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Yarn
- 1. YARN Yet Another Resource Negotiator
- 2. CC BY 2.0 / Richard Bumgardner Been there, done that.
- 3. Agenda • Why YARN? • YARN Architecture and Concepts • Resources & Scheduling – Capacity Scheduler – Fair Scheduler • Configuring the Fair Scheduler • Managing Running Jobs
- 4. Agenda • Why YARN? • YARN Architecture and Concepts • Resources & Scheduling – Capacity Scheduler – Fair Scheduler • Configuring the Fair Scheduler • Managing Running Jobs
- 5. The 1st Generation of Hadoop: Batch HADOOP 1.0 Built for Web-Scale Batch Apps Single App BATCH HDFS Single App INTERACTIVE Single App BATCH HDFS • All other usage patterns must leverage that same infrastructure • Forces the creation of silos for managing mixed workloads Single App BATCH HDFS Single App ONLINE
- 6. Hadoop MapReduce Classic • JobTracker – Manages cluster resources and job scheduling • TaskTracker – Per-‐node agent – Manage tasks
- 7. MapReduce Classic: Limitations • Scalability – Maximum Cluster size – 4,000 nodes – Maximum concurrent tasks – 40,000 – Coarse synchronizaPon in JobTracker • Availability – Failure kills all queued and running jobs • Hard parPPon of resources into map and reduce slots – Low resource uPlizaPon • Lacks support for alternate paradigms and services – IteraPve applicaPons implemented using MapReduce are 10x slower
- 8. Our Vision: Hadoop as Next-Gen Platform MapReduce (cluster resource management & data processing) HDFS (redundant, reliable storage) Single Use System Batch Apps HADOOP 1.0 Multi Purpose Platform Batch, Interactive, Online, Streaming, … HADOOP 2.0 Others (data processing) YARN (cluster resource management) HDFS2 (redundant, reliable storage) MapReduce (data processing)
- 9. YARN: Talking Hadoop Beyond Batch YARN (Cluster Resource Management) HDFS2 (Redundant, Reliable Storage) BATCH (MapReduce) INTERACTIVE (Tez) STREAMING (Storm, S4,…) GRAPH (Giraph) IN-‐MEMORY (Spark) HPC MPI (OpenMPI) ONLINE (HBase) Store ALL DATA in one place… Interact with that data in MULTIPLE WAYS with Predictable Performance and Quality of Service ApplicaRons Run NaRvely IN Hadoop OTHER (Search) (Weave…)
- 10. Why YARN / MR2 ? • Scalability – JobTracker kept track of individual tasks and wouldn’t scale • UPlizaPon – All slots are equal even if the work is not equal • MulP-‐tenancy – Every framework shouldn’t need to write its own execuPon engine – All frameworks should share the resources on a cluster
- 11. Multiple levels of scheduling • YARN – Which applicaPon (framework) to give resources to ? • ApplicaPon (Framework – MR etc.) – Which task within the applicaPon should use these resources ?
