Flink rocksdb memory leak

RocksDB is a versatile, embeddable persistent key-value store designed for fast storage including memory (RAM), flash, SSD, and NVMe. My task will do a savepoint every hour, so every hour will do a savepoint. localdir commented out in the flink config file. Other rocksdb configuration parameters that might affect memory usage: rocksdb. Starting Flink 1. fixed-per-tm" (memory size, no default) cluster-level (yaml only) used by a job only if neither 'state. RocksDB). If I have more insight about memory consumption or leaks I will open a new ticket. Currently, due to the limitation of RocksDB (see issue-6247 ), we cannot create a strict-capacity-limit LRUCache which shared among rocksDB instance (s). ROCKSDB is set by default. Through a case study, this blog post illustrates a Jun 20, 2023 · Apache Flink has been designed for, and is mostly used with large-scale real-time data processing use-cases. fraction增加Flink的托管内存。 Description. If you need to read 5 pages to answer a query, read amplification is 5. checkpoint-storage: filesystem (or jobmanager) # if specified, implies 'filesystem' checkpoint-storage. However, this is not trivial, because Flink will use one RocksDB instance for each instance of a stateful operator. Feb 25, 2024 · 立即试用. 10开始，Flink默认将RocksDB的内存分配配置为每个任务槽的托管内存(managed memory)量。改善内存相关性能问题的主要机制是通过Flink配置taskmanager. 10版本就实现了RocksDB的托管内存 (managed memory)机制。. collect in the loop of the program, which didn’t result in any noticeable changes. dir: s3://xxx state. Working with Speedb eliminates the concerns that things might crash and gives us the ability Mar 23, 2022 · 关于RocksDB使用托管内存，Flink官方文档给出了一段简短的解释：. managed: true: Boolean Nov 23, 2021 · Here are some configs we setup for rocksDB backend. Apr 20, 2015 · Using an 8K block size used up more memory for filters, but not by too much. A similar issue is outlined here: RocksJava - Clearing Column Family Doesn't Free Memory #10324. managed configuration key. If you upgrade Flink from earlier versions, check the migration guide because many changes were introduced with the Feb 18, 2022 · [FLINK-24540] - Fix Resource leak due to Files. 12. 2. managed to false from its default value of true. type 设置默认的 State Backend。. You have to make sure that Flink leaves enough memory for RocksDB. cc rocksdb::Arena::AllocateNewBlock (unsigned long) 55d8c0841838: 333. Firstly, I tried calling GC. when the RocksDB blockcache reachs maximum size, restart the job. As we can see, apart from the os. We’ll consider a lapsed listener problem as an example of a memory leak. Eclipse Memory Analyzer (Eclipse MAT): Eclipse MAT is a Java heap analyzer used to inspect JVM heap dumps for memory utilization, finding memory leaks, etc. Nov 15, 2022 · The problem when I set state. Here we try to explain how RocksDB uses memory. That would reduce the overall memory consumption in exchange for resource isolation. contrib Unfortunately, it seems like there is a memory leak somewhere in the job submission logic. Configure memory for standalone deployment # It is recommended to configure total Flink memory (taskmanager. Proposed changes Configuration. This documentation is for an out-of-date version of Apache Flink. managed. Cache testing. This issue tracks this problem and offer the ability of strict mode once we could enable this feature. size) or its components for standalone deployment where you Jan 16, 2024 · We’ll also discuss various methods to detect memory leaks, including logging, profiling, verbose garbage collection, and heap dumps. Create a Memory Leak. larger size of JVM direct memory or there is a direct memory leak. backend. In Stephan Ewen commented on FLINK-16142: ----- [~Xeli] The issue you mentioned seemed to be largely about the RocksDB memory footprint. They are both persistent key value stores. try setting the flink config state. managed: true , 4GB usage appears on managed memory for each running task It seems that memory is not being reclaimed even after closing the rocksdb object. backend: rocksdb state. 