CloudCom 2012 Taipei, Taiwan December 5, 2012 viraj@yahoo-inc.com

Hadoop 23 - dotnext CloudCom 2012 Taipei, Taiwan December 5, 2012 viraj@yahoo-inc.com

About Me Principal Engg in the Yahoo! Grid Team since May 2008 PhD from Rutgers University, NJ Specialization in Data Streaming, Grid, Autonomic Computing Worked on streaming data from live simulations executing in NERSC (CA), ORNL (TN) to Princeton Plasma Physics Lab (PPPL - NJ) Library introduce less then 5% overhead on computation PhD Thesis on In-Transit data processing for peta-scale simulation workflows Developed CorbaCoG kit for Globus Active contributor to Hadoop Apache, Pig, HCat and developer of Hadoop Vaidya - 2 -

Agenda - (10:30am -12pm) Overview and Introduction HDFS Federation YARN Hadoop 23 User Impact - 3 -

Hadoop Technology Stack at Yahoo! HDFS Distributed File System Map/Reduce Data Processing Paradigm HBase and HFile columnar storage PIG Data Processing Language HIVE SQL like query processing language HCatalog Table abstraction on top of big data allows interaction with Pig and Hive Oozie Workflow Management System Oozie HCatalog Hive PIG Map Reduce HBase File Format (HFile) HDFS 4-4 -

Evolution of Big Data Systems Low-Latency Analytic Processing 2009 Yahoo! S4 2010 Google Percolator 2010 Microsoft Stream Insight 2011 Twitter Storm 2012 Berkeley Spark 2012 Cloudera Impala 2009 IBM Streams 2008 Hive Big Data 2006 PIG 2007 HBase 2006 Google Dremel 2005 2004 Hadoop Google Map Reduce, BigTable - 5 -

Map & Reduce Primitives in Lisp (& Other functional languages) 1970s Google Paper 2004 http://labs.google.com/papers/mapreduce.html - 6 -

Map Output_List = Map (Input_List) Square (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) = (1, 4, 9, 16, 25, 36,49, 64, 81, 100) - 7 -

Reduce Output_Element = Reduce (Input_List) Sum (1, 4, 9, 16, 25, 36,49, 64, 81, 100) = 385-8 -

Parallelism Map is inherently parallel Each list element processed independently Reduce is inherently sequential Unless processing multiple lists Grouping to produce multiple lists - 9 -

Apache Hadoop Version Stable Version: 0.20.205 (aka Hadoop 1.0) Stable release of Hadoop currently run at Yahoo! Latest Version: 0.23.4 Being tested for certification in Yahoo! Hadoop version 2.0.2 in process of development in conjunction with Hortonworks - 10 -

HDFS Data is organized into files and directories Files are divided into uniform sized blocks (default 64MB) and distributed across cluster nodes HDFS exposes block placement so that computation can be migrated to data - 11 -

Hadoop 0.23 (dotnext) Highlights Major Hadoop release adopted by Yahoo! in over 2 years (after Hadoop 0.20 /Hadoop 1.0.2) Primary focus is scalability HDFS Federation larger namespace & scalability Larger aggregated namespace Helps for better Grid consolidation YARN aka MRv2 Job run reliability Agility & Evolution Hadoop 23 initial release does not target availability Addressed in future releases of Hadoop - 12 -

Hadoop 23 Story at Yahoo! Extra effort is being taken to certify applications with Hadoop 23 Sufficient time is provided for users to test their applications in Hadoop 23 Users are encouraged to get accounts to test if their applications run on a sandbox cluster which has Hadoop 23 installed Roll Out Plan Q4-2012 through Q1 2013 Hadoop 23 will be installed in a phased manner on 50k nodes - 13 -

HADOOP 23 FEATURES HDFS FEDERATION - 14 -

Non Federated HDFS Architecture - 15 -

Block Storage Namespace Non Federated HDFS Architecture Two main layers Namespace Namenode NS Block Management Consists of dirs, files and blocks Supports create, delete, modify and list files or dirs operations Datanode Datanode Block Storage Storage Block Management Datanode cluster membership Supports create/delete/modify/get block location operations Manages replication and replica placement Storage - provides read and write access to blocks - 16 -

Block Storage Namespace Non Federated HDFS Architecture Implemented as Namenode NS Block Management Single Namespace Volume Namespace Volume = Namespace + Blocks Single namenode with a namespace Datanode Storage Datanode Entire namespace is in memory Provides Block Management Datanodes store block replicas Block files stored on local file system - 17 -

