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Hadoop Ecosystem
- 1. Hadoop Ecosystem Lior Sidi Sep 2016
- 2. Hello! I am Lior Sidi
- 4. What is Hadoop? • Hadoop – Open source implementation of MapReduce (MR) • Perform MR Jobs fast and efficient Goal generating Value from large datasets That cannot be analyzed using traditional technologies
- 5. Hadoop Concepts Requirements • Linear horizontal scalability • Jobs run in isolation • Simple programming model Challenges and solution • Ch1: Data access bottleneck • Sol: Store and process data on same node • Ch1: Distributed Programming is Difficult • Sol: Use high level languages API
- 6. Hadoop Timeline 2003 Oct Google File System paper released 2004 Dec MapReduce: Simplified Data Processing on Large Clusters 2006 Oct Hadoop 1.0 released 2007 Oct Yahoo Labs creates Pig 2008 Oct Cloudera, Hadoop distributor is founded 2010 Sep Hive and Pig Graduates 2011 Jan Zookeeper Graduates 2013 Mar Yarn deployed in Yahoo 2014 Feb Apache Spark top Level Apache Project
- 9. Storage / HDFS • “Hadoop Distributed File System” • Design: • Write once – read many times pattern • Cheap hardware • Low latency data access • Concepts: • Block – File is split to Size 128 MB blocks, redundancy - 3 • NameNode (Master) – per cluster - file system namespace for blocks (single point of failure) • DataNode (Worker) – per Node - store and retrieve blocks • Functions: • High availability – run a second NameNode • Block caching – block cached in only one DataNode • Locality - Rack sensitive, network topology • File permissions – like POSIX – r w x – owner/group/mode file/directory • Interfaces – HTTP (proxy/direct), Java API • Cluster balance – evenly spread the block on the cluster
- 10. 2Rack 1Rack Data Block 1 Block 2 Block 3 DataNodeDataNodeDataNodeDataNode Block 1 Block 1 Block 2 Block 2 Block 3 Block 3 Block 1 DataNode Block 2 Block 3 NameNode HDFS proxy Client file is distribution and accessed on Hadoop HDFS
- 12. Resource Management / YARN • “Yet Another Resource Negotiator” • Manage and schedule the cluster resource • Daemons: • Resource Manager – Per Cluster – manage resource across the cluster • Node Manager – Per Node – launch and monitor a Container • Container – execute an app process • Resource requests for containers: • Amount of computers (CPU & Memory) • Locality (node/rack) • Lifespan: application per user job or long-running apps shared by users • Scheduling: • Allocate resource by policy (FIFO, capacity (ordanisation), Fair
- 13. Hadoop Cluster Nodemanager node NodeManager Container Master Client node application Resource manager node ResourceManager Client Nodemanager node NodeManager Container Worker Nodemanager node NodeManager Container Worker launch launch launch launch Launch YARN app heartbeat Job scheduling on top Hadoop Cluster
- 15. Processing / MapReduce • Simplify, large scale, automatic, Fault tolerant development data processing • origin - Google paper 2004 • Batch processing • Hadoop MR: • JobTracker – 1per cluster - master process, schedule tasks on workers, monitor progress • taskTracker – 1 per worker - execute map/reduce tasks locally and report progress
- 17. Hadoop Cluster Nodemanager node NodeManager Container JobTracker Client node MR program Resource manager node ResourceManager Client Nodemanager node NodeManager Container TaskTracker Nodemanager node NodeManager Container TaskTracker launch launch launch launch Launch YARN app heartbeat MR Job scheduling on top Hadoop Cluster
- 19. Storage / HBase • Distributes Column Base database on top HDFS • Real time read/write random access for large data-sets • Region – tables splitting by row • Pheonex - SQL on HBase RowKey Column Family 1 Column Family 2 Col 1.1 Version Data Col 1.2 Col 1.3 Version Data Version Data Hbase Data Model
- 21. Coordination / ZooKeeper • Hadoop’s distributed coordination service • Coordinate read/write action on data • high availability filesystem • Implementation: • Data model: • Tree build from Znodes (1MB data) • Znode – data changes, ACL (access control list ) • Leader - perform write and broadcast an update • Follower – pass atomic request to leader • Lock service • User groups • Replicate mode
- 22. Coordination / ZooKeeper Hadoop Cluster ZooKeaper Service Leader HDFSHBase DataNodeDataNodeDataNode HMaster Other Client RegionRegionRegion NameNode / /HBase HDFS/ Follower / /HBase HDFS/ Follower / /HBase HDFS/ LOCK LOCK ZooKeeper Coordination example
- 24. Row Based Avro • Language natural data serialization system • Share many data formats with many code language • Split able and sortable - Allow easy map reduce • Rich schema resolution – flexible scheme • Other Row Based formats • sequenceFile - Logfile format • MapFile - Sorted sequenceFile
- 25. Row Based Avro Header Block 1 Block 2 Block N Count objs Serialized objs SyncMarker identifier Metadata: Schema & codec SyncMarker Size objs { "Type":"record" "Name":"Person" "Fields": [{ "name":"firstName", "type":"string" "order":"descending" },{ "name":"age", "type":"int" },{... ] } Schema File Structure
- 26. File Structure Parquet • Columnar storage format • Skip unneeded columns • Fast queries & small size • Efficient nested data store Header Block 1 Block 2 Block N Column chunk Column chunk Column chunk Page Page Page Page Magic Number File Metadata Footer Message Person { Required binary name (UTF8); Required int32 age (UTF8); Required group hobbies (LIST) { Required binary array (UTF8); } } Schema