13-jar-with-dependencies. Even if the process crashes, RocksDB can recover all of your data from in-memory filesystem. Memory usage by the binary varies from 1. Our Flink Jobs contains a filter, key by session id and then session window with 30mins gap. Click Save changes. See Improving Memory Efficiency. [jira] [Commented] (FLINK-16142) Memory Leak ca Arvid Heise (Jira) [jira] [Commented] (FLINK-16142) Memory Le Chesnay Schepler (Jira) [jira] [Commented] (FLINK Reproducing Flink RocksDB leakage. size or jobmanager. The best way to find out is to use a representative workload to test the job with saturated block caches and write buffers. Click Configuration. 这个时候为了排除raft对内存消耗 从Flink 1. And I just use the rocksdb open get set api. backend: hashmap (or rocksdb) state. 7 of total 300Gb memory assigned to 150 taskslots, giving each taskslot 1. Is it possible to have a single list state that's larger than the current Java heap size/off heap size? Set up TaskManager Memory. ExecutionGraph - OPERATOR_NAME switched from RUNNING to FAILED. However, RocksDB supports a pluggable API that allows an application to provide its own implementation of a memtable. Default cache of rocksdb (8MB capacity) is used and process's memory size remains constant. 10 comes with significant changes to the memory model of the Task Managers and configuration options for your Flink applications. Memtables. memory keeps climbing until stables at 48GB or so, and jvm min heap size and max heap size is set to be equal to 16G, and we did some java heap profiling (also look at /proc/pid/smaps) and verified java heap memory usage is small (1GB). and memory will keep raising. [jira] [Commented] (FLINK-16142) Memory Leak ca Arvid Heise (Jira) [jira] [Commented] (FLINK-16142) Memory Le Chesnay Schepler (Jira) [jira] [Commented] (FLINK Jun 28, 2017 · 2. The further described memory configuration is applicable starting with the release version 1. How to reproduce: increase RocksDB blockcache size (e. I tried to debug the rocksdb logs. 13, one can switch from one state backend to another one by first making a job savepoint and then Set up TaskManager Memory # The TaskManager runs user code in Flink. I was able to run the job post disabling changing this value. lang. The first is to read through the output of DB::GetProperty("rocksdb. fixed-per-slot' are configured. There are a couple of components in RocksDB that contribute to memory usage: Block cache. Blocks pinned by iterators. write-buffer-ratio` (by default 0. so our question is what cause this Apr 16, 2021 · Right now, my problem is resolved. 使用的是rocksdb, 并行度是5，1个tm Set up TaskManager Memory # The TaskManager runs user code in Flink. Cache is not exposed through JNI layer and default cache used by rocksdb is used. Thank you! Nov 11, 2018 · As I understand it, flink does not need to ser/deserialize for every state access when using fs state backend, because the state is kept in memory (heap), rocks db DOES, and I guess that is what is accounting for the slowdown (and backpressure, and checkpoints take MUCH longer, sometimes timeout after 10 minutes). free -m output as follows: total used free shared buff/cache available. fixed-per-slot or state. We can also take a look at the path mentioned in the above image to check the persistence that our application is having with RocksDB as the backend. 2GB -> 2GB (depends on compactions and other unrelated caching I do). Sry about the pool formatting. rocksdb. On Windows or macOS it grows and grows and grows so much that after a few days it's like 7-9 GB consumption and growing. 简介： Apache Flink是由Apache软件基金会开发的开源流处理框架，其核心是用Java和Scala编写的分布式流数据流引擎。. 前言概述. managed to false and configure RocksDB via ColumnFamilyOptions. Dec 21, 2023 · We currently have the state. size或taskmanager. It is popular because of its performance capabilities and versatility which are due in large part to how RocksDB memory management works and the ability to tune its many parameters and options. Running in-memory database with RocksDB is easy – just mount your RocksDB directory on tmpfs or ramfs [1]. This option only has an effect when 'state. – 周天钜. I have 2 kafka streams state stores implemented. ==19550== Reachable blocks (those to which a pointer was found) are not shown. If you upgrade Flink from earlier versions, check the migration guide because many changes were introduced with the Jun 4, 2023 · Target Audience: If you’re interested in gaining a deeper understanding of the internals of NoSQL databases like Amazon’s DynamoDB, Cassandra, and embedded key-value stores such as LevelDB and RocksDB, as well as familiar with the basic