Extreme Scaling in Alibaba JDK

    Many are suspicious of the prospect of something to fork and finish their own. Often the price is too high. Especially strange to hear about their own JDK, which allegedly is in each fairly large company. What the hell, with fat rage? In this article there will be a detailed story about the company, to which all this brings real commercial benefits, and which has done a monstrous job, because they:


    • Developed a multi-tenant Java virtual machine;
    • Invented the mechanism of operation of objects that do not bring an overhead projector for garbage collection;
    • Made something like a ReadyNow counterpart from Azul Zing;
    • We wrote down our own korutin with yield and continuations (and are even willing to share the experience with Loom, which I wrote about in the fall );
    • Screwed to all these wonders own diagnostic subsystem.

    As always, a video, full text transcript and slides are waiting for you under the cut. Welcome to the hell of one of the most difficult ways to adapt open source projects!



    Doctor, where do you get these pictures from? The O'Reilly Covers Corner: The background for KDPV is provided by Joshua Newton and depicts the sacred dance Sangyang Jaran in the city of Ubud, Indonesia. This is a classic Balinese performance consisting of fire and trance dance. A man with uncovered heels moves around a bonfire, bred on coconut husks, shoving his legs different and dancing in a trance state under the influence of horse spirit. The perfect illustration for your own JDK, right?



    Slides and description of the report (you will not need them, in this habratopeke there is everything you need).




    Hello, my name is Sanhong Lee, I work at Alibaba, and I would like to tell you about the changes we made to OpenJDK for the needs of our business. The post consists of three parts. In the first one, I’ll talk about how Alibaba uses Java. The second part, in my opinion, is the most important - in it we will discuss how we customize OpenJDK for the needs of our business. The third part will be about the tools we created for diagnostics.


    But before moving on to the first part, I would like to briefly tell you about our company.



    The diagram shows the internal structure of Alibaba. It consists of various companies whose main specialization is the organization of the electronic market and the provision of financial and logistics platforms. I think in Russia, most are familiar with AliExpress. Alibaba has a dedicated team of programmers who are dedicated to developing and supporting the entire distributed stack, which provides customer service for Aliexpress around the world.


    To get an idea of ​​the scale of the work of Alibaba, let's see what happens in China on the Day of Bachelors . It is celebrated every year on November 11th, and on this day people buy especially a lot of goods through Alibaba. As far as I know, from holidays all over the world, this is where the most purchases occur.


    In the picture above, you see a diagram that shows the load on our support system. The red line shows the work of our service orders and shows the peak number of transactions per second, last year it was 325 thousand. The blue line refers to the payment service, and this figure is 256 thousand. I would like to talk about how to optimize the stack serving so many transactions.


    Let's discuss the main technologies that work in Alibaba with Java. First of all, it must be said that a number of open source applications are our basis. To handle big data we use HBase Hadoop. We use Tomcat and OSGi as a container. Java is used on a colossal scale - millions of JVM instances are deployed in our data center. It is also necessary to say that our architecture is service-oriented, that is, we create many services that communicate with each other using RPC calls. Finally, our architecture is heterogeneous. To improve performance, many algorithms are written using C and C ++ libraries, so they communicate with Java using JNI calls.



    The history of our work with OpenJDK began in 2011, during OpenJDK 6. There are three important reasons why we chose OpenJDK. First, we can directly modify its code in accordance with the needs of the business. Secondly, when urgent problems arise, we can resolve them on our own faster than waiting for an official release. For our business, it is vital. Thirdly, our Java developers use our own tools for quick and high-quality debugging and diagnostics.


    Before turning to technical issues, I would like to list the main difficulties that we have to overcome. First, we have launched a huge number of copies of the JVM - in this situation there is an urgent need to reduce the costs associated with the hardware. Secondly, I have already said that we serve a huge number of transactions. Thanks to the garbage collector, Java promises us "infinite memory." In addition, it gains performance at a low level thanks to the JIT compiler. But it also has a downside: a longer stop-the-world time when collecting garbage. In addition, Java needs additional CPU cycles to compile Java methods. This means that compilers compete for CPU cycles. Both problems are exacerbated as the complexity of the application.


