToMany(cascade = {CascadeType.PERSIST, CascadeType.MERGE}) @JoinTable(name = "Book_Author", joinColumns = { @JoinColumn( name = "book_id", referencedColumnName = "id" ) }, inverseJoinColumns = { @JoinColumn( name = "author_id", referencedColumnName = "id" ) } ) private List authors = new ArrayList<>(); private Book() {} public Book(String title) { this.title = title; } } Cascading the many-to-many persist operation Persisting the Author entities will persist the Books as well: Author _John_Smith = new Author("John Smith"); Author _Michelle_Diangello = new Author("Michelle Diangello"); Author _Mark_Armstrong = new Author("Mark Armstrong"); Book _Day_Dreaming = new Book("Day Dreaming"); Book _Day_Dreaming_2nd = new Book("Day Dreaming, Second Edition"); _John_Smith.addBook(_Day_Dreaming); _Michelle_Diangello.addBook(_Day_Dreaming); _John_Smith.addBook(_Day_Dreaming_2nd); _Michelle_Diangello.addBook(_Day_Dreaming_2nd); _Mark_Armstrong.addBook(_Day_Dreaming_2nd); session.persist(_John_Smith); session.persist(_Michelle_Diangello); session.persist(_Mark_Armstrong); The Book and the Book_Author rows are inserted along with the Authors: insert into Author (id, full_name) values (default, 'John Smith') insert into Book (id, title) values (default, 'Day Dreaming') insert into Author (id, full_name) values (default, 'Michelle Diangello') insert into Book (id, title) values (default, 'Day Dreaming, Second Edition') insert into Author (id, full_name) values (default, 'Mark Armstrong') insert into Book_Author (book_id, author_id) values (1, 1) insert into Book_Author (book_id, author_id) values (1, 2) insert into Book_Author (book_id, author_id) values (2, 1) insert into Book_Author (book_id, author_id) values (2, 2) insert into Book_Author (book_id, author_id) values (3, 1) Dissociating one side of the many-to-many association To delete an Author, we need to dissociate all Book_Author relations belonging to the removable entity: doInTransaction(session -> { Author _Mark_Armstrong = getByName(session, "Mark Armstrong"); _Mark_Armstrong.remove(); session.delete(_Mark_Armstrong); }); This use case generates the following output: SELECT manytomany0_.id AS id1_0_0_, manytomany2_.id AS id1_1_1_, manytomany0_.full_name AS full_nam2_0_0_, manytomany2_.title AS title2_1_1_, books1_.author_id AS author_i2_0_0__, books1_.book_id AS book_id1_2_0__ FROM author manytomany0_ INNER JOIN book_author books1_ ON manytomany0_.id = books1_.author_id INNER JOIN book manytomany2_ ON books1_.book_id = manytomany2_.id WHERE manytomany0_.full_name = 'Mark Armstrong' SELECT books0_.author_id AS author_i2_0_0_, books0_.book_id AS book_id1_2_0_, manytomany1_.id AS id1_1_1_, manytomany1_.title AS title2_1_1_ FROM book_author books0_ INNER JOIN book manytomany1_ ON books0_.book_id = manytomany1_.id WHERE books0_.author_id = 2 delete from Book_Author where book_id = 2 insert into Book_Author (book_id, author_id) values (2, 1) insert into Book_Author (book_id, author_id) values (2, 2) delete from Author where id = 3 The many-to-many association generates way too many redundant SQL statements and often, they are very difficult to tune. Next, I’m going to demonstrate the many-to-many CascadeType.REMOVE hidden dangers. The many-to-many CascadeType.REMOVE gotchas The many-to-many CascadeType.ALL is another code smell, I often bump into while reviewing code. The CascadeType.REMOVE is automatically inherited when usingCascadeType.ALL, but the entity removal is not only applied to the link table, but to the other side of the association as well. Let’s change the Author entity books many-to-many association to use theCascadeType.ALL instead: @ManyToMany(mappedBy = "authors", cascade = CascadeType.ALL) private List books = new ArrayList<>(); When deleting one Author: doInTransaction(session -> { Author _Mark_Armstrong = getByName(session, "Mark Armstrong"); session.delete(_Mark_Armstrong); Author _John_Smith = getByName(session, "John Smith"); assertEquals(1, _John_Smith.books.size()); }); All books belonging to the deleted Author are getting deleted, even if other Authorswe’re still associated to the deleted Books: SELECT manytomany0_.id AS id1_0_, manytomany0_.full_name AS full_nam2_0_ FROM author manytomany0_ WHERE manytomany0_.full_name = 'Mark Armstrong' SELECT books0_.author_id AS author_i2_0_0_, books0_.book_id AS book_id1_2_0_, manytomany1_.id