components of a database management system (DBMS), particularly storage engines, then the following information will be relevant to you. Below you will find a list of all bugfixes and improvements (excluding improvements to the build infrastructure and build stability). fixed-per-slot is not set and state. RocksDB memory is not included in Flink's memory parameters. When cache size is bumped up to say 512MB, process's memory space Nov 29, 2023 · The Apache Flink Community is pleased to announce the second bug fix release of the Flink 1. (I do not care about Savepoints and Checkpoint mechanism about in flink, I am just creating state which almost 5 mins ttl) However memory consumption for all nodes are always increasing and I have to Managed memory: 托管内存; 由 Flink 管理的原生托管内存，保留用于排序、哈希表、中间结果缓存和 RocksDB 状态后端。 托管内存由 Flink 管理并分配为原生内存（堆外）。以下工作负载使用托管内存： 流式作业可以将其用于 RocksDB 状态后端。流和批处理作业都可以使用 为了解决Flink作业使用RocksDB状态后端时的内存超用问题，Flink早在1. Analyzing Memory & Garbage Collection Behaviour # Memory usage and garbage collection can have a profound impact on your application. size) or its components for standalone deployment where you Managed Memory for RocksDB. checkpoints. Kubernetes, Yarn, Mesos), providing strict control over its memory consumption. With 8K block size rocksdb. From the memory monitoring, it can be seen that the memory of each hour will soar up, although the memory will drop a little later, but from every hour From the point of view of the memory peak on the whole point, the memory continues to rise little by To control memory manually, you can set state. Step 1a Note If you configure the total Flink memory Flink will implicitly add JVM memory components to derive the total process memory and request a container with the memory of that derived size. The session window will need to accumulate all the event for the session, and process them using ProcessWindowFunction. Managed Memory for RocksDB. Feb 2, 2021 · As with any memory leak investigation, we started by taking a heap dump of one of the app instances and then analyzed it using YourKit. 2 的 内存溢出问题. The fully in-memory heap-based state backend is a higher performance alternative that offers the same exactly-once fault-tolerance guarantees, but it doesn't spill to disk like RocksDB, so you are more limited in how much state can be managed. The memory manager governs the memory that Flink uses for sorting, hashing, caching or off-heap state backends (e. The managed memory consumption is the same when we use EAXCTLY_ONCE checkpointing. The problem that I am facing is the compaction of rocksdb is happening only in one of the state stores and the other state store is just piling on more sst files and in turn increasing the disk space. That should have changed significantly in Flink 1. This contains a docker-compose file for reproducing a somewhat obscure "bug" in Flink where a timeouted Flink checkpoint leaks files created by RocksDB's checkpoint mechanism. It provides fast and efficient storage for maintaining the state of streaming application. Flink framework and its dependencies also consume the. All key/value state (including windows) is stored in the key/value index of RocksDB. Conclusion. this case 'taskmanager. As I know, rocksdb will use a lot of memory when doing incremental checkpoint, there is already a JIRA An investigation revealed this was due to interleaving allocations of long-term and short-term objects, causing memory usage to slowly grow, similar to when there is a memory leak. We will describe each of them in turn. 0 许可协议。. size) or its components for standalone deployment where you This can mean two things: either job(s) require(s) a. My current flink application runs with 48 task slots on 3 nodes. fraction，即可满足多数 Apr 25, 2021 · 官方答疑：Flink如何及何时使用RocksDB状态后端. . Flink does not directly manage RocksDB’s native memory allocations, but configures RocksDB in a certain way to ensure it uses exactly as much memory as Flink has for its managed memory budget. backend and select HASHMAP or ROCKSDB based on your requirements. We would like to show you a description here but the site won’t allow us. Select Flink from the list of services. 本合集提供有关Apache Flink相关技术、使用技巧和最佳实践的资源。. On Linux memory usage is rock solid at 800- 900MB even if the process is up for a month. 