    The third difficulty is connected with the fact that we have a lot of applications running. I think everyone here is familiar with the tools that come with OpenJDK, such as JConsole or VisualVM. The problem is that they do not give us the exact information we need to configure. In addition, when we use these tools (for example, JConsole or VisualVM) in production, a low overhead projector is not just a wish, but a necessary requirement. I had to write my own diagnostic tools.



    The picture presents in general terms the changes we made to OpenJDK. Let's take a look at how we overcame the difficulties that I mentioned above.


    JVM Multi-Tenant


    One solution we call the multi-tenant JVM. It allows you to safely run multiple web applications in a single container. Another solution is called GCIH (GC Invisible Heap). This is the mechanism that provides you with full-fledged Java objects that do not require the cost of garbage collection. Further, in order to reduce the costs of thread contexts, we implemented corutines on our Java platform. In addition, we wrote a mechanism called JWarmup - its function is very similar to ReadyNow. Douglas Hawkins seems to have mentioned it in his report . Finally, we developed our own profiling tool, ZProfiler.


    Let's take a closer look at how we implement OpenJDK based multi-tenancy.



    Take a look at the picture above - I think most of you are familiar with this scheme. Compare the traditional approach with multi-tenant. If your application is running using Apache Tomcat, you can also run multiple instances in the same container. But Tomcat does not provide a stable resource consumption for each of them. Say, if one of the running applications needs more CPU time than others, how will you control the CPU time allocation? How to ensure that this application does not affect the work of others? Mainly this question made us turn to multi-tenant technology.



    The picture is a schematic representation of how we implement it. We create several containers for tenants inside the JVM. Each of these containers provides reliable control of resource consumption for each Java module. Multiple modules can be deployed in one container. Each module can be associated with one thread or a group of threads in runtime.


    Let's take a look at how the tenant container API looks like. We have a tenant configuration class that stores information about resource consumption. Next, there is the class of the container itself.



    In the presented code snippet, we create one tenant, and then indicate how long the CPU and memory are given to it. The first indicator is an integer number, which means the share of CPU time available to the tenant, in this case we indicated 512. We use a very similar approach in the case of cgroups, I’ll stay at this point in more detail. The second indicator is the maximum heap size that a tenant can use.


    Consider how the tenant interacts with the thread. The class TenantContainerprovides the method .run(), and when the thread enters it, it is automatically attached to the tenant, and when it leaves it, the reverse procedure occurs. So all the code is executed inside the method .run(). In addition, any thread created inside the method .run()is attached to the tenant of the parent thread.


    We come to a very important question - how is the CPU managed in a multi-tenant JVM? Our solution has just been implemented on a Linux x64 platform. There exists a mechanism of control groups, cgroups. It allows you to separate the process into a separate group, and then specify your own mode of resource consumption for each group. Let's try to move this approach into the context of the Hotspot JVM. In Hotstpot, Java threads are organized as native threads.



    This is shown in the diagram above: each Java thread is in one-to-one correspondence with a native thread. In our example, we have a container TenantAin which there are two native threads. To be able to control the CPU time distribution, we put both the native threads in the same control group. Thanks to this, we can regulate resource consumption by relying solely on the functionality of [control groups] (https://en.wikipedia.org/wiki/Cgroups ).


    Let's take a look at a more detailed example.



    Linux control groups are mapped to a directory. In our example, we created a catalog /t0for tenant 0. In this catalog there is a catalog /t0/tasks, all threads for will be here t0. Another important file is /t0/cpu.shares. It indicates how long the CPU will be allocated to this tenant. This whole structure is inherited from the control groups — we simply provided a direct correspondence between the Java thread, the native thread, and the control group.


    Another important question relates to managing a bunch of each tenant.



