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The Latest Microservices Topics

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Percona XtraDB Cluster (PXC): How Many Nodes Do You Need?
Written by Stephane Combaudon. A question I often hear when customers want to set up a production PXC cluster is: “How many nodes should we use?” Three nodes is the most common deployment, but when are more nodes needed? They also ask: “Do we always need to use an even number of nodes?” This is what we’ll clarify in this post. This is all about quorum I explained in a previous post that a quorum vote is held each time one node becomes unreachable. With this vote, the remaining nodes will estimate whether it is safe to keep on serving queries. If quorum is not reached, all remaining nodes will set themselves in a state where they cannot process any query (even reads). To get the right size for you cluster, the only question you should answer is: how many nodes can simultaneously fail while leaving the cluster operational? If the answer is 1 node, then you need 3 nodes: when 1 node fails, the two remaining nodes have quorum. If the answer is 2 nodes, then you need 5 nodes. If the answer is 3 nodes, then you need 7 nodes. And so on and so forth. Remember that group communication is not free, so the more nodes in the cluster, the more expensive group communication will be. That’s why it would be a bad idea to have a cluster with 15 nodes for instance. In general we recommend that you talk to us if you think you need more than 10 nodes. What about an even number of nodes? The recommendation above always specifies odd number of nodes, so is there anything bad with an even number of nodes? Let’s take a 4-node cluster and see what happens if nodes fail: If 1 node fails, 3 nodes are remaining: they have quorum. If 2 nodes fail, 2 nodes are remaining: they no longer have quorum (remember 50% is NOT quorum). Conclusion: availability of a 4-node cluster is no better than the availability of a 3-node cluster, so why bother with a 4th node? The next question is: is a 4-node cluster less available than a 3-node cluster? Many people think so, specifically after reading this sentence from the manual: Clusters that have an even number of nodes risk split-brain conditions. Many people read this as “as soon as one node fails, this is a split-brain condition and the whole cluster stop working”. This is not correct! In a 4-node cluster, you can lose 1 node without any problem, exactly like in a 3-node cluster. This is not better but not worse. By the way the manual is not wrong! The sentence makes sense with its context. There could actually reasons why you might want to have an even number of nodes, but we will discuss that topic in the next section. Quorum with multiple data centers To provide more availability, spreading nodes in several datacenters is a common practice: if power fails in one DC, nodes are available elsewhere. The typical implementation is 3 nodes in 2 DCs: Notice that while this setup can handle any single node failure, it can’t handle all single DC failures: if we lose DC1, 2 nodes leave the cluster and the remaining node has not quorum. You can try with 4, 5 or any number of nodes and it will be easy to convince yourself that in all cases, losing one DC can make the whole cluster stop operating. If you want to be resilient to a single DC failure, you must have 3 DCs, for instance like this: Other considerations Sometimes other factors will make you choose a higher number of nodes. For instance, look at these requirements: All traffic is directed to a single node. The application should be able to fail over to another node in the same datacenter if possible. The cluster must keep operating even if one datacenter fails. The following architecture is an option (and yes, it has an even number of nodes!): Conclusion Regarding availability, it is easy to estimate the number of nodes you need for your PXC cluster. But node failures are not the only aspect to consider: Resilience to a datacenter failure can, for instance, influence the number of nodes you will be using.
June 24, 2015
by Peter Zaitsev
· 1,439 Views
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It's Time to Start Programming (for) Adults
This week we're in Boston at DevNation, an awesome, young (second ever), and relatively intimate (~500 attendees) conference on anything and everything hard-core, cool-and-hot (DevOps, big data, Angular, IoT, you name it), and of course -- since the conference is organized by Red Hat -- totally open-source. So far I've had in-depth conversations with five super-amazing engineers, attended several inspiring keynotes, and chatted with one skilled developer after another. We'll transcribe the deeper interviews shortly, including some on topics totally unrelated to this post. But meanwhile I'd like to offer some thoughts inspired by the first day of the event. The general theme is: we're just beginning to get serious about separation of concerns. The metaphor that keeps popping into my head comes from the first keynote: machines have finally grown up. Imperatives: telling really unintelligent agents what to do (and then they sort of do whatever they please) It is trivial to observe that computers are incredibly stupid. Turing's fundamental paper is about how to figure out whether a theoretical computer will keep calculating the values of a function until the heat death of the universe (okay that's a slight oversimplification). The fact that Edsger Dijkstra felt the need to rail gently against all goto statements in any higher-level language than machine code suggests that, in 1968, far too many computers needed instructions about how to read the instructions that tell them what to do in the first place. Richard Feynman's famous lecture on computer heuristics is the condescension of the man who conceived quantum computing to the level of functional composition (hmmm) and file systems (double sigh). Stupid agents need to be told exactly what to do. Then they need to be told to pay attention to the exact part of the command that tells them exactly what they have been told to do (dude, just goto line 1343 already and shut up). Then they don't do what you told them (optimistically we call this an 'exception'), and then you send them into time out / set a break point and try to figure out where the idiot state muted off the rails. They stare blankly at the wall / variable / register and either do nothing or repeat another unintelligibly wrong result until you notice that your increment is (apparently meaninglessly to you) one bracket too deep. You sigh and tell them what to do again, and after a while they hit age thirty (life-years/debug-hours) and maybe do something useful with their (process-)lives. Well, maybe I'm straining the metaphor a little here, but you get the point because it cuts too close to home. We spend far too much time fixing stupid mistakes that we didn't even know we were making because -- like all actual human beings -- we assumed that the agent we commanded will use their common sense to iron out those few whiffs of, admit it, frank nonsense that our step-by-step instructions will probably always contain. So, at least, goes the imperative programming paradigm. The machine does what you tell it to; and the universe collapses onto itself before the last real number is computed. Functions: reliable, predictable adults Time to give credit where it's due: I'm really just riffing on the metaphor Venkat Subramanian offered in his highly enjoyable keynote on The Joy of Functional Programming yesterday morning His not-so-smart agents -- the 'programmed' of imperative programming -- were toddlers. Since I don't have any kids, I can't presume to understand this experience fully (although I did grow up with three younger brothers..). But the general idea is: imperative programming is tricky because, when you spell everything out super literally, it's very hard to tell exactly why what you thought should happen didn't. Venkat's talk was a whirlwind of functional concepts, from the thrill of immutability to the self-evident utility of memoization. For random (Myers-Briggs?) reasons, the object-oriented paradigm never seemed very intuitive to me -- I've gravitated towards functional style even when the problem domain wasn't actually modeled very well by functions -- but Venkat's side-by-side implementations of simple calculations in OO and functional Java showed the readability delta very clearly. Functional code is beautiful because it looks like its purpose. It tells you flat-out: here is what I do; and then it does it. But immutable functions are also beautiful because they do exactly the same thing every time. I couldn't count on my two year old brother very much at all because given a certain input I had pretty much no idea what would come out. But we all count on our grown-up collaborators to output exactly what they should, given a definite input, predictably and reliably every time. Of course, people also do more than expected -- every intervention of intelligence is an injection of creativity, not generated by the definition of the function -- but at least they do what you need them to do and no less. Containers: grown-ups with good boundaries I'm picking out just one aspect of the resurgent 'joy' of functional programming because the renaissance of containerization (another 'old' technology that is just now really taking off) is, I think, a part of the same shift toward, let's say, treating computers as adults. If functions are reliable agents, then applications in well-defined containers are self-sufficient agents who know exactly what they need from others and neither require nor demand anything more. If apps on dedicated VMs are teenagers negotiating personal boundaries by waking/booting up independently (and taking far too long -- and far too many resources -- to do so, given their meager output) -- or bubble boys, isolated in ways that are unfortunate in order to isolate in ways that are absolutely necessary -- then containerized applications are subway-riders who jam into the train without offending anyone or campers who can live anywhere with just a backpack of just the stuff they need. Of course, subway-riders and campers do more than just not-mess-up. But what's kind of neat about containers is that -- like an adult with good boundaries -- clearly defined bounds and interfaces free up the application / mind to do whatever world-changing thing the developer / human has cooked up. I'll come back to this metaphor in a later article. (Mesh networks, SDN, and ad-hoc computing are all part of the same picture, I think. Kubernetes probably is too, along with event-driven and reactive programming, the actor model, dreams of Smalltalk, and of course REST, at least of the HATEOAS flavor.) But maybe this isn't a good way to think about some of these recent sparks in devworld within a single paradigm -- and maybe my perpetual discomfort with OO is influencing me too much. What do you think?
June 23, 2015
by John Esposito
· 2,333 Views · 1 Like
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AppFabric Coming Apart? 5 Reasons to Move to Redis Labs
Written by Leena Joshi Microsoft recently announced that Microsoft AppFabric 1.1 for Windows Server will be at the end of support on April 2, 2016. Less than a year away! Don’t panic yet, there is another, better solution. Redis is the product of choice for thousands of developers worldwide who want to accelerate their applications. Redis is the fastest growing NoSQL datastore, that runs in-memory and delivers millions of transactions at sub millisecond latencies. Redis Labs provides enterprise class Redis: as a downloadable product Redis Labs Enterprise Cluster (RLEC) or as a seamlessly scalable, highly available service, Redis Cloud. Here are the top 5 reasons to move applications using Microsoft AppFabric to Redis Cloud or Redis Labs Enterprise Cluster : Performance: One of the biggest reasons to move is the blazing fast performance and versatility of Redis. While AppFabric performs simple GET and SET commands, Redis comes with a variety of data structures (strings, lists, hashes, sets, sorted sets) and a sophisticated set of commands, embedded Lua scripting and bit operations that helps you address more than high speed caching – it lets you implement high speed transactions, real time analytics, in-app social functionality, messaging, job and queue management and much more. Scalability: The current AppFabric replacement offered by MS runs on Azure, however it is not a service that scales seamlessly like Redis Cloud. Redis Cloud with 4900+ customers to date is proven to be highly scalable and available and provides all the functionality of Redis with none of the deployment overhead. Also, if you don’t really want a cloud solution, RLEC (Redis Labs Enterprise Cluster) runs on-premises or wherever you are deployed and relieves you of any provisioning, configuring, scaling, clustering, monitoring – it fully automates all those tasks and makes it super-easy to deploy Redis clusters. Portability: AppFabric is Windows and .Net specific while Redis supports many environments and languages.Thanks to our community, Redis supports many languages including Python, Ruby, Java, PHP, Node, C, C# . So, if you have a mixed environment, now you can even extend your use of in-memory, high speed technologies. Built-in Monitoring: Both Redis Cloud and RLEC come with a dashboard to view different operational metrics as well as built-in alerts to receive notification on important events. This is very much more cumbersome with AppFabric and you would have had to use external tools to get the same level of manageability. Ease of use: Redis is simple yet very sophisticated and used by thousands of developers worldwide. It is #3 among NoSQL database adoption (source:DBengines) and #12 among all tools used by developers (source:Stackshare). This means that a talent pool of Redis users and a worldwide community is always there to help! RLEC is free to download and Redis Cloud has a free tier as well – so it is really easy to try out both those products. If you have any questions about migrating from AppFabric to Redis Labs, our solutions consultants are always here to help. Email us at [email protected] and we can set up some time to walk you through the migration! - See more at: https://redislabs.com/blog/appfabric-coming-apart-top-5-reasons-to-move-to-redis#.VYSGzucYGL4
June 22, 2015
by Itamar Haber
· 994 Views
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Devnation Keynote 6/22 #2: The Future of Development with Kubernetes and Docker
From the DevNation Agenda site: You've probably heard a lot about Linux containers and the exciting potential they hold. In this presentation, Matt Hicks will cover how Docker and Kubernetes have evolved to fundamentally change how you will approach development and operations. If you are looking for an understanding of the technology and how it relates to the common roles in IT today, this is the talk to watch. Speaker: Matt Hicks -- Vice President of engineering, Red Hat Matt Hicks is a founding member of the OpenShift by Red Hat team. He has spent more than a decade in software engineering, with a variety of roles in development, operations, architecture, and management. His real expertise is in bridging the gap between developing code and actually running it in production. An expert in IT and cloud-based architectures, he spends his time these days evolving OpenShift to use the power of cloud and make developers more productive.