- 12. Agenda • Why YARN? • YARN Architecture and Concepts • Resources & Scheduling – Capacity Scheduler – Fair Scheduler • Configuring the Fair Scheduler • Managing Running Jobs
- 13. YARN Concepts • ApplicaPon – ApplicaPon is a job submi^ed to the framework – Example – Map Reduce Job • Container – Basic unit of allocaPon – Fine-‐grained resource allocaPon across mulPple resource types (memory, cpu, disk, network, gpu etc.) • container_0 = 2GB, 1 CPU • container_1 = 1GB, 6 CPU – Replaces the fixed map/reduce slots
- 14. YARN Architecture NodeManager NodeManager NodeManager NodeManager Container 1.1 Container 2.4 ResourceManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager Container 1.2 Container 1.3 AM 1 Container 2.1 Container 2.2 Container 2.3 AM2 Scheduler -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ ApplicaRons Manager (AsM)
- 15. Architecture • Resource Manager – Global resource scheduler – Hierarchical queues • Node Manager – Per-‐machine agent – Manages the life-‐cycle of container – Container resource monitoring • ApplicaPon Master – Per-‐applicaPon – Manages applicaPon scheduling and task execuPon – E.g. MapReduce ApplicaPon Master
- 16. Design Centre • Split up the two major funcPons of JobTracker – Cluster resource management – ApplicaPon life-‐cycle management • MapReduce becomes user-‐land library
- 17. YARN Architecture - Walkthrough NodeManager NodeManager NodeManager NodeManager Container 1.1 Container 2.4 ResourceManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager Container 1.2 Container 1.3 AM 1 Container 2.2 Container 2.1 Container 2.3 AM2 Scheduler Client2
- 18. Control Flow: Submit application
- 19. Control Flow: Get application updates
- 20. Control Flow: AM asking for resources
- 21. Control Flow: AM using containers
- 22. Execution Modes • Local mode • Uber mode
- 23. Container Types • DefaultContainerExecutor – Unix’s process-‐based Executor by using ulimit • LinuxContainerExecutor – Linux container-‐based Executor by using cgroups • Choose it based on isolaPon level you need
- 24. Agenda • Why YARN? • YARN Architecture and Concepts • Resources & Scheduling – Capacity Scheduler – Fair Scheduler • Configuring the Fair Scheduler • Managing Running Jobs
- 26. Resource Model and Capacities • Resource vectors – e.g. 1024 MB, 2 vcores, … – No more task slots! • Nodes specify the amount of resources they have – yarn.nodemanager.resource.memory-‐mb – yarn.nodemanager.resource.cpu-‐vcores • vcores to cores relaPon, not really “virtual”
- 27. Resources and Scheduling • What you request is what you get – No more fixed-‐size slots – Framework/applicaPon requests resources for a task • MR AM requests resources for map and reduce tasks, these requests can potenPally be for different amounts of resources
- 28. YARN Scheduling ResourceManager ApplicaPon Master 1 ApplicaPon Master 2 Node 1 Node 2 Node 3 I want 2 containers with 1024 MB and a 1 core each Noted I’m sPll here I’ll reserve some space on node1 for AM1 Got anything for me? Here’s a security token to let you launch a container on Node 1 Hey, launch my container with this shell command Container
- 29. YARN Schedulers • Same as MR1 • 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 – SupporPng preempPon • Default is Capacity Scheduler
- 30. Capacity Scheduler 50% queue-‐1 queue-‐2 queue-‐3 Apps Apps Apps Guaranteed Resources 30% 20%
- 31. YARN Capacity Scheduler • ConfiguraPon in capacity-‐scheduler.xml • Take some Pme to setup your queues! • Queues have per-‐queue ACLs to restrict queue access – Access can be dynamically changed • ElasPcity can be limited on a per-‐queue basis – use yarn.scheduler.capacity.<queue-‐path>.maximum-‐capacity • Use yarn.scheduler.capacity.<queue-‐path>.state to drain queues – ‘Decommissioning’ a queue • yarn rmadmin –refreshQueues to make runPme changes
- 32. YARN Fair Scheduler • The Fair Scheduler is the default YARN scheduler in CDH5 • The only YARN scheduler that Cloudera recommends for producPon clusters • Provides fine-‐grained resource allocaPon for mulPple resource types – Memory (by default) – CPU (opPonal)
- 33. Goals of the Fair Scheduler • Should allow short interacPve jobs to coexist with long producPon jobs • Should allow resources to be controlled proporPonally • Should ensure that the cluster is efficiently uPlized
- 34. The Fair Scheduler • The Fair Scheduler promotes fairness between schedulable enPPes • The Fair Scheduler awards resources to pools that are most underserved – Gives a container to the pool that has the fewest resources allocated
