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8 Lessons in Deployment Tooling Lessons Learned
It didn’t take long. A few months after we released an open source continuous integration tool (Anthill) in 2001, we were asked, “It’s great that I have the build setup, now how do I deploy to the test lab?” That email started a clear transformation in our thinking. Lesson 1: Builds generally exist to be tested or released to customers.The corollary is: Continuous integration is not about build, it is about quality and checking quality generally requires a deployment or six. In 2005, we updated our AnthillPro 2.5 with a shiny new deployment capability. It worked ok, but with that generation of tool being so build oriented, it was never a clean fit. Lesson 2: Deployments are a serious challenge, and can not be bolted on to a build tool. This lesson has been reinforced over the years as we’ve watched the results of various tools tacking on deployments. In 2006, we released AnthillPro 3. Oh boy, what a change. Now deployments and other stuff you do to builds days and weeks later were their own thing. Demonstrating a stunning lack of marketing savvy, we called them “non-originating workflows” (processes that don’t make builds) and declared a new type of tool, an “Application Lifecycle Automation Server”. Today, you’d call it a Continuous Delivery tool (thank you Jez and Dave for the better name). To my knowledge, AnthillPro was the first tool to really take a build lifecycle or build pipeline seriously and had the ambition to manage from continuous integration build through to production release quickly and with a solid audit trail. That brings us to Lesson 3: When you come from the development side of the house as either an engineer or a vendor, you have a lot to learn about audit and security. Around the same time we also learned a great deal about the Dev / Ops gap. While some of our customers implemented the tool as intended, most of the time it was used either by a dev organization doing continuous integration and deployment to the early test environments or a production support / release management group that only did the “official” builds and focused on the production release. The failure of the developer initiated efforts to get to production provided a key insight. Lesson 4: When it comes to deployment automation, start with production, and work your way to the lower environments, treating them as a simpler case of the hard problem. We also learned about the limitations of a build pipeline approach in complex applications. Lesson 5: Deployments of complex systems often require a coordinated release of many builds. The need to start at production style deployments (and not care as much about the build) and the emphasis on deployment time dependencies focused our efforts on a deployment centric tool, uDeploy to compliment the pipeline approach in AnthillPro. Production and scaled usage come together to make the deployment tool a critical piece of infrastructure. If you lose a datacenter, can you recover the tool and use it to recover other applications into a backup datacenter?Lesson 6: Your release and deployment systems need to be configured with high availability and recoverability in mind. For the better part of the last decade, we’ve been trying to streamline deployments and kill off the release weekend. Many of customers have eliminated or shrunk those big events down to a more manageable size. Big releases though still can require the coordination of dozens of people across various teams. One time events like configuring the deployment tool with the password for a new database in each environment, or routine manual steps like putting eyeballs on the new production system before putting it out to the public still need to be managed. Lesson 7: Deployment is still just one part of release. With that in mind, we’ve added uRelease to the portfolio. Different people in the application delivery value chain need different tools. Developers need continuous integration and great testing. Change management needs great auditing around production deployments. Testers need their environments to be not broken, updated regularly and close enough to production to make their testing worthwhile. The patterns and needs overlap. Everyone needs deployments that work. Everyone benefits from understanding the lifecycle of a build (or other version) as well as what is in a given environment. Everyone benefits when release planning meetings are shorter. Lesson 8: No single tool is enough. An integrated toolchain that exchanges information is required. So there you go. Twelve years of tooling, eight with deployments. Summed up in six over-simplified lessons. I’m sure we’ll be updating this next year. Lesson 1: Builds generally exist to be tested or released to customers. Lesson 2: Deployments are a serious challenge, and can not be bolted on to a build tool. Lesson 3: When you come from the development side of the house as either an engineer or a vendor, you have a lot to learn about audit and security. Lesson 4: When it comes to deployment automation, start with production, and work your way to the lower environments, treating them as a simpler case of the hard problem. Lesson 5: Deployments of complex systems often require a coordinated release of many builds. Lesson 6: Release and deployment systems need to be configured with high availability and recoverability in mind. Lesson 7: Deployment is still just one part of release Lesson 8: No single tool does “DevOps”. An integrated toolchain that exchanges information is required. What are the biggest lessons you’ve learned about deployments in the last few years? Share in the comments below!
March 4, 2013
by Eric Minick
· 7,332 Views
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SAP Integration with Talend Components / Connectors (BAPI, RFC, IDoc, BW, SOAP)
talend has several connectors to integrate sap systems. however, this guide is no introduction to talend’s sap components. instead, this guide helps to understand different alternatives to integrate sap systems with talend set up a local sap system configure talend studio for using sap components use talend’s sap wizard run a first talend job which connects to sap all further required information and example use cases for talend’s sap components should be available in the talend component guide at www.help.talend.com . if that’s not the case, please create a jira documentation ticket ( https://jira.talendforge.org/browse/doct )! now let’s take a look at different alternatives for integration of sap systems with talend. alternatives for sap integration three protocols exist for communication between sap and external programs: dynamic information and action gateway (diag): e.g. used by sap gui remote function call (rfc): a function call with input and output parameters (like a java interface) hypertext transfer protocol (http): internet standard the following alternatives are available for integrating sap systems using some of these protocols. file sap supports the direct import of files (call-transaction-program, batch-input, direct input). files have to be in a specific format to be imported. transformation and integration can be realized with talend’s various file components such as tfileinputdelimited. rfc remote function call is the proprietary sap ag interface for communication between a sap system and other sap or third-party compatible system over tcp/ip or cpi-c connections. remote function calls may be associated with sap software and abap programming, and provide a way for an external program (written in languages such as php, asp, java, or c, c++) to use data returned from the server. data transactions are not limited to getting data from the server, but can insert data into server records as well. sap can act as the client or server in an rfc call. a remote function call (rfc) is the call or remote execution of a remote function module in an external system. in the sap system, these functions are provided by the rfc interface system. the rfc interface system enables function calls between two sap systems, or between a sap system and an external system. tsapinput and tsapoutput are talend’s components to use rfcs. business application programming interface (bapi) a bapi is an object-oriented view on most data and transactions of a sap system (called “business objects”). object types of the business objects are stored in the business object repository (bor). bapis are always implemented as rfcs and therefore can be called the same way. additionally, they have the following characteristics (compared to rfcs): stable interface no view layer no exceptions, instead export parameter: “return” most business objects offer the following standard bapis: getlist getdetail change creationfromdata tsapinput and tsapoutput are talend’s components to use bapis. application link enabling (ale) application link enabling (ale) is used for asynchronous messaging between different systems via “intermediate documents (idoc)”. idoc is a sap document format for business transaction data transfers. it is used to realize distributed business processes. idoc is similar to xml in purpose, but differs in syntax. both serve the purpose of data exchange and automation in computer systems, but the idoc technology takes a different approach. while xml allows having some metadata about the document itself, an idoc is obligated to have information at its header like its creator, creation time, etc. while xml has a tag-like tree structure containing data and meta-data, idocs use a table with the data and meta-data. idocs also have a session that explains all the processes which the document passed or will pass, allowing one to debug and trace the status of the document. an idoc consists of control record (it contains the type of idoc, port of the partner, release of sap r/3 which produced the idoc, etc.) data records of different types. the number and type of segments is mostly fixed for each idoc type, but there is some flexibility (for example an sd order can have any number of items). status records containing messages such as 'idoc created', 'the recipient exists', 'idoc was successfully passed to the port', 'could not book the invoice because...' different idoc types are available to handle different types of messages. for example, the idoc format orders01 may be used for both purchase orders and order confirmations. tsapidocinput and tsapidocoutput are talend’s components to use ale / idoc. bapis can also be called asynchronously via ale. all new idocs are even based on bapis. soap web services sap supports soap web services. not just sap as java, but also sap as abap! integration can be realized with talend’s esb / web service components such as tesbrequest, tesbresponse, or tesbconsumer. installation of sap server and client installation can take about 6 to 8 hours, but it is an “all in one installation”, i.e. you can install it overnight. steps for installation: get yourself a windows 7 64 bit laptop or vm with 8+ gb ram and 50+gb free disc space get a sap community account (for free, just register): http://scn.sap.com/welcome download sap netweaver (software downloads --> sap netweaver main releases: http://www.sdn.sap.com/irj/scn/nw-downloads download current version of sap netweaver application server abap 64-bit trial install sap server: follow installation guide – a html website included in the download in root of extracted download folder (start.htm --> there click on “installation” link) install sap gui (rich client frontend): start.htm --> there click on “install sap gui” link and follow instructions download the sap jco for the operating system on which your connector is running. the sap jco is available for download from sap's website at http://service.sap.com/connectors . you must have an sapnet account to access the sap jco (if you do not already have one, contact your local sap basis administrator). usage of sap server hint: you have to use a windows user which has a password (as you need to enter windows credentials when stopping sap). if you have a windows user without a password (for instance if you use windows within a vm on your mac), sap cannot process these credentials (i.e. it cannot process an empty password field) --> change your windows password before starting sap start the management console (windows startmenu --> programs --> sap management console) start and stop the sap server (right click on “nsp” --> start / stop) default user: sap* (sap system super user) password: the one which you entered at installation of sap netweaver, e.g. admin123 usage of sap client a sap client should be used to get information about the sap system (functions, data, etc.) similarly to using e.g. mysql workbench to get information from a mysql database. sap gui (view layer) communicates with sap as abap (business logic layer). the application server communicates with the relational database (db layer). different clients are available for sap: sap gui windows sap gui java web browser external rfc-program for local development demos, sap gui windows is probably the best alternative. start sap gui windows by: clicking shortcut “windows start menu --> sap frontend --> sap logon” entering username and password clicking logon sap transactions in sap, you call sap programs via sap transaction codes. important transactions codes are for example: bapi: bapi explorer, view all sap bapi's se16: data browser, view/add table data se38: program editor here is a list of several other important transaction codes: http://www.sapdev.co.uk/tcodes/tcodes.htm installation of demo data the sap installation includes some demo data. as most people do not want to install “real” sap modules such as sap fi, sap crm or sap bi on their local system, this demo data is perfect for demos using talend’s sap connectors. to install the flight demo on a local sap system, you just have to open the abap editor (transaction: se38) and execute the program sapbc_data_generator. this program generates example data within the flight tables and does some further initializations. here is a good tutorial with more information and how to test the flight application: http://help.sap.com/saphelp_erp60_sp/helpdata/de/db/7c623cf568896be10000000a11405a/content.htm configuration of talend studio to use sap components talend’s sap components are already included in the studio. however, two further steps are required to be able to use them: copy sapjco3.dll to the directory c:/windows/system32 sap java connector jar must be added copy sapjco3.jar to the directory “talend/studio/lib/java” (re-) start talend studio check if sap library is added successfully open view “talend modules” (eclipse --> windows --> show view --> talend --> modules) sort by column “context” look for “tsap*” contexts and check if sapjco3.jar has status “installed” usage of sap components with talend studio this section describes how to use talend’s sap components and the sap wizard in general (using one specific example for calling a bapi). detailed descriptions of all sap components (for using bapis, rfcs, idocs, bw, etc.) are available in the documentation talend_components_rg_x.y.z.pdf at www.help.talend.com . connection to a sap system a connection to a sap system can be done “built-in” or via “metadata --> sap connections” (the latter only in enterprise version). using the latter has several advantages: reuse connection configuration quick check if connection to sap works wizards for retrieving functions from sap (instead of handwriting without wizard) quick test with test parameters if function works before finishing development lifecycle for a sap job development lifecycle for sap job: create connection (if not existing yet) right click on metadata --> sap connection create sap connection follow wizard sap jco version: 3 client: “001” userid: “sap*” password: “admin123” --> as you defined it while installation language: “en” hostname: “localhost” system number: “00” retrieve function (bapi / rfc) right click on created connection click on “retrieve sap function” enter search filter (e.g. bapi_fl*) click on “search” select and double click on your function (e.g bapi_flcust_getlist) you see all input, output and table parameters for this sap function click on “test in” --> here you see parameters in more detail: you now have to define which input and output parameters you want to use --> remove all other by selecting them and clicking “remove” button hint: if you do not remove an input parameter, you usually have to enter a value for it! select the output type - can be a single (single record), a table (list of records), or a structure output hint: difference between table and structure in sap: http://www.sapfans.com/forums/viewtopic.php?f=12&t=119794 if you want to do a quick test: enter values for input parameters (if there are any for your function call), then click “launch” button in this example, there is only an optional input parameter max_rows you should see data in the output fields in this example, you see the record with custname “sap ag” and street “neurottstr. 16” click “finish” button under “metadata --> sap connections --> “your connection” --> sap functions: there you can now see your function (in this example: bapi_flcust_getlist) create sap job drag&drop the created function into a job (without the wizard, you also can enter all data by hand) tsapinput component is proposed automatically. click ok to add it to your job go to “initialize input” and add parameter values in this example, there is just the parameter “max_rows” hint: the parameter value can be changed from a hardcoded value to a variable, of course (just click control space on your keyboard to get access to all available variables via code completion in your studio) go to the tsapinput component and add the desired output mapping (i.e. which values you want to process further with other components scroll to the bottom to “outputs” add the correct table / structure name (in this example: "customer_list") click on mapping (which is empty and has to be filled) click on “mapping”, then click on “…” add the wanted output columns of your sap function add the same names at the column “schema xpathqueries” (do not forget the double quotes here!) click “ok” button connect the tsapinput component to a tlogrowcomponent and synchronize the schema hint: always try out if this works before adding further logic to your job! run and test your job (you will see five rows logged (as you have configured max_rows = 5 that's it. now enjoy talend's sap components :-) best regards, kai wähner (twitter: @kaiwaehner) content from my blog: http://www.kai-waehner.de/blog/2013/03/03/sap-integration-with-talend-components-connectors-bapi-rfc-idoc-bw-soap/