Limitation - Single Namespace Scalability Storage scales horizontally - namespace doesn t Limited number of files, dirs and blocks 250 million files and blocks at 64GB Namenode heap size Performance File system operations throughput limited by a single node 120K read ops/sec and 6000 write ops/sec Poor Isolation All the tenants share a single namespace Separate volume for tenants is not possible Lacks separate namespace for different categories of applications Experimental apps can affect production apps Example - HBase could use its own namespace Isolation is problem, even in a small cluster - 18 -

Block Storage Namespace Limitation Tight coupling Namespace and Block Management are distinct services Tightly coupled due to co-location Scaling block management independent of namespace is simpler Simplifies Namespace and scaling it Block Storage could be a generic service Namespace is one of the applications to use the service Other services can be built directly on Block Storage HBase Foreign namespaces Namenode NS Block Management Datanode Datanode Storage 19-19 -

Block Storage Namespace HDFS Federation NN-1 NN-k NN-n NS1 NS k...... Foreign NS n Pool 1 Pool k Block Pools Pool n Datanode 1 Datanode 2 Datanode m......... Common Storage It is an administrative/operational feature for better managing resources Multiple independent Namenodes and Namespace Volumes in a cluster Namespace Volume = Namespace + Block Pool Block Storage as generic storage service Set of blocks for a Namespace Volume is called a Block Pool DNs store blocks for all the Namespace Volumes no partitioning - 20 -

Managing Namespaces Federation has multiple namespaces Don t you need a single global namespace? Key is to share the data and the names used to access the data A global namespace is one way to do that Client-side mount table is another way to share. Shared mount-table => global shared view Personalized mount-table => per-application view Share the data that matter by mounting it Client-side implementation of mount tables No single point of failure No hotspot for root and top level directories NS1 data project NS2 / home Client-side mount-table NS3 tmp NS4-21 -

viewfs:// schema instead of hdfs:// schema With striped HDFS, user's applications were forced to use explicit URL of the source strip to read the data Federation hides all that detail. User sees only one single Virtual storage: The viewfs: URI schema can be used as the default file system replacing the hdfs schema - 22 -

Client-Side Mount Table and VIEWFS Client-Side Mount Table is a type of file name indirection analogous to mount points in a conventional file system Indirection table available to the client application "client-side" is truly client side as HDFS client library is involved Namenodes are not part of the implementation. Data can be moved from one namespace to another without requiring changes in user applications An appropriate Client Side Mount Table should be provided - 23 -

Client-Side Mount Table and VIEWFS The mount table is specified in a config file, like all other Hadoop configurations, core-site.xml The Client Side Mount Table definitions will by supplied by the Operations team 0.20.XXX <property> <name>fs.default.name</name> <value>hdfs://namenode:port/</value> </property> 0.23 <property> <name>fs.default.name</name> <value>viewfs://clustername/</value> </property> - 24 -

Client-Side Mount Table Example mounttable.xml has a definition of the mount table called "KrRd" for the cluster MyCluster. MyCluster is a federation of the three name spaces managed by the three Namenodes nn1" nn2" and nn3" /user and /tmp managed by nn1. /projects/foo managed by nn2 /projects/bar managed by nn3" - 25 -

Client-Side Mount Table XML Example <configuration> <property> <name>fs.viewfs.mounttable.krrd.link./user</name> <value> hdfs://nn1/user </value> </property> <property> <name>fs.viewfs.mounttable.krrd.link./tmp</name> <value> hdfs://nn1/tmp </value> </property> <property> <name>fs.viewfs.mounttable.krrd.link./projects/foo</name> <value> hdfs://nn2/projects/foo </value> </property> <property> <name>fs.viewfs.mounttable.krrd.link./projects/bar</name> <value> hdfs://nn3/projects/bar</value> </property> </configuration> - 26 -

HDFS Federation - Wire Compatibility Wire Backward Compatibility Hadoop 23 is NOT RPC wire compatible with prior version s of Hadoop (0.20.X) Client must be updated to use the same version of Hadoop client library as installed on the server Application must be recompiled with new version of HDFS library API compatible - 27 -

HDFS Federation: Append Functionality Append Functionality HDFS Federation has full support of append functionality along with flush. The hflush call by the writing client, ensures that all previously written bytes are visible to all new reading clients. - 28 -

HDFS Federation - Sticky Bits Sticky Bits for Directories Directories (not files) have sticky-bits. A file in a sticky directory may only be removed or renamed by: a user if the user has write permission for the directory and the user is the owner of the file, or the owner of the directory, or the super-user. - 29 -