June 22, 2015
by N A
· 1,128 Views · 2 Likes
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Long-Term Log Analysis with AWS Redshift
You will aggregate a lot of logs over the lifetime of your product and codebase, so it’s important to be able to search through them. In the rare case of a security issue, not having that capability is incredibly painful. You might be able to use services that allow you to search through the logs of the last two weeks quickly. But what if you want to search through the last six months, a year, or even further? That availability can be rather expensive or not even an option at all with existing services. Many hosted log services provide S3 archival support which we can use to build a long-term log analysis infrastructure with AWS Redshift. Recently I’ve set up scripts to be able to create that infrastructure whenever we need it at Codeship. AWS Redshift AWS Redshift is a data warehousing solution by AWS. It has an easy clustering and ingestion mechanism ideal for loading large log files and then searching through them with SQL. As it automatically balances your log files across several machines, you can easily scale up if you need more speed. As I said earlier, looking through large amounts of log files is a relatively rare occasion; you don’t need this infrastructure to be around all the time, which makes it a perfect use case for AWS. Setting Up Your Log Analysis Let’s walk through the scripts that drive our long-term log analysis infrastructure. You can check them out in the flomotlik/redshift-logging GitHub repository. I’ll take you step by step through configuring the whole setup of the environment variables needed, as well as starting the creation of the cluster and searching the logs. But first, let’s get a high-level overview of what the setup script is doing before going into all the different options that you can set: Creates an AWS Redshift cluster. You can configure the number of servers and which server type should be used. Waits for the cluster to become ready. Creates a SQL table inside the Redshift cluster to load the log files into. Ingests all log files into the Redshift cluster from AWS S3. Cleans up the database and prints the psql access command to connect into the cluster. Be sure to check out the script on GitHub before we go into all the different options that you can set through the .env file. Options to set The following is a list of all the options available to you. You can simply copy the .env.template file to .env and then fill in all the options to get picked up. AWS_ACCESS_KEY_ID AWS key of the account that should run the Redshift cluster. AWS_SECRET_ACCESS_KEY AWS secret key of the account that should run the Redshift cluster. AWS_REGION=us-east-1 AWS region the cluster should run in, default us-east-1. Make sure to use the same region that is used for archiving your logs to S3 to have them close. REDSHIFT_USERNAME Username to connect with psql into the cluster. REDSHIFT_PASSWORD Password to connect with psql into the cluster. S3_AWS_ACCESS_KEY_ID AWS key that has access to the S3 bucket you want to pull your logs from. We run the log analysis cluster in our AWS Sandbox account but pull the logs from our production AWS account so the Redshift cluster doesn’t impact production in any way. S3_AWS_SECRET_ACCESS_KEY AWS secret key that has access to the S3 bucket you want to pull your logs from. PORT=5439 Port to connect to with psql. CLUSTER_TYPE=single-node The cluster type can be single-node or multi-node. Multi-node clusters get auto-balanced which gives you more speed at a higher cost. NODE_TYPE Instance type that’s used for the nodes of the cluster. Check out the Redshift Documentation for details on the instance types and their differences. NUMBER_OF_NODES=10 Number of nodes when running in multi-mode. CLUSTER_IDENTIFIER=log-analysis DB_NAME=log-analysis S3_PATH=s3://your_s3_bucket/papertrail/logs/862693/dt=2015 Database format and failed loads When ingesting log statements into the cluster, make sure to check the amount of failed loads that are happening. You might have to edit the database format to fit to your specific log output style. You can debug this easily by creating a single-node cluster first that only loads a small subset of your logs and is very fast as a result. Make sure to have none or nearly no failed loads before you extend to the whole cluster. In case there are issues, check out the documentation of the copy command which loads your logs into the database and the parameters in the setup script for that. Example and benchmarks It’s a quick thing to set up the whole cluster and run example queries against it. For example, I’ll load all of our logs of the last nine months into a Redshift cluster and run several queries against it. I haven’t spent any time on optimizing the table, but you could definitely gain some more speed out of the whole system if necessary. It’s just fast enough already for us out of the box. As you can see here, loading all logs of May — more than 600 million log lines — took only 12 minutes on a cluster of 10 machines. We could easily load more than one month into that 10-machine cluster since there’s more than enough storage available, but for this post, one month is enough. After that, we’re able to search through the history of all of our applications and past servers through SQL. We connect with our psql client and send of SQL queries against the “events’ database. For example, what if we want to know how many build servers reported logs in May: loganalysis=# select count(distinct(source_name)) from events where source_name LIKE 'i-%'; count ------- 801 (1 row) So in May, we had 801 EC2 build servers running for our customers. That query took ~3 seconds to finish. Or let’s say we want to know how many people accessed the configuration page of our main repository (the project ID is hidden with XXXX): loganalysis=# select count(*) from events where source_name = 'mothership' and program LIKE 'app/web%' and message LIKE 'method=GET path=/projects/XXXX/configure_tests%'; count ------- 15 (1 row) So now we know that there were 15 accesses on that configuration page throughout May. We can also get all the details, including who accessed it when through our logs. This could help in case of any security issues we’d need to look into. The query took about 40 seconds to go though all of our logs, but it could be optimized on Redshift even more. Those are just some of the queries you could use to look through your logs, gaining more insight into your customers’ use of your system. And you et all of that with a setup that costs $2.50 an hour, can be shut down immediately, and recreated any time you need access to that data again. Conclusions Being able to search through and learn from your history is incredibly important for building a large infrastructure. You need to be able to look into your history easily, especially when it comes to security issues. With AWS Redshift, you have a great tool in hand that allows you to start an ad hoc analytics infrastructure that’s fast and cheap for short-term reviews. Of course, Redshift can do a lot more as well. Let us know what your processes and tools around logging, storage, and search are in the comments.
June 21, 2015
by Florian Motlik
· 1,513 Views
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Ode to a Workstation
Every now and then I get work done in the home office. I’ve written previously about my setup, but after churning out some solution design today, I sat back and really took some time to appreciate the workspace. I’m really pleased with the configuration, it’s probably the best setup I’ve had in years. The desk is a former QLD police desk from the 1940s, so it wasn’t built for modern computers – not a problem, the cables run down the back which is just a minor annoyance. The keyboard and mouse are gaming varieties so that they perform well – the old Sennheiser (RF) wireless headset has been with me since 2006 and still works very well. The wooden clock (recently reviewed) acts as external speakers, a Bluetooth receiver and has a built in microphone so it can be used as a hands-free option for conference calls. It also features Qi wireless charging capability and also features a thermostat. Under the second monitor is a HDD caddy which supports USB3, and features 4 bays which can be used in parallel. I try to keep the desk reasonably neat, and there’s plenty of space so it doesn’t get too cluttered. I have a nice view out the window to a small courtyard which gets early morning sun.
June 21, 2015
by Rob Sanders
· 1,110 Views
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Diff'ing Software Architecture Diagrams
robert annett wrote a post titled diagrams for system evolution where he describes a simple approach to showing how to visually describe changes to a software architecture. in essence, in order to show how a system is to change, he'll draw different versions of the same diagram and use colour-coding to highlight the elements/relationships that will be added, removed or modified. i've typically used a similar approach for describing as-is and to-be architectures in the past too. it's a technique that works well. although you can version control diagrams, it's still tricky to diff them using a tool. one solution that addresses this problem is to not create diagrams, but instead create a textual description of your software architecture model that is then subsequently rendered with some tooling. you could do this with an architecture description language (such as darwin ) although i would much rather use my regular programming language instead. creating a software architecture model as code this is exactly what structurizr is designed to do. i've recreated robert's diagrams with structurizr as follows. and since the diagrams were created by a model described as java code , that description can be diff'ed using your regular toolchain. code provides opportunities this perhaps isn't as obvious as robert's visual approach, and i would likely still highlight the actual differences on diagrams using notation as robert did too. creating a textual description of a software architecture model does provide some interesting opportunities though.
June 19, 2015
by Simon Brown
· 1,447 Views
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Building Microservices: Using an API Gateway
Learn about using the microservice architecture pattern to build microservices and API gateways--compared to the usage of monolithic application architecture.
June 16, 2015
by Patrick Nommensen
· 121,206 Views · 40 Likes
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Why 12 Factor Application Patterns, Microservices and CloudFoundry Matter (Part 2)
Learn why 12 Factor Application Patterns, Microservices and CloudFoundry matter when trying to change the way your product is produced.
June 12, 2015
by Tim Spann DZone Core CORE
· 15,723 Views · 4 Likes
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Top 80 Thread- Java Interview Questions and Answers (Part 2)
PART 1 > THREADS - Top 80 interview questions and answers (detailed explanation with programs) Question 61. class MyRunnable implements Runnable{ public void run(){ for(int i=0;i<3;i++){ System.out.println("i="+i+" ,ThreadName="+Thread.currentThread().getName()); } } } public class MyClass { public static void main(String...args){ MyRunnable runnable=new MyRunnable(); System.out.println("start main() method"); Thread thread1=new Thread(runnable); Thread thread2=new Thread(runnable); thread1.start(); thread2.start(); System.out.println("end main() method"); } } Answer. Thread behaviour is unpredictable because execution of Threads depends on Thread scheduler, start main() method will be the printed first, but after that we cannot guarantee the order of thread1, thread2 and main thread they might run simultaneously or sequentially, so order of end main() method will not be guaranteed. /*OUTPUT start main() method end main() method i=0 ,ThreadName=Thread-0 i=0 ,ThreadName=Thread-1 i=1 ,ThreadName=Thread-0 i=2 ,ThreadName=Thread-0 i=1 ,ThreadName=Thread-1 i=2 ,ThreadName=Thread-1 */ Question 62. class MyRunnable implements Runnable{ public void run(){ for(int i=0;i<3;i++){ System.out.println("i="+i+" ,ThreadName="+Thread.currentThread().getName()); } } } public class MyClass { public static void main(String...args) throws InterruptedException{ System.out.println("In main() method"); MyRunnable runnable=new MyRunnable(); Thread thread1=new Thread(runnable); Thread thread2=new Thread(runnable); thread1.start(); thread1.join(); thread2.start(); thread2.join(); System.out.println("end main() method"); } } Answer. We use join() methodto ensure all threads that started from main must end in order in which they started and also main should end in last. In other words join() method waited for this thread to die. /*OUTPUT In main() method i=0 ,ThreadName=Thread-0 i=1 ,ThreadName=Thread-0 i=2 ,ThreadName=Thread-0 i=0 ,ThreadName=Thread-1 i=1 ,ThreadName=Thread-1 i=2 ,ThreadName=Thread-1 end main() method */ Question 63. class MyRunnable implements Runnable { public void run() { try { while (!Thread.currentThread().isInterrupted()) { Thread.sleep(1000); System.out.println("x"); } } catch (InterruptedException e) { System.out.println(Thread.currentThread().getName() + " ENDED"); } } } public class MyClass { public static void main(String args[]) throws Exception { MyRunnable obj = new MyRunnable(); Thread t = new Thread(obj, "Thread-1"); t.start(); System.out.println("press enter"); System.in.read(); t.interrupt(); } } Answer. "press enter" will be printed first then thread1 will keep on printing x until enter is pressed, once enter is pressed "Thread-1 ENDED" will be printed. System.in.read() causes main thread to go from running to waiting state (thread waits for user input) /* OUTPUT press enter x x x x Thread-1 ENDED */ Question 64. class MyRunnable implements Runnable{ public void run(){ synchronized (this) { System.out.println("1 "); try { this.wait(); System.out.println("2 "); } catch (InterruptedException e) { e.printStackTrace(); } } } } public class MyClass { public static void main(String[] args) { MyRunnable myRunnable=new MyRunnable(); Thread thread1=new Thread(myRunnable,"Thread-1"); thread1.start(); } } Answer. Thread acquires lock on myRunnable object so 1 was printed but notify wasn't called so 2 will never be printed, this is called frozen process. Deadlock is formed, These type of deadlocksare called Frozen processes. /*OUTPUT 1 */ Question 65. import java.util.ArrayList; /* Producer is producing, Producer will allow consumer to * consume only when 10 products have been produced (i.e. when production is over). */ class Producer implements Runnable{ ArrayList sharedQueue; Producer(){ sharedQueue=new ArrayList(); } @Override public void run(){ synchronized (this) { for(int i=1;i<=3;i++){ //Producer will produce 10 products sharedQueue.add(i); System.out.println("Producer is still Producing, Produced : "+i); try{ Thread.sleep(1000); }catch(InterruptedException e){e.printStackTrace();} } System.out.println("Production is over, consumer can consume."); this.notify(); } } } class Consumer extends Thread{ Producer prod; Consumer(Producer obj){ prod=obj; } public void run(){ synchronized (this.prod) { System.out.println("Consumer waiting for production to get over."); try{ this.prod.wait(); }catch(InterruptedException e){e.printStackTrace();} } int productSize=this.prod.sharedQueue.size(); for(int i=0;i Q61- Q80
June 6, 2015
by Ankit Mittal
· 13,746 Views · 3 Likes
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Mounting an EBS Volume to Docker on AWS Elastic Beanstalk
Mounting an EBS volume to a Docker instance running on Amazon Elastic Beanstalk (EB) is surprisingly tricky. The good news is that it is possible. I will describe how to automatically create and mount a new EBS volume (optionally based on a snapshot). If you would prefer to mount a specific, existing EBS volume, you should check out leg100’s docker-ebs-attach (using AWS API to mount the volume) that you can use either in a multi-container setup or just include the relevant parts in your own Dockerfile. The problem with EBS volumes is that, if I am correct, a volume can only be mounted to a single EC2 instance – and thus doesn’t play well with EB’s autoscaling. That is why EB supports only creating and mounting a fresh volume for each instance. Why would you want to use an auto-created EBS volume? You can already use a docker VOLUME to mount a directory on the host system’s ephemeral storage to make data persistent across docker restarts/redeploys. The only advantage of EBS is that it survives restarts of the EC2 instance but that is something that, I suppose, happens rarely. I suspect that in most cases EB actually creates a new EC2 instance and then destroys the old one. One possible benefit of an EBS volume is that you can take a snapshot of it and use that to launch future instances. I’m now inclined to believe that a better solution in most cases is to set up automatic backup to and restore from S3, f.ex. using duplicity with its S3 backend (as I do for my NAS). Anyway, here is how I got EBS volume mounting working. There are 4 parts to the solution: Configure EB to create an EBS mount for your instances Add custom EB commands to format and mount the volume upon first use Restart the Docker daemon after the volume is mounted so that it will see it (see this discussion) Configure Docker to mount the (mounted) volume inside the container 1-3.: .ebextensions/01-ebs.config: # .ebextensions/01-ebs.config commands: 01format-volume: command: mkfs -t ext3 /dev/sdh test: file -sL /dev/sdh | grep -v 'ext3 filesystem' # ^ prints '/dev/sdh: data' if not formatted 02attach-volume: ### Note: The volume may be renamed by the Kernel, e.g. sdh -> xvdh but # /dev/ will then contain a symlink from the old to the new name command: | mkdir /media/ebs_volume mount /dev/sdh /media/ebs_volume service docker restart # We must restart Docker daemon or it wont' see the new mount test: sh -c "! grep -qs '/media/ebs_volume' /proc/mounts" option_settings: # Tell EB to create a 100GB volume and mount it to /dev/sdh - namespace: aws:autoscaling:launchconfiguration option_name: BlockDeviceMappings value: /dev/sdh=:100 4.: Dockerrun.aws.json and Dockerfile: Dockerrun.aws.json: mount the host’s /media/ebs_volume as /var/easydeploy/share inside the container: { "AWSEBDockerrunVersion": "1", "Volumes": [ { "HostDirectory": "/media/ebs_volume", "ContainerDirectory": "/var/easydeploy/share" } ] } Dockerfile: Tell Docker to use a directory on the host system as /var/easydeploy/share – either a randomly generated one or the one given via the -m mount option to docker run: ... VOLUME ["/var/easydeploy/share"] ...