March 4, 2013
by Kai Wähner DZone Core CORE
· 32,926 Views · 1 Like
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JUnit testing of Spring MVC application: Testing the Service Layer
In continuation of my earlier blogs on Introduction to Spring MVC and Testing DAO layer in Spring MVC, in this blog I will demonstrate how to test Service layer in Spring MVC. The objective of this demo is 2 fold, to build the Service layer using TDD and increase the code coverage during JUnit testing of Service layer. For people in hurry, get the latest code from Github and run the below command mvn clean test -Dtest=com.example.bookstore.service.AccountServiceTest Since in my earlier blog, we have already tested the DAO layer, in this blog we only need to focus on testing service layer. We need to mock the DAO layer so that we can control the behavior in Service layer and cover various scenarios. Mockito is a good framework which is used to mock a method and return known data and assert that in the JUnit. As a first step we define the AccountServiceTestContextConfiguration class with AccountServiceTest class. If you notice there are 2 beans defined in that class and we marked the as a @Configuration which shows that it is a Spring Context class. In the JUnit test we @Autowired AccountService class. And AccountServiceImpl @Autowired the AccountRepository class. When creating the Bean in the configuration file we also stubbed the AccountRepository class using Mockito, @RunWith(SpringJUnit4ClassRunner.class) @ContextConfiguration public class AccountServiceTest { @Configuration static class AccountServiceTestContextConfiguration { @Bean public AccountService accountService() { return new AccountServiceImpl(); } @Bean public AccountRepository accountRepository() { return Mockito.mock(AccountRepository.class); } } //We Autowired the AccountService bean so that it is injected from the configuration @Autowired private AccountService accountService; @Autowired private AccountRepository accountRepository; During the setup of the JUnit we use Mockito mock findByUsername method to return a predefined account object as below @Before public void setup() { Account account = new AccountBuilder() { { address("Herve", "4650", "Rue de la gare", "1", null, "Belgium"); credentials("john", "secret"); name("John", "Doe"); } }.build(true); Mockito.when(accountRepository.findByUsername("john")).thenReturn(account); } Now we write the tests as below and test both the positive and negative scenarios, @Test(expected = AuthenticationException.class) public void testLoginFailure() throws AuthenticationException { accountService.login("john", "fail"); } @Test() public void testLoginSuccess() throws AuthenticationException { Account account = accountService.login("john", "secret"); assertEquals("John", account.getFirstName()); assertEquals("Doe", account.getLastName()); } } Finally we verify if the findByUsername method is called only once successfully as below in the teardown, @After public void verify() { Mockito.verify(accountRepository, VerificationModeFactory.times(1)).findByUsername(Mockito.anyString()); // This is allowed here: using container injected mocks Mockito.reset(accountRepository); } AccountService class looks as below, @Service @Transactional(readOnly = true) public class AccountServiceImpl implements AccountService { @Autowired private AccountRepository accountRepository; @Override public Account login(String username, String password) throws AuthenticationException { Account account = this.accountRepository.findByUsername(username, password); } else { throw new AuthenticationException("Wrong username/password", "invalid.username"); } return account; } } I hope this blog helped you. In my next blog, I will demo how to build a controller JUnit test. Reference: Pro Spring MVC: With Web Flow by by Marten Deinum, Koen Serneels
March 3, 2013
by Krishna Prasad
· 81,667 Views · 3 Likes
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Using UriTemplate to Change Path Parameters in a URI
I have been recently working on a client generator in the wadl.java.net project and I was solving a bug where I wanted to change path parameter on a URI. This turned out to be quite easy with existing classes that are part of the Jersey project. First of all you need a instance of UriTemplate, depending on the very of Jersey this is in a slightly different package - you IDE's automatic import will do the work for you. String template = "http://example.com/name/{name}/age/{age}"; UriTemplate uriTemplate = new UriTemplate(template); Then you use match(...) to extract them: String uri = "http://example.com/name/Bob/age/47"; Map parameters = new HashMap<>(); // Not this method returns false if the URI doesn't match, ignored // for the purposes of the this blog. uriTemplate.match(uri, parameters); Then you can replace the parameters at will and rebuild the URI: parameters.put("name","Arnold"); UriBuilder builder = UriBuilder.fromPath(template); URI output = builder.build(parameters);
March 1, 2013
by Gerard Davison
· 43,855 Views · 2 Likes
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JUnit testing of Spring MVC application: Testing DAO layer
In continuation of my blog JUnit testing of Spring MVC application – Introduction, in this blog, I will show how to design and implement DAO layer for the Bookstore Spring MVC web application using Test Driven development. For people in hurry, get the latest code from Github and run the below command mvn clean test -Dtest=com.example.bookstore.repository.JpaBookRepositoryTest As a part of TDD, Write a basic CRUD (create, read, update, delete) operations on a Book DAO class com.example.bookstore.repository.JpaBookRepository. Don’t have the database wiring yet in this DAO class. Once we build the JUnit tests, we use JPA as a persistence layer. We also use H2 as a inmemory database for testing purpose. Create Book POJO class Create the JUnit test as below, public class JpaBookRepositoryTest { @Test public void testFindById() { Book book = bookRepository.findById(this.book.getId()); assertEquals(this.book.getAuthor(), book.getAuthor()); assertEquals(this.book.getDescription(), book.getDescription()); assertEquals(this.book.getIsbn(), book.getIsbn()); } @Test public void testFindByCategory() { List books = bookRepository.findByCategory(category); assertEquals(1, books.size()); for (Book book : books) { assertEquals(this.book.getCategory().getId(), category.getId()); assertEquals(this.book.getAuthor(), book.getAuthor()); assertEquals(this.book.getDescription(), book.getDescription()); assertEquals(this.book.getIsbn(), book.getIsbn()); } } @Test @Rollback(true) public void testStoreBook() { Book book = new BookBuilder() { { description("Something"); author("JohnDoe"); title("John Doe's life"); isbn("1234567890123"); category(category); } }.build(); bookRepository.storeBook(book); Book book1 = bookRepository.findById(book.getId()); assertEquals(book1.getAuthor(), book.getAuthor()); assertEquals(book1.getDescription(), book.getDescription()); assertEquals(book1.getIsbn(), book.getIsbn()); } } If you notice since the JpaBookRepository is only a skeleton class without implementation, all the tests will fail. As a next step, we need to create a Configuration and wire a datasource, and for the test purpose we will be using H2 database. And we also need to wire this back to JUnit test as below, @Configuration public class InfrastructureContextConfiguration { @Autowired private DataSource dataSource; //some more configurations.. @Bean public DataSource dataSource() { EmbeddedDatabaseBuilder builder = new EmbeddedDatabaseBuilder(); builder.setType(EmbeddedDatabaseType.H2); return builder.build(); } } //JUnit test wiring is as below @RunWith(SpringJUnit4ClassRunner.class) @ContextConfiguration(classes = { InfrastructureContextConfiguration.class, TestDataContextConfiguration.class }) @Transactional public class JpaBookRepositoryTest { //the test methods } Next step is to setup and teardown sample data in the JUnit test case as below, public class JpaBookRepositoryTest { @PersistenceContext private EntityManager entityManager; private Book book; private Category category; @Before public void setupData() { EntityBuilderManager.setEntityManager(entityManager); category = new CategoryBuilder() { { name("Evolution"); } }.build(); book = new BookBuilder() { { description("Richard Dawkins' brilliant reformulation of the theory of natural selection"); author("Richard Dawkins"); title("The Selfish Gene: 30th Anniversary Edition"); isbn("9780199291151"); category(category); } }.build(); } @After public void tearDown() { EntityBuilderManager.clearEntityManager(); } } Once we do the wiring, we need to implement the com.example.bookstore.repository.JpaBookRepository and use JPA to do the CRUD on the database and run the tests. The tests will succeed. Finally if you run Cobertura for this example from STS, we will get over 90% of line coverage for com.example.bookstore.repository.JpaBookRepository. In case you want to try few exercises you can implement repository for Account and User. I hope this blog helped you. In my next blog I will talk about Mochito and Implementing the Service layer.
March 1, 2013
by Krishna Prasad
· 80,299 Views
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Parallelizing Work with Redis
Curator's Note: Here's a Redis how-to from back in 2011. Anyone who has heard of Redis has probably also heard of Resque, which is a lightweight queue'ing system. To the uninitiated it might seem strange, or maybe even impossible, to construct a queue'ing system using just a key-value store. In this article, I’m going to break down some of the primitives redis exposes that make building a queue'ing system over it trivial and show how Redis is so much more than just a key-value store. The problem Let’s say, you are a mathematician and have just come up with this super performant way of computing factors of numbers. You decide to write up the following sinatra service: def compute_factors(number) factors = crazy_performant_computation number end get "/compute_factors" do number, post_back_url = params[:number].to_i, params[:post_back_url] RestClient.post post_back_url, factors => compute_factors(number).to_json "OK" end You soon start seeing crazy traffic and realize, performant as your factor computation algorithm is, it’s not fast enough to keep up with the speed at which you are getting requests to your service. First pass at optimization by forking You realize that it’s going to be far more efficient to fork off a new Process or Thread and have that perform the computation and post back the result. So your code now changes to: get "/compute_factors" do number, post_back_url = params[:number].to_i, params[:post_back_url] Process.fork do RestClient.post post_back_url, factors => compute_factors(number).to_json end "OK" end While, this is great you soon realize that filling up the process table in your OS is not such a good idea. Capping process creation using a process pool It is exactly this problem that a process pool was meant to solve. The basic idea is that you would still like to perform your time-intensive task in the background, but would like to put a cap on the number of background processes you have running. There are some excellent libraries that solve this problem such as Delayed Job and Resque. However, being the hacker that you are, you decide to roll one yourself. There are however a bunch of issues that these libraries solve and you decide to pull a pen and paper and note them down to ensure that you are not missing anything: Cap how many workers you create You need to have a way to cap the number of background workers you create, that way you don’t have the same problem you were having before. Control worker creation and destruction You would like to be able to boot up and bring down your workers reasonably gracefully. Handle race conditions You realize, that spinning new processes means that you now have to ensure your code is concurrent-safe. Redis provides, some wonderful atomic operations out-of-the-box so this shouldn’t be too hard. Second pass using BRPOP Redis supports a couple of interesting data-structures including lists, sets and hashes. Redis lists have a command called RPOP which basically lets you pop an item off the tail of a list, in essence treating it like a queue. The RPOP command comes with a blocking variant of itself called BRPOP that blocks on the call to popping an element from the list. You can also specify a timeout for how long (in seconds) you would like to block on the call. def compute_factors(number) factors = crazy_performant_computation number end NUMBER_OF_WORKERS = (ENV['NUMBER_OF_WORKERS'] || 50).to_i NUMBER_OF_WORKERS.times do Process.fork do redis = Redis.new loop do val = redis.brpop "work_queue", 1 unless val puts "Process: #{Process.pid} is exiting" exit 0 end number, postback_url = Marshal.load val.last RestClient.post postback_url, factors => compute_factors(number).to_json end end end redis = Redis.new get "/compute_factors" do number, post_back_url = params[:number].to_i, params[:post_back_url] redis.lpush "work_queue", Marshal.dump([number, post_back_url]) "OK" end So you now have solved a bunch of problems in this new approach. We have a fixed number of workers running to handle our background processing – so now our process table getting filled is not subject to traffic conditions. Race conditions are handled for us by Redis, since BRPOP is atomic and guarantees no two workers will do duplicate work. And finally, workers destroy themselves if they break out of the brpop call due to their timeout being hit, in this case 1 second. So, that’s quite a slew of problems that have been solved for us by virtue of just using redis. We soon start, seeing a different problem though. As traffic in our site lags, workers seem to be dying off since their timeout is being hit. We’d really like to now have the workers block for a longer time than just 1 second, while also having the option to kill them off sooner if we need to. That way, they’ll not be hanging around for any longer than they have to. Gracefully shutting down workers Our mandate now is to shutdown our workers gracefully, using redis and little bit of UNIX signals magic (for examples of using signals in this area checkout Unicorn Is Unix and the Unicorn web-server. Our code now morphs to: def compute_factors(number) factors = crazy_performant_computation number end NUMBER_OF_WORKERS = (ENV['NUMBER_OF_WORKERS'] || 50).to_i NUMBER_OF_WORKERS.times do Process.fork do redis = Redis.new loop do val = redis.brpop "work_queue", 30 unless val puts "Process: #{Process.pid} is signing off due to timeout!" exit 0 end if val.last == "DIE!" puts "Process: #{Process.pid} has been asked to kill itself by parent" exit 0 end number, postback_url = Marshal.load val.last RestClient.post postback_url, factors => compute_factors(number).to_json end end end redis = Redis.new get "/compute_factors" do number, post_back_url = params[:number].to_i, params[:post_back_url] redis.lpush "work_queue", Marshal.dump([number, post_back_url]) "OK" end `echo #{Process.pid} > /tmp/factors.pid` puts "Parent process wrote PID to /tmp/factors.pid" trap('QUIT') do NUMBER_OF_WORKERS.times do redis.lpush "work_queue", "DIE!" end end We have now bumped up the timeout to 30 seconds and also have in place a way to bring down the workers near instantly. This is accomplished by the web-server trapping the QUIT signal and when it does, it pushes a “DIE!” message onto the redis “work_queue”. It pushes this message the same number of times as the NUMBER_OF_WORKERS. And since BRPOP is an atomic and concurrent-safe operation we are now supporting the bringing down of workers via redis. How cool is that! To gracefully shutdown the server and workers we just need to: kill -s QUIT `cat /tmp/factors.pid` Conclusion The next time you need to get some background job action going, stop yourself from just grabbing a library. Instead, toy around with redis lists a little. You’ll be surprised by how much you can accomplish with just straight redis primitives.