HDFS Federation - FileContext File Context: New API for access to HDFS features. Replacement for the existing File System interface. FileContext is intended for application developers. FileSystem is intended for Service Provider - 30 -

HDFS Federation - Symbolic Links Symbolic links allow the redirection of a filename to full URI Symbolic links may cross file systems No requirement for the target to exist when the link is created Symbolic links are available only via the File Context interface. - 31 -

HDFS Federation - Hadoop ARchive (HAR) har://scheme-hostname:port/archivepath/fileinarchive If no scheme is provided it assumes the underlying filesystem har:///archivepath/fileinarchive Naming scheme with viewfs har:///viewfs://cluster-name/foo/bar har:///foo/bar, if the default file system is viewfs: har:///hdfs://name-server/a/b/foo/bar - 32 -

HDFS Federation - MapReduce Hadoop framework transparently handles initialization of delegation token for all the Namenodes in the current Federated HDFS User job requires to access external HDFS Set mapreduce.job.hdfs-servers with a comma separated list of the Namenodes - 33 -

YET ANOTHER RESOURCE NEGOTIATOR (YARN) NEXT GENERATION OF HADOOP MAP-REDUCE - 34 -

Hadoop MapReduce Today JobTracker Manages cluster resources and job scheduling TaskTracker Per-node agent Manage tasks - 35 -

Current Limitations of the Job Tracker Scalability Maximum Cluster size 4,000 nodes Maximum concurrent tasks 40,000 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 - 36 -

Current Limitations of the Job Tracker Lacks support for alternate paradigms Iterative applications implemented using MapReduce are 10x slower. 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 - 37 -

Design Theme for YARN Reliability Availability Scalability - Clusters of 6,000-10,000 machines Each machine with 16 cores, 48G/96G RAM, 24TB/36TB disks 100,000 concurrent tasks 10,000 concurrent jobs Wire Compatibility Agility & Evolution Ability for customers to control upgrades to the grid software stack. - 38 -

Design Methodology Split up the two major functions of JobTracker Cluster resource management Application life-cycle management MapReduce becomes user-land library - 39 -

Architecture - 40 -

Architecture - 41 -

Architecture - 42 -

Architecture of YARN 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 - 43 -

Improvements vis-à-vis current Job Tracker 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 6,000 2012 machines > 12,000 2009 machines <16+ cores, 48/96G, 24TB> v/s <8 cores, 16G, 4TB> - 44 -

Improvements vis-à-vis current Job Tracker Availability Resource Manager No single point of failure availability via ZooKeeper Targeted in Future release of Hadoop 23 Application Masters are restarted automatically on RM restart Applications continue to progress with existing resources during restart, new resources aren t allocated Application Master Optional failover via application-specific checkpoint MapReduce applications pick up where they left off - 45 -

Improvements vis-à-vis current Job Tracker Wire Compatibility Protocols are wire-compatible Old clients can talk to new servers Rolling upgrades - 46 -

Improvements vis-à-vis current Job Tracker Innovation and Agility MapReduce now becomes a user-land library Multiple versions of MapReduce (& ecosystems) can run in the same cluster Faster deployment cycles for improvements Customers upgrade MapReduce versions on their schedule Users can run customized versions of MapReduce HOP (Hadoop Online Prototype) modified version of Hadoop MapReduce that allows data to be pipelined between tasks and between jobs - 47 -

Improvements vis-à-vis current Job Tracker Utilization Generic resource model Memory (in 23 the rest are for future releases) CPU Disk b/w Network b/w Remove fixed partition of map and reduce slots - 48 -

Improvements vis-à-vis current Job Tracker Support for programming paradigms other than MapReduce MPI : Work already in progress Master-Worker Machine Learning Iterative processing Enabled by allowing use of paradigm-specific Application Master Run all on the same Hadoop cluster - 49 -

Performance Improvements Small Job Optimizations Runs all tasks of Small job (i.e. job with up to 3/4 tasks) entirely in Application Master's JVM Reduces JVM startup time and also eliminates inter-node and inter-process data transfer during the shuffle phase. Transparent to the user Several Other improvements Speculation: Less aggressive Overhauled Shuffling algorithm yielding 30% improvement. - 50 -

Experiences of YARN High Points Scalable Largest YARN cluster in the world built at Yahoo! running on (Hadoop 0.23.4), with no scalability issues so far Ran tests to validate that YARN should scale to 10,000 nodes. Surprisingly Stable Web Services Better Utilization of Resources at Yahoo! No fixed partitioning between Map and Reduce Tasks Latency from resource available to resource re-assigned is far better than 1.x in big clusters - 51 -

