Taming YARN @ Hadoop Conference Japan 2014

2Copyright©2014 NTT corp. All Rights Reserved. • Tsuyoshi Ozawa • Researcher & Engineer @ NTT Twitter: @oza_x86_64 • A Hadoop Contributor • Merged patches – 29 patches! • Developing ResourceManager HA with community • Author of “Hadoop 徹底入門 2nd Edition” Chapter 22(YARN) About me

3Copyright©2014 NTT corp. All Rights Reserved. • Overview of YARN • Components • ResourceManager • NodeManager • ApplicationMaster • Configuration • Capacity Planning on YARN • Scheduler • Health Check on ResourceManager • Threads • ResourceManager HA Agenda

5Copyright©2014 NTT corp. All Rights Reserved. YARN • Generic resource management framework • YARN = Yet Another Resource Negotiator • Proposed by Arun C Murthy in 2011 • Container-level resource management • Container is more generic unit of resource than slots • Separate JobTracker’s role • Job Scheduling/Resource Management/Isolation • Task Scheduling What’s YARN? JobTracker MRv1 architecture MRv2 and YARN Architecture YARN ResourceManager Impala Master Spark MasterMRv2 Master TaskTracker YARN NodeManager map slot reduce slot containercontainercontainer

6Copyright©2014 NTT corp. All Rights Reserved. • Running various processing frameworks on same cluster • Batch processing with MapReduce • Interactive query with Impala • Interactive deep analytics(e.g. Machine Learning) with Spark Why YARN?(Use case) MRv2/Tez YARN HDFS Impala Spark Periodic long batch query Interactive Aggregation query Interactive Machine Learning query

7Copyright©2014 NTT corp. All Rights Reserved. • More effective resource management for multiple processing frameworks • difficult to use entire resources without thrashing • Cannot move *Real* big data from HDFS/S3 Why YARN?(Technical reason) Master for MapReduce Master for Impala Slave Impala slave map slot reduce slot MapReduce slave Slave Slave Slave HDFS slave Each frameworks has own schedulerJob2Job1 Job1 thrashing

8Copyright©2014 NTT corp. All Rights Reserved. • Resource is managed by JobTracker • Job-level Scheduling • Resource Management MRv1 Architecture Master for MapReduce Slave map slot reduce slot MapReduce slave Slave map slot reduce slot MapReduce slave Slave map slot reduce slot MapReduce slave Master for Impala Schedulers only now own resource usages

9Copyright©2014 NTT corp. All Rights Reserved. • Idea • One global resource manager(ResourceManager) • Common resource pool for all frameworks(NodeManager and Container) • Schedulers for each frameworks(AppMaster) YARN Architecture ResourceManager Slave NodeManager Container Container Container Slave NodeManager Container Container Container Slave NodeManager Container Container Container Master Slave Slave MasterSlave SlaveMaster Slave Slave Client 1. Submit jobs 2. Launch Master 3. Launch Slaves

10Copyright©2014 NTT corp. All Rights Reserved. YARN and Mesos YARN • AppMaster is launched for each jobs • More scalability • Higher latency • One container per req • One Master per Job Mesos • AppMaster is launched for each app(framework) • Less scalability • Lower latency • Bundle of containers per req • One Master per Framework ResourceManager NM NM NM ResourceMaster Slave Slave Slave Master1 Master2 Master1 Master2 Policy/Philosophy is different

11Copyright©2014 NTT corp. All Rights Reserved. • MapReduce • Of course, it works • DAG-style processing framework • Spark on YARN • Hive on Tez on YARN • Interactive Query • Impala on YARN(via llama) • Users • Yahoo! • Twitter • LinkdedIn • Hadoop 2 @ Twitter http://www.slideshare.net/Hadoop_Summit/t- 235p210-cvijayarenuv2 YARN Eco-system

13Copyright©2014 NTT corp. All Rights Reserved. • Master Node of YARN • Role • Accepting requests from 1. Application Masters for allocating containers 2. Clients for submitting jobs • Managing Cluster Resources • Job-level Scheduling • Container Management • Launching Application-level Master(e.g. for MapReduce) ResourceManager(RM) ResourceManager Client Slave NodeManager Container Container Master 4.Container allocation requests to NodeManager 1. Submitting Jobs 2. Launching Master of jobs 3.Container allocation requests

14Copyright©2014 NTT corp. All Rights Reserved. • Slave Node of YARN • Role • Accepting requests from RM • Monitoring local machine and report it to RM • Health Check • Managing local resources NodeManager(NM) NodeManagerResourceManager 2. Allocating containers Clients Master or 3. Launching containers containers 4. Containers information (host, port, etc.) 1. Request containers Periodic health check via heartbeat

15Copyright©2014 NTT corp. All Rights Reserved. • Master of Applications (e.g. Master of MapReduce, Tez , Spark etc.) • Run on Containers • Roles • Getting containers from ResourceManager • Application-level Scheduling • How much and where Map tasks run? • When reduce tasks will be launched? ApplicationMaster(AM) NodeManager Container Master of MapReduce ResourceManager 1. Request containers 2. List of Allocated containers