- 35. Fair Scheduler Pools • Each job is assigned to a pool – Also known as a queue in YARN terminology • All pools in YARN descend from the root pool • Physical resource are not bound to any specific pool • Pools can be predefined or defined dynamically by specifying a pool name when you submit a job • Pools and subpools are defined in the fair-scheduler.xml file Total: 30GB Alice Bob 15GB15GB
- 36. In Which Pool Will a Job Run • The default pool for a job is root.username – For example, root.Alice and root.Bob – You can drop root when referring to a pool • For example, you can refer to root.Alice simply as Alice • Jobs can be assigned to arbitrarily-‐named pools – To specify the pool name when submirng a MapReduce job, use • -D mapreduce.job.queuename
- 37. When Will a Job Run Within a Pool? • The Fair Scheduler grants resources to a pool, but which job’s task will get resources? • The policies for assigning resources to jobs within a pool are defined in fair-scheduler.xml • The Fair Scheduler uses three techniques for prioriPzing jobs within pools: – Single resource fairness – Dominant resource fairness – FIFO • You can also configure the Fair Scheduler to delay assignment of resources when a preferred rack or node is not available
- 38. Single Resource Fairness • Single resource fairness – Is the default Fair Scheduler policy – Schedules jobs using memory • Example – Two pools: Alice has 15GB allocated, and Bob has 5GB – Both pools request a 10GB container of memory – Bob has less resources and will be granted the next 10GB that becomes available Total: 30GB Alice Bob 10GB 15GB 5GB
- 39. Adding Pools Redistributes Resources • The user Charlie now submits a job to a new pool – Resource allocaPons are adjusted – Each pool receives a fair share of cluster resources Total: 30GB Alice Bob Charlie 10GB 10GB10GB
- 40. Determining the Fair Share • The fair share of resources assigned to the pool is based on – The total resources available across the cluster – The number of pools compePng for cluster resources • Excess cluster capacity is spread across all pools – The aim is to maintain the most even allocaPon possible so every pool receives its fair share of resources • The fair share will never be higher than the actual demand • Pools can use more than their fair share when other pools are not in need of resources – This happens when there are no tasks eligible to run in other pools
- 41. Minimum Resources • A pool with minimum resources defined receives priority during resource allocaPon • The minimum resources, minResources, are the minimum amount of resources that must be allocated to the pool prior to fair share allocaPon – Minimum resources are allocated to each pool assuming there is cluster capacity – Pools that have minimum resources specified will receive priority in resource assignment
- 42. Minimum Resource Allocation Example • First, fill up the Production pool to the 20GB minimum guarantee • Then distribute the remaining 10GB evenly across Alice and Bob Total: 30GB ProducPon BobAlice Demand: 100GB Demand: 30GB Demand: 25GB minResources: 20GB 5GB 5GB 20GB
- 43. Minimum Resource Allocation Example 2: Production Pool Empty • Production has no demand, so no resources are allocated to it • All resources are allocated evenly between Alice and Bob Total: 30GB ProducPon 15GB BobAlice Demand: 0GB Demand: 30GB Demand: 25GB minResources: 20GB 15GB
- 44. • Combined minResources of Production and Research exceed capacity • Minimum resources are assigned proporPonally based on defined minResources unPl available resources are exhausted • No memory remains for pools without minResources defined (i.e.,Bob) Minimum Resource Allocation Example 3: MinResources Exceed Resources Total: 30GB ProducPon BobResearch Demand: 100GB Demand: 30GB Demand: 25GB minResources: 50GB minResources: 25GB 20GB 10GB
- 45. • Production is filled to minResources • Remaining 25GB is distributed across all pools • Production pool receives more than its minResources, to maintain fairness Minimum Resource Allocation Example 4: MinResources < Fair Share Total: 30GB ProducPon BobAlice Demand: 100GB Demand: 30GB Demand: 25GB minResources: 5GB 10GB 10GB10GB
- 46. Pools with Weights • Instead of (or in addiPon to) serng minResources, pools can be assigned a weight • Pools with higher weight receive more resources during allocaPon • ‘Even water glass height’ analogy: – Think of the weight as controlling the ‘width’ of the glass