Performance (0.23.3 vs. 1.0.2) HDFS Read (Throughput 5.37% higher) MapReduce Sort (Runtime 4.59% smaller, Throughput 3.98% higher) Shuffle (Shuffle Time 13.25% smaller) Gridmix (Runtime 5.29% smaller) Small Jobs Uber AM (Word Count 3.5x faster, 27.7x fewer resources) - 52 -

YARN Synergy with new Compute Paradigms MPI (www.open-mpi.org nightly snapshot) Machine Learning (Spark) Real-time Streaming (S4 and Storm) Graph Processing (GIRAPH-13) - 53 -

The Not So Good Oozie on YARN can have potential deadlocks (MAPREDUCE- 4304) UberAM can mitigate this Some UI scalability issues (YARN-151, MAPREDUCE-4720) Some pages download very large tables and paginate in JavaScript Minor incompatibilities in the distributed cache No generic history server (MAPREDUCE-3061) AM failures hard to debug (MAPREDUCE-4428, MAPREDUCE- 3688) - 54 -

HADOOP 23 IMPACT ON END USERS - 55 -

Hadoop 23 Compatibility Command Line Users should depend on environment variables: $HADOOP_COMMON_HOME $HADOOP_MAPRED_HOME $HADOOP_HDFS_HOME hadoop command to execute mapred or hdfs subcommands has been deprecated Old usage (will work) New Usage hadoop queue showacls hadoop fs -ls hadoop mapred job -kill <job_id> mapred queue -showacls hdfs dfs ls <path> mapred job -kill <job_id> - 56 -

Hadoop 23 Compatibility Map Reduce An application that is using a version of Hadoop 20 will not work in Hadoop 0.23 Hadoop 0.23 version is API compatible with Hadoop 0.20 But not binary compatible Hadoop Java programs will not require any code change, However users have to recompile with Hadoop 0.23 If code change is required, please let us know. Streaming applications should work without modifications Hadoop Pipes (using C/C++ interface) application will require a recompilation with new libraries - 57 -

Hadoop 23 Compatibility - Pipes Although not deprecated, no future enhancements are planned. Potential of being deprecated in future As of now, it should work as expected. Recompilation with new Hadoop library is required. Recommended use of Hadoop Streaming for any new development. - 58 -

Hadoop 23 Compatibility - Ecosystems Applications relying on default setup of Oozie, Pig and Hive should continue to work. Pig and Hive scripts should continue to work as expected Pig and Hive UDFs written in Java/Python should continue to function as expected Recompilation of Java UDF s against Hadoop 23 is required - 59 -

Hadoop 23 Compatibility Matrix for Pig Pig Version Hadoop 0.20.204 or 0.20.205 Hadoop 23 0.9.1 Works with Hadoop 20.205 Incompatible with Hadoop 23 0.9.2 Works with Hadoop 20.205 Works with Hadoop 23 0.10 and upwards Works with Hadoop 20.205 release Works with Hadoop 23-60 -

Hadoop 23 Compatibility - Pig Pig versions 0.9.2 and future releases will be fully supported on Hadoop 0.23 No Changes in Pig script if it uses relative paths in HDFS Changes in pig script is required if HDFS absolute path (hdfs:// ) is used HDFS Federation part of Hadoop 23 requires the usage of viewfs:// (HDFS discussion to follow) Change hdfs:// schema to use viewfs:// schema Java UDF s must be re-compiled with Pig 0.9.2 jar Ensures if user is using incompatible or deprecated APIs Code change might not be required for most cases - 61 -

Hadoop 23 Compatibility Matrix for Oozie Oozie Version Hadoop 0.20.205 Hadoop 23 3.1.2 Works with Hadoop 20 release Incompatible with Hadoop 23 3.1.4 Incompatible with Hadoop 20 release Works with Hadoop 23-62 -

Hadoop 23 Compatibility - Oozie Oozie 3.1.4 and later versions compatible with Hadoop 23 Existing user workflow and coordinator definition (XML) should continue to work as expected No need to redeploy the Oozie coordinator jobs Users will need to update workflow definition to use viewfs:// instead of hdfs:// schema Due to HDFS Federation (discussion to follow) If HDFS is updated to have multi-volume (i.e. Federated) and there is a need to relocate the data - 63 -

Hadoop 23 Compatibility Oozie Actions All Java actions must be recompiled with Hadoop 23 libraries distcp action or Java action invoking distcp requires Hadoop 23 compatible distcp jar Users responsibility to package Hadoop 23 compatible jars with their workflow definition Pig 0.9.2 jar needs to be packaged for Pig action to function in Hadoop 23-64 -