June 3, 2015
by Jakub Holý
· 14,839 Views
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Ecosystem of Hadoop Animal Zoo
hadoop is best known for map reduce and it's distributed file system (hdfs). recently other productivity tools developed on top of these will form a complete ecosystem of hadoop. most of the projects are hosted under apache software foundation . hadoop ecosystem projects are listed below. hadoop common a set of components and interfaces for distributed file system and i/o (serialization, java rpc, persistent data structures) http://hadoop.apache.org/ hadoop ecosystem hdfs a distributed file system that runs on large clusters of commodity hardware. hadoop distributed file system, hdfs renamed form ndfs. scalable data store that stores semi-structured, un-structured and structured data. http://hadoop.apache.org/docs/r2.3.0/hadoop-project-dist/hadoop-hdfs/hdfsuserguide.html http://wiki.apache.org/hadoop/hdfs map reduce map reduce is the distributed, parallel computing programming model for hadoop. inspired from google map reduce research paper . hadoop includes implementation of map reduce programming model. in map reduce there are two phases, not surprisingly map and reduce. to be precise in between map and reduce phase, there is another phase called sort and shuffle. job tracker in name node machine manages other cluster nodes. map reduce programming can be written in java. if you like sql or other non- java languages, you are still in luck. you can use utility called hadoop streaming. http://wiki.apache.org/hadoop/hadoopmapreduce hadoop streaming a utility to enable map reduce code in many languages like c, perl, python, c++, bash etc., examples include a python mapper and awk reducer. http://hadoop.apache.org/docs/r1.2.1/streaming.html avro a serialization system for efficient, cross-language rpc and persistent data storage. avro is a framework for performing remote procedure calls and data serialization. in the context of hadoop, it can be used to pass data from one program or language to another, e.g. from c to pig. it is particularly suited for use with scripting languages such as pig, because data is always stored with its schema in avro. http://avro.apache.org/ apache thrift apache thrift allows you to define data types and service interfaces in a simple definition file. taking that file as input, the compiler generates code to be used to easily build rpc clients and servers that communicate seamlessly across programming languages. instead of writing a load of boilerplate code to serialize and transport your objects and invoke remote methods, you can get right down to business. http://thrift.apache.org/ hive and hue if you like sql, you would be delighted to hear that you can write sql and hive convert it to a map reduce job. but, you don't get a full ansi-sql environment. hue gives you a browser based graphical interface to do your hive work. hue features a file browser for hdfs, a job browser for map reduce/yarn, an hbase browser, query editors for hive, pig, cloudera impala and sqoop2.it also ships with an oozie application for creating and monitoring workflows, a zookeeper browser and an sdk. pig a high-level programming data flow language and execution environment to do map reduce coding the pig language is called pig latin. you may find naming conventions some what un-conventional, but you get incredible price-performance and high availability. https://pig.apache.org/ jaql jaql is a functional, declarative programming language designed especially for working with large volumes of structured, semi-structured and unstructured data. as its name implies, a primary use of jaql is to handle data stored as json documents, but jaql can work on various types of data. for example, it can support xml, comma-separated values (csv) data and flat files. a "sql within jaql" capability lets programmers work with structured sql data while employing a json data model that's less restrictive than its structured query language counterparts. 1. jaql in google code 2. what is jaql? by ibm sqoop sqoop provides a bi-directional data transfer between hadoop -hdfs and your favorite relational database. for example you might be storing your app data in relational store such as oracle, now you want to scale your application with hadoop so you can migrate oracle database data to hadoop hdfs using sqoop. http://sqoop.apache.org/ oozie manages hadoop workflow. this doesn't replace your scheduler or BPM tooling, but it will provide if-then-else branching and control with hadoop jobs. https://oozie.apache.org/ zookeeper a distributed, highly available coordination service. zookeeper provides primitives such as distributed locks that can be used for building the highly scalable applications. it is used to manage synchronization for cluster. http://zookeeper.apache.org/ hbase based on google's bigtable , hbase "is an open-source, distributed, version, column-oriented store" that sits on top of hdfs. a super scalable key-value store. it works very much like a persistent hash-map (for python developers think like a dictionary). it is not a conventional relational database. it is a distributed, column oriented database. hbase uses hdfs for it's underlying. supports both batch-style computations using map reduce and point queries for random reads. https://hbase.apache.org/ cassandra a column oriented nosql data store which offers scalability, high availability with out compromising on performance. it perfect platform for commodity hardware and cloud infrastructure.cassandra's data model offers the convenience of column indexes with the performance of log-structured updates, strong support for de-normalization and materialized views , and powerful built-in caching. http://cassandra.apache.org/ flume a real time loader for streaming your data into hadoop. it stores data in hdfs and hbase.flume "channels" data between "sources" and "sinks" and its data harvesting can either be scheduled or event-driven. possible sources for flume include avro, files, and system logs, and possible sinks include hdfs and hbase. http://flume.apache.org/ mahout machine learning for hadoop, used for predictive analytics and other advanced analysis. there are currently four main groups of algorithms in mahout: recommendations, a.k.a. collective filtering classification, a.k.a categorization clustering frequent item set mining, a.k.a parallel frequent pattern mining mahout is not simply a collection of pre-existing algorithms; many machine learning algorithms are intrinsically non-scalable; that is, given the types of operations they perform, they cannot be executed as a set of parallel processes. algorithms in the mahout library belong to the subset that can be executed in a distributed fashion. http://en.wikipedia.org/wiki/list_of_machine_learning_algorithms https://www.coursera.org/course/machlearning https://mahout.apache.org/ fuse makes the hdfs system to look like a regular file system so that you can use ls, rm, cd etc., directly on hdfs data. whirr apache whirr is a set of libraries for running cloud services. whirr provides a cloud-neutral way to run services. you don't have to worry about the idiosyncrasies of each provider.a common service api. the details of provisioning are particular to the service. smart defaults for services. you can get a properly configured system running quickly, while still being able to override settings as needed. you can also use whirr as a command line tool for deploying clusters. https://whirr.apache.org/ giraph an open source graph processing api like pregel from google https://giraph.apache.org/ chukwa chukwa, an incubator project on apache, is a data collection and analysis system built on top of hdfs and map reduce. tailored for collecting logs and other data from distributed monitoring systems, chukwa provides a workflow that allows for incremental data collection, processing and storage in hadoop. it is included in the apache hadoop distribution as an independent module. https://chukwa.apache.org/ drill apache drill, an incubator project on apache, is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. drill is the open source version of google's dremel system which is available as an iaas service called google big query. one explicitly stated design goal is that drill is able to scale to 10,000 servers or more and to be able to process petabytes of data and trillions of records in seconds. http://incubator.apache.org/drill/ impala (cloudera) released by cloudera, impala is an open-source project which, like apache drill, was inspired by google's paper on dremel; the purpose of both is to facilitate real-time querying of data in hdfs or hbase. impala uses an sql-like language that, though similar to hiveql, is currently more limited than hiveql. because impala relies on the hive meta store, hive must be installed on a cluster in order for impala to work. the secret behind impala's speed is that it "circumvents map reduce to directly access the data through a specialized distributed query engine that is very similar to those found in commercial parallel rdbmss." (source: cloudera) http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html http://training.cloudera.com/elearning/impala/
June 3, 2015
by Umashankar Ankuri
· 23,956 Views · 3 Likes
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Top 80 Thread- Java Interview Questions and Answers (Part 1)
Question 1. What is Thread in java? Answer. Threads consumes CPU in best possible manner, hence enables multi processing. Multi threading reduces idle time of CPU which improves performance of application. Thread are light weight process. A thread class belongs to java.lang package. We can create multiple threads in java, even if we don’t create any Thread, one Thread at least do exist i.e. main thread. Multiple threads run parallely in java. Threads have their own stack. Advantage of Thread : Suppose one thread needs 10 minutes to get certain task, 10 threads used at a time could complete that task in 1 minute, because threads can run parallely. Question 2. What is difference between Process and Thread in java? Answer. One process can have multiple Threads, Thread are subdivision of Process. One or more Threads runs in the context of process. Threads can execute any part of process. And same part of process can be executed by multiple Threads. Processes have their own copy of the data segment of the parent process while Threads have direct access to the data segment of its process. Processes have their own address while Threads share the address space of the process that created it. Process creation needs whole lot of stuff to be done, we might need to copy whole parent process, but Thread can be easily created. Processes can easily communicate with child processes but interprocess communication is difficult. While, Threads can easily communicate with other threads of the same process using wait() and notify() methods. In process all threads share system resource like heap Memory etc. while Thread has its own stack. Any change made to process does not affect child processes, but any change made to thread can affect the behavior of the other threads of the process. Example to see where threads on are created on different processes and same process. Question 3. How to implement Threads in java? Answer. This is very basic threading question. Threads can be created in two ways i.e. by implementing java.lang.Runnable interface or extending java.lang.Thread class and then extending run method. Thread has its own variables and methods, it lives and dies on the heap. But a thread of execution is an individual process that has its own call stack. Thread are lightweight process in java. Thread creation by implementingjava.lang.Runnableinterface. We will create object of class which implements Runnable interface : MyRunnable runnable=new MyRunnable(); Thread thread=new Thread(runnable); 2) And then create Thread object by calling constructor and passing reference of Runnable interface i.e. runnable object : Thread thread=new Thread(runnable); Question 4 . Does Thread implements their own Stack, if yes how? (Important) Answer. Yes, Threads have their own stack. This is very interesting question, where interviewer tends to check your basic knowledge about how threads internally maintains their own stacks. I’ll be explaining you the concept by diagram. Question 5. We should implement Runnable interface or extend Thread class. What are differences between implementing Runnable and extending Thread? Answer. Well the answer is you must extend Thread only when you are looking to modify run() and other methods as well. If you are simply looking to modify only the run() method implementing Runnable is the best option (Runnable interface has only one abstract method i.e. run() ). Differences between implementing Runnable interface and extending Thread class - Multiple inheritance in not allowed in java : When we implement Runnable interface we can extend another class as well, but if we extend Thread class we cannot extend any other class because java does not allow multiple inheritance. So, same work is done by implementing Runnable and extending Thread but in case of implementing Runnable we are still left with option of extending some other class. So, it’s better to implement Runnable. Thread safety : When we implement Runnable interface, same object is shared amongst multiple threads, but when we extend Thread class each and every thread gets associated with new object. Inheritance (Implementing Runnable is lightweight operation) : When we extend Thread unnecessary all Thread class features are inherited, but when we implement Runnable interface no extra feature are inherited, as Runnable only consists only of one abstract method i.e. run() method. So, implementing Runnable is lightweight operation. Coding to interface : Even java recommends coding to interface. So, we must implement Runnable rather than extending thread. Also, Thread class implements Runnable interface. Don’t extend unless you wanna modify fundamental behaviour of class, Runnable interface has only one abstract method i.e. run() : We must extend Thread only when you are looking to modify run() and other methods as well. If you are simply looking to modify only the run() method implementing Runnable is the best option (Runnable interface has only one abstract method i.e. run() ). We must not extend Thread class unless we're looking to modify fundamental behaviour of Thread class. Flexibility in code when we implement Runnable : When we extend Thread first a fall all thread features are inherited and our class becomes direct subclass of Thread , so whatever action we are doing is in Thread class. But, when we implement Runnable we create a new thread and pass runnable object as parameter,we could pass runnable object to executorService & much more. So, we have more options when we implement Runnable and our code becomes more flexible. ExecutorService : If we implement Runnable, we can start multiple thread created on runnable object with ExecutorService (because we can start Runnable object with new threads), but not in the case when we extend Thread (because thread can be started only once). Question 6. How can you say Thread behaviour is unpredictable? (Important) Answer. The solution to question is quite simple, Thread behaviour is unpredictable because execution of Threads depends on Thread scheduler, thread scheduler may have different implementation on different platforms like windows, unix etc. Same threading program may produce different output in subsequent executions even on same platform. To achieve we are going to create 2 threads on same Runnable Object, create for loop in run() method and start both threads. There is no surety that which threads will complete first, both threads will enter anonymously in for loop. Question 7 . When threads are not lightweight process in java? Answer. Threads are lightweight process only if threads of same process are executing concurrently. But if threads of different processes are executing concurrently then threads are heavy weight process. Question 8. How can you ensure all threads that started from main must end in order in which they started and also main should end in last? (Important) Answer. Interviewers tend to know interviewees knowledge about Thread methods. So this is time to prove your point by answering correctly. We can use join() methodto ensure all threads that started from main must end in order in which they started and also main should end in last.In other words waits for this thread to die. Calling join() method internally calls join(0); DETAILED DESCRIPTION : Join() method - ensure all threads that started from main must end in order in which they started and also main should end in last. Types of join() method with programs- 10 salient features of join. Question 9.What is difference between starting thread with run() and start() method? (Important) Answer. This is quite interesting question, it might confuse you a bit and at time may make you think is there really any difference between starting thread with run() and start() method. When you call start() method, main thread internally calls run() method to start newly created Thread, so run() method is ultimately called by newly created thread. When you call run() method main thread rather than starting run() method with newly thread it start run() method by itself. Question 10. What is significance of using Volatile keyword? (Important) Answer. Java allows threads to access shared variables. As a rule, to ensure that shared variables are consistently updated, a thread should ensure that it has exclusive use of such variables by obtaining a lock that enforces mutual exclusion for those shared variables. If a field is declared volatile, in that case the Java memory model ensures that all threads see a consistent value for the variable. Few small questions> Q. Can we have volatile methods in java? No, volatile is only a keyword, can be used only with variables. Q. Can we have synchronized variable in java? No, synchronized can be used only with methods, i.e. in method declaration. Question 11. Differences between synchronized and volatile keyword in Java? (Important) Answer.Its very important question from interview perspective. Volatilecan be used as a keyword against the variable, we cannot use volatile against method declaration. volatile void method1(){} //it’s illegal, compilation error. While synchronization can be used in method declaration or we can create synchronization blocks (In both cases thread acquires lock on object’s monitor). Variables cannot be synchronized. Synchronized method: synchronized void method2(){} //legal Synchronized block: void method2(){ synchronized (this) { //code inside synchronized block. } } Synchronized variable (illegal): synchronized int i;//it’s illegal, compilatiomn error. Volatile does not acquire any lock on variable or object, but Synchronization acquires lock on method or block in which it is used. Volatile variables are not cached, but variables used inside synchronized method or block are cached. When volatile is used will never create deadlock in program, as volatile never obtains any kind of lock . But in case if synchronization is not done properly, we might end up creating dedlock in program. Synchronization may cost us performance issues, as one thread might be waiting for another thread to release lock on object. But volatile is never expensive in terms of performance. DETAILED DESCRIPTION : Differences between synchronized and volatile keyword in detail with programs. Question 12. Can you again start Thread? Answer.No, we cannot start Thread again, doing so will throw runtimeException java.lang.IllegalThreadStateException. The reason is once run() method is executed by Thread, it goes into dead state. Let’s take an example- Thinking of starting thread again and calling start() method on it (which internally is going to call run() method) for us is some what like asking dead man to wake up and run. As, after completing his life person goes to dead state. Question 13. What is race condition in multithreading and how can we solve it? (Important) Answer. This is very important question, this forms the core of multi threading, you should be able to explain about race condition in detail. When more than one thread try to access same resource without synchronization causes race condition. So we can solve race condition by using either synchronized block or synchronized method. When no two threads can access same resource at a time phenomenon is also called as mutual exclusion. Few sub questions> What if two threads try to read same resource without synchronization? When two threads try to read on same resource without synchronization, it’s never going to create any problem. What if two threads try to write to same resource without synchronization? When two threads try to write to same resource without synchronization, it’s going to create synchronization problems. Question 14. How threads communicate between each other? Answer. This is very must know question for all the interviewees, you will most probably face this question in almost every time you go for interview. Threads can communicate with each other by using wait(), notify() and notifyAll() methods. Question 15. Why wait(), notify() and notifyAll() are in Object class and not in Thread class? (Important) Answer. Every Object has a monitor, acquiring that monitors allow thread to hold lock on object. But Thread class does not have any monitors. wait(), notify() and notifyAll()are called on objects only >When wait() method is called on object by thread it waits for another thread on that object to release object monitor by calling notify() or notifyAll() method on that object. When notify() method is called on object by thread it notifies all the threads which are waiting for that object monitor that object monitor is available now. So, this shows that wait(), notify() and notifyAll() are called on objects only. Now, Straight forward question that comes to mind is how thread acquires object lock by acquiring object monitor? Let’s try to understand this basic concept in detail? Wait(), notify() and notifyAll() method being in Object class allows all the threads created on that object to communicate with other. . As multiple threads exists on same object. Only one thread can hold object monitor at a time. As a result thread can notify other threads of same object that lock is available now. But, thread having these methods does not make any sense because multiple threads exists on object its not other way around (i.e. multiple objects exists on thread). Now let’s discuss one hypothetical scenario, what will happen if Thread class contains wait(), notify() and notifyAll() methods? Having wait(), notify() and notifyAll() methods means Thread class also must have their monitor. Every thread having their monitor will create few problems - >Thread communication problem. >Synchronization on object won’t be possible- Because object has monitor, one object can have multiple threads and thread hold lock on object by holding object monitor. But if each thread will have monitor, we won’t have any way of achieving synchronization. >Inconsistency in state of object (because synchronization won't be possible). Question 16. Is it important to acquire object lock before calling wait(), notify() and notifyAll()? Answer.Yes, it’s mandatory to acquire object lock before calling these methods on object. As discussed above wait(), notify() and notifyAll() methods are always called from Synchronized block only, and as soon as thread enters synchronized block it acquires object lock (by holding object monitor). If we call these methods without acquiring object lock i.e. from outside synchronize block then java.lang. IllegalMonitorStateException is thrown at runtime. Wait() method needs to enclosed in try-catch block, because it throws compile time exception i.e. InterruptedException. Question 17. How can you solve consumer producer problem by using wait() and notify() method? (Important) Answer. Here come the time to answer very very important question from interview perspective. Interviewers tends to check how sound you are in threads inter communication. Because for solving this problem we got to use synchronization blocks, wait() and notify() method very cautiously. If you misplace synchronization block or any of the method, that may cause your program to go horribly wrong. So, before going into this question first i’ll recommend you to understand how to use synchronized blocks, wait() and notify() methods. Key points we need to ensure before programming : >Producer will produce total of 10 products and cannot produce more than 2 products at a time until products are being consumed by consumer. Example> when sharedQueue’s size is 2, wait for consumer to consume (consumer will consume by calling remove(0) method on sharedQueue and reduce sharedQueue’s size). As soon as size is less than 2, producer will start producing. >Consumer can consume only when there are some products to consume. Example> when sharedQueue’s size is 0, wait for producer to produce (producer will produce by calling add() method on sharedQueue and increase sharedQueue’s size). As soon as size is greater than 0, consumer will start consuming. Explanation of Logic > We will create sharedQueue that will be shared amongst Producer and Consumer. We will now start consumer and producer thread. Note: it does not matter order in which threads are started (because rest of code has taken care of synchronization and key points mentioned above) First we will start consumerThread > consumerThread.start(); consumerThread will enter run method and call consume() method. There it will check for sharedQueue’s size. -if size is equal to 0 that means producer hasn’t produced any product, wait for producer to produce by using below piece of code- synchronized (sharedQueue) { while (sharedQueue.size() == 0) { sharedQueue.wait(); } } -if size is greater than 0, consumer will start consuming by using below piece of code. synchronized (sharedQueue) { Thread.sleep((long)(Math.random() * 2000)); System.out.println("consumed : "+ sharedQueue.remove(0)); sharedQueue.notify(); } Than we will start producerThread > producerThread.start(); producerThread will enter run method and call produce() method. There it will check for sharedQueue’s size. -if size is equal to 2 (i.e. maximum number of products which sharedQueue can hold at a time), wait for consumer to consume by using below piece of code- synchronized (sharedQueue) { while (sharedQueue.size() == maxSize) { //maxsize is 2 sharedQueue.wait(); } } -if size is less than 2, producer will start producing by using below piece of code. synchronized (sharedQueue) { System.out.println("Produced : " + i); sharedQueue.add(i); Thread.sleep((long)(Math.random() * 1000)); sharedQueue.notify(); } DETAILED DESCRIPTION with program : Solve Consumer Producer problem by using wait() and notify() methods in multithreading. Question 18. How to solve Consumer Producer problem without using wait() and notify() methods, where consumer can consume only when production is over.? Answer. In this problem, producer will allow consumer to consume only when 10 products have been produced (i.e. when production is over). We will approach by keeping one boolean variable productionInProcess and initially setting it to true, and later when production will be over we will set it to false. Question 19. How can you solve consumer producer pattern by using BlockingQueue? (Important) Answer. Now it’s time to gear up to face question which is most probably going to be followed up by previous question i.e. after how to solve consumer producer problem using wait() and notify() method. Generally you might wonder why interviewer's are so much interested in asking about solving consumer producer problem using BlockingQueue, answer is they want to know how strong knowledge you have about java concurrent Api’s, this Api use consumer producer pattern in very optimized manner, BlockingQueue is designed is such a manner that it offer us the best performance. BlockingQueue is a interface and we will use its implementation class LinkedBlockingQueue. Key methods for solving consumer producer pattern are > put(i); //used by producer to put/produce in sharedQueue. take();//used by consumer to take/consume from sharedQueue. Question 20. What is deadlock in multithreading? Write a program to form DeadLock in multi threading and also how to solve DeadLock situation. What measures you should take to avoid deadlock? (Important) Answer. This is very important question from interview perspective. But, what makes this question important is it checks interviewees capability of creating and detecting deadlock. If you can write a code to form deadlock, than I am sure you must be well capable in solving that deadlock as well. If not, later on this post we will learn how to solve deadlock as well. First question comes to mind is, what is deadlock in multi threading program? Deadlock is a situation where two threads are waiting for each other to release lock holded by them on resources. But how deadlock could be formed : Thread-1 acquires lock on String.class and then calls sleep() method which gives Thread-2 the chance to execute immediately after Thread-1 has acquired lock on String.class and Thread-2 acquires lock on Object.class then calls sleep() method and now it waits for Thread-1 to release lock on String.class. Conclusion: Now, Thread-1 is waiting for Thread-2 to release lock on Object.class and Thread-2 is waiting for Thread-1 to release lock on String.class and deadlock is formed. //Code called by Thread-1 public void run() { synchronized (String.class) { Thread.sleep(100); synchronized (Object.class) { } } } //Code called by Thread-2 publicvoid run() { synchronized (Object.class) { Thread.sleep(100); synchronized (String.class) { } } } Here comes the important part, how above formed deadlock could be solved : Thread-1 acquires lock on String.class and then calls sleep() method which gives Thread-2 the chance to execute immediately after Thread-1 has acquired lock on String.class and Thread-2 tries to acquire lock on String.class but lock is holded by Thread-1. Meanwhile, Thread-1 completes successfully. As Thread-1 has completed successfully it releases lock on String.class, Thread-2 can now acquire lock on String.class and complete successfully without any deadlock formation. Conclusion: No deadlock is formed. //Code called by Thread-1 publicvoid run() { synchronized (String.class) { Thread.sleep(100); synchronized (Object.class) { } } } //Code called by Thread-2 publicvoid run() { synchronized (String.class) { Thread.sleep(100); synchronized (Object.class) { } } } Few important measures to avoid Deadlock > Lock specific member variables of class rather than locking whole class: We must try to lock specific member variables of class rather than locking whole class. Use join() method: If possible try touse join() method, although it may refrain us from taking full advantage of multithreading environment because threads will start and end sequentially, but it can be handy in avoiding deadlocks. If possible try avoid using nested synchronization blocks. Question 21. Have you ever generated thread dumps or analyzed Thread Dumps? (Important) Answer. Answering this questions will show your in depth knowledge of Threads. Every experienced must know how to generate Thread Dumps. VisualVM is most popular way to generate Thread Dump and is most widely used by developers. It’s important to understand usage of VisualVM for in depth knowledge of VisualVM. I’ll recommend every developer must understand this topic to become master in multi threading. It helps us in analyzing threads performance, thread states, CPU consumed by threads, garbage collection and much more. For detailed information see Generating and analyzing Thread Dumps using VisualVM - step by step detail to setup VisualVM with screenshots jstack is very easy way to generate Thread dump and is widely used by developers. I’ll recommend every developer must understand this topic to become master in multi threading. For creating Thread dumps we need not to download any jar or any extra software. For detailed information see Generating and analyzing Thread Dumps using JSATCK - step by step detail to setup JSTACK with screenshots. Question 22. What is life cycle of Thread, explain thread states? (Important) Answer. Thread states/ Thread life cycle is very basic question, before going deep into concepts we must understand Thread life cycle. Thread have following states > New Runnable Running Waiting/blocked/sleeping Terminated (Dead) Thread states/ Thread life cycle in diagram > Thread states in detail > New : When instance of thread is created using new operator it is in new state, but the start() method has not been invoked on the thread yet, thread is not eligible to run yet. Runnable : When start() method is called on thread it enters runnable state. Running : Thread scheduler selects thread to go fromrunnable to running state. In running state Thread starts executing by entering run() method. Waiting/blocked/sleeping : In this state a thread is not eligible to run. >Thread is still alive, but currently it’s not eligible to run. In other words. > How can Thread go from running to waiting state? By calling wait()method thread go from running to waiting state. In waiting state it will wait for other threads to release object monitor/lock. > How can Thread go from running to sleeping state? By calling sleep() methodthread go from running to sleeping state. In sleeping state it will wait for sleep time to get over. Terminated (Dead) : A thread is considered dead when its run() method completes. Question 23. Are you aware of preemptive scheduling and time slicing? Answer. In preemptive scheduling, the highest priority thread executes until it enters into the waiting or dead state. In time slicing, a thread executes for a certain predefined time and then enters runnable pool. Than thread can enter running state when selected by thread scheduler. Question 24. What are daemon threads? Answer.Daemon threads are low priority threads which runs intermittently in background for doing garbage collection. 12 Few salient features of daemon() threads> Thread scheduler schedules these threads only when CPU is idle. Daemon threads are service oriented threads, they serves all other threads. These threads are created before user threads are created and die after all other user threads dies. Priority of daemon threads is always 1 (i.e. MIN_PRIORITY). User created threads are non daemon threads. JVM can exit when only daemon threads exist in system. we can use isDaemon() method to check whether thread is daemon thread or not. we can use setDaemon(boolean on) method to make any user method a daemon thread. If setDaemon(boolean on) is called on thread after calling start() method than IllegalThreadStateException is thrown. You may like to see how daemon threads work, for that you can use VisualVM or jStack. I have provided Thread dumps over there which shows daemon threads which were intermittently running in background. Some of the daemon threads which intermittently run in background are > "RMI TCP Connection(3)-10.175.2.71" daemon"RMI TCP Connection(idle)" daemon"RMI Scheduler(0)" daemon"C2 CompilerThread1" daemon "GC task thread#0 (ParallelGC)" Question 25. Why suspend() and resume() methods are deprecated? Answer.Suspend() method is deadlock prone. If the target thread holds a lock on object when it is suspended, no thread can lock this object until the target thread is resumed. If the thread that would resume the target thread attempts to lock this monitor prior to calling resume, it results in deadlock formation. These deadlocksare generally called Frozen processes. Suspend() method puts thread from running to waiting state. And thread can go from waiting to runnable state only when resume() method is called on thread. It is deprecated method. Resume() method is only used with suspend() method that’s why it’s also deprecated method. Question 26. Why destroy() methods is deprecated? Answer. This question is again going to check your in depth knowledge of thread methods i.e. destroy() method is deadlock prone. If the target thread holds a lock on object when it is destroyed, no thread can lock this object (Deadlock formed are similar to deadlock formed when suspend() and resume() methods are used improperly). It results in deadlock formation. These deadlocksare generally called Frozen processes. Additionally you must know calling destroy() method on Threads throw runtimeException i.e. NoSuchMethodError. Destroy() method puts thread from running to dead state. Question 27. As stop() method is deprecated, How can we terminate or stop infinitely running thread in java? (Important) Answer. This is very interesting question where interviewees thread basics basic will be tested. Interviewers tend to know user’s knowledge about main thread’s and thread invoked by main thread. We will try to address the problem by creating new thread which will run infinitely until certain condition is satisfied and will be called by main Thread. Infinitely running thread can be stopped using boolean variable. Infinitely running thread can be stopped using interrupt() method. Let’s understand Why stop() method is deprecated : Stopping a thread with Thread.stop() causes it to release all of the monitors that it has locked. If any of the objects previously protected by these monitors were in an inconsistent state, the damaged