February 28, 2013
by Santosh Kumar
· 8,276 Views
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Spring, JMS, Listener Adapters, and Containers
In order to receive JMS messages, Spring provides the concept of message listener containers. These are beans that can be tied to receive messages that arrive at certain destinations. This post will examine the different ways in which containers can be configured. A simple example is below where the DefaultMessageListenerContianer has been configured to watch one queue (the property jms.queue.name) and has a reference to a myMessageListener bean which implements the MessageListener interface (ie onMessage): This is all very well but means that the myMessageListener bean will have to handle the JMS Message object and process accordingly depending upon the type of javax.jms.Message and its payload. For example: if (message instanceof MapMessage) { // cast, get object, do something } An alternative is to use a MessageListenerAdapter. This class abstracts away the above processing and leaves your code to deal with just the message's payload. For example: The delegate is a reference to a myMessageReceiverDelegate bean which has one or more methods called processMessage. It does not need to implement the MessageListener interface. This method can be overload to handle different payload types. Spring behind the scenes will determine which gets called. For example: public void processMessage(final HashMap message) { // do something } public void processMessage(final String message) { // do something } For the given approach though, only one queue can be tied to the container. Another approach is to tie many listeners (therefore many queues) to the one container, The below Spring XML, using the jms namespace, shows how two listeners for different queues can be tied to one container: The myMessageReceiverDelegate bean is treated as an adapter delegate, therefore does not need to implement the MessageListener interface. Each listener can have a different delegate but for the above example, all messages arriving at the two queues are processed by the one receiver bean ie myMessageReceiverDelegate. If there is a need to check the message type and extract the payload, then the listener can use a class which implements the MessageListener interface (eg the myMessageListener bean used in the first example). The onMessage method will then be called when messages arrive at the specified destination:
February 28, 2013
by Geraint Jones
· 72,602 Views · 2 Likes
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Understanding TCP/IP Network Stack & Writing Network Apps
We cannot imagine Internet service without TCP/IP. All Internet services we have developed and used at NHN are based on a solid basis, TCP/IP. Understanding how data is transferred via the network will help you to improve performance through tuning, troubleshooting, or introduction to a new technology. This article will describe the overall operation scheme of the network stack based on data flow and control flow in Linux OS and the hardware layer. Key Characteristics of TCP/IP How should I design a network protocol to transmit data quickly while keeping the data order without any data loss? TCP/IP has been designed with this consideration. The following are the key characteristics of TCP/IP required to understand the concept of the stack. TCP and IP Technically, since TCP and IP have different layer structures, it would be correct to describe them separately. However, here we will describe them as one. 1. Connection-oriented First, a connection is made between two endpoints (local and remote) and then data is transferred. Here, the "TCP connection identifier" is a combination of addresses of the two endpoints, having type. 2. Bidirectional Byte Stream Bidirectional data communication is made by using byte stream. 3. In-order Delivery A receiver receives data in the order of sending data from a sender. For that, the order of data is required. To mark the order, 32-bit integer data type is used. 4. Reliability through ACK When a sender did not receive ACK (acknowledgement) from a receiver after sending data to the receiver, the sender TCP re-sends the data to the receiver. Therefore, the sender TCP buffers unacknowledged data from the receiver. 5. Flow Control A sender sends as much data as a receiver can afford. A receiver sends the maximum number of bytes that it can receive (unused buffer size, receive window) to the sender. The sender sends as much data as the size of bytes that the receiver's receive window allows. 6. Congestion Control The congestion window is used separately from the receive window to prevent network congestion by limiting the volume of data flowing in the network. Like the receive window, the sender sends as much data as the size of bytes that the receiver's congestion window allows by using a variety of algorithms such as TCP Vegas, Westwood, BIC, and CUBIC. Different from flow control, congestion control is implemented by the sender only. Data Transmission As indicated by its name, a network stack has many layers. The following Figure 1 shows the layer types. Figure 1: Operation Process by Each Layer of TCP/IP Network Stack for Data Transmission. There are several layers and the layers are briefly classified into three areas: User area Kernel area Device area Tasks at the user area and the kernel area are performed by the CPU. The user area and the kernel area are called "host" to distinguish them from the device area. Here, the device is the Network Interface Card (NIC) that sends and receives packets. It is a more accurate term than the commonly used "LAN card". Let's take a look at the user area. First, the application creates data to send (the "User data" box in Figure 1) and then calls the write() system call to send the data. Assume that the socket (fd in Figure 1) has been already created. When the system call is called, the area is switched to the kernel area. POSIX-series operating systems including Linux and Unix expose the socket to the application by using a file descriptor. In the POSIX-series operating system, the socket is a kind of a file. The file layer executes a simple examination and calls the socket function by using the socket structure connected to the file structure. The kernel socket has two buffers. One is the send socket buffer for sending; And the other is the receive socket buffer for receiving. When the write system call is called, the data in the user area is copied to the kernel memory and then added to the end of the send socket buffer. This is to send data in order. In the Figure 1, the light-gray box refers to the data in the socket buffer. Then, TCP is called. There is the TCP Control Block (TCB) structure connected to the socket. The TCB includes data required for processing the TCP connection. Data in the TCB are connection state (LISTEN, ESTABLISHED, TIME_WAIT),receive window, congestion window, sequence number, resending timer, etc. If the current TCP state allows for data transmission, a new TCP segment (in other words, a packet) is created. If data transmission is impossible due to flow control or such a reason, the system call is ended here and then the mode is returned to the user mode (in other words, the control is passed to the application). There are two TCP segments as shown in Figure 2: TCP header; And payload. Figure 2: TCP Frame Structure (source). The payload includes the data saved in the unacknowledged send socket buffer. The maximum length of the payload is the maximum value among the receive window, congestion window, and maximum segment size (MSS). Then, TCP checksum is computed. In this checksum computation, pseudo header information (IP addresses, segment length, and protocol number) is included. One or more packets can be transmitted according to the TCP state. In fact, since the current network stack uses the checksum offload, the TCP checksum is computed by NIC, not by the kernel. However, we assume that the kernel computes the TCP checksum for convenience. The created TCP segment goes down to the IP layer. The IP layer adds an IP header to the TCP segment and performs IP routing. IP routing is a procedure of searching the next hop IP in order to go to the destination IP. After the IP layer has computed and added the IP header checksum, it sends the data to the Ethernet layer. The Ethernet layer searches for the MAC address of the next hop IP by using the Address Resolution Protocol (ARP). It then adds the Ethernet header to the packet. The host packet is completed by adding the Ethernet header. After IP routing is performed, the transmit interface (NIC) is known as the result of IP routing. The interface is used for transmitting a packet to the next hop IP and the IP. Therefore, the transmit NIC driver is called. At this time, if a packet capture program such as tcpdump or Wireshark is running, the kernel copies the packet data onto the memory buffer that the program uses. In that way, the receiving packet is directly captured on the driver. Generally, the traffic shaper function is implemented to run on this layer. The driver requests packet transmission according to the driver-NIC communication protocol defined by the NIC manufacturer. After receiving the packet transmission request, the NIC copies the packets from the main memory to its memory and then sends it to the network line. At this time, by complying with the Ethernet standard, it adds the IFG (Inter-Frame Gap), preamble, and CRC to the packet. The IFG and preamble are used to distinguish the start of the packet (as a networking term, framing), and the CRC is used to protect the data (the same purpose as TCP and IP checksum). Packet transmission is started based on the physical speed of the Ethernet and the condition of Ethernet flow control. It is like getting the floor and speaking in a conference room. When an NIC sends a packet, the NIC generates interrupts on the host CPU. Every interrupt has its own interrupt number and the OS searches an adequate driver to handle the interrupt by using the number. The driver registers a function to handle the interrupt (an interrupt handler) when the driver is started. The OS calls the interrupt handler and then the interrupt handler returns the transmitted packet to the OS. So far we have discussed the procedure of data transmission through the kernel and the device when an application performs write. However, without a direct write request from the application, the kernel can transmit a packet by directly calling TCP. For example, when an ACK is received and the receive window is expanded, the kernel creates a TCP segment including the data left in the socket buffer and sends the TCP segment to the receiver. Data Receiving Now, let's take a look at how data is received. Data receiving is a procedure for how the network stack handles a packet coming in. Figure 3 shows how the network stack handles a packet received. Figure 3: Operation Process by Each Layer of TCP/IP Network Stack for Handling Data Received. First, the NIC writes the packet onto its memory. It checks whether the packet is valid by performing the CRC check and then sends the packet to the memory buffer of the host. This buffer is a memory that has already been requested by the driver to the kernel and allocated for receiving packets. After the buffer has been allocated, the driver tells the memory address and size to the NIC. When there is no host memory buffer allocated by the driver even though the NIC receives a packet, the NIC may drop the packet. After sending the packet to the host memory buffer, the NIC sends an interrupt to the host OS. Then, the driver checks whether it can handle the new packet or not. So far, the driver-NIC communication protocol defined by the manufacturer is used. When the driver should send a packet to the upper layer, the packet must be wrapped in a packet structure that the OS uses for the OS to understand the packet. For example, sk_buff of Linux, mbuf of BSD-series kernel, and NET_BUFFER_LIST of Microsoft Windows are the packet structures of the corresponding OS. The driver sends the wrapped packets to the upper layer. The Ethernet layer checks whether the packet is valid and then de-multiplexes the upper protocol (network protocol). At this time, it uses the ethertype value of the Ethernet header. The IPv4 ethertype value is 0x0800. It removes the Ethernet header and then sends the packet to the IP layer. The IP layer also checks whether the packet is valid. In other words, it checks the IP header checksum. It logically determines whether it should perform IP routing and make the local system handle the packet, or send the packet to the other system. If the packet must be handled by the local system, the IP layer de-multiplexes the upper protocol (transport protocol) by referring to the proto value of the IP header. The TCP proto value is 6. It removes the IP header and then sends the packet to the TCP layer. Like the lower layer, the TCP layer checks whether the packet is valid. It also checks the TCP checksum. As mentioned before, since the current network stack uses the checksum offload, the TCP checksum is computed by NIC, not by the kernel. Then it searches the TCP control block where the packet is connected. At this time, of the packet is used as an identifier. After searching the connection, it performs the protocol to handle the packet. If it has received new data, it adds the data to the receive socket buffer. According to the TCP state, it can send a new TCP packet (for example, an ACK packet). Now TCP/IP receiving packet handling has completed. The size of the receive socket buffer is the TCP receive window. To a certain point, the TCP throughput increases when the receive window is large. In the past, the socket buffer size had been adjusted on the application or the OS configuration. The latest network stack has a function to adjust the receive socket buffer size, i.e., the receive window, automatically. When the application calls the read system call, the area is changed to the kernel area and the data in the socket buffer is copied to the memory in the user area. The copied data is removed from the socket buffer. And then the TCP is called. The TCP increases the receive window because there is new space in the socket buffer. And it sends a packet according to the protocol status. If no packet is transferred, the system call is terminated. Network Stack Development Direction The functions of network stack layers described so far are the most basic functions. The network stack in the early 1990s had few more functions than the functions described above. However, the latest network stack has many more functions and complexity as the network stack implementation structure gets higher. The latest network stack is classified by purpose as follows. Packet Processing Procedure Manipulation It is a function like Netfilter (firewall, NAT) and traffic control. By inserting the user-controllable code to the basic processing flow, the function can work differently according to the user configuration. Protocol Performance It aims to improve the throughput, latency, and stability that the TCP protocol can achieve within the given network environment. Various congestion control algorithms and additional TCP functions such as SACK are the typical examples. The protocol improvement will not be discussed here since it is out of the scope. Packet Processing Efficiency The packet processing efficiency aims to improve the maximum number of packets that can be processed per second by reducing the CPU cycle, memory usage, and memory accesses that one system consumes to process packets. There have been several attempts to reduce the latency in the system. The attempts include stack parallel processing, header prediction, zero-copy, single-copy, checksum offload, TSO, LRO, RSS, etc. Control Flow in the Stack Now, we will take a more detailed look at the internal flow of the Linux network stack. Like a subsystem which is not a network stack, a network stack basically runs as the event-driven way that reacts when the event occurs. Therefore, there is no separated thread to execute the stack. Figure 1 and Figure 3 showed the simplified diagrams of control flow. Figure 4 below illustrates more exact control flow. Figure 4: Control Flow in the Stack. At Flow (1) in Figure 4, an application calls a system call to execute (use) the TCP. For example, calls the read system call and the write system call and then executes TCP. However, there is no packet transmission. Flow (2) is same as Flow (1) if it requires packet transmission after executing TCP. It creates a packet and sends down the packet to the driver. A queue is in front of the driver. The packet comes into the queue first, and then the queue implementation structure decides the time to send the packet to the driver. This is queue discipline (qdisc) of Linux. The function of Linux traffic control is to manipulate the qdisc. The default qdisc is a simple First-In-First-Out (FIFO) queue. By using another qdisc, operators can achieve various effects such as artificial packet loss, packet delay, transmission rate limit, etc. At Flow (1) and Flow (2), the process thread of the application also executes the driver. Flow (3) shows the case in which the timer used by the TCP has expired. For example, when the TIME_WAITtimer has expired, the TCP is called to delete the connection. Like Flow (3), Flow (4) is the case in which the timer used by the TCP has expired and the TCP execution result packet should be transmitted. For example, when the retransmit timer has expired, the packet of which ACK has not been received is transmitted. Flow (3) and Flow (4) show the procedure of executing the timer softirq that has processed the timer interrupt. When the NIC driver receives an interrupt, it frees the transmitted packet. In most cases, execution of the driver is terminated here. Flow (5) is the case of packet accumulation in the transmit queue. The driver requests softirq and the softirq handler executes the transmit queue to send the accumulated packet to the driver. When the NIC driver receives an interrupt and finds a newly received packet, it requests softirq. The softirq that processes the received packet calls the driver and transmits the received packet to the upper layer. In Linux, processing the received packet as shown above is called New API (NAPI). It is similar to polling because the driver does not directly transmit the packet to the upper layer, but the upper layer directly gets the packet. The actual code is called NAPI poll or poll. Flow (6) shows the case that completes execution of TCP, and Flow (7) shows the case that requires additional packet transmission. All of Flow (5), (6), and (7) are executed by the softirq which has processed the NIC interrupt. How to Process Interrupt and Received Packet Interrupt processing is complex; however, you need to understand the performance issue related to processing of packets received. Figure 5 shows the procedure of processing an interrupt. Figure 5: Processing Interrupt, softirq, and Received Packet. Assume that the CPU 0 is executing an application program (user program). At this time, the NIC receives a packet and generates an interrupt for the CPU 0. Then the CPU executes the kernel interrupt (called irq) handler. This handler refers to the interrupt number and then calls the driver interrupt handler. The driver frees the packet transmitted and then calls the napi_schedule() function to process the received packet. This function requests the softirq (software interrupt). After execution of the driver interrupt handler has been terminated, the control is passed to the kernel handler. The kernel handler executes the interrupt handler for the softirq. After the interrupt context has been executed, the softirq context will be executed. The interrupt context and the softirq context are executed by an identical thread. However, they use different stacks. And, the interrupt context blocks hardware interrupts; however, the softirq context allows for