Hadoop 23 - Oozie Challenges Learning curve for maven builds Build iterations, local maven staging repo staleness Queue configurations, container allocations require revisiting the design Many iterations of Hadoop 23 deployment Overhead to test Oozie compatibility with new release Initial deployment of YARN did not have a view of the Application Master (AM) logs Manual ssh to AM for debugging launcher jobs - 65 -

Hadoop 23 Compatibility - Hive Hive version 0.8.1 and upwards are fully supported Hive SQL/scripts should continue to work without any modification Java UDF s in Hive must be re-compiled with Hadoop 23 compatible hive.jar Ensures if user is using incompatible or deprecated APIs - 66 -

Hadoop 23 Hive Challenges Deprecation of code in MiniMRCluster that fetches the stack trace from the JobTracker no longer works Extra amount of time in debugging and rewriting test cases Incompatibility of HDFS commands between Hadoop 1.0.2 and 0.23 -rmr vs. -rm -r mkdir vs. mkdir p Results in fixing tests in new ways or inventing workarounds so that they run in both Hadoop 1.0.2 and Hadoop 0.23 As Hive uses MapRed API s; more work required for certification Would be good to move to MapReduce API s (for example: Pig) - 67 -

Hadoop 23 Compatibility - HCat HCat 0.4 and upwards version will be certified to work with Hadoop 23-68 -

Hadoop 23 New Features User Logs in User Dir User logs (stdout/stderr/syslog from the job) go into /user/ HDFS dir and are subject to quotas User logs have potential to fill up user HDFS quota User has to periodically clean up Previously in Hadoop 20 were stored on task tracker machines Deleted after a fixed interval Storing of User logs fails if User quota on HDFS maxed out Application Master logs (counters, start time, #maps, #reducers) Stored on the system directories Cleaned up on a periodic basis - 69 -

Hadoop 23 Compatibility - Job History API Log Format History API & Log format are changed Affects all applications and tools that directly use Hadoop History API Stored as Avro serialization in JSon format Applications and tools are recommended to use Rumen Data extraction and analysis tool for Map-Reduce https://issues.apache.org/jira/secure/attachment/12450044/ru men.pdf - 70 -

Hadoop 23 Queue Changes Hadoop 23 has support for Hierarchical Queues Container Queues contain other Leaf/Job Queues Jobs are submitted to Leaf queues Higher level of controls to the administrators Better scheduling of jobs with competing resources within the container queues Queues (as before) can steal resources beyond their capacity subject to their Max-Capacity setting - 71 -

Hadoop 23 Compatibility - Web-UI Different Look and Feel to Web UI Customizable by the user Any user applications/tools depending on Web UI screen-scrapping to extract data will fail Users should depend on the web services API instead - 72 -

Resource Manager - 73 -

32 bits 64 bit JDK for Hadoop 23? Only 32 bit JDK is certified for Hadoop 23 64 bit JDK would be bundled but not certified 64 bit support postponed to post Hadoop 23 deployment - 74 -

Hadoop 23 Operations and Services Grid Operations at Yahoo! transitioned Hadoop 1.0.2 Namenode to Hadoop 23 smoothly No data was lost Matched the container configurations on Hadoop 23 clusters with the old Map Reduce slots Map Reduce slots were configured based on memory hence transition was smooth Scheduling, planning and migration of Hadoop 1.0.2 applications to Hadoop 23 for about 100+ customers was a major task for solutions Many issues were caught in the last minute needed emergency fixes (globbing, pig.jar packaging, change in mkdir command ) Hadoop 0.23.4 build planned - 75 -

Acknowledgements YARN Robert Evans, Thomas Graves, Jason Lowe Pig - Rohini Paliniswamy Hive and HCatalog Chris Drome Oozie Mona Chitnis and Mohammad Islam Services and Operations Rajiv Chittajallu and Kimsukh Kundu - 76 -

References 0.23 Documentation http://people.apache.org/~acmurthy/hadoop-0.23/ 0.23 Release Notes http://people.apache.org/~acmurthy/hadoop-0.23/hadoopproject-dist/hadoop-common/releasenotes.html YARN Documentation http://people.apache.org/~acmurthy/hadoop-0.23/hadoopyarn/hadoop-yarn-site/yarn.html HDFS Federation Documentation http://people.apache.org/~acmurthy/hadoop-0.23/hadoopyarn/hadoop-yarn-site/federation.html - 77 -