objects become visible to other threads, which might lead to unpredictable behavior. Question 28. what is significance of yield() method, what state does it put thread in? yield() is a native method it’s implementation in java 6 has been changed as compared to its implementation java 5. As method is native it’s implementation is provided by JVM. In java 5, yield() method internally used to call sleep() method giving all the other threads of same or higher priority to execute before yielded thread by leaving allocated CPU for time gap of 15 millisec. But java 6, calling yield() method gives a hint to the thread scheduler that the current thread is willing to yield its current use of a processor. The thread scheduler is free to ignore this hint. So, sometimes even after using yield() method, you may not notice any difference in output. salient features of yield() method > Definition : yield() method when called on thread gives a hint to the thread scheduler that the current thread is willing to yield its current use of a processor.The thread scheduler is free to ignore this hint. Thread state : when yield() method is called on thread it goes from running to runnable state, not in waiting state. Thread is eligible to run but not running and could be picked by scheduler at anytime. Waiting time : yield() method stops thread for unpredictable time. Static method : yield()is a static method, hence calling Thread.yield() causes currently executing thread to yield. Native method : implementation of yield() method is provided by JVM. Let’s see definition of yield() method as given in java.lang.Thread - public static native void yield(); synchronized block : thread need not to to acquire object lock before calling yield()method i.e. yield() method can be called from outside synchronized block. Question 29.What is significance of sleep() method in detail, what statedoes it put thread in ? sleep() is a native method, it’s implementation is provided by JVM. 10 salient features of sleep() method > Definition : sleep() methods causes current thread to sleep for specified number of milliseconds (i.e. time passed in sleep method as parameter). Ex- Thread.sleep(10) causes currently executing thread to sleep for 10 millisec. Thread state : when sleep() is called on thread it goes from running to waiting state and can return to runnable state when sleep time is up. Exception : sleep() method must catch or throw compile time exception i.e. InterruptedException. Waiting time : sleep() method have got few options. sleep(long millis) - Causes the currently executing thread to sleep for the specified number of milliseconds public static native void sleep(long millis) throws InterruptedException; sleep(long millis, int nanos) - Causes the currently executing thread to sleep for the specified number of milliseconds plus the specified number of nanoseconds. public static native void sleep(long millis,int nanos) throws InterruptedException; static method : sleep()is a static method, causes the currently executing thread to sleep for the specified number of milliseconds. Belongs to which class :sleep() method belongs to java.lang.Thread class. synchronized block : thread need not to to acquire object lock before calling sleep()method i.e. sleep() method can be called from outside synchronized block. Question 30. Difference between wait() and sleep() ? (Important) Answer. Should be called from synchronized block :wait() method is always called from synchronized block i.e. wait() method needs to lock object monitor before object on which it is called. But sleep() method can be called from outside synchronized block i.e. sleep() method doesn’t need any object monitor. IllegalMonitorStateException : if wait() method is called without acquiring object lock than IllegalMonitorStateException is thrown at runtime, but sleep() methodnever throws such exception. Belongs to which class : wait() method belongs to java.lang.Object class but sleep() method belongs to java.lang.Thread class. Called on object or thread : wait() method is called on objects but sleep() method is called on Threads not objects. Thread state : when wait() method is called on object, thread that holded object’s monitor goes from running to waiting state and can return to runnable state only when notify() or notifyAll()method is called on that object. And later thread scheduler schedules that thread to go from from runnable to running state. when sleep() is called on thread it goes from running to waiting state and can return to runnable state when sleep time is up. When called from synchronized block :when wait() method is called thread leaves the object lock. But sleep()method when called from synchronized block or method thread doesn’t leaves object lock. Question 31. Differences and similarities between yield() and sleep()? Answer. Differences yield() and sleep() : Definition : yield() method when called on thread gives a hint to the thread scheduler that the current thread is willing to yield its current use of a processor.The thread scheduler is free to ignore this hint. sleep() methods causes current thread to sleep for specified number of milliseconds (i.e. time passed in sleep method as parameter). Ex- Thread.sleep(10) causes currently executing thread to sleep for 10 millisec. Thread state : when sleep() is called on thread it goes from running to waiting state and can return to runnable state when sleep time is up. when yield() method is called on thread it goes from running to runnable state, not in waiting state. Thread is eligible to run but not running and could be picked by scheduler at anytime. Exception : yield() method need not to catch or throw any exception. But sleep() method must catch or throw compile time exception i.e. InterruptedException. Waiting time : yield() method stops thread for unpredictable time, that depends on thread scheduler. But sleep() method have got few options. sleep(long millis) - Causes the currently executing thread to sleep for the specified number of milliseconds sleep(long millis, int nanos) - Causes the currently executing thread to sleep for the specified number of milliseconds plus the specified number of nanoseconds. similarity between yield() and sleep(): > yield() and sleep() method belongs to java.lang.Thread class. > yield() and sleep() method can be called from outside synchronized block. > yield() and sleep() method are called on Threads not objects. Question 32. Mention some guidelines to write thread safe code, most important point we must take care of in multithreading programs? Answer. In multithreading environment it’s important very important to write thread safe code, thread unsafe code can cause a major threat to your application. I have posted many articles regarding thread safety. So overall this will be revision of what we have learned so far i.e. writing thread safe healthy code and avoiding any kind of deadlocks. If method is exposed in multithreading environment and it’s not synchronized (thread unsafe) than it might lead us to race condition, we must try to use synchronized block and synchronized methods. Multiple threads may exist on same object but only one thread of that object can enter synchronized method at a time, though threads on different object can enter same method at same time. Even static variables are not thread safe, they are used in static methods and if static methods are not synchronized then thread on same or different object can enter method concurrently. Multiple threads may exist on same or different objects of class but only one thread can enter static synchronized method at a time, we must consider making static methods as synchronized. If possible, try to use volatile variables. If a field is declared volatile all threads see a consistent value for the variable. Volatile variables at times can be used as alternate to synchronized methods as well. Final variables are thread safe because once assigned some reference of object they cannot point to reference of other object. s is pointing to String object. public class MyClass { final String s=new String("a"); void method(){ s="b"; //compilation error, s cannot point to new reference. } } If final is holding some primitive value it cannot point to other value. public class MyClass { final inti=0; void method(){ i=0; //compilation error, i cannot point to new value. } } Usage of local variables : If possible try to use local variables, local variables are thread safe, because every thread has its own stack, i.e. every thread has its own local variables and its pushes all the local variables on stack. public class MyClass { void method(){ inti=0; //Local variable, is thread safe. } } Using thread safe collections : Rather than using ArrayList we must Vector and in place of using HashMap we must use ConcurrentHashMap or HashTable. We must use VisualVM or jstack to detect problems such as deadlocks and time taken by threads to complete in multi threading programs. Using ThreadLocal:ThreadLocal is a class which provides thread-local variables. Every thread has its own ThreadLocal value that makes ThreadLocal value threadsafe as well. Rather than StringBuffer try using immutable classes such as String. Any change to String produces new String. Question 33. How thread can enter waiting, sleeping and blocked state and how can they go to runnable state ? Answer. This is very prominently asked question in interview which will test your knowledge about thread states. And it’s very important for developers to have in depth knowledge of this thread state transition. I will try to explain this thread state transition by framing few sub questions. I hope reading sub questions will be quite interesting. > How can Thread go from running to waiting state ? By calling wait()method thread go from running to waiting state. In waiting state it will wait for other threads to release object monitor/lock. > How can Thread return from waiting to runnable state ? Once notify() or notifyAll()method is called object monitor/lock becomes available and thread can again return to runnable state. > How can Thread go from running to sleeping state ? By calling sleep() methodthread go from running to sleeping state. In sleeping state it will wait for sleep time to get over. > How can Thread return from sleeping to runnable state ? Once specified sleep time is up thread can again return to runnable state. Suspend() method can be used to put thread in waiting state and resume() method is the only way which could put thread in runnable state. Thread also may go from running to waiting state if it is waiting for some I/O operation to take place. Once input is available thread may return to running state. >When threads are in running state, yield()method can make thread to go in Runnable state. Question 34. Difference between notify() and notifyAll() methods, can you write a code to prove your point? Answer. Goodness. Theoretically you must have heard or you must be aware of differences between notify() and notifyAll().But have you created program to achieve it? If not let’s do it. First, I will like give you a brief description of what notify() and notifyAll() methods do. notify()- Wakes up a single thread that is waiting on this object's monitor. If any threads are waiting on this object, one of them is chosen to be awakened. The choice is random and occurs at the discretion of the implementation. A thread waits on an object's monitor by calling one of the wait methods. The awakened threads will not be able to proceed until the current thread relinquishes the lock on this object. public final native void notify(); notifyAll()- Wakes up all threads that are waiting on this object's monitor. A thread waits on an object's monitor by calling one of the wait methods. The awakened threads will not be able to proceed until the current thread relinquishes the lock on this object. public final native void notifyAll(); Now it’s time to write down a program to prove the point. Question 35. Does thread leaves object lock when sleep() method is called? Answer. When sleep() method is called Thread does not leaves object lock and goes from running to waiting state. Thread waits for sleep time to over and once sleep time is up it goes from waiting to runnable state. Question 36. Does thread leaves object lock when wait() method is called? Answer. When wait() method is called Thread leaves the object lock and goes from running to waiting state. Thread waits for other threads on same object to call notify() or notifyAll() and once any of notify() or notifyAll() is called it goes from waiting to runnable state and again acquires object lock. Question 37. What will happen if we don’t override run method? Answer. This question will test your basic knowledge how start and run methods work internally in Thread Api. When we call start() method on thread, it internally calls run() method with newly created thread. So, if we don’t override run() method newly created thread won’t be called and nothing will happen. class MyThread extends Thread { //don't override run() method } publicclass DontOverrideRun { publicstaticvoid main(String[] args) { System.out.println("main has started."); MyThread thread1=new MyThread(); thread1.start(); System.out.println("main has ended."); } } /*OUTPUT main has started. main has ended. */ As we saw in output, we didn’t override run() method that’s why on calling start() method nothing happened. Question 38. What will happen if we override start method? Answer. This question will again test your basic core java knowledge how overriding works at runtime, what what will be called at runtime and how start and run methods work internally in Thread Api. When we call start() method on thread, it internally calls run() method with newly created thread. So, if we override start() method, run() method will not be called until we write code for calling run() method. class MyThread extends Thread { @Override publicvoid run() { System.out.println("in run() method"); } @Override publicvoid start(){ System.out.println("In start() method"); } } publicclass OverrideStartMethod { publicstaticvoid main(String[] args) { System.out.println("main has started."); MyThread thread1=new MyThread(); thread1.start(); System.out.println("main has ended."); } } /*OUTPUT main has started. In start() method main has ended. */ If we note output. we have overridden start method and didn’t called run() method from it, so, run() method wasn’t call. Question 39. Can we acquire lock on class? What are ways in which you can acquire lock on class? Answer. Yes, we can acquire lock on class’s class object in 2 ways to acquire lock on class. Thread can acquire lock on class’s class object by- Entering synchronized block or Let’s say there is one class MyClass. Now we can create synchronization block, and parameter passed with synchronization tells which class has to be synchronized. In below code, we have synchronized MyClass synchronized (MyClass.class) { //thread has acquired lock on MyClass’s class object. } by entering static synchronized methods. public staticsynchronizedvoid method1() { //thread has acquired lock on MyRunnable’s class object. } As soon as thread entered Synchronization method, thread acquired lock on class’s class object. Thread will leave lock when it exits static synchronized method. Question 40. Difference between object lock and class lock? Answer. It is very important question from multithreading point of view. We must understand difference between object lock and class lock to answer interview, ocjp answers correctly. Object lock Class lock Thread can acquire object lock by- Entering synchronized block or by entering synchronized methods. Thread can acquire lock on class’s class object by- Entering synchronized block or by entering static synchronized methods. Multiple threads may exist on same object but only one thread of that object can enter synchronized method at a time. Threads on different object can enter same method at same time. Multiple threads may exist on same or different objects of class but only one thread can enter static synchronized method at a time. Multiple objects of class may exist and every object has it’s own lock. Multiple objects of class may exist but there is always one class’s class object lock available. First let’s acquire object lock by entering synchronized block. Example- Let’s say there is one class MyClassand we have created it’s object and reference to that object is myClass. Now we can create synchronization block, and parameter passed with synchronization tells which object has to be synchronized. In below code, we have synchronized object reference by myClass. MyClass myClass=newMyclass(); synchronized (myClass) { } As soon thread entered Synchronization block, thread acquired object lock on object referenced by myClass (by acquiring object’s monitor.) Thread will leave lock when it exits synchronized block. First let’s acquire lock on class’s class object by entering synchronized block. Example- Let’s say there is one class MyClass. Now we can create synchronization block, and parameter passed with synchronization tells which class has to be synchronized. In below code, we have synchronized MyClass synchronized (MyClass.class) { } As soon as thread entered Synchronization block, thread acquired MyClass’s class object. Thread will leave lock when it exits synchronized block. publicsynchronizedvoid method1() { } As soon as thread entered Synchronization method, thread acquired object lock. Thread will leave lock when it exits synchronized method. public staticsynchronizedvoid method1() {} As soon as thread entered static Synchronization method, thread acquired lock on class’s class object. Thread will leave lock when it exits synchronized method. Let’s me give you some tricky situation based question, Question 41. Suppose you have 2 threads (Thread-1 and Thread-2) on same object. Thread-1 is in synchronized method1(), can Thread-2 enter synchronized method2() at same time? Answer.No, here when Thread-1 is in synchronized method1() it must be holding lock on object’s monitor and will release lock on object’s monitor only when it exits synchronized method1(). So, Thread-2 will have to waitfor Thread-1 to release lock on object’s monitor so that it could enter synchronized method2(). Likewise, Thread-2 even cannot enter synchronized method1() which is being executed by Thread-1. Thread-2 will have to wait for Thread-1 to release lock on object’s monitor so that it could enter synchronized method1(). Now, let’s see a program to prove our point. Question 42. Suppose you have 2 threads (Thread-1 and Thread-2) on same object. Thread-1 is in static synchronized method1(), can Thread-2 enter static synchronized method2() at same time? Answer.No, here when Thread-1 is in static synchronized method1() it must be holding lock on class class’s object and will release lock on class’s classobject only when it exits static synchronized method1(). So, Thread-2 will have to wait for Thread-1 to release lock on class’s classobject so that it could enter static synchronized method2(). Likewise, Thread-2 even cannot enter static synchronized method1() which is being executed by Thread-1. Thread-2 will have to wait for Thread-1 to release lock on class’s classobject so that it could enter static synchronized method1(). Now, let’s see a program to prove our point. Question 43. Suppose you have 2 threads (Thread-1 and Thread-2) on same object. Thread-1 is in synchronized method1(), can Thread-2 enter static synchronized method2() at same time? Answer.Yes, here when Thread-1 is in synchronized method1() it must be holding lock on object’s monitor and Thread-2 can enter static synchronized method2() by acquiring lock on class’s class object. Now, let’s see a program to prove our point. Question 44. Suppose you have thread and it is in synchronized method and now can thread enter other synchronized method from that method? Answer.Yes, here when thread is in synchronized method it must be holding lock on object’s monitor and using that lock thread can enter other synchronized method. Now, let’s see a program to prove our point. Question 45. Suppose you have thread and it is in static synchronized method and now can thread enter other static synchronized method from that method? Answer. Yes, here when thread is in static synchronized method it must be holding lock on class’s class object and using that lock thread can enter other static synchronized method. Now, let’s see a program to prove our point. Question 46. Suppose you have thread and it is in static synchronized method and now can thread enter other non static synchronized method from that method? Answer.Yes, here when thread is in static synchronized method it must be holding lock on class’s class object and when it enters synchronized method it will hold lock on object’s monitor as well. So, now thread holds 2 locks (it’s also called nested synchronization)- >first one on class’s class object. >second one on object’s monitor (This lock will be released when thread exits non static method).Now, let’s see a program to prove our point. Question 47. Suppose you have thread and it is in synchronized method and now can thread enter other static synchronized method from that method? Answer.Yes, here when thread is in synchronized method it must be holding lock on object’s monitor and when it enters static synchronized method it will hold lock on class’s class object as well. So, now thread holds 2 locks (it’s also called nested synchronization)- >first one on object’s monitor. >second one on class’s class object.