AS id1_1_1_, manytomany1_.title AS title2_1_1_ FROM book_author books0_ INNER JOIN book manytomany1_ ON books0_.book_id = manytomany1_.id WHERE books0_.author_id = 3 delete from Book_Author where book_id=2 delete from Book where id=2 delete from Author where id=3 Most often, this behavior doesn’t match the business logic expectations, only being discovered upon the first entity removal. We can push this issue even further, if we set the CascadeType.ALL to the Book entity side as well: @ManyToMany(cascade = CascadeType.ALL) @JoinTable(name = "Book_Author", joinColumns = { @JoinColumn( name = "book_id", referencedColumnName = "id" ) }, inverseJoinColumns = { @JoinColumn( name = "author_id", referencedColumnName = "id" ) } ) This time, not only the Books are being deleted, but Authors are deleted as well: doInTransaction(session -> { Author _Mark_Armstrong = getByName(session, "Mark Armstrong"); session.delete(_Mark_Armstrong); Author _John_Smith = getByName(session, "John Smith"); assertNull(_John_Smith); }); The Author removal triggers the deletion of all associated Books, which further triggers the removal of all associated Authors. This is a very dangerous operation, resulting in a massive entity deletion that’s rarely the expected behavior. If you enjoyed this article, I bet you are going to love my book as well. SELECT manytomany0_.id AS id1_0_, manytomany0_.full_name AS full_nam2_0_ FROM author manytomany0_ WHERE manytomany0_.full_name = 'Mark Armstrong' SELECT books0_.author_id AS author_i2_0_0_, books0_.book_id AS book_id1_2_0_, manytomany1_.id AS id1_1_1_, manytomany1_.title AS title2_1_1_ FROM book_author books0_ INNER JOIN book manytomany1_ ON books0_.book_id = manytomany1_.id WHERE books0_.author_id = 3 SELECT authors0_.book_id AS book_id1_1_0_, authors0_.author_id AS author_i2_2_0_, manytomany1_.id AS id1_0_1_, manytomany1_.full_name AS full_nam2_0_1_ FROM book_author authors0_ INNER JOIN author manytomany1_ ON authors0_.author_id = manytomany1_.id WHERE authors0_.book_id = 2 SELECT books0_.author_id AS author_i2_0_0_, books0_.book_id AS book_id1_2_0_, manytomany1_.id AS id1_1_1_, manytomany1_.title AS title2_1_1_ FROM book_author books0_ INNER JOIN book manytomany1_ ON books0_.book_id = manytomany1_.id WHERE books0_.author_id = 1 SELECT authors0_.book_id AS book_id1_1_0_, authors0_.author_id AS author_i2_2_0_, manytomany1_.id AS id1_0_1_, manytomany1_.full_name AS full_nam2_0_1_ FROM book_author authors0_ INNER JOIN author manytomany1_ ON authors0_.author_id = manytomany1_.id WHERE authors0_.book_id = 1 SELECT books0_.author_id AS author_i2_0_0_, books0_.book_id AS book_id1_2_0_, manytomany1_.id AS id1_1_1_, manytomany1_.title AS title2_1_1_ FROM book_author books0_ INNER JOIN book manytomany1_ ON books0_.book_id = manytomany1_.id WHERE books0_.author_id = 2 delete from Book_Author where book_id=2 delete from Book_Author where book_id=1 delete from Author where id=2 delete from Book where id=1 delete from Author where id=1 delete from Book where id=2 delete from Author where id=3 This use case is wrong in so many ways. There are a plethora of unnecessary SELECT statements and eventually we end up deleting all Authors and all their Books. That’s why CascadeType.ALL should raise your eyebrow, whenever you spot it on a many-to-many association. When it comes to Hibernate mappings, you should always strive for simplicity. TheHibernate documentation confirms this assumption as well: Practical test cases for real many-to-many associations are rare. Most of the time you need additional information stored in the “link table”. In this case, it is much better to use two one-to-many associations to an intermediate link class. In fact, most associations are one-to-many and many-to-one. For this reason, you should proceed cautiously when using any other association style. Conclusion Cascading is a handy ORM feature, but it’s not free of issues. You should only cascade from Parent entities to Children and not the other way around. You should always use only the casacde operations that are demanded by your business logic requirements, and not turn the CascadeType.ALL into a default Parent-Child association entity state propagation configuration. Code available on GitHub.
March 13, 2015
by Vlad Mihalcea
· 97,371 Views · 8 Likes
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