2. Mar 8, 2022 · We found this useful for observing memory trends over longer periods of time, helping us detect memory leaks in RocksDB for one of our applications. When configuring the state backend in Cloudera Manager, the configuration serves as a default 问题现象是（同事给出的，我们只看到一个结果） 随着IO的持续写入，大概每个节点rocksdb数据的存储量都达到20G以上之后，top看到的IO 进程物理内存资源和实际的抓取的rocksdb tcmalloc分配的堆内存大小无法匹配，差距达到2-3倍。. Do you still see issues connected to that in Flink 1. The TaskManager runs user code in Flink. size` or `taskmanager. After FLINK-7289, we could control the memory usage of RocksDB state backend. ==19550== The main thread stack size used in this run was 8388608. This means the number of RocksDB instances per TaskManager depends on the number of stateful Description. The bookie version is 4. Indexes and bloom filters. These recently-introduced changes make Flink more adaptable to all kinds of deployment environments (e. If there is a leak, then it is likely in RocksDB (native memory) and not the (Java) application on top of it, as the Java heap is limited. Thanks! That is exactly what I was missing. incremental: true state. This means disk performance may have an impact on the performance of Flink jobs using RocksDB. memory. 可选值包括 jobmanager (HashMapStateBackend), rocksdb (EmbeddedRocksDBStateBackend)， 或使用实现了 state backend 工厂 StateBackendFactory 的类的全限定类名， 例如： EmbeddedRocksDBStateBackend 对应为 org. For persistence against loss of machines, please configure a CheckpointStorage instance for the Job. ) and fire a. The heap dump will allow you to analyze potential memory leaks in your user code. We highly Apr 4, 2016 · But then again after restart memory usage by application remained unchanged for a couple of days now so the issue might not be present anymore. 2 migration). 转载请注明出处！. num-retained: 3. 问题一： flink-1. size) or its components for standalone deployment where you RocksDB 官方提供了性能优化指南 [5]，也可以根据这些来进行参数调优。 同样的，1. If the memory leak should be caused by Flink, then please reach out to the dev mailing list. By default user could set the RocksDB memory boundary through `taskmanager. dir: file:///checkpoint-dir/. The second is to divide your disk write bandwidth (you can use iostat) by your DB write rate. The direct memory can be allocated by user code or some of its dependencies. This feature is active by default and can be (de)activated via the state. May 31, 2019 · Now I'm using incremental Checkpoint in Flink with RocksDB, running on a container environment. 10. fraction`, tune the write/read memory ratio through `state. Public signup for this instance is disabled. java. The RocksDB state backend uses a combination of fast in-memory cache and optimized disk based lookups to manage Sep 29, 2022 · The memory usage is all occupied by the Bookie process, so the only place to suspect is to call the sub-JNI part, and only rocksdb is left. This release includes 82 bug fixes, vulnerability fixes, and minor improvements for Flink 1. When a memtable fills up, it is flushed to a static sorted table (SST) file on storage. Rest of app is tight. stats", &stats). 72MiB/99. Read amplification is the number of disk reads per query. flink. ==19550== main thread stack using the --main-stacksize= flag. g. 为避免应用程序故障时造成数据丢失 Jul 7, 2020 · heap memory和direct memory被jvm控制了，显然不会被os kill，而是OOM，可以被flink 捕捉而爆出异常的，被os kill只有托管给rocksdb的native memory了。 如何分析native memory的leak呢，就需要引入jemalloc。 We would like to show you a description here but the site won’t allow us. malloc and 这样，Rocksdb的缓存是两层的：块缓存和页缓存。反直觉的一件事情是，减小块缓存不会增加IO。存起来的内存可能会被用于页缓存，所以会有更多的数据被缓存起来。然而，CPU使用量可能会增加，因为Rocksdb需要解压他从页缓存读取的数据。 Mar 27, 2024 · I think I might have been overthinking in trying to fine tune rocksDB managed memory (because previously in Flink 1. Alternatively, you can use the above mentioned cache/buffer-manager mechanism, but set the memory size to a fixed amount independent of Flink’s managed memory size (state. Jun 8, 2023 · Shani explains that “for cyber security companies, the bigger the network, the bigger the problems. David Anderson. Jul 17, 2023 · Streaming State: RocksDB is used as a state store in stream processing frameworks like Apache Flink. I hope this provides a clearer overview of the situation. apache. To debug the leak, we used jemalloc and jprof to profile the sources of malloc calls from the java process and attached are the profiles generated at various stages of the job. 