    In the picture you can see the scheme of how it is implemented. Our approach is based on G1GC. At the bottom of the picture, it is shown that G1GC divides the heap into sections of equal size. Based on these, we create Tenant Allocation Contexts, TAC-and, with which the tenant manages his heap site. Through TAC, we limit the size of the heap area available to the tenant. Here, the principle that every section of the heap contains objects of only one tenant operates. To implement it, we needed to make changes to the process of copying an object during garbage collection — it was necessary to ensure that the object was copied to the correct part of the heap.



    Schematically, this process is depicted in the diagram above. As I said, our implementation is based on G1GC. G1GC is a copying garbage collector, so during garbage collection we need to make sure that the object is copied to the correct part of the heap. On the slide, all objects created Tenant-1should be copied to its heap section, similarly to Tenant-2.


    There are other considerations that arise when tenants are isolated from each other. Here you need to say about TLAB (Thread Local Allocation Buffer) - this is a quick memory allocation mechanism. The TLAB space depends on the part of the heap. As I already said, different tenants have different groups of heap areas.



    The specifics of working with TLAB is shown on the slide - when the thread switches from Tenant 1to Tenant 2, we need to make sure that the correct heap area is used for the TLAB space. This can be achieved in two ways. The first way - when Thread Aswitched to Tenant 1on Tenant 2, we just dispose of old, and create a new one Tenant 2. This method is relatively easy to implement, but it wastes space in the TLAB, which is undesirable. The second way is more difficult - to make TLAB aware of tenants. This means that we will have several TLAB buffers for one thread. When Thread Aswitching from Tenant 1to Tenant 2, we need to change the buffer and use the one that was created in Tenant 2.


    Another mechanism that needs to be mentioned in connection with the delimitation of tenants is the IHOP (Initiating Thread Occupancy Percent). Initially, the IHOP was calculated on the basis of the entire heap, but in the case of a multi-tenant mechanism, it must be calculated on the basis of only one segment of the heap.


    Let's take a closer look at what GCIH (GC Invisible Heap) is. This mechanism creates a plot in the heap, hidden from the garbage collector, and, accordingly, not affected by the garbage collection. This plot is managed by a tenant GCIH.



    Here it is important to say that we provide a public API to our Java developers. An example of working with him can be seen on the screen. It allows using the method to moveIn()move objects from a regular heap to the heap GCIH. Its advantage is that you can still interact with these objects, as with ordinary Java objects, they are very similar in structure. But at the same time they do not require the cost of garbage collection. The conclusion, in my opinion, is that if you want to speed up garbage collection, you need to customize the behavior of the garbage collector in accordance with the needs of your application.



    The picture shows a high-level GCIH scheme. On the right is the usual Java heap, on the left is the space allocated for GCIH. Links from a regular heap to objects in GCIH are valid, but links from GCIH to a regular heap are not. To understand why this is so, consider an example. We have object “A” in GCIH, which contains a reference to object “B” in the regular heap. The problem is that object “B” can be moved by the garbage collector. As I have already said, we do not make updates in GCIH, so that after the garbage collector has been running, object “A” may contain an invalid reference to object “B”. This problem can be solved with the help of the pre-write barrier - they were discussed in the previous report. As an example, suppose that someone needs to save a link from a regular Java heap to GCIH before the save we assumed


    As for the specific application, the multi-tenant JVM is used in our Taobao Personalization Platform, abbreviated TPP. This is a recommendation system for our e-shopping application. TPP can deploy several microservices in one container, and with the help of a multi-tenant JVM, we adjust the memory and CPU time provided to each microservice.


    As for GCIH, it is used in our other system, the UM Platform. This is an online discount application. The owner of this application uses GCIH to pre-cache GCIH data on the local machine so as not to access objects from a remote cache server or remote database. As a result, we lighten the load on the network and perform less serialization and deserialization.



    The picture shows a diagram in which the blue color shows the load when using the usual JDK, and the red one shows the GCIH. As you can see, we are reducing the CPU usage by over 18%.


    As far as I know, BellSoft solved a similar problem , and their solution was similar to GCIH, but they used a different approach to reduce serialization and deserialization costs.