hardware interrupts. The softirq handler that processes the received packet is the net_rx_action() function. This function calls thepoll() function of the driver. The poll() function calls the netif_receive_skb() function and then sends the received packets one by one to the upper layer. After processing the softirq, the application restarts execution from the stopped point in order to request a system call. Therefore, the CPU that has received the interrupt processes the received packets from the first to the last. In Linux, BSD, and Microsoft Windows, the processing procedure is basically the same on this wise. When you check the server CPU utilization, sometimes you can check that only one CPU executes the softirq hard among the server CPUs. The phenomenon occurs due to the way of processing received packets explained so far. To solve the problem, multi-queue NIC, RSS, and RPS have been developed. Data Structure The followings are some key data structures. Take a look at them and review the code. sk_buff structure First, there is the sk_buff structure or skb structure that means a packet. Figure 6 shows some of the sk_buffstructure. As the functions have been advanced, they get more complicated. However, the basic functions are very common that anyone can think. Figure 6: Packet Structure sk_buff. Including Packet Data and meta data The structure directly includes the packet data or refers to it by using a pointer. In Figure 6, some of the packets (from Ethernet to buffer) refer to using the data pointer and the additional data (frags) refer to the actual page. The necessary information such as header and payload length is saved in the meta data area. For example, inFigure 6, the mac_header, the network_header, and the transport_header have the corresponding pointer data that points the starting position of the Ethernet header, IP header and TCP header, respectively. This way makes TCP protocol processing easy. How to Add or Delete a Header The header is added or deleted as up and down each layer of the network stack. Pointers are used for more efficient processing. For example, to remove the Ethernet header, just increase the head pointer. How to Combine and Divide Packet The linked list is used for efficient execution of tasks such as adding or deleting packet payload data to the socket buffer, or packet chain. The next pointer and the prev pointer are used for this purpose. Quick Allocation and Free As a structure is allocated whenever creating a packet, the quick allocator is used. For example, if data is transmitted at the speed of 10-Gigabit Ethernet, more than one million packets per second must be created and deleted. TCP Control Block Second, there is a structure that represents the TCP connection. Previously, it was abstractly called a TCP control block. Linux uses tcp_sock for the structure. In Figure 7, you can see the relationship among the file, the socket, and the tcp_sock. Figure 7: TCP Connection Structure. When a system call has occurred, it searches the file in the file descriptor used by the application that has called the system call. For the Unix-series OS, the socket, the file and the device for general file system for storage are abstracted to a file. Therefore, the file structure includes the least information. For a socket, a separate socket structure saves the socket-related information and the file refers to the socket as a pointer. The socket refers to the tcp_sock again. The tcp_sock is classified into sock, inet_sock, etc to support various protocols except TCP. It may be considered as a kind of polymorphism. All status information used by the TCP protocol is saved in the tcp_sock. For example, the sequence number, receive window, congestion control, and retransmit timer are saved in the tcp_sock. The send socket buffer and the receive socket buffer are the sk_buff lists and they include the tcp_sock. The dst_entry, the IP routing result, is referred to in order to avoid too frequent routing. The dst_entry allows for easy search of the ARP result, i.e., the destination MAC address. The dst_entry is part of the routing table. The structure of the routing table is very complex that it will not be discussed in this document. The NIC to be used for packet transmission is searched by using the dst_entry. The NIC is expressed as the net_device structure. Therefore, by searching just the file, it is very easy to find all structures (from the file to the driver) required to process the TCP connection with the pointer. The size of the structures is the memory size used by one TCP connection. The memory size is a few KBs (excluding the packet data). As more functions have been added, the memory usage has been gradually increased. Finally, let's see the TCP connection lookup table. It is a hash table used to search the TCP connection where the received packet belongs. The hash value is calculated by using the input data of of the packet and the Jenkins hash algorithm. It is told that the hash function has been selected by considering defense against attacks to the hash table. Following Code: How to Transmit Data We will check the key tasks performed by the stack by following the actual Linux kernel source code. Here, we will observe two paths which are frequently used. First, this is a path used to transmit data when an application calls the write system call. SYSCALL_DEFINE3(write, unsigned int, fd, const char __user *, buf, ...) { struct file *file; [...] file = fget_light(fd, &fput_needed); [...] ===> ret = filp->f_op->aio_write(&kiocb, &iov, 1, kiocb.ki_pos); struct file_operations { [...] ssize_t (*aio_read) (struct kiocb *, const struct iovec *, ...) ssize_t (*aio_write) (struct kiocb *, const struct iovec *, ...) [...] }; static const struct file_operations socket_file_ops = { [...] .aio_read = sock_aio_read, .aio_write = sock_aio_write, [...] }; When the application calls the write system call, the kernel performs the write() function of the file layer. First, the actual file structure of the file descriptor fd is fetched. And then the aio_write is called. This is the function pointer. In the file structure, you will see the file_operations structure pointer. The structure is generally called function table and includes the function pointers such as aio_read and aio_write. The actual table for the socket is socket_file_ops. The aio_write function used by the socket is sock_aio_write. The function table is used for the purpose that is similar to the Java interface. It is generally used for the kernel to perform code abstraction or refactoring. static ssize_t sock_aio_write(struct kiocb *iocb, const struct iovec *iov, ..) { [...] struct socket *sock = file->private_data; [...] ===> return sock->ops->sendmsg(iocb, sock, msg, size); struct socket { [...] struct file *file; struct sock *sk; const struct proto_ops *ops; }; const struct proto_ops inet_stream_ops = { .family = PF_INET, [...] .connect = inet_stream_connect, .accept = inet_accept, .listen = inet_listen, .sendmsg = tcp_sendmsg, .recvmsg = inet_recvmsg, [...] }; struct proto_ops { [...] int (*connect) (struct socket *sock, ...) int (*accept) (struct socket *sock, ...) int (*listen) (struct socket *sock, int len); int (*sendmsg) (struct kiocb *iocb, struct socket *sock, ...) int (*recvmsg) (struct kiocb *iocb, struct socket *sock, ...) [...] }; The sock_aio_write() function gets the socket structure from the file and then calls sendmsg. It is also the function pointer. The socket structure includes the proto_ops function table. The proto_ops implemented by the IPv4 TCP is inet_stream_ops and the sendmsg is implemented by tcp_sendmsg. int tcp_sendmsg(struct kiocb *iocb, struct socket *sock, struct msghdr *msg, size_t size) { struct sock *sk = sock->sk; struct iovec *iov; struct tcp_sock *tp = tcp_sk(sk); struct sk_buff *skb; [...] mss_now = tcp_send_mss(sk, &size_goal, flags); /* Ok commence sending. */ iovlen = msg->msg_iovlen; iov = msg->msg_iov; copied = 0; [...] while (--iovlen >= 0) { int seglen = iov->iov_len; unsigned char __user *from = iov->iov_base; iov++; while (seglen > 0) { int copy = 0; int max = size_goal; [...] skb = sk_stream_alloc_skb(sk, select_size(sk, sg), sk->sk_allocation); if (!skb) goto wait_for_memory; /* * Check whether we can use HW checksum. */ if (sk->sk_route_caps & NETIF_F_ALL_CSUM) skb->ip_summed = CHECKSUM_PARTIAL; [...] skb_entail(sk, skb); [...] /* Where to copy to? */ if (skb_tailroom(skb) > 0) { /* We have some space in skb head. Superb! */ if (copy > skb_tailroom(skb)) copy = skb_tailroom(skb); if ((err = skb_add_data(skb, from, copy)) != 0) goto do_fault; [...] if (copied) tcp_push(sk, flags, mss_now, tp->nonagle); [...] } tcp_sengmsg gets tcp_sock (i.e.,TCP control block) from the socket and copies the data that the application has requested to transmit to the send socket buffer. When copying data to sk_buff, how many bytes will one sk_buff include? One sk_buff copies and includes MSS (tcp_send_mss) bytes to help the code that actually creates packets. Maximum Segment Size (MSS) stands for the maximum payload size that one TCP packet includes. By using TSO and GSO, one sk_buff can save more data than MSS. This will be discussed later, not in this document. The sk_stream_alloc_skb function creates a new sk_buff, and skb_entail adds the new sk_buff to the tail of the send_socket_buffer. The skb_add_data function copies the actual application data to the data buffer of thesk_buff. All the data is copied by repeating the procedure (creating an sk_buff and adding it to the send socket buffer) several times. Therefore, sk_buffs at the size of the MSS are in the send socket buffer as a list. Finally, the tcp_push is called to make the data which can be transmitted now as a packet, and the packet is sent. static inline void tcp_push(struct sock *sk, int flags, int mss_now, ...) [...] ===> static int tcp_write_xmit(struct sock *sk, unsigned int mss_now, ...) int nonagle, { struct tcp_sock *tp = tcp_sk(sk); struct sk_buff *skb; [...] while ((skb = tcp_send_head(sk))) { [...] cwnd_quota = tcp_cwnd_test(tp, skb); if (!cwnd_quota) break; if (unlikely(!tcp_snd_wnd_test(tp, skb, mss_now))) break; [...] if (unlikely(tcp_transmit_skb(sk, skb, 1, gfp))) break; /* Advance the send_head. This one is sent out. * This call will increment packets_out. */ tcp_event_new_data_sent(sk, skb); [...] The tcp_push function transmits as many of the sk_buffs in the send socket buffer as the TCP allows in sequence. First, the tcp_send_head is called to get the first sk_buff in the socket buffer and thetcp_cwnd_test and the tcp_snd_wnd_test are performed to check whether the congestion window and the receive window of the receiving TCP allow new packets to be transmitted. Then, the tcp_transmit_skb function is called to create a packet. static int tcp_transmit_skb(struct sock *sk, struct sk_buff *skb, int clone_it, gfp_t gfp_mask) { const struct inet_connection_sock *icsk = inet_csk(sk); struct inet_sock *inet; struct tcp_sock *tp; [...] if (likely(clone_it)) { if (unlikely(skb_cloned(skb))) skb = pskb_copy(skb, gfp_mask); else skb = skb_clone(skb, gfp_mask); if (unlikely(!skb)) return -ENOBUFS; } [...] skb_push(skb, tcp_header_size); skb_reset_transport_header(skb); skb_set_owner_w(skb, sk); /* Build TCP header and checksum it. */ th = tcp_hdr(skb); th->source = inet->inet_sport; th->dest = inet->inet_dport; th->seq = htonl(tcb->seq); th->ack_seq = htonl(tp->rcv_nxt); [...] icsk->icsk_af_ops->send_check(sk, skb); [...] err = icsk->icsk_af_ops->queue_xmit(skb); if (likely(err <= 0)) return err; tcp_enter_cwr(sk, 1); return net_xmit_eval(err); } tcp_transmit_skb creates the copy of the given sk_buff (pskb_copy). At this time, it does not copy the entire data of the application but the metadata. And then it calls skb_push to secure the header area and records the header field value. Send_check computes the TCP checksum. With the checksum offload, the payload data is not computed. Finally, queue_xmit is called to send the packet to the IP layer. Queue_xmit for IPv4 is implemented by the ip_queue_xmit function. int ip_queue_xmit(struct sk_buff *skb) [...] rt = (struct rtable *)__sk_dst_check(sk, 0); [...] /* OK, we know where to send it, allocate and build IP header. */ skb_push(skb, sizeof(struct iphdr) + (opt ? opt->optlen : 0)); skb_reset_network_header(skb); iph = ip_hdr(skb); *((__be16 *)iph) = htons((4 << 12) | (5 << 8) | (inet->tos & 0xff)); if (ip_dont_fragment(sk, &rt->dst) && !skb->local_df) iph->frag_off = htons(IP_DF); else iph->frag_off = 0; iph->ttl = ip_select_ttl(inet, &rt->dst); iph->protocol = sk->sk_protocol; iph->saddr = rt->rt_src; iph->daddr = rt->rt_dst; [...] res = ip_local_out(skb); [...] ===> int __ip_local_out(struct sk_buff *skb) [...] ip_send_check(iph); return nf_hook(NFPROTO_IPV4, NF_INET_LOCAL_OUT, skb, NULL, skb_dst(skb)->dev, dst_output); [...] ===> int ip_output(struct sk_buff *skb) { struct net_device *dev = skb_dst(skb)->dev; [...] skb->dev = dev; skb->protocol = htons(ETH_P_IP); return NF_HOOK_COND(NFPROTO_IPV4, NF_INET_POST_ROUTING, skb, NULL, dev, ip_finish_output, [...] ===> static int ip_finish_output(struct sk_buff *skb) [...] if (skb->len > ip_skb_dst_mtu(skb) && !skb_is_gso(skb)) return ip_fragment(skb, ip_finish_output2); else return ip_finish_output2(skb); The ip_queue_xmit function executes tasks required by the IP layers. __sk_dst_check checks whether the cached route is valid. If there is no cached route or the cached route is invalid, it performs IP routing. And then it calls skb_push to secure the IP header area and records the IP header field value. After that, as following the function call, ip_send_check computes the IP header checksum and calls the netfilter function. IP fragment is created when ip_finish_output function needs IP fragmentation. No fragmentation is generated when TCP is used. Therefore, ip_finish_output2 is called and it adds the Ethernet header. Finally, a packet is completed. int dev_queue_xmit(struct sk_buff *skb) [...] ===> static inline int __dev_xmit_skb(struct sk_buff *skb, struct Qdisc *q, ...) [...] if (...) { .... } else if ((q->flags & TCQ_F_CAN_BYPASS) && !qdisc_qlen(q) && qdisc_run_begin(q)) { [...] if (sch_direct_xmit(skb, q, dev, txq, root_lock)) { [...] ===> int sch_direct_xmit(struct sk_buff *skb, struct Qdisc *q, ...) [...] HARD_TX_LOCK(dev, txq, smp_processor_id()); if (!netif_tx_queue_frozen_or_stopped(txq)) ret = dev_hard_start_xmit(skb, dev, txq); HARD_TX_UNLOCK(dev, txq); [...] } int dev_hard_start_xmit(struct sk_buff *skb, struct net_device *dev, ...) [...] if (!list_empty(&ptype_all)) dev_queue_xmit_nit(skb, dev); [...] rc = ops->ndo_start_xmit(skb, dev); [...] } The completed packet is transmitted through the dev_queue_xmit function. First, the packet passes via the qdisc. If the default qdisc is used and the queue is empty, the sch_direct_xmit function is called to directly send down the packet to the driver, skipping the queue. Dev_hard_start_xmit function calls the actual driver. Before calling the driver, the device TX is locked first. This is to prevent several threads from accessing the device simultaneously. As the kernel locks the device TX, the driver transmission code does not need an additional lock. It is closely related to the parallel processing that will be discussed next time. Ndo_start_xmit function calls the driver code. Just before, you will see ptype_all and dev_queue_xmit_nit. The ptype_all is a list that includes the modules such as packet capture. If a capture program is running, the packet is copied by ptype_all to the separate program. Therefore, the packet that tcpdump shows is the packet transmitted to the driver. When checksum offload or TSO is used, the NIC manipulates the packet. So the tcpdump packet is different from the packet transmitted to the network line. After completing packet transmission, the driver interrupt handler returns the sk_buff. Following Code: How to Receive Data The general executed path is to receive a packet and then to add the data to the receive socket buffer. After executing the driver interrupt handler, follow the napi poll handle first. static void net_rx_action(struct softirq_action *h) { struct softnet_data *sd = &__get_cpu_var(softnet_data); unsigned long time_limit = jiffies + 2; int budget = netdev_budget; void *have; local_irq_disable(); while (!list_empty(&sd->poll_list)) { struct napi_struct *n; [...] n = list_first_entry(&sd->poll_list, struct napi_struct, poll_list); if (test_bit(NAPI_STATE_SCHED, &n->state)) { work = n->poll(n, weight); trace_napi_poll(n); } [...] } int netif_receive_skb(struct sk_buff *skb) [...] ===> static int __netif_receive_skb(struct sk_buff *skb) { struct packet_type *ptype, *pt_prev; [...] __be16 type; [...] list_for_each_entry_rcu(ptype, &ptype_all, list) { if (!ptype->dev || ptype->dev == skb->dev) { if (pt_prev) ret = deliver_skb(skb, pt_prev, orig_dev); pt_prev = ptype; } } [...] type = skb->protocol; list_for_each_entry_rcu(ptype, &ptype_base[ntohs(type) & PTYPE_HASH_MASK], list) { if (ptype->type == type && (ptype->dev == null_or_dev || ptype->dev == skb->dev || ptype->dev == orig_dev)) { if (pt_prev) ret = deliver_skb(skb, pt_prev, orig_dev); pt_prev = ptype; } } if (pt_prev) { ret = pt_prev->func(skb, skb->dev, pt_prev, orig_dev); static struct packet_type ip_packet_type __read_mostly = { .type = cpu_to_be16(ETH_P_IP), .func = ip_rcv, [...] }; As mentioned before, the net_rx_action function is the softirq handler that receives a packet. First, the driver that has requested the napi poll is retrieved from the poll_list and the poll handler of the driver is called. The driver wraps the received packet with sk_buff and then calls netif_receive_skb. When there is a module that requests all packets, the netif_receive_skb sends packets to the module. Like packet transmission, the packets are transmitted to the module registered to the ptype_all list. The packets are captured here. Then, the packets are transmitted to the upper layer based on the packet type. The Ethernet packet includes 2-byte ethertype field in the header. The value indicates the packet type. The driver records the value in sk_buff(skb->protocol). Each protocol has its own packet_type structure and registers the pointer of the structure to the ptype_base hash table. IPv4 uses ip_packet_type. The Type field value is the IPv4 ethertype (ETH_P_IP) value. Therefore, the IPv4 packet calls the ip_rcv function. int ip_rcv(struct sk_buff *skb, struct net_device *dev, ...) { struct iphdr *iph; u32 len; [...] iph = ip_hdr(skb); [...] if (iph->ihl < 5 || iph->version != 4) goto inhdr_error; if (!pskb_may_pull(skb, iph->ihl*4)) goto inhdr_error; iph = ip_hdr(skb); if (unlikely(ip_fast_csum((u8 *)iph, iph->ihl))) goto inhdr_error; len = ntohs(iph->tot_len); if (skb->len < len) { IP_INC_STATS_BH(dev_net(dev), IPSTATS_MIB_INTRUNCATEDPKTS); goto drop; } else if (len < (iph->ihl*4)) goto inhdr_error; [...] return NF_HOOK(NFPROTO_IPV4, NF_INET_PRE_ROUTING, skb, dev, NULL, ip_rcv_finish); [...] ===> int ip_local_deliver(struct sk_buff *skb) [...] if (ip_hdr(skb)->frag_off & htons(IP_MF | IP_OFFSET)) { if (ip_defrag(skb, IP_DEFRAG_LOCAL_DELIVER)) return 0; } return NF_HOOK(NFPROTO_IPV4, NF_INET_LOCAL_IN, skb, skb->dev, NULL, ip_local_deliver_finish); [...] ===> static int ip_local_deliver_finish(struct sk_buff *skb) [...] __skb_pull(skb, ip_hdrlen(skb)); [...] int protocol = ip_hdr(skb)->protocol; int hash, raw; const struct net_protocol *ipprot; [...] hash = protocol & (MAX_INET_PROTOS - 1); ipprot = rcu_dereference(inet_protos[hash]); if (ipprot != NULL) { [...] ret = ipprot->handler(skb); [...] ===> static const struct net_protocol tcp_protocol = { .handler = tcp_v4_rcv, [...] }; The ip_rcv function executes tasks required by the IP layers. It examines packets such as the length and header checksum. After passing through the netfilter code, it performs the ip_local_deliver function. If required, it assembles IP fragments. Then, it calls ip_local_deliver_finish through the netfilter code. Theip_local_deliver_finish function removes the IP header by using the __skb_pull and then searches