(This lock will be released when thread exits static method).Now, let’s see a program to prove our point. Question 48. Suppose you have 2 threads (Thread-1 on object1 and Thread-2 on object2). Thread-1 is in synchronized method1(), can Thread-2 enter synchronized method2() at same time? Answer.Yes, here when Thread-1 is in synchronized method1() it must be holding lock on object1’s monitor. Thread-2 will acquire lock on object2’s monitor and enter synchronized method2(). Likewise, Thread-2 even enter synchronized method1() as well which is being executed by Thread-1 (because threads are created on different objects). Now, let’s see a program to prove our point. Question 49. Suppose you have 2 threads (Thread-1 on object1 and Thread-2 on object2). Thread-1 is in static synchronized method1(), can Thread-2 enter static synchronized method2() at same time? Answer.No, it might confuse you a bit that threads are created on different objects. But, not to forgot that multiple objects may exist but there is always one class’s class object lock available. Here, when Thread-1 is in static synchronized method1() it must be holding lock on class class’s object and will release lock on class’s classobject only when it exits static synchronized method1(). So, Thread-2 will have to wait for Thread-1 to release lock on class’s classobject so that it could enter static synchronized method2(). Likewise, Thread-2 even cannot enter static synchronized method1() which is being executed by Thread-1. Thread-2 will have to wait for Thread-1 to release lock on class’s classobject so that it could enter static synchronized method1(). Now, let’s see a program to prove our point. Question 50. Difference between wait() and wait(long timeout), What are thread states when these method are called? Answer. wait() wait(long timeout) When wait() method is called on object, it causes causes the current thread to wait until another thread invokes the notify() or notifyAll() method for this object. wait(long timeout) - Causes the current thread to wait until either another thread invokes the notify() or notifyAll() methods for this object, or a specified timeout time has elapsed. When wait() is called on object - Thread enters from running to waiting state. It waits for some other thread to call notify so that it could enter runnable state. When wait(1000) is called on object - Thread enters from running to waiting state. Than even if notify() or notifyAll() is not called after timeout time has elapsed thread will go from waiting to runnable state. Question 51. How can you implement your own Thread Pool in java? Answer. What is ThreadPool? ThreadPool is a pool of threads which reuses a fixed number of threads to execute tasks. At any point, at most nThreads threads will be active processing tasks. If additional tasks are submitted when all threads are active, they will wait in the queue until a thread is available. ThreadPool implementation internally uses LinkedBlockingQueue for adding and removing tasks. In this post i will be using LinkedBlockingQueue provide by java Api, you can refer this post for implementing ThreadPool using custom LinkedBlockingQueue. Need/Advantage of ThreadPool? Instead of creating new thread every time for executing tasks, we can create ThreadPool which reuses a fixed number of threads for executing tasks. As threads are reused, performance of our application improves drastically. How ThreadPool works? We will instantiate ThreadPool, in ThreadPool’s constructor nThreads number of threads are created and started. ThreadPool threadPool=new ThreadPool(2); Here 2 threads will be created and started in ThreadPool. Then, threads will enter run() method of ThreadPoolsThread class and will call take() method on taskQueue. If tasks are available thread will execute task by entering run() method of task (As tasks executed always implements Runnable). publicvoid run() { . . . while (true) { . . . Runnable runnable = taskQueue.take(); runnable.run(); . . . } . . . } Else waits for tasks to become available. When tasks are added? When execute() method of ThreadPool is called, it internally calls put() method on taskQueue to add tasks. taskQueue.put(task); Once tasks are available all waiting threads are notified that task is available. Question 52. What is significance of using ThreadLocal? Answer. This question will test your command in multi threading, can you really create some perfect multithreading application or not. ThreadLocal is a class which provides thread-local variables. What is ThreadLocal ? ThreadLocal is a class which provides thread-local variables. Every thread has its own ThreadLocal value that makes ThreadLocal value threadsafe as well. For how long Thread holds ThreadLocal value? Thread holds ThreadLocal value till it hasn’t entered dead state. Can one thread see other thread’s ThreadLocal value? No, thread can see only it’s ThreadLocal value. Are ThreadLocal variables thread safe. Why? Yes, ThreadLocal variables are thread safe. As every thread has its own ThreadLocal value and one thread can’t see other threads ThreadLocal value. Application of ThreadLocal? ThreadLocal are used by many web frameworks for maintaining some context (may be session or request) related value. In any single threaded application, same thread is assigned for every request made to same action, so ThreadLocal values will be available in next request as well. In multi threaded application, different thread is assigned for every request made to same action, so ThreadLocal values will be different for every request. When threads have started at different time they might like to store time at which they have started. So, thread’s start time can be stored in ThreadLocal. Creating ThreadLocal > private ThreadLocal threadLocal = new ThreadLocal(); We will create instance of ThreadLocal. ThreadLocal is a generic class, i will be using String to demonstrate threadLocal. All threads will see same instance of ThreadLocal, but a thread will be able to see value which was set by it only. How thread set value of ThreadLocal > threadLocal.set( new Date().toString()); Thread set value of ThreadLocal by calling set(“”) method on threadLocal. How thread get value of ThreadLocal > threadLocal.get() Thread get value of ThreadLocal by calling get() method on threadLocal. See here for detailed explanation of threadLocal. Question 53. What is busy spin? Answer. What is busy spin? When one thread loops continuously waiting for another thread to signal. Performance point of view - Busy spin is very bad from performance point of view, because one thread keeps on looping continuously ( and consumes CPU) waiting for another thread to signal. Solution to busy spin - We must use sleep() or wait() and notify() method. Using wait() is better option. Why using wait() and notify() is much better option to solve busy spin? Because in case when we use sleep() method, thread will wake up again and again after specified sleep time until boolean variable is true. But, in case of wait() thread will wake up only when when notified by calling notify() or notifyAll(), hence end up consuming CPU in best possible manner. Program - Consumer Producer problem with busy spin > Consumer thread continuously execute (busy spin) in while loop tillproductionInProcess is true. Once producer thread has ended it will make boolean variable productionInProcess false and busy spin will be over. while(productionInProcess){ System.out.println("BUSY SPIN - Consumer waiting for production to get over"); } Question 54. Can a constructor be synchronized? Answer. No, constructor cannot be synchronized. Because constructor is used for instantiating object, when we are in constructor object is under creation. So, until object is not instantiated it does not need any synchronization. Enclosing constructor in synchronized block will generate compilation error. Using synchronized in constructor definition will also show compilation error. COMPILATION ERROR = Illegal modifier for the constructor in type ConstructorSynchronizeTest; only public, protected & private are permitted Though we can use synchronized block inside constructor. Read More about : Constructor in java cannot be synchronized Question 55. Can you find whether thread holds lock on object or not? Answer. holdsLock(object) method can be used to find out whether current thread holds the lock on monitor of specified object. holdsLock(object) method returns true if the current thread holds the lock on monitor of specified object. Question 56. What do you mean by thread starvation? Answer. When thread does not enough CPU for its execution Thread starvation happens. Thread starvation may happen in following scenarios > Low priority threads gets less CPU (time for execution) as compared to high priority threads. Lower priority thread may starve away waiting to get enough CPU to perform calculations. In deadlock two threads waits for each other to release lock holded by them on resources. There both Threads starves away to get CPU. Thread might be waiting indefinitely for lock on object’s monitor (by calling wait() method), because no other thread is calling notify()/notifAll() method on object. In that case, Thread starves away to get CPU. Thread might be waiting indefinitely for lock on object’s monitor (by calling wait() method), but notify() may be repeatedly awakening some other threads. In that case also Thread starves away to get CPU. Question 57. What is addShutdownHook method in java? Answer. addShutdownHook method in java > addShutdownHook method registers a new virtual-machine shutdown hook. A shutdown hook is a initialized but unstarted thread. When JVM starts its shutdown it will start all registered shutdown hooks in some unspecified order and let them run concurrently. When JVM (Java virtual machine) shuts down > When the last non-daemon thread finishes, or when the System.exit is called. Once JVM’s shutdown has begunnew shutdown hook cannot be registered neither previously-registered hook can be de-registered. Any attempt made to do any of these operations causes an IllegalStateException. For more detail with program read : Threads addShutdownHook method in java Question 58. How you can handle uncaught runtime exception generated in run method? Answer. We can use setDefaultUncaughtExceptionHandler method which can handle uncaught unchecked(runtime) exception generated in run() method. What is setDefaultUncaughtExceptionHandler method? setDefaultUncaughtExceptionHandler method sets the default handler which is called when a thread terminates due to an uncaught unchecked(runtime) exception. setDefaultUncaughtExceptionHandler method features > setDefaultUncaughtExceptionHandler method sets the default handler which is called when a thread terminates due to an uncaught unchecked(runtime) exception. setDefaultUncaughtExceptionHandler is a static method method, so we can directly call Thread.setDefaultUncaughtExceptionHandler to set the default handler to handle uncaught unchecked(runtime) exception. It avoids abrupt termination of thread caused by uncaught runtime exceptions. Defining setDefaultUncaughtExceptionHandler method > Thread.setDefaultUncaughtExceptionHandler(new Thread.UncaughtExceptionHandler(){ publicvoid uncaughtException(Thread thread, Throwable throwable) { System.out.println(thread.getName() + " has thrown " + throwable); } }); Question 59. What is ThreadGroup in java, What is default priority of newly created threadGroup, mention some important ThreadGroup methods ? Answer. When program starts JVM creates a ThreadGroup named main. Unless specified, all newly created threads become members of the main thread group. ThreadGroup is initialized with default priority of 10. ThreadGroup important methods > getName() name of ThreadGroup. activeGroupCount() count of active groups in ThreadGroup. activeCount() count of active threads in ThreadGroup. list() list() method has prints ThreadGroups information getMaxPriority() Method returns the maximum priority of ThreadGroup. setMaxPriority(int pri) Sets the maximum priority of ThreadGroup. Question 60. What are thread priorities? Answer. Thread Priority range is from 1 to 10. Where 1 is minimum priority and 10 is maximum priority. Thread class provides variables of final static int type for setting thread priority. /* The minimum priority that a thread can have. */ publicfinalstaticintMIN_PRIORITY= 1; /* The default priority that is assigned to a thread. */ publicfinalstaticintNORM_PRIORITY= 5; /* The maximum priority that a thread can have. */ publicfinalstaticintMAX_PRIORITY= 10; Thread with MAX_PRIORITY is likely to get more CPU as compared to low priority threads. But occasionally low priority thread might get more CPU. Because thread scheduler schedules thread on discretion of implementation and thread behaviour is totally unpredictable. Thread with MIN_PRIORITY is likely to get less CPU as compared to high priority threads. But occasionally high priority thread might less CPU. Because thread scheduler schedules thread on discretion of implementation and thread behaviour is totally unpredictable. setPriority()method is used for Changing the priority of thread. getPriority()method returns the thread’s priority.
May 29, 2015
by Ankit Mittal
· 338,604 Views · 38 Likes
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Converting to/from Unix Timestamp in C#
a few days ago, visual studio 2015 rc was released. among the many updates to .net framework 4.6 with this release, we now have some new utility methods allowing conversion to/from unix timestamps. although these were added primarily to enable more cross-platform support in .net core framework , unix timestamps are also sometimes useful in a windows environment. for instance, unix timestamps are often used to facilitate redis sorted sets where the score is a datetime (since the score can only be a double ). unix timestamp conversion before .net 4.6 until now, you had to implement conversions to/from unix time yourself. that actually isn’t hard to do. by definition , unix time is the number of seconds since 1st january 1970, 00:00:00 utc. thus we can convert from a local datetime to unix time as follows: var datetime = new datetime(2015, 05, 24, 10, 2, 0, datetimekind.local); var epoch = new datetime(1970, 1, 1, 0, 0, 0, datetimekind.utc); var unixdatetime = (datetime.touniversaltime() - epoch).totalseconds; we can convert back to a local datetime as follows: var timespan = timespan.fromseconds(unixdatetime); var localdatetime = new datetime(timespan.ticks).tolocaltime(); unix timestamp conversion in .net 4.6 quoting the visual studio 2015 rc release notes : new methods have been added to support converting datetime to or from unix time. the following apis have been added to datetimeoffset: static datetimeoffset fromunixtimeseconds(long seconds) static datetimeoffset fromunixtimemilliseconds(long milliseconds) long tounixtimeseconds() long tounixtimemilliseconds() so .net 4.6 gives us some new methods, but to use them, you’ll first have to convert from datetime to datetimeoffset. first, make sure you’re targeting the right version of the .net framework: you can then use the new methods: var datetime = new datetime(2015, 05, 24, 10, 2, 0, datetimekind.local); var datetimeoffset = new datetimeoffset(datetime); var unixdatetime = datetimeoffset.tounixtimeseconds(); …and to change back… var localdatetimeoffset = datetimeoffset.fromunixtimeseconds(unixdatetime) .datetime.tolocaltime();
May 26, 2015
by Daniel D'agostino
· 94,805 Views · 1 Like
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Server and Storage I/O Benchmark Tools: Microsoft Diskspd (Part I)
A key to improving performance is benchmarking. Read about Microsoft Diskspd's tools for storage and server benchmarking, and boost your I/O performance.
May 22, 2015
by Greg Schulz
· 14,953 Views
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Efficient Cassandra Write Pattern for Micro-Batching
The best way to write to a Cassandra cluster are concurrent asynchronous writes. In cases where data exhibits strong temporal locality, speed can be improved.