- 27. Data Injection Resource Management coordination Processing Storage Data Formats Hadoop Ecosystem
- 28. Data Integration / Sqoop • Import/export structural data • Sqoop connector: • import/export from a database • Sqoop1- command line • Sqoop2 – service • Connectors – connect RDBs Hadoop Cluster Export MapReduce Job Database Table Sqoop client Import MapReduce Job Hdfs Hdfs Map Map Hdfs Hdfs Map Map metadata launch launch ExportImport
- 29. Data Integration / Flume • Event base data injection into Hadoop • Flume agent components: • Sources – spoolingDir (create events), Avro(RPC), Http (requests) • Channel • Sink – Avro, HDFS, HBase, Solr(=near real time) • Reliability - Use separate transaction • Fan out – one source many sinks • Scale - agent tiers for aggregation multiple sources • Sink grouping- avoid failure and load balancing
- 30. Fan Out Data Integration / Flume Hadoop Data File system Flume Agent Source Channel Sink Tier 1 Flume Agent Tier 1 Flume Agent Tier 1 Flume Agent Tier 2 Flume Agent Tier 2 Flume Agent Tier 3 Flume Agent Tier 3 Flume Agent File system Sink GroupingScale HDFS HBase Data
- 31. Data Integration / Kafka • distributed publish-subscribe messaging system • Fast, scalable, durable • Components: • Topics – categories of feeds messages • Procedures – process that publish messages to topic • Message consumer – processes that subscribe for topic • Broker – kafka servers on cluster • Distribution • Leader – allow read/write • Follower – replicate
- 33. Data Integration / Streaming • Stream processing • Kafka Stream - Process and analyze data in Kafka • Storm – real-time computation • Spark streaming – process live data and can apply Spark MLib and graphX Flume Agent 1 Data Kafka Spark Streaming Flume Agent 2 Storm Topic A Topic B HDFS 1 1 1 2 2
- 35. • Cluster Computing Framework • In Memory processing • Language: Scala, Java and Python • RDD – resilience Distributed dataset • Read only collection spread in the cluster • Computation of transformation happened when Action • DAG engine – schedule many transformations to one optimal Job • Spark context • parallel jobs • Caching • Broadcast variables (Data/Functions) • Cluster Manager of executors: • Local, Standalone, Mesos , Yarn Computation / Spark
- 36. Computation / Spark Hadoop Driver SparkContext Spark Program DAG Scheduler Task Scheduler Scheduler backend Executer Executer Executer Job Job Stages Tasks Task Task Task
- 37. Scripting / Pig • Data flow programming language - Map reduce abstraction • support: User defined functions (UDF), Streaming, nested data • Don’t support: random read/write • Pig Latin - Scripting language • Load, store, filtering, Group, Join, Sort, Union and Split, UDF, Co-group • Modes • Local – small datasets • MR mode – run on cluster • Execution - script, grunt (shell), embedded (java) • Parameter substitution – run script with different parameters • Similar • Crunch – MR pipeline with Java (no UDF)
- 38. Query / Hive • Components • MetaStore – tables description • HiveQL – SQL dialect (SQL: 2003) • tables Management • warehouse directory • external tables • functionality • Bucketing and Partitions by column • Support UDF and UDAF (aggregate) • Insert Update Delete: • Saved in delta files • Background MR Jobs • (Available Transaction context) • Lock table (avoid drop)
- 39. Query / Comparison SparkSql (shark)ImpalaHive Procedural development BI & SQL analyticsBatchUsage OKBestbadSpeed MemoryDedicated Deamons on DataNode MapReduceimplementation Persto , Drill (SQL: 2011) Hive On sparkSimilar tools
- 41. Workflow / Oozie • Schedule Hadoop jobs • Job types: • Workflows – sequence of jobs via Directed Graphs (DAGs) • Coordinator - trigger jobs by time or availability start Sqoop Fork Pig PigMR Sub workflow FS (HDFS) Join End Control flow Action Email
- 43. Search / Solr • Full- text search over Hadoop • Near real time indexing • REST API • Based on Apache Lucene java search library
- 45. Visualization / Hue • Open source Web interface for analyzing data with any Hadoop. • Application: • File Browser: HDFS, Hbase • Scheduling of jobs and workflows : Oozie • Job Browser: YARN • SQL : Hive, Impala • Data analysis: Pig, UDF • Dynamic Search: Solr • Notebooks: Spark • Data Transfer: Sqoop 2
- 47. Cluster Management / Cloudera • 100% open source • The most complete and tested distribution of Hadoop • Integrate all Hadoop project • Express – free, end to end administration • Enterprise – Extra features and support
- 48. Cluster Management / Comparison https://talendexpert.com/cloudera-vs-honworks-vs-mapr
- 49. MasterMasterMaster Other Servers Worker Basic Cluster configuration Resource manager Standby Resource Manager NodeManager DataNode Cloudera Manager Hive GW ZooKeeper Impala Daemon Impala State Sqoop GW Spark GW NameNode Master ZooKeeper Secondary NameNode Worker NodeManager DataNode Impala Daemon Worker NodeManager DataNode Impala Daemon Worker NodeManager DataNode Impala Daemon
Hinweis der Redaktion
- HDFS – manage the file system across network of machines Design to store big files Master worker pattern Namenode maintain the directory tree –doesn’t maintain a perstistent location but reconstract when reboot Namenode is the most important component in the cluster when it lost the entire access to the cluster is lost therefore it possible to create high availabuility where we
- Design to support map reduce but is used for other operations