the upper protocol whose value is identical to the IP header protocol value. Similar to the Ptype_base, each transport protocol registers its own net_protocol structure in inet_protos. IPv4 TCP uses tcp_protocol and callstcp_v4_rcv that has been registered as a handler. When packets come into the TCP layer, the packet processing flow varies depending on the TCP status and the packet type. Here, we will see the packet processing procedure when the expected next data packet has been received in the ESTABLISHED status of the TCP connection. This path is frequently executed by the server receiving data when there is no packet loss or out-of-order delivery. int tcp_v4_rcv(struct sk_buff *skb) { const struct iphdr *iph; struct tcphdr *th; struct sock *sk; [...] th = tcp_hdr(skb); if (th->doff < sizeof(struct tcphdr) / 4) goto bad_packet; if (!pskb_may_pull(skb, th->doff * 4)) goto discard_it; [...] th = tcp_hdr(skb); iph = ip_hdr(skb); TCP_SKB_CB(skb)->seq = ntohl(th->seq); TCP_SKB_CB(skb)->end_seq = (TCP_SKB_CB(skb)->seq + th->syn + th->fin + skb->len - th->doff * 4); TCP_SKB_CB(skb)->ack_seq = ntohl(th->ack_seq); TCP_SKB_CB(skb)->when = 0; TCP_SKB_CB(skb)->flags = iph->tos; TCP_SKB_CB(skb)->sacked = 0; sk = __inet_lookup_skb(&tcp_hashinfo, skb, th->source, th->dest); [...] ret = tcp_v4_do_rcv(sk, skb); First, the tcp_v4_rcv function validates the received packets. When the header size is larger than the data offset (th->doff < sizeof(struct tcphdr) / 4), it is the header error. And then __inet_lookup_skb is called to look for the connection where the packet belongs from the TCP connection hash table. From the sock structure found, all required structures such as tcp_sock and socket can be got. int tcp_v4_do_rcv(struct sock *sk, struct sk_buff *skb) [...] if (sk->sk_state == TCP_ESTABLISHED) { /* Fast path */ sock_rps_save_rxhash(sk, skb->rxhash); if (tcp_rcv_established(sk, skb, tcp_hdr(skb), skb->len)) { [...] ===> int tcp_rcv_established(struct sock *sk, struct sk_buff *skb, [...] /* * Header prediction. */ if ((tcp_flag_word(th) & TCP_HP_BITS) == tp->pred_flags && TCP_SKB_CB(skb)->seq == tp->rcv_nxt && !after(TCP_SKB_CB(skb)->ack_seq, tp->snd_nxt))) { [...] if ((int)skb->truesize > sk->sk_forward_alloc) goto step5; NET_INC_STATS_BH(sock_net(sk), LINUX_MIB_TCPHPHITS); /* Bulk data transfer: receiver */ __skb_pull(skb, tcp_header_len); __skb_queue_tail(&sk->sk_receive_queue, skb); skb_set_owner_r(skb, sk); tp->rcv_nxt = TCP_SKB_CB(skb)->end_seq; [...] if (!copied_early || tp->rcv_nxt != tp->rcv_wup) __tcp_ack_snd_check(sk, 0); [...] step5: if (th->ack && tcp_ack(sk, skb, FLAG_SLOWPATH) < 0) goto discard; tcp_rcv_rtt_measure_ts(sk, skb); /* Process urgent data. */ tcp_urg(sk, skb, th); /* step 7: process the segment text */ tcp_data_queue(sk, skb); tcp_data_snd_check(sk); tcp_ack_snd_check(sk); return 0; [...] } The actual protocol is executed from the tcp_v4_do_rcv function. If the TCP is in the ESTABLISHED status,tcp_rcv_esablished is called. Processing of the ESTABLISHED status is separately handled and optimized since it is the most common status. The tcp_rcv_established first executes the header prediction code. The header prediction is also quickly processed to detect in the common state. The common case here is that there is no data to transmit and the received data packet is the packet that must be received next time, i.e., the sequence number is the sequence number that the receiving TCP expects. In this case, the procedure is completed by adding the data to the socket buffer and then transmitting ACK. Go forward and you will see the sentence comparing truesize with sk_forward_alloc. It is to check whether there is any free space in the receive socket buffer to add new packet data. If there is, header prediction is "hit" (prediction succeeded). Then __skb_pull is called to remove the TCP header. After that, __skb_queue_tail is called to add the packet to the receive socket buffer. Finally, __tcp_ack_snd_check is called for transmitting ACK if necessary. In this way, packet processing is completed. If there is not enough free space, a slow path is executed. The tcp_data_queue function newly allocates the buffer space and adds the data packet to the socket buffer. At this time, the receive socket buffer size is automatically increased if possible. Different from the quick path, tcp_data_snd_check is called to transmit a new data packet if possible. Finally, tcp_ack_snd_check is called to create and transmit the ACK packet if necessary. The amount of code executed by the two paths is not much. This is accomplished by optimizing the common case. In other words, it means that the uncommon case will be processed significantly more slowly. The out-of-order delivery is one of the uncommon cases. How to Communicate between Driver and NIC Communication between a driver and the NIC is the bottom of the stack and most people do not care about it. However, the NIC is executing more and more tasks to solve the performance issue. Understanding the basic operation scheme will help you understand the additional technology. A driver and the NIC asynchronously communicate. First, a driver requests packet transmission (call) and the CPU performs another task without waiting for the response. And then the NIC sends packets and notifies the CPU of that, the driver returns the received packets (returns the result). Like packet transmission, packet receiving is asynchronous. First, a driver requests packet receiving and the CPU performs another task (call). Then, the NIC receives packets and notifies the CPU of that, and the driver processes the received packets received (returns the result). Therefore, a space to save the request and the response is necessary. In most cases, the NIC uses the ring structure. The ring is similar to the common queue structure. With the fixed number of entries, one entry saves one request data or one response data. The entries are sequentially used in turn. The name "ring" is generally used since the fixed entries are reused in turn. As following the packet transmission procedure shown in the following Figure 8, you will see how the ring is used. Figure 8: Driver-NIC Communication: How to Transmit Packet. The driver receives packets from the upper layer and creates the send descriptor that the NIC can understand. The send descriptor includes the packet size and the memory address by default. As the NIC needs the physical address to access the memory, the driver should change the virtual address of the packets to the physical address. Then, it adds the send descriptor to the TX ring (1). The TX ring is the send descriptor ring. Next, it notifies the NIC of the new request (2). The driver directly writes the data to a specific NIC memory address. In this way, Programmed I/O (PIO) is the data transmission method in which the CPU directly sends data to the device. The notified NIC gets the send descriptor of the TX ring from the host memory (3). Since the device directly accesses the memory without intervention of the CPU, the access is called Direct Memory Access (DMA). After getting the send descriptor, the NIC determines the packet address and the size and then gets the actual packets from the host memory (4). With the checksum offload, the NIC computes the checksum when the NIC gets the packet data from the memory. Therefore, overhead rarely occurs. The NIC sends packets (5) and then writes the number of packets that are sent to the host memory (6). Then, it sends an interrupt (7). The driver reads the number of packets that are sent and then returns the packets that have been sent so far. In the following Figure 9, you will see the procedure of receiving packets. Figure 9: Driver-NIC Communication: How to Receive Packets. First, the driver allocates the host memory buffer for receiving packets and then creates the receive descriptor. The receive descriptor includes the buffer size and the memory address by default. Like the send descriptor, it saves the physical address that the DMA uses in the receive descriptor. Then, it adds the receive descriptor to the RX ring (1). It is the receive request and the RX ring is the receive request ring. Through the PIO, the driver notifies that there is a new descriptor in the NIC (2). The NIC gets the new descriptor of the RX ring. And then it saves the size and location of the buffer included in the descriptor to the NIC memory (3). After the packets have been received (4), the NIC sends the packets to the host memory buffer (5). If the checksum offload function is existing, the NIC computes the checksum at this time. The actual size of received packets, the checksum result, and any other information are saved in the separate ring (the receive return ring) (6). The receive return ring saves the result of processing the receive request, i.e., the response. And then the NIC sends an interrupt (7). The driver gets packet information from the receive return ring and processes the received packets. If necessary, it allocates new memory buffer and repeats Step (1) and Step (2). To tune the stack, most people say that the ring and interrupt setting should be adjusted. When the TX ring is large, a lot of send requests can be made at once. When the RX ring is large, a lot of packet receives can be done at once. A large ring is useful for the workload that has a huge burst of packet transmission/receiving. In most cases, the NIC uses a timer to reduce the number of interrupts since the CPU may suffer from large overhead to process interrupts. To avoid flooding the host system with too many interrupts, interrupts are collected and sent regularly(interrupt coalescing) while sending and receiving the packets. Stack Buffer and Flow Control Flow control is executed in several stages in the stack. Figure 10 shows buffers used to transmit data. First, an application creates data and adds it to the send socket buffer. If there is no free space in the buffer, the system call is failed or the blocking occurs in the application thread. Therefore, the application data rate flowing into the kernel must be controlled by using the socket buffer size limit. Figure 10: Buffers Related to Packet Transmission. The TCP creates and sends packets to the driver through the transmit queue (qdisc). It is a typical FIFO queue type and the maximum length of the queue is the value of txqueuelen which can be checked by executing the ifconfig command. Generally, it is thousands of packets. The TX ring is between the driver and the NIC. As mentioned before, it is considered as a transmission request queue. If there is no free space in the queue, no transmission request is made and the packets are accumulated in the transmit queue. If too many packets are accumulated, packets are dropped. The NIC saves the packets to transmit in the internal buffer. The packet rate from this buffer is affected by the physical rate (ex: 1 Gb/s NIC cannot offer performance of 10 Gb/s). And with the Ethernet flow control, packet transmission is stopped if there is no free space in the receive NIC buffer. When the packet rate from the kernel is faster than the packet rate from the NIC, packets are accumulated in the buffer of the NIC. If there is no free space in the buffer, processing of transmission request from the TX ring is stopped. More and more requests are accumulated in the TX ring and finally there is no free space in the queue. The driver cannot make any transmission request and the packets are accumulated in the transmit queue. Like this, backpressure is sent from the bottom to the top through many buffers. Figure 11 shows the buffers that the receive packets are passing. The packets are saved in the receive buffer of the NIC. From the view of flow control, the RX ring between the driver and the NIC is considered as a packet buffer. The driver gets packets coming into the RX ring and then sends them to the upper layer. There is no buffer between the driver and the upper layer since the NIC driver that is used by the server system uses NAPI by default. Therefore, it can be considered as the upper layer directly gets packets from the RX ring. The payload data of packets is saved in the receive socket buffer. The application gets the data from the socket buffer later. Figure 11: Buffers Related to Packet Receiving. The driver that does not support NAPI saves packets in the backlog queue. Later, the NAPI handler gets packets. Therefore, the backlog queue can be considered as a buffer between the upper layer and the driver. If the packet processing rate of the kernel is slower than the packet flow rate into the NIC, the RX ring space is full. And the space of the buffer in the NIC is full, too. When the Ethernet flow control is used, the NIC sends a request to stop transmission to the transmission NIC or makes the packet drop. There is no packet drop due to lack of space in the receive socket buffer because the TCP supports end-to-end flow control. However, packet drop occurs due to lack of space in the socket buffer when the application rate is slow because the UDP does not support flow control. The sizes of the TX ring and the RX ring used by the driver in Figure 10 and Figure 11 are the sizes of the rings shown by the ethtool. For most workloads which regard throughput as important, it will be helpful to increase the ring size and the socket buffer size. Increasing the sizes reduces the possibility of failures caused by lack of space in the buffer while receiving and transmitting a lot of packets at a fast rate. Conclusion Initially, I planned to explain only the things that would be helpful for you to develop network programs, execute performance tests, and perform troubleshooting. In spite of my initial plan, the amount of description included in this document is not small. I hope this document will help you to develop network applications and monitor their performance. The TCP/IP protocol itself is very complicated and has many exceptions. However, you don't need to understand every line of TCP/IP-related code of the OS to understand performance and analyze the phenomena. Just understanding its context will be very helpful for you. With continuous advancement of system performance and implementation of the OS network stack, the latest server can offer 10-20 Gb/s TCP throughput without any problem. These days, there are too many technology types related to performance, such as TSO, LRO, RSS, GSO, GRO, UFO, XPS, IOAT, DDIO, and TOE, just like alphabet soup, to make us confused. In the next article, I will explain about the network stack from the performance perspective and discuss the problems and effects of this technology. By Hyeongyeop Kim, Senior Engineer at Performance Engineering Lab, NHN Corporation.
February 27, 2013
by Esen Sagynov
· 13,867 Views · 1 Like
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Solving RPM installation conflicts
This post comes from Ignacio Nin at the MySQL Performance Blog. Lately we’ve had many reports of the RPM packages for CentOS 5 (mostly) and CentOS 6 having issues when installing different combinations of our products, particularly with Percona Toolkit. Examples of bugs related to these issues are lp:1031427 and lp:1051874. These problems arise when trying to install a package from the distribution that is linked against the version of libmysqlclient.so shipped by the distribution (libmysqlclient.so.15 for CentOS 5/libmysqlclient.so.16 for CentOS 6) and a version of Percona Server that depends on another version of libmysqlclient.so, usually more recent. Bug lp:1031427 is an example of this, and shows how the packages would conflict when trying to install libmysqlclient.so. For example, when installing php-mysql alongside PS 5.5 in CentOS 6: # yum -q install Percona-Server-server-55 php-mysql Installing: Percona-Server-server-55 x86_64 5.5.29-rel29.4.401.rhel6 percona 15 M php-mysql x86_64 5.3.3-14.el6_3 updates 79 k Installing for dependencies: Percona-Server-client-55 x86_64 5.5.29-rel29.4.401.rhel6 percona 7.0 M Percona-Server-shared-51 x86_64 5.1.67-rel14.3.506.rhel6 percona 2.8 M Percona-Server-shared-55 x86_64 5.5.29-rel29.4.401.rhel6 percona 787 k Transaction Summary ===================================================================================================================================================== Install 5 Package(s) Is this ok [y/N]: y Transaction Check Error: file /usr/lib64/libmysqlclient.so conflicts between attempted installs of Percona-Server-shared-51-5.1.67-rel14.3.506.rhel6.x86_64 and Percona-Server-shared-55-5.5.29-rel29.4.401.rhel6.x86_64 file /usr/lib64/libmysqlclient_r.so conflicts between attempted installs of Percona-Server-shared-51-5.1.67-rel14.3.506.rhel6.x86_64 and Percona-Server-shared-55-5.5.29-rel29.4.401.rhel6.x86_64 The traditional solution for this situation was to provide a special package, Percona-Server-shared-compat (modeled after upstream’s MySQL-shared-compat) which would contain ALL versions of libmysqlclient.so.* together and wouldn’t conflict. Probably some of you are familiar with this approach. # yum -q install Percona-Server-server-55 Percona-Server-shared-compat php-mysql Installing: Percona-Server-server-55 x86_64 5.5.29-rel29.4.401.rhel6 percona 15 M Percona-Server-shared-compat x86_64 5.5.29-rel29.4.401.rhel6 percona 3.4 M php-mysql x86_64 5.3.3-14.el6_3 updates 79 k Installing for dependencies: Percona-Server-client-55 x86_64 5.5.29-rel29.4.401.rhel6 percona 7.0 M Percona-Server-shared-55 x86_64 5.5.29-rel29.4.401.rhel6 percona 787 k Transaction Summary ===================================================================================================================================================== Install 5 Package(s) Notice how PS-shared-compat installs along the -shared package, providing the older libmysqlclient.so.16 required by php-mysql. However, this has proved non-intuitive and problematic, since the shared-compat package wouldn’t get selected unless explicitely installed — and many of our users would rather have it “just work” without requiring additional knowledge of what the particular workaround was, etc.. We’re now trying a solution in which our -shared packages won’t conflict anymore at libmysqlclient.so, so we are able to install them side-by-side, modelled after the mysql-libs packages provided by CentOS/Redhat. So even if the user wants to install PS 5.5 alongside packages that depend on 5.1/5.0, the -shared packages will work together. For example installing 5.5 and postfix in CentOS: # yum -q install Percona-Server-server-55 postfix Installing: Percona-Server-server-55 x86_64 5.5.29-rel29.4.402.rhel5 percona-testing 19 M postfix x86_64 2:2.3.3-6.el5 base 3.8 M Installing for dependencies: Percona-SQL-shared-50 x86_64 5.0.92-b23.89.rhel5 percona-testing 1.8 M Percona-Server-client-55 x86_64 5.5.29-rel29.4.402.rhel5 percona-testing 9.1 M Percona-Server-shared-55 x86_64 5.5.29-rel29.4.402.rhel5 percona-testing 993 k … and this will install without problems. Additionally, this has the advantage of allowing an upgrade from 5.1 to 5.5 without uninstalling any software that depended on the old version. # rpm -qa | grep ^Percona Percona-Server-client-51-5.1.67-rel14.3.507.rhel6.x86_64 Percona-Server-shared-51-5.1.67-rel14.3.507.rhel6.x86_64 Percona-Server-server-51-5.1.67-rel14.3.507.rhel6.x86_64 In this case only Percona-Server-client-51 and Percona-Server-server-51 need be removed, allowing any package that depends on Percona-Server-shared-51 (providing libmysqlclient.so.16) to remain installed. After the server and client packages are uninstalled, you can install PS 5.5 without conflict. The current package candidates for versions 5.0.92 (which required an update), 5.1.67-14.3 and 5.5.29-29.4 can be tested from the percona-testing repository. We encourage you to try these out and send us your feedback and/or file any bugs you find. Installation instructions for Percona Testing repositories. We’re aiming to include these fixes in our next releases of 5.1 and 5.5. Percona Toolkit users in particular will enjoy this update since it’ll mean no more trouble when installing it from repository!