May 20, 2015
by John Georgiadis
· 35,158 Views · 1 Like
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How To Set Up a Tomcat, Apache and mod_jk Cluster
In this article I will go through a common set-up for a small production environment. A single tier, load balanced application server cluster. Overview A high level overview of what we will be doing. Downloading and installing Apache HTTP server and mod_jk Downloading Tomcat Downloading Java Configuring two local Tomcat servers Clustering the two Tomcat servers Configuring Apache to use mod_jk to forward request to Tomcat Deploying application to Tomcat server that tests our set-up Introduction What is Apache? Apache is an HTTP server. What is mod_jk? It is an Apache module that allows AJP communication between Apache and a back end application server like Tomcat.I am running this on Ubuntu 14.04LTS installed on a dual boot PC with Windows 7. Download Apache2 We are going to use Ubuntu's APT package maintenance system to obtain and install Apache2. sudo apt-get install apache2 This will install in /etc/apache2 Download and install mod_jk The mod_jk module is not included in the Apache2 download so must be obtained and installed separately. The installation requires that the mod_jk module is visible to Apache and configured to ensure that Apache knows where to look for it and what to do with the requests you want to proxy. sudo apt-get install libapache2-mod-jk This will install in /etc/libapache2-mod-jk also two files have been added to the /etc/apache2/mods-available folder. Downloading and installing Tomcat 8 At the time of writing this Tomcat 8 does not have a package in APT so you must download the binaries from the tomcat website.http://tomcat.apache.org/download-80.cgi select the appropriate binary distribution and extract it as follows. tar xvzf apache-tomcat-8.0.5.tar.gz We need two copies of the Tomcat server to be load balanced. I created two directories in the /opt/ location: /opt/tomcat-server1/ and /opt/tomcat-server2/ and copied tomcat into each one. Download and install Java Download Java from APT as follows: apt-get install openjdk-7-jdk and set JAVA_HOME in .bashrc vim ~/.bashrc export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64 Configure two local Tomcat servers We will edit only the server.xml of the server2 installation of tomcat. We need to change port numbers to avoid conflicts.We change the following: and comment out the HTTP Connector as we only want the web application to be accessible through the load balancer.Here is my server2 Tomcat server.xml configuration. Configure mod_jk Load balancing is configured in the workers.properties file, located /etc/libapache2-mod-jk/ where workers represent actual or virtual workers.We will define two actual workers and two virtual workers which map to the Tomcat servers. In the worker.list property I have defined two virtual workers: status and loadbalancer, I will refer to these later in the Apache configuration.Workers for each server have been defined using values for the server.xml configuration files. I used the port values for the AJP connectors and I have included an lbfactor that sets the preference that the load balancer will show for that server.Finally we define the virtual workers. The loadbalancer worker is set to type lb and set the workers that represent the Tomcat servers in the balancer_workers properties. The status only needs to be set to type status. worker.list=loadbalancer,status worker.server1.port=8009worker.server1.host=localhostworker.server1.type=ajp13 worker.server2.port=9009worker.server2.host=localhostworker.server2.type=ajp13 worker.server1.lbfactor=1worker.server2.lbfactor=1 worker.loadbalancer.type=lbworker.loadbalancer.balance_workers=server1,server2 worker.status.type=status Ensure that you remove any other worker configuration that are not being used. Configure Apache Web Server to forward requests You will need to add the following to the Apache configurations located in etc/apache2/sites-enabled/000-default.conf JkMount /status status JkMount /* loadbalancer Verify the installation To test that all has been configured correctly we need to deploy an application. A sample application that has been used for years to test such configurations is called the ClusterJSP sample application. You can find it by googling in or from the JBoss site.Now deploy the war to the webapps folder on both servers and start each server using the start-up script /opt/tomcat-server1/bin/startup.sh.Go to http://localhost/clusterjsp/HaJsp.jsp and you should see the page show HttpSession information. Now lets look at the mod_jk status page: http://localhost/status. You will see that this page shows information about the load balancer workers and the workers it is balancing. If everything is working you will see the worker error state show OK or OK/IDLE if they are not currently balancing load. Things to try out Enable sticky sessions: Configure jvmRoute in the server.xml configuration. Further reading Loadbalancing with mod_jk and ApacheWorking with mod_jk Connecting Apache's Web Server to Multiple Instances of Tomcat
May 19, 2015
by Alex Theedom
· 10,867 Views · 1 Like
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Docker Machine on Windows - How To Setup You Hosts
I've been playing around with Docker a lot lately. Many reasons for that, one for sure is, that I love to play around with latest technology and even help out to build a demo or two or a lab. The main difference, between what everybody else of my coworkers is doing is, that I run my setup on Windows. Like most of the middleware developers out there. So, If you followed Arun's blog about "Docker Machine to Setup Docker Host" you might have tried to make this work on windows already. Here is the ultimate short how-to guide on using Docker Machine to administrate and spin up your Docker hosts. Docker Machine Machine lets you create Docker hosts on your computer, on cloud providers, and inside your own data center. It creates servers, installs Docker on them, then configures the Docker client to talk to them. You basically don't have to have anything installed on your machine prior to this. Which is a hell lot easier, than having to manually install boot2docker before. So, let's try this out. You want to have at least one thing in place before starting with anything Docker or Machine. Go and get Git for Windows (aka msysgit). It has all kinds of helpful unix tools in his belly, which you need anyway. Prerequisites - The One For All Solution The first is to install the windows boot2docker distribution which I showed in an earlier blog. It contains the following bits configured and ready for you to use: - VirtualBox - Docker Windows Client Prerequisites- The Bits And Pieces I dislike the boot2docker installer for a variety of reasons. Mostly, because I want to know what exactly is going on on my machine. So I played around a bit and here is the bits and pieces installer if you decide against the one-for-all solution. Start with the virtualization solution. We need something like that on Windows, because it just can't run Linux and this is what Docker is based on. At least for now. So, get VirtualBox and ensure that version 4.3.18 is correctly installed on your system (VirtualBox-4.3.18-96516-Win.exe, 105 MB). WARNING: There is a strange issue, when you run Windows itself in Virtualbox. You might run into an issue with starting the host. And while you're at it, go and get the Docker Windows Client. The other is to grab the final from the test servers as a direct download (docker-1.6.0.exe, x86_64, 7.5MB). Rename to "docker" and put it into a folder of your choice (I assume it will be c:\docker\. Now you also need to download Docker Machine, which is another single executable (docker-machine_windows-amd64.exe, 11.5MB). Rename to "docker-machine" and put it into the same folder. Now add this folder to your PATH: set PATH=%PATH%;C:\docker If you change your standard PATH environment variable, this might safe your from a lot of typing. That's it. Now you're ready to create your first Machine managed Docker Host. Create Your Docker Host With Machine All you need is a simple command: docker-machine create --driver virtualbox dev And the output should state: ←[34mINFO←[0m[0000] Creating SSH key... ←[34mINFO←[0m[0001] Creating VirtualBox VM... ←[34mINFO←[0m[0016] Starting VirtualBox VM... ←[34mINFO←[0m[0022] Waiting for VM to start... ←[34mINFO←[0m[0076] "dev" has been created and is now the active machine. ←[34mINFO←[0m[0076] To point your Docker client at it, run this in your shell: eval "$(docker-machine.exe env dev)" This means, you just created a Docker Host using the VirtualBox provider and the name “dev”. Now you need to find out on which IP address the host is running. docker-machine ip 192.168.99.102 If you want to configure your environment variables, needed by the client more easy, just use the following command: docker-machine env dev export DOCKER_TLS_VERIFY=1 export DOCKER_CERT_PATH="C:\\Users\\markus\\.docker\\machine\\machines\\dev" export DOCKER_HOST=tcp://192.168.99.102:2376 Which outputs the Linux version of environment variable definition. All you have to do is to change the "export" keyword to "set", remove the " and the double back-slashes and you are ready to go. C:\Users\markus\Downloads>set DOCKER_TLS_VERIFY=1 C:\Users\markus\Downloads>set DOCKER_CERT_PATH=C:\Users\markus\.docker\machine\machines\dev C:\Users\markus\Downloads>set DOCKER_HOST=tcp://192.168.99.102:2376 Time to test our Docker Client And here we go now run WildFly on your freshly created host: docker run -it -p 8080:8080 jboss/wildfly Watch the container being downloaded and check, that it is running by redirecting your browser to http://192.168.99.102:8080/. Congratulations on having setup your very first docker host with Maschine on Windows.
May 12, 2015
by Markus Eisele
· 20,229 Views
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8 Questions You Need to Ask About Microservices, Containers & Docker in 2015
In containers and microservices, we’re facing the greatest potential change in how we deliver and run software services since the arrival of virtual machines.
May 9, 2015
by Andrew Phillips
· 15,087 Views · 1 Like
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Binding to Data Services with Spring Boot in Cloud Foundry
Written by Dave Syer on the Spring blog In this article we look at how to bind a Spring Boot application to data services (JDBC, NoSQL, messaging etc.) and the various sources of default and automatic behaviour in Cloud Foundry, providing some guidance about which ones to use and which ones will be active under what conditions. Spring Boot provides a lot of autoconfiguration and external binding features, some of which are relevant to Cloud Foundry, and many of which are not. Spring Cloud Connectors is a library that you can use in your application if you want to create your own components programmatically, but it doesn’t do anything “magical” by itself. And finally there is the Cloud Foundry java buildpack which has an “auto-reconfiguration” feature that tries to ease the burden of moving simple applications to the cloud. The key to correctly configuring middleware services, like JDBC or AMQP or Mongo, is to understand what each of these tools provides, how they influence each other at runtime, and and to switch parts of them on and off. The goal should be a smooth transition from local execution of an application on a developer’s desktop to a test environment in Cloud Foundry, and ultimately to production in Cloud Foundry (or otherwise) with no changes in source code or packaging, per the twelve-factor application guidelines. There is some simple source code accompanying this article. To use it you can clone the repository and import it into your favourite IDE. You will need to remove two dependencies from the complete project to get to the same point where we start discussing concrete code samples, namely spring-boot-starter-cloud-connectors and auto-reconfiguration. NOTE: The current co-ordinates for all the libraries being discussed are org.springframework.boot:spring-boot-*:1.2.3.RELEASE,org.springframework.boot:spring-cloud-*-connector:1.1.1.RELEASE,org.cloudfoundry:auto-reconfiguration:1.7.0.RELEASE. TIP: The source code in github includes a docker-compose.yml file (docs here). You can use that to create a local MySQL database if you don’t have one running already. You don’t actually need it to run most of the code below, but it might be useful to validate that it will actually work. Punchline for the Impatient If you want to skip the details, and all you need is a recipe for running locally with H2 and in the cloud with MySQL, then start here and read the rest later when you want to understand in more depth. (Similar options exist for other data services, like RabbitMQ, Redis, Mongo etc.) Your first and simplest option is to simply do nothing: do not define a DataSource at all but put H2 on the classpath. Spring Boot will create the H2 embedded DataSource for you when you run locally. The Cloud Foundry buildpack will detect a database service binding and create a DataSource for you when you run in the cloud. If you add Spring Cloud Connectors as well, your app will also work in other cloud platforms, as long as you include a connector. That might be good enough if you just want to get something working. If you want to run a serious application in production you might want to tweak some of the connection pool settings (e.g. the size of the pool, various timeouts, the important test on borrow flag). In that case the buildpack auto-reconfiguration DataSource will not meet your requirements and you need to choose an alternative, and there are a number of more or less sensible choices. The best choice is probably to create a DataSource explicitly using Spring Cloud Connectors, but guarded by the “cloud” profile: @Configuration @Profile("cloud") public class DataSourceConfiguration { @Bean public Cloud cloud() { return new CloudFactory().getCloud(); } @Bean @ConfigurationProperties(DataSourceProperties.PREFIX) public DataSource dataSource() { return cloud().getSingletonServiceConnector(DataSourceclass, null); } } You can use spring.datasource.* properties (e.g. in application.properties or a profile-specific version of that) to set the additional properties at runtime. The “cloud” profile is automatically activated for you by the buildpack. Now for the details. We need to build up a picture of what’s going on in your application at runtime, so we can learn from that how to make a sensible choice for configuring data services. Layers of Autoconfiguration Let’s take a a simple app with DataSource (similar considerations apply to RabbitMQ, Mongo, Redis): @SpringBootApplication public class CloudApplication { @Autowired private DataSource dataSource; public static void main(String[] args) { SpringApplication.run(CloudApplication.class, args); } } This is a complete application: the DataSource can be @Autowired because it is created for us by Spring Boot. The details of the DataSource (concrete class, JDBC driver, connection URL, etc.) depend on what is on the classpath. Let’s assume that the application uses Spring JDBC via the spring-boot-starter-jdbc (or spring-boot-starter-data-jpa), so it has aDataSource implementation available from Tomcat (even if it isn’t a web application), and this is what Spring Boot uses. Consider what happens when: Classpath contains H2 (only) in addition to the starters: the DataSource is the Tomcat high-performance pool from DataSourceAutoConfiguration and it connects to an in memory database “testdb”. Classpath contains H2 and MySQL: DataSource is still H2 (same as before) because we didn’t provide any additional configuration for MySQL and Spring Boot can’t guess the credentials for connecting. Add spring-boot-starter-cloud-connectors to the classpath: no change inDataSource because the Spring Cloud Connectors do not detect that they are running in a Cloud platform. The providers that come with the starter all look for specific environment variables, which they won’t find unless you set them, or run the app in Cloud Foundry, Heroku, etc. Run the application in “cloud” profile with spring.profiles.active=cloud: no change yet in the DataSource, but this is one of the things that the Java buildpack does when your application runs in Cloud Foundry. Run in “cloud” profile and provide some environment variables to simulate running in Cloud Foundry and binding to a MySQL service: VCAP_APPLICATION={"name":"application","instance_id":"FOO"} VCAP_SERVICES={"mysql":[{"name":"mysql","tags":["mysql"],"credentials":{"uri":"mysql://root:root@localhost/test"}]} (the “tags” provides a hint that we want to create a MySQL DataSource, the “uri” provides the location, and the “name” becomes a bean ID). The DataSource is now using MySQL with the credentials supplied by the VCAP_* environment variables. Spring Boot has some autoconfiguration for the Connectors, so if you looked at the beans in your application you would see a CloudFactory bean, and also the DataSource bean (with ID “mysql”). Theautoconfiguration is equivalent to adding @ServiceScan to your application configuration. It is only active if your application runs in the “cloud” profile, and only if there is no existing @Bean of type Cloud, and the configuration flagspring.cloud.enabled is not “false”. Add the “auto-reconfiguration” JAR from the Java buildpack (Maven co-ordinatesorg.cloudfoundry:auto-reconfiguration:1.7.0.RELEASE). You can add it as a local dependency to simulate running an application in Cloud Foundry, but it wouldn’t be normal to do this with a real application (this is just for experimenting with autoconfiguration). The auto-reconfiguration JAR now has everything it needs to create a DataSource, but it doesn’t (yet) because it detects that you already have a bean of type CloudFactory, one that was added by Spring Boot. Remove the explicit “cloud” profile. The profile will still be active when your app starts because the auto-reconfiguration JAR adds it back again. There is still no change to theDataSource because Spring Boot has created it for you via the @ServiceScan. Remove the spring-boot-starter-cloud-connectors dependency, so that Spring Boot backs off creating a CloudFactory. The auto-reconfiguration JAR actually has its own copy of Spring Cloud Connectors (all the classes with different package names) and it now uses them to create a DataSource (in a BeanFactoryPostProcessor). The Spring Boot autoconfigured DataSource is replaced with one that binds to MySQL via theVCAP_SERVICES. There is no control over pool properties, but it does still use the Tomcat pool if available (no support for Hikari or DBCP2). Remove the auto-reconfiguration JAR and the DataSource reverts to H2. TIP: use web and actuator starters with endpoints.health.sensitive=false to inspect the DataSource quickly through “/health”. You can also use the “/beans”, “/env” and “/autoconfig” endpoints to see what is going in in the autoconfigurations and why. NOTE: Running in Cloud Foundry or including auto-reconfiguration JAR in classpath locally both activate the “cloud” profile (for the same reason). The VCAP_* env vars are the thing that makes Spring Cloud and/or the auto-reconfiguration JAR create beans. NOTE: The URL in the VCAP_SERVICES is actually not a “jdbc” scheme, which should be mandatory for JDBC connections. This is, however, the format that Cloud Foundry normally presents it in because it works for nearly every language other than Java. Spring Cloud Connectors or the buildpack auto-reconfiguration, if they are