17Copyright©2014 NTT corp. All Rights Reserved. • YARN configurations • etc/hadoop/yarn-site.xml • ResourceManager configurations • yarn.resourcemanager.* • NodeManager configurations • yarn.nodemanager.* • Framework-specific configurations • E.g. MapReduce or Tez • MRv2: etc /hadoop/mapred-site.xml • Tez: etc /tez/tez-site.xml Basic knowledge of configuration files

19Copyright©2014 NTT corp. All Rights Reserved. • Define resources with XML (etc/hadoop/yarn-site.xml) Resource definition on NodeManager NodeManager CPU CPU CPU CPU CPU Memory Memory Memory Memory Memory <property> <name>yarn.nodemanager.resource.cpu-vcores</name> <value>8</value> </property> <property> <name>yarn.nodemanager.resource.memory-mb</name> <value>8192</value> </property> 8 CPU cores 8 GB memory

20Copyright©2014 NTT corp. All Rights Reserved. Container allocation on ResourceManager • RM accepts container request and send it to NM, but the request can be rewritten • Small requests will be rounded up to minimum-allocation-mb • Large requests will be rounded down to maximum-allocation-mb <property> <name>yarn.scheduler.minimum-allocation-mb</name> <value>1024</value> </property> <property> <name>yarn.scheduler.maximum-allocation-mb</name> <value>8192</value> </property> ResourceManagerClient Request 512MB NodeManager NodeManager NodeManager Request 1024MB Master

21Copyright©2014 NTT corp. All Rights Reserved. • Define how much MapTasks or ReduceTasks use resource • MapReduce: etc /hadoop/mapred-site.xml Container allocation at framework side NodeManager CPU CPU CPU CPU CPU Memory Memory Memory Memory Memory 8 CPU cores 8 GB memory <property> <name>mapreduce.map.memory.mb</name> <value>1024</value> </property> <property> <name>mapreduce.reduce.memory.mb</name> <value>4096</value> </property> Slave NodeManager Container Container Master Giving us containers For map task - 1024 MB memory, 1 CPU core Container 1024MB memory 1 core

22Copyright©2014 NTT corp. All Rights Reserved. Container Killer • What’s happens when memory usage gets larger than requested? • NodeManager kills containers for isolation • when virtual memory exceeds allocated expected to avoid thrashing by default • Think whether memory check is really needed <property> <name>yarn.nodemanager.vmem-pmem-ratio</name> <value>2.1</value> </property> <property> <name>yarn.nodemanager.vmem-check-enabled</name> <value>true</value> <!– virtual memory check --> </property> NodeManager Container 1024MB memory 1 core Monitoring memory usage<property> <name>yarn.nodemanager.pmem-check-enabled</name> <value>true</value> <!– physical memory check --> </property>

23Copyright©2014 NTT corp. All Rights Reserved. Difficulty of container killer and JVM • -Xmx and -Xx:MaxPermSize is only for heap memory! • JVM can use -Xmx + -Xx:MaxPermSize + α • Please see GC tutorial to understand memory usage on JVM: http://www.oracle.com/webfolder/technetw ork/tutorials/obe/java/gc01/index.html

24Copyright©2014 NTT corp. All Rights Reserved. vs Container Killer • Basically same as OOM • Deciding policy at first • When should containers abort? • Running test query again and again • Profiling and dump heaps when Container killer appears • Check (p,v)mem-check-enabled configuration • pmem-check-enabled • vmem-check-enabled • One proposal is to automatic retry and tuning • MAPREDUCE-5785 • YARN-2091

25Copyright©2014 NTT corp. All Rights Reserved. • LinuxContainerExecutor • Linux container-based executor by using cgroups • DefaultContainerExecutor • Unix’s process-based Executor by using ulimit • Choose it based on isolation level you need • Better isolation with Linux Container Container Types <property> <name>yarn.nodemanager.container-executor.class</name> <value>org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor</value> </property>

26Copyright©2014 NTT corp. All Rights Reserved. • Configurations for cgorups • cgorups’ hierarchy • cgroups’ mount path Enabling LinuxContainerExecutor <property> <name>yarn.nodemanager.linux-container-executor.cgroups.hierarchy /name> <value>/hadoop-yarn </value> </property> <property> <name>yarn.nodemanager.linux-container-executor.cgroups.mount-path</name> <value>/sys/fs/cgroup</value> </property>

28Copyright©2014 NTT corp. All Rights Reserved. Schedulers on ResourceManager • Same as MRv1 • FIFO Scheduler • Processing Jobs in order • Fair Scheduler • Fair to all users, dominant fair scheduler • Capacity Scheduler • Queue shares as percentage of clusters • FIFO scheduling within each queue • Supporting preemption • Default is Capacity Scheduler <property> <name>yarn.resourcemanager.scheduler.class</name> <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value> </property>

30Copyright©2014 NTT corp. All Rights Reserved. Disk health check by NodeManager • NodeManager can check disk health • If the healthy disk is lower than specified disks space, NodeManager will abort <property> <name>yarn.nodemanager.disk-health-checker.min-healthy-disks</name> <value>0.25</value> </property> <property> <name>yarn.nodemanager.disk-health-checker.interval-ms</name> <value>120000</value> </property> NodeManager Monitoring disk health disk disk disk