February 25, 2013
by Peter Zaitsev
· 7,839 Views
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Nested Iterator with WSO2 ESB
Please find the following sample which demonstrates the Nested iterator. The following is the source of the configuration. 15000 $1 Use the following request with soapui in order to test the above sample. SUN9 SUN10 From the second iterate mediator, messages retrieved from the first iterator are again iterated into smaller messages as below SUN10
February 23, 2013
by Achala Chathuranga Aponso
· 7,456 Views
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When to use Aspect Oriented Architecture (AOA/AOD)
When is it appropriate to use aspect oriented architecture? I think the only honest answer to this question is that it depends on the context for which the question is being asked. There really are no hard and fast rules regarding the selection of an architectural model(s) for a project because each model provides good and bad benefits. Every system is built with a unique requirements and constraints. This context will dictate when to use one type of architecture over another or in conjunction with others. To me aspect oriented architecture models should be a sub-phase in the architectural modeling and design process especially when creating enterprise level models. Personally, I like to use this approach to create a base architectural model that is defined by non-functional requirements and system quality attributes. This general model can then be used as a starting point for additional models because it is targets all of the business key quality attributes required by the system. Aspect oriented architecture is a method for modeling non-functional requirements and quality attributes of a system known as aspects. These models do not deal directly with specific functionality. They do categorize functionality of the system. This approach allows a system to be created with a strong emphasis on separating system concerns into individual components. These cross cutting components enables a systems to create with compartmentalization in regards to non-functional requirements or quality attributes. This allows for the reduction in code because an each component maintains an aspect of a system that can be called by other aspects. This approach also allows for a much cleaner and smaller code base during the implementation and support of a system. Additionally, enabling developers to develop systems based on aspect-oriented design projects will be completed faster and will be more reliable because existing components can be shared across a system; thus, the time needed to create and test the functionality is reduced. Example of an effective use of Aspect Oriented Architecture In my experiences, aspect oriented architecture can be very effective with large or more complex systems. Typically, these types of systems have a large number of concerns so the act of defining them is very beneficial for reducing the system’s complexity because components can be developed to address each concern while exposing functionality to the other system components. The benefits to using the aspect oriented approach as the starting point for a system is that it promotes communication between IT and the business due to the fact that the aspect oriented models are quality attributes focused so not much technical understanding is needed to understand the model. An example of this can be in developing a new intranet website. Common Intranet Concerns: Error Handling Security Logging Notifications Database connectivity Example of a not as effective use of Aspect Oriented Architecture Again in my experiences, aspect oriented architecture is not as effective with small or less complex systems in comparison. There is no need to model concerns for a system that has a limited amount of them because the added overhead would not be justified for the actual benefits of creating the aspect oriented architecture model. Furthermore, these types of projects typically have a reduced time schedule and a limited budget. The creation of the Aspect oriented models would increase the overhead of a project and thus increase the time needed to implement the system. An example of this is seen by creating a small application to poll a network share for new files and then FTP them to a new location. The two primary concerns for this project is to monitor a network drive and FTP files to a new location. There is no need to create an aspect model for this system because there will never be a need to share functionality amongst either of these concerns. To add to my point, this system is so small that it could be created with just a few classes so the added layer of componentizing the concerns would be complete overkill for this situation. References: Brichau, Johan; D'Hondt, Theo. (2006) Aspect-Oriented Software Development (AOSD) - An Introduction. Retreived from: http://www.info.ucl.ac.be/~jbrichau/courses/introductionToAOSD.pdf
February 21, 2013
by Todd Merritt
· 9,584 Views
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Spring-Test-MVC Junit Testing Spring Security Layer with Method Level Security
For people in hurry get the code from Github. In continuation of my earlier blog on spring-test-mvc junit testing Spring Security layer with InMemoryDaoImpl, in this blog I will discuss how to use achieve method level access control. Please follow the steps in this blog to setup spring-test-mvc and run the below test case. mvn test -Dtest=com.example.springsecurity.web.controllers.SecurityControllerTest The JUnit test case looks as below, @RunWith(SpringJUnit4ClassRunner.class) @ContextConfiguration(loader = WebContextLoader.class, value = { "classpath:/META-INF/spring/services.xml", "classpath:/META-INF/spring/security.xml", "classpath:/META-INF/spring/mvc-config.xml" }) public class SecurityControllerTest { @Autowired CalendarService calendarService; @Test public void testMyEvents() throws Exception { Authentication auth = new UsernamePasswordAuthenticationToken("[email protected]", "user1"); SecurityContext securityContext = SecurityContextHolder.getContext(); securityContext.setAuthentication(auth); calendarService.findForUser(0); SecurityContextHolder.clearContext(); } @Test(expected = AuthenticationCredentialsNotFoundException.class) public void testForbiddenEvents() throws Exception { calendarService.findForUser(0); } } @Test(expected=AccessDeniedException.class) public void testWrongUserEvents() throws Exception { Authentication auth = new UsernamePasswordAuthenticationToken("[email protected]", "user2"); SecurityContext securityContext = SecurityContextHolder.getContext(); securityContext.setAuthentication(auth); calendarService.findForUser(0); SecurityContextHolder.clearContext(); } If you notice, if the user did not login or if the user is trying to access another users information it will throw an exception. The interface access control is as below, public interface CalendarService { @PreAuthorize("hasRole('ROLE_ADMIN') or principal.id == #userId") List findForUser(int userId); } The PreAuthorize only works on interface so that any implementation that implements this interface has this access control. I hope this blog helps you.
February 21, 2013
by Krishna Prasad
· 23,575 Views
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Apache Camel Meets Redis
The Lamborghini of Key-Value stores Camel is the best of bread Integration framework and in this post I'm going to show you how to make it even more powerful by leveraging another great project - Redis. Camel 2.11 is on its way to be released soon with lots of new features, bug fixes and components. Couple of these new components are authored by me, redis-component being my favourite one. Redis - a ligth key/value store is an amazing piece of Italian software designed for speed (same as Lamborghini - a two-seater Italian car designed for speed). Written in C and having an in-memory closer to the metal nature, Redis performs extremely well (Lamborgini's motto is "Closer to the Road"). Redis is often referred to as a data structure server since keys can contain strings, hashes, lists and sorted sets. A fast and light data structure server is like a super sportscars for software engineers - it just flies. If you want to find out more about Redis' and Lamborghini's unique performance characteristics google around and you will see for yourself. Getting started with Redis is easy: download, make, and start a redis-server. After these steps, you ready to use it from your Camel application. The component uses internally Spring Data which in turn uses Jedis driver, but with possibility to switch to other Redis drivers. Here are few use cases where the camel-redis component is a good fit: Idempotent Repository The term idempotent is used in mathematics to describe a function that produces the same result if it is applied to itself. In Messaging this concepts translates into the a message that has the same effect whether it is received once or multiple times. In Camel this pattern is implemented using the IdempotentConsumer class which uses an Expression to calculate a unique message ID string for a given message exchange; this ID can then be looked up in the IdempotentRepository to see if it has been seen before; if it has the message is consumed; if its not then the message is processed and the ID is added to the repository. RedisIdempotentRepository is using a set structure to store and check for existing Ids. ${in.body.id} Caching One of the main uses of Redis is as LRU cache. It can store data inmemory as Memcached or can be tuned to be durable flushing data to a log file that can be replayed if the node restarts.The various policies when maxmemory is reached allows creating caches for specific needs: volatile-lru remove a key among the ones with an expire set, trying to remove keys not recently used. volatile-ttl remove a key among the ones with an expire set, trying to remove keys with short remaining time to live. volatile-random remove a random key among the ones with an expire set. allkeys-lru like volatile-lru, but will remove every kind of key, both normal keys or keys with an expire set. allkeys-random like volatile-random, but will remove every kind of keys, both normal keys and keys with an expire set. Once your Redis server is configured with the right policies and running, the operation you need to do are SET and GET: SET keyOne valueOne Interap pub/sub with Redis Camel has various components for interacting between routes: direct: provides direct, synchronous invocation in the same camel context. seda: asynchronous behavior, where messages are exchanged on a BlockingQueue, again in the same camel context. vm: asynchronous behavior like seda, but also supports communication across CamelContext as long as they are in the same JVM. Complex applications usually consist of more than one standalone Camel instances running on separate machines. For this kind of scenarios, Camel provides jms, activemq, combination of AWS SNS with SQS, for messaging between instances. Redis has a simpler solution for the Publish/Subscribe messaging paradigm. Subscribers subscribes to one or more channels, by specifying the channel names or using pattern matching for receiving messages from multiple channels. Then the publisher publishes the messages to a channel, and Redis makes sure it reaches all the matching subscribers. PUBLISH testChannel Test Message Other usages Guaranteed Delivery: Camel supports this EIP using JMS, File, JPA and few other components. Here Redis can be used as lightweight key-value persistent store with its transaction support. The Claim Check from the EIP patterns allows you to replace message content with a claim check (a unique key), which can be used to retrieve the message content at a later time. The message content can be stored temporarily in Redis. Redis is also very popular for implementing counters, leaderboards, tagging systems and many more functionalities. Now, with two swiss army knives under your belt, the integrations to make are limited only by your imagination.
February 20, 2013
by Bilgin Ibryam
· 10,936 Views
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Building SOLID Databases: Dependency Inversion and Robust DB Interfaces
Dependency inversion is the idea that interfaces should depend on abstractions not on specifics. According to Wikipedia, the principle states: A. High-level modules should not depend on low-level modules. Both should depend on abstractions. B. Abstractions should not depend upon details. Details should depend upon abstractions. Of course the second part of this principle is impossible if read literally. You can't have an abstraction until you know what details are to be covered, and so the abstraction and details are both co-dependent. If the covered details change sufficiently the abstraction will become either leaky or inadequate and so it is worth seeing these as intertwined to some extent. The focus on abstraction is helpful because it suggests that the interface contract should be designed in such a way that neither side really has to understand any internal details of the other in order to make things work. Both sides depend on well-encapsulated API's and neither side has to worry about what the other side is really doing. This is what is meant by details depending on abstractions rather than the other way around. This concept is quite applicable beyond object oriented programming because it covers a very basic aspect of API contract design, namely how well an API should encapsulate behavior. This principle is first formulated in its current form in the object oriented programming paradigm but is generally applicable elsewhere. SQL as an Abstraction Layer, or Why RDBMS are Still King There are plenty of reasons to dislike SQL, such as the fact that nulls are semantically ambiguous. As a basic disclaimer I am not holding SQL up to be a paragon of programming languages or even db interfaces, but I think it is important to discuss what SQL does right in this regard. SQL is generally understood to be a declarative language which approximates relational mathematics for database access purposes. With SQL, you specify what you want returned, not how to get it, and the planner determines the best way to get it. SQL is thus an interface language rather than a programming language per se. With SQL, you can worry about the logical structure, leaving the implementation details to the db engine. SQL queries are basically very high level specifications of operations, not detailed descriptions of how to do something efficiently. Even update and insert statements (which are by nature more imperative than select statements) leave the underlying implementation entirely to the database management system. I think that this, along with many concessions the language has made to real-world requirements (such as bags instead of sets and the addition of ordering to bags) largely account for the success of this language. SQL, in essence, encapsulates a database behind a mature mathematical, declarative model in the same way that JSON and REST do (in a much less comprehensive way) in many NoSQL db's. In essence SQL provides encapsulation, interface, and abstraction in a very full-featured way and this is why it has been so successful. SQL Abstraction as Imperfect One obvious problem with treating SQL as an abstraction layer in its own right is that one is frequently unable to write details in a way that is clearly separate from the interface. Often storage tables are hit directly, and therefore there is little separation between logical detail and logical interface, and so this can break down when database complexity reaches a certain size. Approaches to managing this problem include using stored procedures or user defined functions, and using views to encapsulate storage tables. Stored Procedures and User Defined Functions Done Wrong Of the above methods, stored procedures and functional interfaces have bad reputations frequently because of bad experiences that many people have with them. These include developers pushing too much logic into stored procedures, and the fact that defining functional interfaces in this way usually produces a very tight binding between database code and application code, often leading to maintainability problems. The first case is quite obvious, and includes the all-too-frequent case of trying to send emails directly from stored procedures (always a bad idea). This mistake leads to certain types of problems, including the fact that ACID-compliant operations may be mixed with non-ACID-compliant ones, leading to cases where a transaction can only be partially rolled back. Oops, we didn't actually record the order as shipped, but we told the customer it was..... MySQL users will also note this is an argument against mixing transactional and nontransactional backend table types in the same db..... However that problem is outside the scope of this post. Additionally, MySQL is not well suited for many applications against a single set of db relations. The second problem, though, is more insidious. The traditional way stored procedures and user defined functions are typically used, the application has to be deeply aware of the interface to the database, but the rollout for these aspects is different leading to the possibility or service interruptions, and a need to very carefully and closely time rollout of db changes with application changes. As more applications use the database, this becomes harder and the chance of something being overlooked becomes greater. For this reason the idea that all operations must go through a set of stored procedures is a decision fraught with hazard as the database and application environment evolves. Typically it is easier to manage backwards-compatibility in schemas than it is in functions and so a key question is how many opportunities you have to create new bugs when a new column is added. There are, of course, more hazards which I have dealt with before, but the point is that stored procedures are potentially harmful and a major part of the reason is that they usually form a fairly brittle contract with the application layer. In a traditional stored procedure, adding a column to be stored will require changing the number of variables in the stored procedure's argument list, the queries to access it, and each application's call to that stored procedure. In this way, they provide (in the absence of other help) at best a leaky abstraction layer around the database details. This is the sort of problem that dependency inversion helps to avoid. Stored Procedures and User Defined Functions Done Right Not all stored procedures are done wrong. In the LedgerSMB project we have at least partially solved the abstraction/brittleness issue by looking to web services for inspiration. Our approach provides an additional mapping layer and dynamic query generation around a stored procedure interface. By using a service locator pattern, and overloading the system tables in PostgreSQL as the service registry, we solve the problem of brittleness. Our approach of course is not perfect and it is not the only possibility. One shortcoming is that our approach is that the invocation of the service locator is relatively spartan. We intend to allow more options there in the future. However one thing I have noticed is the fact that there are far fewer places where bugs can hide and therefore faster and more robust development takes place. Additionally a focus on clarity of code in stored procedures has eliminated a number of important performance bottlenecks, and it limits the number of places where a given change propagates to. Other Important Options in PostgreSQL Stored procedures are not the only abstraction mechanisms available from PostgreSQL. In addition to views, there are also other interesting ways of using functions to accomplish this without insisting that all access goes through stored procedures. In addition these methods can be freely mixed to produce very powerful, intelligent database systems. Such options include custom types, written in C, along with custom operators, functions and the like. These would then be stored in columns and SQL can be used to provide an abstraction layer around the types. In this way SQL becomes the abstraction and the C programs become the details. A future post will cover the use of ip4r in network management with PostgreSQL db's as an example of what can be done here. Additionally, things like triggers and notifications can be used to ensure that appropriate changes trigger other changes in the same transaction or, upon transaction commit, hand off control to other programs in subsequent transactions (allowing for independent processing and error control for things like sending emails). Recommendations Rather than specific recommendations, the overall point here is to look at the database itself as a an application running in an application server (the RDBMS) and design it as an application with an appropriate API. There are many ways to do this, from writing components in C and using SQL as an abstraction mechanism to writing things in SQL and using stored procedures as a mechanism. One could even write code in SQL and still use SQL as an abstraction mechanism. The key point however is to be aware of the need for discoverable abstraction, a need which to date things like ORMs and stored procedures often fill very imperfectly. A well designed db with appropriate abstraction in interfaces, should be able to be seen as an application in its own right, engineered as such, and capable of serving multiple client apps through a robust and discoverable API. As with all things, it starts by recognizing the problems and putting solutions as priorities from the design stage onward.