- 47. Example: Pool with Double Weight • Production is filled to minResources (5Gb) • Remaining 25GB is distributed across all pools • Bob pool receives twice the amount of memory during fair share allocaPon Total: 30GB ProducPon BobAlice Demand: 100GB Demand: 30GB Demand: 25GB minResources: 5GB Weight: 2 8GB 14GB8GB
- 48. Dominant Resource Fairness • The Fair Scheduler can be configured to schedule with both memory and CPU using dominant resource fairness • Scenario #1: – Alice has 6GB and 3 cores, and Bob has 4GB and 2 cores – which pool receives the next resource allocaPon? • Bob will receive the next container because it has less memory and less CPU cores allocated than Alice 6GB 3 cores 4GB 2 cores Alice Usage Bob Usage
- 49. Dominant Resource Fairness Example • Scenario #2: – A cluster has 10GB of total memory and 20 cores – Pool Alice has containers granted for 4GB of memory and 5 cores – Pool Bob has containers granted for 1GB of memory and 10 cores • Alice will receive the next container because its 40% dominant share of memory is less than the Bob pool’s 50% dominant share of CPU 4GB 40% capacity 5 cores 25% capacity 1GB 10% capacity 10 cores 50% capacity Alice Usage Bob Usage
- 50. Achieving Fair Share: The Patient Approach • If shares are imbalanced, pools which are over their fair share may not assign new tasks when their old ones complete – Those resources then become available to pools which are operaPng below their fair share • However, waiPng paPently for a task in another pool to finish may not be acceptable in a producPon environment – Tasks could take a long Pme to complete
- 51. Achieving Fair Share: The Brute Force Approach • With preempPon enabled, the Fair Scheduler acPvely kills tasks that belong to pools operaPng over their fair share – Pools operaPng below fair share receive those reaped resources • There are two types of preempPon available – Minimum share preempPon – Fair share preempPon • PreempPon code avoids killing a task in a pool if it would cause that pool to begin preempPng tasks in other pools – This prevents a potenPally endless cycle of pools killing one another’s tasks
- 52. Minimum Share Preemption • Pools with a minResources configured are operaPng on an SLA (Service Level Agreement) • Pools that are below their minimum share as defined by minResources can preempt tasks in other pools – Set minSharePreemptionTimeout to the number of seconds the pool is under its minimum share before preempPon should begin – Default is infinite (Java’s Long.MAX_VALUE)
- 53. Fair Share Preemption • Pools not receiving their fair share can preempt tasks in other pools – Only pools that exceed their fair share are candidates for preempPon • Use fair share preempPon conservaPvely – Set fairSharePreemptionTimeout to the number of seconds a pool is under fair share before preempPon should begin – Default is infinite (Java’s Long.MAX_VALUE)
- 54. Agenda • Why YARN? • YARN Architecture and Concepts • Resources & Scheduling – Capacity Scheduler – Fair Scheduler • Configuring the Fair Scheduler • Managing Running Jobs
- 55. Configuring Fair Scheduler Capabilities (1) • yarn.scheduler.fair.allow-‐undeclared-‐pools (yarn-‐site.xml) – When true, new pools can be created at applicaPon submission Pme or by the user-‐as-‐default-‐queue property. When false, submirng to a pool that is not specified in the fair-scheduler.xml file causes the applicaPon to be placed in the “default” pool. Default: true. Ignored if a pool placement policy is defined in the fair- scheduler.xml file. • yarn.scheduler.fair.preempPon (yarn-‐site.xml) – Enables preempPon in Fair Scheduler. Set to true if you have pools that must operate on an SLA. Default: false. • yarn.scheduler.fair.user-‐as-‐default-‐queue (yarn-‐site.xml) – Send jobs to pools based on users’ names instead of to the default pool, root.default. Default: true
- 56. Configuring Fair Scheduler Capabilities (2) • yarn.scheduler.fair.locality.threshold.node 、 yarn.scheduler.fair.locality.threshold.rack (yarn-‐site.xml) – For applicaPon that request containers on parPcular nodes or racks, the number of scheduling opportuniPes since the last container assignment to wait before accepPng a placement on another node. Expressed as a float between 0 and 1, which, as a fracPon of the cluster size, is the number of scheduling opportuniPes to pass up.. Default: 1 (don’t pass up any scheduling opportuniPes) • Example: yarn.scheduler.fair.locality.threshold.node = 0.02, cluster size = 100 nodes. At most 2 scheduling opportuniPes can be skipped when preferred placement cannot be met.