creating a DataSource, will translate it into a jdbc:* URL for you. NOTE: The MySQL URL also contains user credentials and a database name which are valid for the Docker container created by the docker-compose.yml in the sample source code. If you have a local MySQL server with different credentials you could substitute those. TIP: If you use a local MySQL server and want to verify that it is connected, you can use the “/health” endpoint from the Spring Boot Actuator (included in the sample code already). Or you could create a schema-mysql.sql file in the root of the classpath and put a simple keep alive query in it (e.g. SELECT 1). Spring Boot will run that on startupso if the app starts successfully you have configured the database correctly. The auto-reconfiguration JAR is always on the classpath in Cloud Foundry (by default) but it backs off creating any DataSource if it finds a org.springframework.cloud.CloudFactorybean (which is provided by Spring Boot if the CloudAutoConfiguration is active). Thus the net effect of adding it to the classpath, if the Connectors are also present in a Spring Boot application, is only to enable the “cloud” profile. You can see it making the decision to skip auto-reconfiguration in the application logs on startup: 015-04-14 15:11:11.765 INFO 12727 --- [ main] urceCloudServiceBeanFactoryPostProcessor : Skipping auto-reconfiguring beans of type javax.sql.DataSource 2015-04-14 15:11:57.650 INFO 12727 --- [ main] ongoCloudServiceBeanFactoryPostProcessor : Skipping auto-reconfiguring beans of type org.springframework.data.mongodb.MongoDbFactory 2015-04-14 15:11:57.650 INFO 12727 --- [ main] bbitCloudServiceBeanFactoryPostProcessor : Skipping auto-reconfiguring beans of type org.springframework.amqp.rabbit.connection.ConnectionFactory 2015-04-14 15:11:57.651 INFO 12727 --- [ main] edisCloudServiceBeanFactoryPostProcessor : Skipping auto-reconfiguring beans of type org.springframework.data.redis.connection.RedisConnectionFactory ... etc. Create your own DataSource The last section walked through most of the important autoconfiguration features in the various libraries. If you want to take control yourself, one thing you could start with is to create your own instance of DataSource. You could do that, for instance, using aDataSourceBuilder which is a convenience class and comes as part of Spring Boot (it chooses an implementation based on the classpath): @SpringBootApplication public class CloudApplication { @Bean public DataSource dataSource() { return DataSourceBuilder.create().build(); } ... } The DataSource as we’ve defined it is useless because it doesn’t have a connection URL or any credentials, but that can easily be fixed. Let’s run this application as if it was in Cloud Foundry: with the VCAP_* environment variables and the auto-reconfiguration JAR but not Spring Cloud Connectors on the classpath and no explicit “cloud” profile. The buildpack activates the “cloud” profile, creates a DataSource and binds it to the VCAP_SERVICES. As already described briefly, it removes your DataSource completely and replaces it with a manually registered singleton (which doesn’t show up in the “/beans” endpoint in Spring Boot). Now add Spring Cloud Connectors back into the classpath the application and see what happens when you run it again. It actually fails on startup! What has happened? The@ServiceScan (from Connectors) goes and looks for bound services, and creates bean definitions for them. That’s a bit like the buildpack, but different because it doesn’t attempt to replace any existing bean definitions of the same type. So you get an autowiring error because there are 2 DataSources and no way to choose one to inject into your application in various places where one is needed. To fix that we are going to have to take control of the Cloud Connectors (or simply not use them). Using a CloudFactory to create a DataSource You can disable the Spring Boot autoconfiguration and the Java buildpack auto-reconfiguration by creating your own Cloud instance as a @Bean: @Bean public Cloud cloud() { return new CloudFactory().getCloud(); } @Bean @ConfigurationProperties(DataSourceProperties.PREFIX) public DataSource dataSource() { return cloud().getSingletonServiceConnector(DataSource.class, null); } Pros: The Connectors autoconfiguration in Spring Boot backed off so there is only oneDataSource. It can be tweaked using application.properties via spring.datasource.*properties, per the Spring Boot User Guide. Cons: It doesn’t work without VCAP_* environment variables (or some other cloud platform). It also relies on user remembering to ceate the Cloud as a @Bean in order to disable the autoconfiguration. Summary: we are still not in a comfortable place (an app that doesn’t run without some intricate wrangling of environment variables is not much use in practice). Dual Running: Local with H2, in the Cloud with MySQL There is a local configuration file option in Spring Cloud Connectors, so you don’t have to be in a real cloud platform to use them, but it’s awkward to set up despite being boiler plate, and you also have to somehow switch it off when you are in a real cloud platform. The last point there is really the important one because you end up needing a local file to run locally, but only running locally, and it can’t be packaged with the rest of the application code (for instance violates the twelve factor guidelines). So to move forward with our explicit @Bean definition it’s probably better to stick to mainstream Spring and Spring Boot features, e.g. using the “cloud” profile to guard the explicit creation of a DataSource: @Configuration @Profile("cloud") public class DataSourceConfiguration { @Bean public Cloud cloud() { return new CloudFactory().getCloud(); } @Bean @ConfigurationProperties(DataSourceProperties.PREFIX) public DataSource dataSource() { return cloud().getSingletonServiceConnector(DataSource.class, null); } } With this in place we have a solution that works smoothly both locally and in Cloud Foundry. Locally Spring Boot will create a DataSource with an H2 embedded database. In Cloud Foundry it will bind to a singleton service of type DataSource and switch off the autconfigured one from Spring Boot. It also has the benefit of working with any platform supported by Spring Cloud Connectors, so the same code will run on Heroku and Cloud Foundry, for instance. Because of the @ConfigurationProperties you can bind additional configuration to the DataSource to tweak connection pool properties and things like that if you need to in production. NOTE: We have been using MySQL as an example database server, but actually PostgreSQL is at least as compelling a choice if not more. When paired with H2 locally, for instance, you can put H2 into its “Postgres compatibility” mode and use the same SQL in both environments. Manually Creating a Local and a Cloud DataSource If you like creating DataSource beans, and you want to do it both locally and in the cloud, you could use 2 profiles (“cloud” and “local”), for example. But then you would have to find a way to activate the “local” profile by default when not in the cloud. There is already a way to do that built into Spring because there is always a default profile called “default” (by default). So this should work: @Configuration @Profile("default") // or "!cloud" public class LocalDataSourceConfiguration { @Bean @ConfigurationProperties(DataSourceProperties.PREFIX) public DataSource dataSource() { return DataSourceBuilder.create().build(); } } @Configuration @Profile("cloud") public class CloudDataSourceConfiguration { @Bean public Cloud cloud() { return new CloudFactory().getCloud(); } @Bean @ConfigurationProperties(DataSourceProperties.PREFIX) public DataSource dataSource() { return cloud().getSingletonServiceConnector(DataSource.class, null); } } The “default” DataSource is actually identical to the autoconfigured one in this simple example, so you wouldn’t do this unless you needed to, e.g. to create a custom concreteDataSource of a type not supported by Spring Boot. You might think it’s all getting a bit complicated, but in fact Spring Boot is not making it any harder, we are just dealing with the consequences of needing to control the DataSource construction in 2 environments. Using a Non-Embedded Database Locally If you don’t want to use H2 or any in-memory database locally, then you can’t really avoid having to configure it (Spring Boot can guess a lot from the URL, but it will need that at least). So at a minimum you need to set some spring.datasource.* properties (the URL for instance). That that isn’t hard to do, and you can easily set different values in different environments using additional profiles, but as soon as you do that you need to switch off the default values when you go into the cloud. To do that you could define thespring.datasource.* properties in a profile-specific file (or document in YAML) for the “default” profile, e.g. application-default.properties, and these will not be used in the “cloud” profile. A Purely Declarative Approach If you prefer not to write Java code, or don’t want to use Spring Cloud Connectors, you might want to try and use Spring Boot autoconfiguration and external properties (or YAML) files for everything. For example Spring Boot creates a DataSource for you if it finds the right stuff on the classpath, and it can be completely controlled through application.properties, including all the granular features on the DataSource that you need in production (like pool sizes and validation queries). So all you need is a way to discover the location and credentials for the service from the environment. The buildpack translates Cloud Foundry VCAP_*environment variables into usable property sources in the Spring Environment. Thus, for instance, a DataSource configuration might look like this: spring.datasource.url: ${cloud.services.mysql.connection.jdbcurl:jdbc:h2:mem:testdb} spring.datasource.username: ${cloud.services.mysql.connection.username:sa} spring.datasource.password: ${cloud.services.mysql.connection.password:} spring.datasource.testOnBorrow: true The “mysql” part of the property names is the service name in Cloud Foundry (so it is set by the user). And of course the same pattern applies to all kinds of services, not just a JDBCDataSource. Generally speaking it is good practice to use external configuration and in particular @ConfigurationProperties since they allow maximum flexibility, for instance to override using System properties or environment variables at runtime. Note: similar features are provided by Spring Boot, which provides vcap.services.*instead of cloud.services.*, so you actually end up with more than one way to do this. However, the JDBC urls are not available from the vcap.services.* properties (non-JDBC services work fine with tthe corresponding vcap.services.*credentials.url). One limitation of this approach is it doesn’t apply if the application needs to configure beans that are not provided by Spring Boot out of the box (e.g. if you need 2 DataSources), in which case you have to write Java code anyway, and may or may not choose to use properties files to parameterize it. Before you try this yourself, though, beware that actually it doesn’t work unless you also disable the buildpack auto-reconfiguration (and Spring Cloud Connectors if they are on the classpath). If you don’t do that, then they create a new DataSource for you and Spring Boot cannot bind it to your properties file. Thus even for this declarative approach, you end up needing an explicit @Bean definition, and you need this part of your “cloud” profile configuration: @Configuration @Profile("cloud") public class CloudDataSourceConfiguration { @Bean public Cloud cloud() { return new CloudFactory().getCloud(); } } This is purely to switch off the buildpack auto-reconfiguration (and the Spring Boot autoconfiguration, but that could have been disabled with a properties file entry). Mixed Declarative and Explicit Bean Definition You can also mix the two approaches: declare a single @Bean definition so that you control the construction of the object, but bind additional configuration to it using@ConfigurationProperties (and do the same locally and in Cloud Foundry). Example: @Configuration public class LocalDataSourceConfiguration { @Bean @ConfigurationProperties(DataSourceProperties.PREFIX) public DataSource dataSource() { return DataSourceBuilder.create().build(); } } (where the DataSourceBuilder would be replaced with whatever fancy logic you need for your use case). And the application.properties would be the same as above, with whatever additional properties you need for your production settings. A Third Way: Discover the Credentials and Bind Manually Another approach that lends itself to platform and environment independence is to declare explicit bean definitions for the @ConfigurationProperties beans that Spring Boot uses to bind its autoconfigured connectors. For instance, to set the default values for a DataSourceyou can declare a @Bean of type DataSourceProperties: @Bean @Primary public DataSourceProperties dataSourceProperties() { DataSourceProperties properties = new DataSourceProperties(); properties.setInitialize(false); return properties; } This sets a default value for the “initialize” flag, and allows other properties to be bound fromapplication.properties (or other external properties). Combine this with the Spring Cloud Connectors and you can control the binding of the credentials when a cloud service is detected: @Autowired(required="false") Cloud cloud; @Bean @Primary public DataSourceProperties dataSourceProperties() { DataSourceProperties properties = new DataSourceProperties(); properties.setInitialize(false); if (cloud != null) { List infos = cloud.getServiceInfos(RelationalServiceInfo.class); if (infos.size()==1) { RelationalServiceInfo info = (RelationalServiceInfo) infos.get(0); properties.setUrl(info.getJdbcUrl()); properties.setUsername(info.getUserName()); properties.setPassword(info.getPassword()); } } return properties; } and you still need to define the Cloud bean in the “cloud” profile. It ends up being quite a lot of code, and is quite unnecessary in this simple use case, but might be handy if you have more complicated bindings, or need to implement some logic to choose a DataSource at runtime. Spring Boot has similar *Properties beans for the other middleware you might commonly use (e.g. RabbitProperties, RedisProperties, MongoProperties). An instance of such a bean marked as @Primary is enough to reset the defaults for the autoconfigured connector. Deploying to Multiple Cloud Platforms So far, we have concentrated on Cloud Foundry as the only cloud platform in which to deploy the application. One of the nice features of Spring Cloud Connectors is that it supports other platforms, either out of the box or as extension points. Thespring-boot-starter-cloud-connectors even includes Heroku support. If you do nothing at all, and rely on the autoconfiguration (the lazy programmer’s approach), then your application will be deployable in all clouds where you have a connector on the classpath (i.e. Cloud Foundry and Heroku if you use the starter). If you take the explicit @Bean approach then you need to ensure that the “cloud” profile is active in the non-Cloud Foundry platforms, e.g. through an environment variable. And if you use the purely declarative approach (or any combination involving properties files) you need to activate the “cloud” profile and probably also another profile specific to your platform, so that the right properties files end up in theEnvironment at runtime. Summary of Autoconfiguration and Provided Behaviour Spring Boot provides DataSource (also RabbitMQ or Redis ConnectionFactory, Mongo etc.) if it finds all the right stuff on the classpath. Using the “spring-boot-starter-*” dependencies is sufficient to activate the behaviour. Spring Boot also provides an autowirable CloudFactory if it finds Spring Cloud Connectors on the classpath (but switches off only if it finds a @Bean of type Cloud). The CloudAutoConfiguration in Spring Boot also effectively adds a @CloudScan to your application, which you would want to switch off if you ever needed to create your ownDataSource (or similar). The Cloud Foundry Java buildpack detects a Spring Boot application and activates the “cloud” profile, unless it is already active. Adding the buildpack auto-reconfiguration JAR does the same thing if you want to try it locally. Through the auto-reconfiguration JAR, the buildpack also kicks in and creates aDataSource (ditto RabbitMQ, Redis, Mongo etc.) if it does not find a CloudFactory bean or a Cloud bean (amongst others). So including Spring Cloud Connectors in a Spring Boot application switches off this part of the “auto-reconfiguration” behaviour (the bean creation). Switching off the Spring Boot CloudAutoConfiguration is easy, but if you do that, you have to remember to switch off the buildpack auto-reconfiguration as well if you don’t want it. The only way to do that is to define a bean definition (can be of type Cloud orCloudFactory for instance). Spring Boot binds application.properties (and other sources of external properties) to@ConfigurationProperties beans, including but not limited to the ones that it autoconfigures. You can use this feature to tweak pool properties and other settings that need to be different in production environments. General Advice and Conclusion We have seen quite a few options and autoconfigurations in this short article, and we’ve only really used thee libraries (Spring Boot, Spring Cloud Connectors, and the Cloud Foundry buildpack auto-reconfiguration JAR) and one platform (Cloud Foundry), not counting local deployment. The buildpack features are really only useful for very simple applications because there is no flexibility to tune the connections in production. That said it is a nice thing to be able to do when prototyping. There are only three main approaches if you want to achieve the goal of deploying the same code locally and in the cloud, yet still being able to make necessary tweaks in production: Use Spring Cloud Connectors to explicitly create DataSource and other middleware connections and protect those @Beans with @Profile("cloud"). The approach always works, but leads to more code than you might need for many applications. Use the Spring Boot default autoconfiguration and declare the cloud bindings usingapplication.properties (or in YAML). To take full advantage you have to expliccitly switch off the buildpack auto-reconfiguration as well. Use Spring Cloud Connectors to discover the credentials, and bind them to the Spring Boot@ConfigurationProperties as default values if present. The three approaches are actually not incompatible, and can be mixed using@ConfigurationProperties to provide profile-specific overrides of default configuration (e.g. for setting up connection pools in a different way in a production environment). If you have a relatively simple Spring Boot application, the only way to choose between the approaches is probably personal taste. If you have a non-Spring Boot application then the explicit @Bean approach will win, and it may also win if you plan to deploy your application in more than one cloud platform (e.g. Heroku and Cloud Foundry). NOTE: This blog has been a journey of discovery (who knew there was so much to learn?). Thanks go to all those who helped with reviews and comments, in particularScott Frederick, who spotted most of the mistakes in the drafts and always had time to look at a new revision.
May 6, 2015
by Pieter Humphrey
· 27,148 Views · 2 Likes
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