February 19, 2013
by Chris Travers
· 5,277 Views
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XML->JSON->HashMap
Yes, it is long time since i posted… Was just trying to see how a XML can be converted to JSON and to HashMap. The situation is very imaginary. import java.io.File; import java.io.FileInputStream; import java.io.IOException; import java.io.InputStream; import java.util.ArrayList; import java.util.List; import java.util.Map; import net.sf.json.JSON; import net.sf.json.xml.XMLSerializer; import org.apache.commons.io.IOUtils; import org.codehaus.jackson.JsonGenerationException; import org.codehaus.jackson.map.JsonMappingException; import org.codehaus.jackson.map.ObjectMapper; import org.codehaus.jackson.type.TypeReference; public class XML2JSONConvertor { public static void main(String[] args) throws Exception { InputStream is = new FileInputStream(new File( “e:\\jagannathan\\personal\\java-projects\\secondtest.xml”)); String xml = IOUtils.toString(is); XMLSerializer xmlSerializer = new XMLSerializer(); JSON json = xmlSerializer.read(xml); System.out.println(json.toString(2)); printJSON(json.toString(2)); } public static void printJSON(String jsonString) { ObjectMapper mapper = new ObjectMapper(); try { Map jsonInMap = mapper.readValue(jsonString, new TypeReference>() { }); List keys = new ArrayList(jsonInMap.keySet()); for (String key : keys) { System.out.println(key + “: ” + jsonInMap.get(key)); } } catch (JsonGenerationException e) { e.printStackTrace(); } catch (JsonMappingException e) { e.printStackTrace(); } catch (IOException e) { e.printStackTrace(); } } } Dependencies net.sf.json-lib json-lib 2.4 jdk15 commons-io commons-io 2.3 compile xom xom 1.2.5 org.codehaus.jackson jackson-mapper-asl 1.9.0 The Input XML Jags Inc Jagan Male 24-jul Satya Male 24-apr The output 7 Feb, 2013 7:20:50 PM net.sf.json.xml.XMLSerializer getType INFO: Using default type string { “name”: “Jags Inc”, “employees”: [ { "name": "Jagan", "sex": "Male", "dob": "24-jul" }, { "name": "Satya", "sex": "Male", "dob": "24-apr" } ] } name: Jags Inc employees: [{name=Jagan, sex=Male, dob=24-jul}, {name=Satya, sex=Male, dob=24-apr}]
February 18, 2013
by Jagannathan Asokan
· 33,583 Views
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CPU Cache Flushing Fallacy
Even from highly experienced technologists I often hear talk about how certain operations cause a CPU cache to "flush". This seems to be illustrating a very common fallacy about how CPU caches work, and how the cache sub-system interacts with the execution cores. In this article I will attempt to explain the function CPU caches fulfil, and how the cores, which execute our programs of instructions, interact with them. For a concrete example I will dive into one of the latest Intel x86 server CPUs. Other CPUs use similar techniques to achieve the same ends. Most modern systems that execute our programs are shared-memory multi-processor systems in design. A shared-memory system has a single memory resource that is accessed by 2 or more independent CPU cores. Latency to main memory is highly variable from 10s to 100s of nanoseconds. Within 100ns it is possible for a 3.0GHz CPU to process up to 1200 instructions. Each Sandy Bridge core is capable of retiring up to 4 instructions-per-cycle (IPC) in parallel. CPUs employ cache sub-systems to hide this latency and allow them to exercise their huge capacity to process instructions. Some of these caches are small, very fast, and local to each core; others are slower, larger, and shared across cores. Together with registers and main-memory, these caches make up our non-persistent memory hierarchy. Next time you are developing an important algorithm, try pondering that a cache-miss is a lost opportunity to have executed ~500 CPU instructions! This is for a single-socket system, on a multi-socket system you can effectively double the lost opportunity as memory requests cross socket interconnects. Memory Hierarchy Figure 1. For the circa 2012 Sandy Bridge E class servers our memory hierarchy can be decomposed as follows: Registers: Within each core are separate register files containing 160 entries for integers and 144 floating point numbers. These registers are accessible within a single cycle and constitute the fastest memory available to our execution cores. Compilers will allocate our local variables and function arguments to these registers. When hyperthreading is enabled these registers are shared between the co-located hyperthreads. Memory Ordering Buffers (MOB): The MOB is comprised of a 64-entry load and 36-entry store buffer. These buffers are used to track in-flight operations while waiting on the cache sub-system. The store buffer is a fully associative queue that can be searched for existing store operations, which have been queued when waiting on the L1 cache. These buffers enable our fast processors to run asynchronously while data is transferred to and from the cache sub-system. When the processor issues asynchronous reads and writes then the results can come back out-of-order. The MOB is used to disambiguate the ordering for compliance to the published memory model. Level 1 Cache: The L1 is a core-local cache split into separate 32K data and 32K instruction caches. Access time is 3 cycles and can be hidden as instructions are pipelined by the core for data already in the L1 cache. Level 2 Cache: The L2 cache is a core-local cache designed to buffer access between the L1 and the shared L3 cache. The L2 cache is 256K in size and acts as an effective queue of memory accesses between the L1 and L3. L2 contains both data and instructions. L2 access latency is 12 cycles. Level 3 Cache: The L3 cache is shared across all cores within a socket. The L3 is split into 2MB segments each connected to a ring-bus network on the socket. Each core is also connected to this ring-bus. Addresses are hashed to segments for greater throughput. Latency can be up to 38 cycles depending on cache size. Cache size can be up to 20MB depending on the number of segments, with each additional hop around the ring taking an additional cycle. The L3 cache is inclusive of all data in the L1 and L2 for each core on the same socket. This inclusiveness, at the cost of space, allows the L3 cache to intercept requests thus removing the burden from private core-local L1 & L2 caches. Main Memory: DRAM channels are connected to each socket with an average latency of ~65ns for socket local access on a full cache-miss. This is however extremely variable, being much less for subsequent accesses to columns in the same row buffer, through to significantly more when queuing effects and memory refresh cycles conflict. 4 memory channels are aggregated together on each socket for throughput, and to hide latency via pipelining on the independent memory channels. NUMA: In a multi-socket server we have non-uniform memory access. It is non-uniform because the required memory maybe on a remote socket having an additional 40ns hop across the QPI bus. Sandy Bridge is a major step forward for 2-socket systems over Westmere and Nehalem. With Sandy Bridge the QPI limit has been raised from 6.4GT/s to 8.0GT/s, and two lanes can be aggregated thus eliminating the bottleneck of the previous systems. For Nehalem and Westmere the QPI link is only capable of ~40% the bandwidth that could be delivered by the memory controller for an individual socket. This limitation made accessing remote memory a choke point. In addition, the QPI link can now forward pre-fetch requests which previous generations could not. Associativity Levels Caches are effectively hardware based hash tables. The hash function is usually a simple masking of some low-order bits for cache indexing. Hash tables need some means to handle a collision for the same slot. The associativity level is the number of slots, also known as ways or sets, which can be used to hold a hashed version of an address. Having more levels of associativity is a trade off between storing more data vs. power requirements and time to search each of the ways. For Sandy Bridge the L1 and L2 are 8-way and the L3 is 12-way associative. Cache Coherence With some caches being local to cores, we need a means of keeping them coherent so all cores can have a consistent view of memory. The cache sub-system is considered the "source of truth" for mainstream systems. If memory is fetched from the cache it is never stale; the cache is the master copy when data exists in both the cache and main-memory. This style of memory management is known as write-back whereby data in the cache is only written back to main-memory when the cache-line is evicted because a new line is taking its place. An x86 cache works on blocks of data that are 64-bytes in size, known as a cache-line. Other processors can use a different size for the cache-line. A larger cache-line size reduces effective latency at the expense of increased bandwidth requirements. To keep the caches coherent the cache controller tracks the state of each cache-line as being in one of a finite number of states. The protocol Intel employs for this is MESIF, AMD employs a variant know as MOESI. Under the MESIF protocol each cache-line can be in 1 of the 5 following states: Modified: Indicates the cache-line is dirty and must be written back to memory at a later stage. When written back to main-memory the state transitions to Exclusive. Exclusive: Indicates the cache-line is held exclusively and that it matches main-memory. When written to, the state then transitions to Modified. To achieve this state a Request-For-Ownership (RFO) message is sent which involves a read plus an invalidate broadcast to all other copies. Shared: Indicates a clean copy of a cache-line that matches main-memory. Invalid: Indicates an unused cache-line. Forward: Indicates a specialised version of the shared state i.e. this is the designated cache which should respond to other caches in a NUMA system. To transition from one state to another, a series of messages are sent between the caches to effect state changes. Previous to Nehalem for Intel, and Opteron for AMD, this cache coherence traffic between sockets had to share the memory bus which greatly limited scalability. These days the memory controller traffic is on a separate bus. The Intel QPI, and AMD HyperTransport, buses are used for cache coherence between sockets. The cache controller exists as a module within each L3 cache segment that is connected to the on-socket ring-bus network. Each core, L3 cache segment, QPI controller, memory controller, and integrated graphics sub-system are connected to this ring-bus. The ring is made up of 4 independent lanes for: request, snoop, acknowledge, and 32-bytes data per cycle. The L3 cache is inclusive in that any cache-line held in the L1 or L2 caches is also held in the L3. This provides for rapid identification of the core containing a modified line when snooping for changes. The cache controller for the L3 segment keeps track of which core could have a modified version of a cache-line it owns. If a core wants to read some memory, and it does not have it in a Shared, Exclusive, or Modified state; then it must make a read on the ring bus. It will then either be read from main-memory if not in the cache sub-systems, or read from L3 if clean, or snooped from another core if Modified. In any case the read will never return a stale copy from the cache sub-system, it is guaranteed to be coherent. Concurrent Programming If our caches are always coherent then why do we worry about visibility when writing concurrent programs? This is because within our cores, in their quest for ever greater performance, data modifications can appear out-of-order to other threads. There are 2 major reasons for this. Firstly, our compilers can generate programs that store variables in registers for relatively long periods of time for performance reasons, e.g. variables used repeatedly within a loop. If we need these variables to be visible across cores then the updates must not be register allocated. This is achieved in C by qualifying a variable as "volatile". Beware that C/C++ volatile is inadequate for telling the compiler to order other instructions. For this you need fences/barriers. The second major issue with ordering we have to be aware of is a thread could write a variable and then, if it reads it shortly after, could see the value in its store buffer which may be older than the latest value in the cache sub-system. This is never an issue for algorithms following the Single Writer Principle but is an issue for the likes of the Dekker and Peterson lock algorithms. To overcome this issue, and ensure the latest value is observed, the thread must wait for the store buffer to drain on that core. This can be achieved by issuing a fence instruction. The write of a volatile variable in Java, in addition to never being register allocated, is accompanied by a full fence instruction. This fence instruction on x86 has a significant performance impact by preventing progress on the issuing thread until the store buffer is drained. Fences on other processors can have more efficient implementations that simply put a marker in the store buffer for the search boundary, e.g. the Azul Vega does this. If you want to ensure memory ordering across Java threads when following the Single Writer Principle, and avoid the store fence, it is possible by using the j.u.c.Atomic(Int|Long|Reference).lazySet() method, as opposed to setting a volatile variable. The Fallacy Returning to the fallacy of "flushing the cache" as part of a concurrent algorithm. I think we can safely say that we never "flush" the CPU cache within our user space programs. I believe the source of this fallacy is the need to flush, mark or drain to a point, the store buffer for some classes of concurrent algorithms so the latest value can be observed on a subsequent load operation. For this we require a memory ordering fence and not a cache flush. Another possible source of this fallacy is that L1 caches, or the TLB, may need to be flushed based on address indexing policy on a context switch. ARM, previous to ARMv6, did not use address space tags on TLB entries thus requiring the whole L1 cache to be flushed on a context switch. Many processors require the L1 instruction cache to be flushed for similar reasons, in many cases this is simply because instruction caches are not required to be kept coherent. The bottom line is, context switching is expensive and a bit off topic, so in addition to the cache pollution of the L2, a context switch can also cause the TLB and/or L1 caches to require a flush. Intel x86 processors require only a TLB flush on context switch.