    Jokes in java


    Let's now go back to Alibaba and see how you can implement Korutinas in Java. But first, let's talk about the origins, why in general this needs to be addressed. In Java, it was always very easy to write applications with multithreading. But the problem with creating such applications is that, as I said, in the Hotspot Java threads are already implemented as native threads. Therefore, when there are many threads in your application, the costs of changing the context of the thread become very high.



    Consider an example in which we will have 4 I / O threads and 200 threads with the logic of your application. The table on the screen shows the results of running this simple demo - you can see how much time the CPU takes to change contexts. The solution for this problem can be the implementation of Corutin in Java.


    To provide it, we needed two things. First, Alibaba JDK needed to add support for sequels. This work was based on the JKU patch, we will dwell on it in more detail. Secondly, we have added a user-mode sheduler, which will be responsible for continuing in the thread. Thirdly, Alibaba has a lot of applications. Therefore, our solution is very important for our Java developers, and it was necessary to make it absolutely transparent for them. And this means that in our business application there should be practically no changes in the code. We called our solution Wisp. Our coroutine implementation in Java is widely used in Alibaba, so it can be considered proven that it works in Java. Get to know him more.



    Let's start with an example, the code of which is presented above - this is quite a normal Java-application. First, a thread pool is created. Then another runnable task is created that accepts a socket. After that, read from the stream. Next, we create another Runnable task, with which we connect to the server and, finally, write data to the stream. As you can see, everything looks quite standard. If you run the code on a regular JDK, each of these Runnable tasks will be executed in a separate thread. But in our decision the mechanics will be completely different.



    As can be seen from the dump thread presented on the slide, we create two coroutines in one thread, not two threads. Now you need to make this solution work. The main thing here is to generate generation of yieldTo-events at all possible points of blocking. In our example, these points are serverSocket.accept(), is.read(buf), and the socket connection os.write(buf). Thanks to the yield events at these points we will be able to transfer control from one cortina to another within the same thread. To summarize, our approach is that we achieve asynchronous performance using Coroutine, but our programmers can write code in a synchronous style, since such code is much simpler and easier to maintain and debug.


    Let's look at exactly how we provided support for sequels in Alibaba JDK. As I said, this work is based on a multi-lingual virtual machine project created by the community - it is in the public domain. We used this patch in Alibaba JDK and fixed some bugs that occurred in our production environment.



    As can be seen in the diagram, here in one thread there can be several coroutines, and for each a separate stack is created. In addition, the patch I mentioned provides us with the most important API here - yieldTo, with the help of which the control is transferred from one cortina to another.


    Let us turn to how we implemented a user-mode sheduler for corutin. We use a selector, and with the help of it we register several channels. When an I / O event (socket read, socket write, socket connect, or socket accept) occurs, it is recorded as the key for the selector. Therefore, when this event ends, we receive an alert from the selector. Thus, we use a selector to schedule coroutines in case of an I / O lock. Consider an example of how this will work.



    In the picture we see a socket and a synchronous call. client.read(buffer). At the bottom of the slide is written the code that will be executed inside this call. First, it checks whether it is possible to read from the channel or not. If yes, then we return the result. The most interesting thing happens if reading cannot be done. Then we register the read event in our scheduler with selector. This makes it possible to schedule the execution of some other cortinae. Take a look at how this happens. We have a thread in which to create a scheduler. The thread and our korutina are in one-to-one correspondence with each other. Sheduler allows us to manage the korutinami this thread. What happens if I / O is blocked? When an I / O event occurs, the scheduler receives an alert, and in this situation it relies entirely on the selector.


