next generation of hadoop mapreduce
TRANSCRIPT
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
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• Machines are getting bigger and faster
• Moving toward 12 2TB disks instead of 4 1TB disks
• Enables more tasks per a machine
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?
http://developer.yahoo.com/blogs/hadoop/posts/2011/02/mapreduce-nextgen/http://developer.yahoo.com/blogs/hadoop/posts/2011/03/mapreduce-nextgen-scheduler/