February 15, 2013
by Martin Thompson
· 11,578 Views · 3 Likes
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Synchronising Multithreaded Integration Tests
Testing threads is hard, very hard and this makes writing good integration tests for multithreaded systems under test... hard. This is because in JUnit there's no built in synchronisation between the test code, the object under test and any threads. This means that problems usually arise when you have to write a test for a method that creates and runs a thread. One of the most common scenarios in this domain is in making a call to a method under test, which starts a new thread running before returning. At some point in the future when the thread's job is done you need assert that everything went well. Examples of this scenario could include asynchronously reading data from a socket or carrying out a long and complex set of operations on a database. For example, the ThreadWrapper class below contains a single public method: doWork(). Calling doWork() sets the ball rolling and at some point in the future, at the discretion of the JVM, a thread runs adding data to a database. public class ThreadWrapper { /** * Start the thread running so that it does some work. */ public void doWork() { Thread thread = new Thread() { /** * Run method adding data to a fictitious database */ @Override public void run() { System.out.println("Start of the thread"); addDataToDB(); System.out.println("End of the thread method"); } private void addDataToDB() { // Dummy Code... try { Thread.sleep(4000); } catch (InterruptedException e) { e.printStackTrace(); } } }; thread.start(); System.out.println("Off and running..."); } } A straightforward test for this code would be to call the doWork() method and then check the database for the result. The problem is that, owing to the use of a thread, there's no co-ordination between the object under test, the test and the thread. A common way of achieving some co-ordination when writing this kind of test is to put some kind of delay in between the call to the method under test and checking the results in the database as demonstrated below: public class ThreadWrapperTest { @Test public void testDoWork() throws InterruptedException { ThreadWrapper instance = new ThreadWrapper(); instance.doWork(); Thread.sleep(10000); boolean result = getResultFromDatabase(); assertTrue(result); } /** * Dummy database method - just return true */ private boolean getResultFromDatabase() { return true; } } In the code above there is a simple Thread.sleep(10000) between two method calls. This technique has the benefit of being incredabile simple; however it's also very risky. This is because it introduces a race condition between the test and the worker thread as the JVM makes no guarantees about when threads will run. Often it'll work on a developer's machine only to fail consistently on the build machine. Even if it does work on the build machine it atificially lengthens the duration of the test; remember that quick builds are important. The only sure way of getting this right is to synchronise the two different threads and one technique for doing this is to inject a simple CountDownLatch into the instance under test. In the example below I've modified the ThreadWrapper class's doWork() method adding the CountDownLatch as an argument. public class ThreadWrapper { /** * Start the thread running so that it does some work. */ public void doWork(final CountDownLatch latch) { Thread thread = new Thread() { /** * Run method adding data to a fictitious database */ @Override public void run() { System.out.println("Start of the thread"); addDataToDB(); System.out.println("End of the thread method"); countDown(); } private void addDataToDB() { try { Thread.sleep(4000); } catch (InterruptedException e) { e.printStackTrace(); } } private void countDown() { if (isNotNull(latch)) { latch.countDown(); } } private boolean isNotNull(Object obj) { return latch != null; } }; thread.start(); System.out.println("Off and running..."); } } he Javadoc API describes a count down latch as: A synchronization aid that allows one or more threads to wait until a set of operations being performed in other threads completes. A CountDownLatch is initialized with a given count. The await methods block until the current count reaches zero due to invocations of the countDown() method, after which all waiting threads are released and any subsequent invocations of await return immediately. This is a one-shot phenomenon -- the count cannot be reset. If you need a version that resets the count, consider using a CyclicBarrier. A CountDownLatch is a versatile synchronization tool and can be used for a number of purposes. A CountDownLatch initialized with a count of one serves as a simple on/off latch, or gate: all threads invoking await wait at the gate until it is opened by a thread invoking countDown(). A CountDownLatchinitialized to N can be used to make one thread wait until N threads have completed some action, or some action has been completed N times. A useful property of a CountDownLatch is that it doesn't require that threads calling countDown wait for the count to reach zero before proceeding, it simply prevents any thread from proceeding past an await until all threads could pass. The idea here is that the test code will never check the database for the results until the run() method of the worker thread has called latch.countdown(). This is because the test code thread is blocking at the call to latch.await(). latch.countdown() decrements latch's count and once this is zero the blocking call the latch.await() returns and the test code continues executing, safe in the knowledge that any results which should be in the database, are in the database. The test can then retrieve these results and make a valid assertion. Obviously, the code above merely fakes the database connection and operations. The thing is you may not want to, or need to, inject a CountDownLatch directly into your code; after all it's not used in production and it doesn't look particularly clean or elegant. One quick way around this is to simply make the doWork(CountDownLatch latch) method package private and expose it through a public doWork() method. public class ThreadWrapper { /** * Start the thread running so that it does some work. */ public void doWork() { doWork(null); } @VisibleForTesting void doWork(final CountDownLatch latch) { Thread thread = new Thread() { /** * Run method adding data to a fictitious database */ @Override public void run() { System.out.println("Start of the thread"); addDataToDB(); System.out.println("End of the thread method"); countDown(); } private void addDataToDB() { try { Thread.sleep(4000); } catch (InterruptedException e) { e.printStackTrace(); } } private void countDown() { if (isNotNull(latch)) { latch.countDown(); } } private boolean isNotNull(Object obj) { return latch != null; } }; thread.start(); System.out.println("Off and running..."); } } The code above uses Google's Guava @VisibleForTesting annotation to tell us that the doWork(CountDownLatch latch) method visibility has been relaxed slightly for testing purposes. Now I realise that making a method call package private for testing purposes in highly controversial; some people hate the idea, whilst others include it everywhere. I could write a whole blog on this subject (and may do one day), but for me it should be used judiciously, when there's no other choice, for example when you're writing characterisation tests for legacy code. If possible it should be avoided, but never ruled out. After all tested code is better than untested code. With this in mind the next iteration of ThreadWrapper designs out the need for a method marked as @VisibleForTesting together with the need to inject a CountDownLatch into your production code. The idea here is to use the Strategy Pattern and separate the Runnable implementation from the Thread. Hence, we have a very simple ThreadWrapper public class ThreadWrapper { /** * Start the thread running so that it does some work. */ public void doWork(Runnable job) { Thread thread = new Thread(job); thread.start(); System.out.println("Off and running..."); } } and a separate job: public class DatabaseJob implements Runnable { /** * Run method adding data to a fictitious database */ @Override public void run() { System.out.println("Start of the thread"); addDataToDB(); System.out.println("End of the thread method"); } private void addDataToDB() { try { Thread.sleep(4000); } catch (InterruptedException e) { e.printStackTrace(); } } } You'll notice that the DatabaseJob class doesn't use a CountDownLatch. How is it synchronised? The answer lies in the test code below... public class ThreadWrapperTest { @Test public void testDoWork() throws InterruptedException { ThreadWrapper instance = new ThreadWrapper(); CountDownLatch latch = new CountDownLatch(1); DatabaseJobTester tester = new DatabaseJobTester(latch); instance.doWork(tester); latch.await(); boolean result = getResultFromDatabase(); assertTrue(result); } /** * Dummy database method - just return true */ private boolean getResultFromDatabase() { return true; } private class DatabaseJobTester extends DatabaseJob { private final CountDownLatch latch; public DatabaseJobTester(CountDownLatch latch) { super(); this.latch = latch; } @Override public void run() { super.run(); latch.countDown(); } } } The test code above contains an inner class DatabaseJobTester, which extends DatabaseJob. In this class the run() method has been overridden to include a call to latch.countDown() after our fake database has been updated via the call to super.run(). This works because the test passes a DatabaseJobTester instance to the doWork(Runnable job) method adding in the required thread testing capability. The idea of sub-classing objects under test is something I've mentioned before in one of my blogs on testing techniques and is a really powerful technique. So, to conclude: Testing threads is hard. Testing anonymous inner classes is almost impossible. Using Thead.sleep(...) is a risky idea and should be avoided. You can refactor out these problems using the Strategy Pattern. Programming is the Art of Making the Right Decision ...and that relaxing a method's visibility for testing may or may not be a good idea, but more on that later... The code above is available on Github in the captain debug repository (git://github.com/roghughe/captaindebug.git) under the unit-testing-threads project.
February 13, 2013
by Roger Hughes
· 14,040 Views · 12 Likes
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Eclipse Workspace Tips
usually, one of the first things i see if i launch eclipse is this dialog: select a workspace dialog actually, that ‘workspace’ thing is one of the most important things in eclipse to understand. to mess around it can cause a lot of pain. so i have collected some ‘lessons learned’ around workspaces. the workspace .metadata folder the workspace is where eclipse stores that .metadata folder: workspace metadata folder in this folder, eclipse stores all the workspace settings or preferences i configure e.g. using the menu window > preferences . sometimes that .metadata folder is named ‘framework’ too. e.g. if i’m are asked by eclipse to store some settings in the ‘frame work’ then this means it will be stored in the .metadata. eclipse uses this folder as well to store internal files and data structures. and many plugins store their settings in here to. consider the content of this folder as a ‘black box’: so do not change it, do not touch it unless you *really* know what you are doing! do not copy or move that folder. if you want to copy/share your workspace settings, then do *not* copy the .metadata folder, as typically you cannot use this folder on another machine or for another user. if you want to transfer/copy your settings, then see this post . workspace and eclipse versions as eclipse stores information in the .metadata workspace structure, the data/format might be different from version of eclipse to another (e.g. from one version of codewarrior to another). while using the same workspace with different versions of eclipse might work, it is *not* recommended. the eclipse community tries hard to keep things compatible, but using a different workspace for different eclipse versions is what i recommend. i started to name my workspace(s) like ‘wsp_lecture_10.2′ or ‘wsp_lecture_10.3′ to show that i’m using it for a specific version of codewarrior. workspace and projects this leads to the question: “do i have to duplicate then my projects if using with different versions of eclipse in parallel?” the answer is ‘no’. because the workspace folder does *not* have to have the projects in it (as folders). they can, but it is not needed. for example i have different workspaces (“wsp_10.3″, “wsp_10.2″), but my projects are in the “projects” folder somewhere else on my disk. what i do is to import the projects into each workspace, keep the projects in their original folder location. the menu file > import > general > existing projects into workspace can be used, with ‘copy projects into workspace’ * unchecked *: importing projects into workspace an easy trick is to drag&drop the project folders into eclipse: that’s much faster and simpler in my view than using above dialog. tip: showing the current workspace in the title bar using multiple workspaces can be confusing at some time. see this post how you can show the workspace in the application title: workspace shown in title bar processor expert processor expert has a special setting in the workspace pointing to its ‘data base’. that data base is inside the installation folder, in the mcu\processorexpert folder. if a launch eclipse and use a workspace from a different installation, i get a warning dialog: processor expert workspace warning: current worksapce is configured to use data from another installation of processor expert. that path setting of processor expert (pointing to the installation folder) is in my view the biggest argument to *not* share a workspace between different versions of eclipse. pressing the ‘open preferences’ opens the settings, and with ‘restore defaults’ it will (after a restart of eclipse) use the new installation path: processor expert directory that warning might not come up if using multiple installations of codewarrior. it seems that as long there is a valid path to a processor expert data base, the warning might not show up, and it will use that data base. that can lead to weird behaviour, so better check that the path in the workspace setting is pointing to the right folder. workspace dialog on startup remember that dialog at the beginning of this post? if i want to change if (and what) is shown at eclipse startup, then this is configured with the menu window > preferences > general > startup and shutdown : startup and workspaces in this dialog i can remove items from the list (e.g. if a workspace folder does not exist any more). tabula rasa as eclipse stores a lot of information in the workspace .metadata, that folder can grow to a substantial size (several hundreds of mbytes, depending on usage and configuration). one reason is because eclipse stores local history and undo information into that .metadata folder. just have a look at .metadata\.plugins\org.eclipse.core.resources\.history so from time to time (especially if i think that eclipse is slowing down), i export my workspace settings ( file > export > general > preferences , see copy my workspace settings ) and re-import it again into the new workspace. summary eclipse stores all its workspace settings and files in the .metadata folder. never touch/copy/change/move the .metadata folder. use different workspaces for each eclipse version. store the projects outside of the workspace if you want to share them across different eclipse versions. happy workspacing
February 11, 2013
by Erich Styger
· 100,582 Views · 3 Likes
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Top 10 Customization of Eclipse Settings
the great thing with eclipse is that you can configure a lot. in general, i’m happy with most of the defaults in eclipse and codewarrior. here are my top 10 things i change in eclipse to make it even better: add -showlocation to the eclipse startup command line: show workspace location in the title bar disable the heuristik settings for the indexer : fixing the eclipse index disable build (if required) for the debugger: speeding up the debug launch in codewarrior using spaces and not tabs: spaces vs. tabs in eclipse highlight the selected line : color makes the difference! configuring more hovers : hovering and debugging enabling static software analysis : free static code analysis with eclipse processor expert expert settings: enabling the expert level in processor expert custom dictionary settings: eclipse spell checker show line numbers : eclipse and line numbers i don’t have to waste time to change the settings for each of my workspaces: after i have changed the settings, i can simply export and import them again into another workspace: change my preferences using the menu window > preferences use the menu file > export > general > preferences and save the settings in a file: file export switch to the new workspace use the menu file > import > general > preferences to import the settings from the file: file import happy customizing
February 10, 2013
by Erich Styger
· 40,212 Views
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How Do You Organise Maven Sub-Modules?
Being an itinerant programmer one of the things I've noticed over the years is that every project you come across seems to have a slightly different way of organising its Maven modules. There seems to be no conventional way of characterising the contents of a project's sub-modules and not that much discussion on it either. This is strange, as defining the responsibilities of your Maven modules seems to me to be as critical as good class design and coding technique to a project's success. So, in light of this dearth of wisdom, here's my two penneth worth... When you first come across a new project, you'll generally find a layout convention that vaguely matches that defined by the Better Builds With Maven manual. The 'clean' project directory usually contains a POM file, a src folder and several sub-modules, each in their own subdirectory, as shown in the diagram below: If we all agree that this is the standard way of approaching the top level of project layout (and I have seen it done slightly differently) then there seems to be three different approaches taken when organising the responsibilities of each of a project's sub-modules. These are: Totally haphazardly. By class type. By functional area. I'm not going to linger on those projects that are organised seemingly without any structure or order except to say that they probably started off well organised but were not designed well enough to endure the changes forced upon them. In saying that a project's sub-modules are organised 'by class type', I mean that modules are used to group together all classes that comprise, but are not limited to, a layer in the program's architecture. For example a module could contain all classes that make up the program's service or persistence layers or a module could contain all model class (i.e. beans). Conversely, in saying that a project's sub-modules are organised by functional area I'm talking about a situation where each module contains, as close as possible, a vertical slice of the application, including model beans, service layer, controllers etc. If the truth be told then there are any number of ways to organise your project's sub-modules. Most project set-ups are fairly flat in structure, which is what I've demonstrated above; however, if you take a look at Erik Putrycz's 2009 talk Maven – or how to automate java builds, tests and version management with open source tools, he demonstrates that you can have modules within modules within modules. In order to explore this a little further, I'm going to invent my usual preposterously contrived scenario and in this scenario, you've got to write a program for a Welsh dental practice owned by a man called Jones also known locally as 'Jones The Driller'. The requirements would be pretty standard, I suspect, for a dental practice and would include handling: Patients details: name, address, DOB, phone number etc. Medical records, including treatments and outcomes. Appointments. Accounting, e.g. sales, purchase, wages etc. Auditing: as in who did what to whom... As a solution to Jones The Driller's problem, you propose that you write a multi-module web application based upon Spring, MVC and tomcat that, when assembled, has a standard 'n' tier design of a mySQL database, a database layer, service layer, a set of controllers and some JSPs that comprise the view. In creating your project your idea is to organise your sub-modules 'by class type' and you come up with the following module organisation, shown below roughly in build dependency order dentists-model dentists-utils dentist-repository dentists-services dentists-controllers dentists-web ...which on your screen looks something like this: Your dentists-model module contains the project's beans that model object used from the persistence layer right up to the JSPs. dentists-repository, dentists-services and dentists-controllers reflect the various layers of your application, with dentists-web module containing all the JSPs, CSS and other view paraphernalia. As for dentists-utils, well every project has a utils module where all the really useful, but disparate classes end up. Meanwhile, in a different universe, a different version of you decides to organise your project's sub-modules by functional area and you come up with the following breakdown: dentists-utils dentists-audit dentists-user-details dentists-medical-records dentists-appointments dentists-accounts dentists-repository dentists-integration dentists-web In this scenario, the build order is somewhat different; virtually all modules will depend upon dentists-utils and, depending upon your exact audit requirements, most modules will rely upon dentists-audit. You can also see in the following images that the sub-module package structure has been arranged on layer and type boundaries in that each module has its own model, repository (which contains interface definitions only) services and controller packages and that the layout of each module is identical at the top level. Another discussion to have here is the organisation of your project's package structure, where you can ask the same kind of questions: do you organise 'by class type' or 'by functional area' as shown above? You may have noticed that the dentists-repository modules can be fairly near the end of the build cycle as it only contains the implementation of the repository classes and not their interface definitions. You may have also noticed that dentists-web is again a separate module. This is because you're a pretty savvy business guy and in keeping the JSPs etc. in their own module, you hope to re-skin your app and sell it to that other Welsh dentist down the road: Williams The Puller. From a test perspective, each module contains its own unit tests, but there's a separate integration test module that, as it'll take longer to execute can be run when required. There are generally two ways of defining integration tests: firstly by putting them in their own module, which I prefer, and secondly by using a integration test naming convention such as somefileIT.java, and running all *IT.java files separately to all *Test.java files. Your two identical selves have proposed two different solutions to the same problem, so I guess that it's now time to takes a look at the pros and cons of each. Taking the 'by class type' solution first, what can be said about it? On the plus side, it's pretty maintainable in that you always known where to find stuff. Need a service class? Then that's in the dentist-service module. Also, the build order is very straight forward. On the down side, organisation 'by class type' is prone to problems with circular dependencies creeping in and classes with totally different responsibilities are all mixed up together making it difficult to re-use functionality in other projects without unnecessarily dragging in the who shebang. So, what about the pros and cons of the 'by functional area' approach? To my way of thinking, given the package structure of each module, it's just about as easy to locate a class using this technique as it is when using 'by class type'. The real benefit of using this approach is that it's far simpler to re-use a functional area of code in other projects. For example, I've worked on many projects in different companies and have implemented auditing several times. Each time I implement it I usually do it in roughly the same way, so wouldn't it be good just to reuse the first implementation? Not withstanding code ownership issues... The same idea also applies to dentists-user-details; the requirement to manage names and addresses applies equally as well to a shoe sales web site as it does a dental practice. And the downside? One of the benefits of this approach is that the modules are highly decoupled, but from experience no matter how hard you try, you always end up with more coupling that you'd like. You may have already spotted that both of these proposals are not 100% pure; 'by class type' contains a bit of 'by functionality' and conversely 'by functional area' contains a couple of 'by class type' modules. This may be avoidable, but I'm purposely being pragmatic. As I said earlier you always see a utils module in a project. Furthermore creating a separate database module allows you to change your project's database implementation fairly easily, which may make testing easier in some circumstances and likewise, having a separate web module allows you to re-skin your code should you be lucky enough to sell the same product to multiple customers with their own branding. Finally, one of the unwritten truths in software development is that once you've organised your project into its sub-modules you'll rarely get the opportunity to reorganise and improve them: there usually isn't the time or the political will as doing so costs money; however, it should be remembered that, in Agile terms, project module composition is, like code, a form of technical debt, which if done badly also costs you a lot of cash. It therefore seems a really good idea, as a team, to plan out your project thoroughly before starting to code. So be radical, do some design or have a meeting, you know it'll be worth it in the end.
February 8, 2013
by Roger Hughes
· 28,691 Views · 1 Like
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