    Let's summarize the review of the work of our sheduler, which we called WispEngine. For each of our threads we allocate a separate WispEngine. When a coroutine lock occurs, we register certain events (socket read / write and so on) with WispEngine. Some events are associated with the parking thread, for example, if you call thread.sleep()with a delay of 100 milliseconds. In this case, you will generate a thread parking event, which will then be registered in the selector. Another important question is when the sheduler assigns the execution of the next available cortina. There are two main conditions. The first is when certain events are generated, such as I / O events or timeout events. Everything is pretty simple here: suppose you are making a callthread.sleep()with a delay of 200 milliseconds. When they expire, the sheduler has the ability to perform the next available quortin. Or here we can talk about some unpacking events that are generated, say, during a call object.notify()or the object.notifyAll()Second condition - when the user submits new requests, and we create a quortenine to service these requests, and then the sheduler assigns its execution.


    It also needs to be said about the service we created, WispThreadExecutor.



    The screen shows a sample code, and we see that this is the usual ExecutorService, created in the same way. His methods are available .execute()and submit()for the Runnable-tasks, but the problem is that all pass through the method of submit()Runnable-tasks will be performed in korutine, rather than thread. This solution is completely transparent to those who will implement our application, they will be able to use our API for coroutines.



    I come to the last difficult part of the post - how to solve the issue of synchronization in the korutinas. This is a difficult question, so let's look at it in a simplified example. Here we have Korutina A ( test::foo) and Korutina В( test::bar). Initially, we assign the execution test:footo Coroutin А. Then korutina Аcalls wait(). If nothing is done, the current thread will be blocked by the call wait(). As you can see from this dump thread, there will be a deadlock, and we will not be able to schedule the execution of the next cortina.


    How to solve this problem? Hotspot provides three types of locks. The first is fast lock. Here the owner of the lock is determined by the address in the stack. As I said before, each of our corutin has a separate stack. Therefore, in the case of fast lock, we do not need to do any additional work. There is no similar support for biased lock in our system. We tried it on our production and it turned out that in the absence of biased lock, performance does not decrease. For us it is quite suitable.



    Let's talk about a more difficult case - inflated lock. Take another look at the example I gave above. We have korutina А( .foo()) and korutina B( .bar()). First, we assign the execution of the coroutine Аand run it. Then she calls Object.wait, after which she falls into the waiting list. After this we take a very important step: we generate an event yieldTothat transfers control to the main thread. Next, we run corutin B. It makes a call Object.notifyand generates corresponding events unpark. Eventually they will wake the coroutine А. After the execution is complete bar(), it will be possible to transfer control to the coruntine.А. Thus, the deadlock, which I mentioned earlier, is completely overcome.


    Let's now discuss performance. We use Korutin in one of our online applications Carts. Based on it, we can compare the work of a corutin with the work of a regular JDK.



    As you can see, they allow us to reduce the CPU consumption by almost 10%. I understand that most of you most likely do not have the ability to directly make such complex changes to the JDK code. But the main conclusion here, in my opinion, is that if productivity losses cost money and the resulting amount is large enough, you can try to improve performance with the help of the korutin library.


    Jwarmup


    Let's move on to our other tool - JWarmup. It is very similar to another tool, ReadyNow. As we know, Java has a warm-up problem — the compiler at this stage requires additional CPU cycles. This caused us problems - for example, a TimeOut Error occurred. When scaling, these problems only get worse, and in our case we are talking about a very complex application - more than 20 thousand classes and more than 50 thousand methods.


    Before we started using JWarmup, the owners of our application used simulated data to warm up. On this data, the JIT compiler performed a preliminary compilation while no requests had yet been received. But the simulated data is different from the real, so for the compiler they are not representative. In some cases, unexpected deoptimization occurred, performance suffered. The solution to this problem was JWarmup. He has two main stages of work - recording and compilation. Alibaba has two types of media, beta and production. Both those and others receive real requests from users, after which the same version of the application is deployed in these two environments. In a beta environment, only profiling data is collected, on the basis of which production is then precompiled.



    Let's take a closer look at what kind of information we collect. We need to record exactly which classes are initialized, which methods are compiled, then this data is dumped into a log on the hard disk, which is accessible to the compiler. The most difficult moment is the initialization of classes. Its order is completely dependent on the application logic. The slide shows an example - class initialization Barshould occur after execution Foo.test(), as it uses foo.count. In this situation, we perform initialization at the moment when all the necessary logic is already executed.