31Copyright©2014 NTT corp. All Rights Reserved. User-defined health check by NodeManager • NodeManager can specify health-check script • If the scripts return strings “ERROR”, NodeManager will be marked as “unhealthy” <property> <name>yarn.nodemanager.health-checker.script.timeout-ms</name> <value>1200000</value> </property> <property> <name>yarn.nodemanager.health-checker.script.path</name> <value>/usr/bin/health-check-script.sh</value> </property> <property> <name>yarn.nodemanager.health-checker.script.opts</name> <value></value> </property>

33Copyright©2014 NTT corp. All Rights Reserved. Thread tuning on ResourceManager ResourceManager Client Slave NodeManager Container Container Master Admin Admin commands Submitting jobs Accept requests Heartbeat

34Copyright©2014 NTT corp. All Rights Reserved. Thread tuning on ResourceManager ResourceManager Client Slave NodeManager Container Container Master yarn.resourcemanager. client.thread-count(default=50) Admin Admin commands yarn.resourcemanager.scheduler. client.thread-count(default=50) yarn.resourcemanager.resource- tracker.client.thread-count(default=50) yarn.resourcemanager.admin.client .thread-count(default=1) Submitting jobs Accept requests Heartbeat

38Copyright©2014 NTT corp. All Rights Reserved. • What’s happen when ResourceManager fails? • cannot submit new jobs • NOTE: • Launched Apps continues to run • AppMaster recover is done in each frameworks • MRv2 ResourceManager High Availability ResourceManager Slave NodeManager Container Container Container Slave NodeManager Container Container Container Slave NodeManager Container Container Container Master Slave Slave MasterSlave SlaveMaster Slave Slave Client Submit jobs Continue to run each jobs

39Copyright©2014 NTT corp. All Rights Reserved. • Approach • Storing RM information to ZooKeeper • Automatic Failover by Embedded Elector • Manual Failover by RMHAUtils • NodeManagers uses local RMProxy to access them ResourceManager High Availability ResourceManager Active ResourceManager Standby ZooKeeper ZooKeeper ZooKeeper 2. failure 3. Embedded Detects failure EmbeddedElector EmbeddedElector 4. Failover RMState RMState RMState 1. Active Node stores all state into RMStateStore 3. Standby Node become active 5. Load states from RMStateStore

40Copyright©2014 NTT corp. All Rights Reserved. cluster1 • Cluster ID, RM Ids need to be specified Basic configuration(yarn-site.xml) <property> <name>yarn.resourcemanager.ha.enabled</name> <value>true</value> </property> <property> <name>yarn.resourcemanager.cluster-id</name> <value>cluster1</value> </property> <property> <name>yarn.resourcemanager.ha.rm-ids</name> <value>rm1,rm2</value> </property> <property> <name>yarn.resourcemanager.hostname.rm1</name> <value>master1</value> </property> <property> <name>yarn.resourcemanager.hostname.rm2</name> <value>master2</value> </property> ResourceManager Active(rm1) master1 ResourceManager Standby(rm2) master2

41Copyright©2014 NTT corp. All Rights Reserved. • To enable RM-HA, specify ZooKeeper as RMStateStore ZooKeeper Setting(yarn-site.xml) <property> <name>yarn.resourcemanager.store.class</name> <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value> </property> <property> <name>yarn.resourcemanager.zk-address</name> <value>zk1:2181,zk2:2181,zk3:2181</value> </property> <property> <name>yarn.resourcemanager.zk-address</name> <value>zk1:2181,zk2:2181,zk3:2181</value> </property>

42Copyright©2014 NTT corp. All Rights Reserved. • Depends on… • ZooKeeper’s connection timeout • yarn.resourcemanager.zk-timeout-ms • Number of znodes • Utility to benchmark ZKRMStateStore#loadState(YARN-1514) Estimating failover time $ bin/hadoop jar ./hadoop-yarn-server-resourcemanager-3.0.0-SNAPSHOT-tests.jar TestZKRMStateStorePerf -appSize 100 -appattemptsize 100 -hostPort localhost:2181 > ZKRMStateStore takes 2791 msec to loadState. ResourceManager Active ResourceManager Standby ZooKeeper ZooKeeper ZooKeeper EmbeddedElector EmbeddedElector RMState RMState RMState Load states from RMStateStore Failover

43Copyright©2014 NTT corp. All Rights Reserved. • YARN is a new layer for managing resources • New components from V2 • ResourceManager • NodeManager • Application Master • There are lots tuning points • Capacity Planning • Health check on NM • RM and NM threads • ResourceManager HA • Questions -> user@hadoop.apache.org • Issue -> https://issues.apache.org/jira/browse/YARN/ Summary

Taming YARN @ Hadoop Conference Japan 2014

by Tsuyoshi Ozawa, Researcher at NTT

on Jul 09, 2014

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Taming YARN @ Hadoop Conference Japan 2014 Presentation Transcript