- 57. Configuring Resource Allocation for Pools and Users (1) • You configure Fair Scheduler pools in the /etc/hadoop/ conf/fair-scheduler.xml file • The Fair Scheduler rereads this file every 10 seconds – ResourceManager restart is not required when the file changes • The fair-scheduler.xml file must contain an <allocations> element • Use the <queue> element to configure resource allocaPon for a pool • Use the <user> element to configure resource allocaPon for a user across mulPple pools
- 58. Configuring Resource Allocation for Pools and Users (2) • To specify resource allocaPons, use the <queue> or <user> element with any or all of the the following subelements – <minResources> • The minimum resources to which the pool is enPtled • Format is x mb, y vcores • Example: 10000mb, 5 vcores – <maxResources> • The maximum resources to which the pool is enPtled • Format is x mb, y vcores
- 59. Configuring Resource Allocation for Pools and Users (3) • AddiPonal sub-‐elements of <queue> or <user> to use when specifying resource allocaPons – <maxRunningApps> • The maximum applicaPons in the pool that can be run concurrently – <weight> • Used for non-‐proporPonate sharing with other pools • The default is 1 – <minSharePreemptionTimeout> • Time to wait before pre-‐empPng tasks – <schedulingPolicy> • SRF for single resource fairness (the default) • SRF for dominant resource fairness • FIFO for first-‐in, first-‐out
- 60. fair-scheduler.xml Example (1) • Allow users to run three jobs, but allow Bob to run six jobs <?xml version=“1.0”?> <allocaPons> <userMaxAppsDefault>3</userMaxAppsDefault> <user name=“bob”> <maxRunningApps>6</maxRunningApps> </user> </allocaPons>
- 61. fair-scheduler.xml Example (2) • Add a fair share Pmeout <?xml version=“1.0”?> <allocaPons> <userMaxAppsDefault>3</userMaxAppsDefault> <user name=“bob”> <maxRunningApps>6</maxRunningApps> </user> <fairSharePreempPonTimeout>300</fairSharePreempPonTimeout> </allocaPons>
- 62. fair-scheduler.xml Example (3) • Define the producPon pool with a weight of 2 and a resource allocaPon of 10000 MB and 1 core <?xml version=“1.0”?> <allocaPons> <userMaxAppsDefault>3</userMaxAppsDefault> <queue name=“producPon”> <minResources>10000 mb, 1 vcores</minResources> <weight>2.0</weight> </queue > </allocaPons>
- 63. fair-scheduler.xml Example (4) • Add an SLA to the producPon pool <?xml version=“1.0”?> <allocaPons> <userMaxAppsDefault>3</userMaxAppsDefault> <queue name=“producPon”> <minResources>10000 mb, 1 vcores</minResources> <weight>2.0</weight> <minSharePreempPonTimeout>60</minSharePreempPonTimeout> </queue > </allocaPons>
- 64. The Fair Scheduler User Interface • h^p://<resource_manager_host>:8088/cluster/scheduler
- 65. Agenda • Why YARN? • YARN Architecture and Concepts • Resources & Scheduling – Capacity Scheduler – Fair Scheduler • Configuring the Fair Scheduler • Managing Running Jobs
- 66. Displaying Jobs • To view jobs currently running on the cluster – yarn application –list – List all running jobs, including the applicaPon ID for each • To view all jobs on the cluster, including completed jobs – yarn application –list all • To display the status of an individual job – yarn application –status <application_ID> • You can also use the ResourceManager Web UI, Hue, Ambari, Cloudera Manager to display jobs
- 67. Killing Jobs • It is important to note that once a user has submi^ed a job, they can not stop it just by hirng CTRL-‐C on their terminal – This stops job output appearing on the user’s console – The job is sPll running on the cluster! • To kill a job running on the cluster – yarn application –kill <application_ID> • You can also kill job from Cloudera Manager
- 69. 目录 • aaa