    The picture shows a comparison of JWarmup performance and tiered compilation, red and blue graphics, respectively. Time is plotted on the x axis, CPU time is on the y axis. At the first stage, JWarmup pre-compiles the code, so it consumes more CPU time than a regular JDK. But then, when real requests from users begin to arrive, we see a significant improvement in performance compared to the standard JDK. Finally, at the last stage with stepwise compilation, all the most frequently used methods are already compiled, and the resource consumption drops again.


    It is necessary to say a few more words about JWarmup. We cannot write a class if it was generated dynamically by, say, some groovy script, or using Java reflection, or a proxy. We simply ignore such classes. In addition, we have to disable some optimizations, for example, “null check elimination”. Otherwise, unexpected deoptimization may occur. Finally, our current implementation of JWarmup is incompatible with stepwise compilation, so if you want to use JWarmup, you must disable it.


    Diagnostic tools


    And finally, let's talk about the diagnostic tools we have created in Alibaba.



    The scheme describes their functioning. Here are the components of the JVM - garbage collector, bytecode interpreter and compiler, as well as threads in runtime. In terms of memory, we have a Java heap, metaspace, VM data (intended for internal use in the VM) and a code cache for the JIT compiler. We have added significantly more profiling capabilities to OpenJDK. First, the garbage collector now works on the basis of much more accurate information, which allows us to significantly improve its performance. Secondly, we implemented two important features for running threads. The first is called HotMethodProfiling, it allows you to determine which methods use the most CPU time. By the way, if you need to profile your methods, I suggest using Honest Profiler, it is a very good open source tool, it works on the same principle as our HotMethodProfiling feature. Another feature is called MethodTracing. We instrument the input and output methods at the compilation level, so we know how long it takes to execute. In addition, we added the ability to create a dump for metaspace and code cache. Based on the code cache dump, we can tell our Java developers which class loader consumes more memory of this cache. Thanks to the metaspace dump, you can understand whether it is fragmented or not. This is very useful when developing in Java.


    Next, we also created a diagnostic tool called ZProfiler.



    Schematically, his work is depicted in the picture above. For it, we developed a JVMTi agent that runs inside the JVM process (in the diagram on the left). In addition, we created a ZProfiler server based on Apache Tomcat. It is directly deployed in our data center. This allows the ZProfiler server to directly access the target JVM. Finally, ZProfiler has a web UI that our developers can use. ZProfiler provides two main functionalities. First, by simply clicking on the UI, you can get very accurate information about the target JVM. Secondly, ZProfiler provides post-mortem diagnostics. For example, if an OutOfMemoryError error occurred in our production environment, you can generate a heap dump with one click, and this file will be downloaded from the target JVM server to the ZProfiler server, after which the results of the analysis will be available to developers. This is a very effective solution that allows you to do without, say, Eclipse MAT.


    To summarize. We have created several solutions for the problems we faced. This is a multi-tenant JVM, GCIH, Cortina for Alibaba JDK, as well as JWarmup - a tool very similar to ReadyNow and the commercial Zing JVM. Finally, we created the ZProfiler tool. In conclusion, I would like to say that we are happy to provide the community with the improvements that we have created on the basis of OpenJDK. On this occasion, the dialogue is already underway, in particular, the possibility of adding JWarmup to OpenJDK is being discussed. In addition, we plan to participate in an OpenJDK project called Loom, this is a implementation of Corutin for Java. At this I have everything, thank you for your attention.


    Minute advertising. The report you just read was made at the JPoint conference in 2018. It is already 2019, and the next JPoint will be held in Moscow on April 5-6. The program is still at the stage of formation, but you can already see such famous comrades as Rafael Winterhalter and Sebastian Daschner. Tickets can be purchased on the official conference website . To assess the quality of the remaining reports from the last conference, you can watch the archive of videos on YouTube . See you at the JPoint!

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