用户只需启用state. backend Jun 29, 2020 · Checkpoints of Flink Job. executiongraph. For some ongoing projects that improve memory efficiency. 4Gb managed memory, so I have replicated the same in my Flink 1. 17. We are getting this error: 2020-02-18 10:22:10,020 INFO org. 17 series. Working with Speedb eliminates the concerns that things might crash and gives us the ability 在 Flink 配置文件 可以通过键 state. This state backend can store very large state that exceeds memory and spills to local disk. task. go through step 2-3 few more times. But what if you need to process low-throughput streams? Running a full, distributed Flink cluster might be an overkill, as there’s quite a bit of overhead for distributed coordination. 5) and the reserved memory fraction for index/filters through `state. Then, I created a memory dump using gcore, whic We are consuming gzip compressed messages from Kafka using KafkaConnector09 and use rocksDB backend for checkpoint storage. when I try to use valgrind , it told me there is memory leak. What I can see in the heap dump is the following * The user code class loaders in Flink are not kept alive any more, they have no GC roots. Memory tuning guide # In addition to the main memory setup guide, this section explains how to set up memory depending on the use case and which options are important for each case. Run it: Then, wait for at least one checkpoint to timeout (Checking the webinterface at :8081 or the output. and monitor the memory usage (k8s pod working set) of the TM. fixed-per-slot: 6000m is on Flink UI, in task manager page, I cant see the allocated managed memory anymore. Companies report about TBs of data being processed per second, or TBs of state in huge clusters. Jan 28, 2023 · I am trying to tune memory configuration for my flink job deployed using FlinkKubernetesOperator. This is an excellent way to learn about memory allocation in Java and garbage collection. The user was able to address this issue by configuring RocksDB to allocate blocks in the block cache (which tend to be long-lived) from a separate jemalloc arena. cache 1: (C) func rocksdb/memory/arena. Aug 7, 2023 · Disadvantages: Increased resource consumption due to RocksDB's memory usage. OutOfMemoryError: Metaspace. start a job using RocksDB statebackend. See full list on flink. here is a chain of references to given memory: rocksdb::Arena::AllocateNewBlock (unsigned long) Jun 8, 2023 · Shani explains that “for cyber security companies, the bigger the network, the bigger the problems. 流处理应用程序通常是有状态的，通过保存已处理事件的信息，用于影响未来事件的处理。. I’ve added a new service rocksdb-statebackend that uses the apache/flink-statebackend-rocksdb image, which 为了达到上述目标，Flink 默认将 RocksDB 的可用内存配置为任务管理器的单槽（per-slot）托管内存量。这将为大多数应用程序提供良好的开箱即用体验，即大多数应用程序不需要调整 RocksDB 配置，简单的增加 Flink 的托管内存即可改善内存相关性能问题。 Dec 5, 2019 · As I understand, RocksDB data is stored off-heap in RocksDB instances or on disk until the data is deserialized in a RocksDBState class in Flink. Operators allocate the memory either by requesting a number of memory segments or by reserving You can choose between RocksDB and Hashmap as a state backend for your Flink streaming application. Mar 25, 2019 · This long standing ticket shows how challenging RocksDB memory capacity planning is in Flink. 用File Leak Detector这个工具检查一下,官网下载得到file-leak-detector-1. In. It can be used to read heap dumps 为了达到上述目标，Flink 默认将 RocksDB 的可用内存配置为任务管理器的单槽（per-slot）托管内存量。这将为大多数应用程序提供良好的开箱即用体验，即大多数应用程序不需要调整 RocksDB 配置，简单的增加 Flink 的托管内存即可改善内存相关性能问题。 Apr 17, 2021 · Flink RocksDB 内存管理. Aug 10, 2020 · heap memory和direct memory被jvm控制了，显然不会被os kill，而是OOM，可以被flink 捕捉而爆出异常的，被os kill只有托管给rocksdb的native memory了。 如何分析native memory的leak呢，就需要引入jemalloc。 Managed Memory for RocksDB. introduce "state. jar。 先取消job后把TaskManager容器重启，进入容器，attach到taskmanager进程上。 java -jar /flink/file-leak-detector-1. fixed-per Apr 5, 2020 · 使用File Leak Detector分析句柄. Does that mean RocksDB is not being used? In the flink UI I can see keys getting added to RocksDB does that mean it’s only storing in memory and not on disk? Not sure if it still uses disk when using Kubernetes without any local storage enabled in flink. While Hashmap stores data as an object on Java heap, RocksDB can be used to store a larger state that does not fit easily in memory. The fraction of cache memory that is reserved for high-priority data like index, filter, and compression dictionary blocks. 