Next Generation of Apache Hadoop MapReduce Owen

What is Hadoop?  A framework for storing and processing big data on lots of commodity machines. - Up to 4,000 machines in a cluster - Up to 20 PB in a cluster  Open Source Apache project  High reliability done in software - Automated failover for data and computation  Implemented in Java  Primary data analysis platform at Yahoo! - 40,000+ machines running Hadoop

What is Hadoop?  HDFS – Distributed File System - Combines cluster’s local storage into a single namespace. - All data is replicated to multiple machines. - Provides locality information to clients  MapReduce - Batch computation framework - Tasks re-executed on failure - User code wrapped around a distributed sort - Optimizes for data locality of input

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Hadoop MapReduce Today  JobTracker - Manages cluster resources and job scheduling  TaskTracker - Per-node agent - Manage tasks

Current Limitations  Scalability - Maximum Cluster size – 4,000 nodes - Maximum concurrent tasks – 40,000 - Coarse synchronization in JobTracker  Single point of failure - Failure kills all queued and running jobs - Jobs need to be re-submitted by users  Restart is very tricky due to complex state  Hard partition of resources into map and reduce slots

Current Limitations  Lacks support for alternate paradigms - Iterative applications implemented using MapReduce are 10x slower. - Users use MapReduce to run arbitrary code - Example: K-Means, PageRank  Lack of wire-compatible protocols - Client and cluster must be of same version - Applications and workflows cannot migrate to different clusters

MapReduce Requirements for 2011  Reliability  Availability  Scalability - Clusters of 6,000 machines - Each machine with 16 cores, 48G RAM, 24TB disks - 100,000 concurrent tasks - 10,000 concurrent jobs  Wire Compatibility  Agility & Evolution – Ability for customers to control upgrades to the grid software stack.

MapReduce – Design Focus  Split up the two major functions of JobTracker - Cluster resource management - Application life-cycle management  MapReduce becomes user-land library

Architecture

 Resource Manager - Global resource scheduler - Hierarchical queues  Node Manager - Per-machine agent - Manages the life-cycle of container - Container resource monitoring  Application Master - Per-application - Manages application scheduling and task execution - E.g. MapReduce Application Master

Improvements vis-à-vis current MapReduce  Scalability - Application life-cycle management is very expensive - Partition resource management and application life-cycle management - Application management is distributed - Hardware trends - Currently run clusters of 4,000 machines 6, machines > 12, machines v/s

Improvements vis-à-vis current MapReduce  Availability - Application Master Optional failover via application-specific checkpoint MapReduce applications pick up where they left off - Resource Manager No single point of failure - failover via ZooKeeper Application Masters are restarted automatically

Improvements vis-à-vis current MapReduce  Wire Compatibility - Protocols are wire-compatible - Old clients can talk to new servers - Evolution toward rolling upgrades

Improvements vis-à-vis current MapReduce  Innovation and Agility - MapReduce now becomes a user-land library - Multiple versions of MapReduce can run in the same cluster (a la Apache Pig) Faster deployment cycles for improvements - Customers upgrade MapReduce versions on their schedule - Users can use customized MapReduce versions without affecting everyone!

Improvements vis-à-vis current MapReduce  Utilization - Generic resource model Memory CPU Disk b/w Network b/w - Remove fixed partition of map and reduce slots

Improvements vis-à-vis current MapReduce  Support for programming paradigms other than MapReduce - MPI - Master-Worker - Machine Learning and Iterative processing - Enabled by paradigm-specific Application Master - All can run on the same Hadoop cluster

Summary  Takes Hadoop to the next level - Scale-out even further - High availability - Cluster Utilization - Support for paradigms other than MapReduce

Questions?