11. I will observe it. The job uses rocks db for state management. We are using Flink 1. Memory is represented either in MemorySegment s of equal size or in reserved chunks of certain size. 9, 128 containers with 20G memory in total to run Sep 23, 2018 · chllcy commented on Sep 23, 2018. answered Jan 30, 2022 at 10:25. Any insights or suggestions would be greatly appreciated. 0, and the version of rocksdb is 6. 15. 8, we had a fraction of 0. jar 13 http=19999,threshold=20000,strong May 31, 2019 · 2. Jan 28, 2022 · You don't have to use Flink with RocksDB. In RocksDB, the default implementation of the memtable is a skip list. RocksDB 的内存管理在 Flink 1. Another consequence of per sub-task RocksDB instances is that the TaskManager process may use a large amount of open file Class MemoryManager. In this post, we describe Flink’s memory model, as it stands in Memory tuning guide # In addition to the main memory setup guide, this section explains how to set up memory depending on the use case and which options are important for each case. size' configuration option should be increased. 10? Mar 10, 2021 · A memtable is an in-memory structure where data is buffered. 10 新版的 Flink 提供了全自动的 RocksDB 内存管理（Managed）[6]，以大大简化容器环境下内存的最大用量设置，避免因为超过了允许使用的最大内存量而被系统 KILL 或发生 OOM. I am configuring only Dec 8, 2013 · Rocksdb is loaded as part of JAVA process and DB operations are accessed through JNI layer. managed' or 'state. 1G), it is easier to monitor and reproduce. 10 版本之前是不受管控的，社区利用 RocksDB 已有的一些内存控制上的优化，在去年对 Flink 中 RocksDB 的使用做了一系列 Apr 21, 2020 · Apache Flink 1. yaml, use. Nov 22, 2014 · with the latest rocksdb jni, we found the following memory usage pattern. org Jul 21, 2018 · But on macOS and on Windows it leaks memory. fixed-per-slot' nor 'state. The TLDR of it is that RocksDB memory usage can grow A StateBackend that stores its state in an embedded RocksDB instance. Also I am using rocksdb as state management. Flink 中保存的事件信息，即状态，会被存储在已经配置的状态后端中。. Mar 31, 2023 · 1. list [FLINK-24543] - Zookeeper connection issue causes inconsistent state in Flink [FLINK-24563] - Comparing timstamp_ltz with random string throws NullPointerException Oct 20, 2020 · I tracked down where memory in RocksDB is being stored: It turns out that rocksdb has caches, which are responsible for memory usage. {{< hint warning >}} Warning: If Flink or user code allocates unmanaged off-heap (native) memory beyond the container size the job can fail because the Flink application. Mar 29, 2021 · As covered in a recent blog post, RocksDB is a state backend in Flink that allows a job to have state larger than the amount of available memory as the state backend can spill state to local disk. Oct 16, 2020 · High Flink network buffer usage, which causes Kafka lagging. estimate-table-readers-mem is 869,905,706 (829MB), and with 32K block size it is 664,033,011 (633MB). We recommend you use the latest stable version. fixed-per-tm options). This is done on a per-slot level (managed memory is accounted per Mar 27, 2014 · With growing RAM sizes and strict low-latency requirements, lots of applications decide to keep their entire data in memory. I am now closing this ticket, as the first aspect ("swap usage") is not a RocksDB issue. 版权声明： 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 3. Stephan Ewen edited comment on FLINK-16142 at 2/26/20 4:23 PM: ----- Interesting. Configure Total Memory. off-heap. managed参数 (默认即为true)，再设定合适的TaskManager托管内存比例taskmanager. Following are the memory settings. Open your cluster in Cloduera Manager. Mem: 63088 58063 337 268 4688 4639. Search for state. estimate-table-readers-mem=5474305559 after 24hr and remained in this range for a couple of days now. For a complete list of all changes see: JIRA. This setting override configs set as a part of the rocksdb options factory. To set in flink-conf. In this Jul 4, 2021 · I noticed that the memory usage of the program I wrote using this gem increases over time, leading me to believe I have a memory leak. state. runtime. Attachments. I'm pretty sure the leak is inside rocksdb. . Go to our Self serve sign up page to request an account. As you can see when state. Configuring memory usage for your needs can greatly reduce Flink’s resource footprint and improve Job stability. sa cj jf vy id kf eg ka mb bz