Hadoop: Distributed Data Processing

Editor's Notes

• #5: The solution is to *augment* the current RDBMSes with a “smart” storage/processing system. The original event level data is kept in this smart storage layer and can be mined as needed. The aggregate data is kept in the RDBMSes for interactive reporting and analytics.
• #6: The system is self-healing in the sense that it automatically routes around failure. If a node fails then its workload and data are transparently shifted some where else.The system is intelligent in the sense that the MapReduce scheduler optimizes for the processing to happen on the same node storing the associated data (or co-located on the same leaf Ethernet switch), it also speculatively executes redundant tasks if certain nodes are detected to be slow.One of the key benefits of Hadoop is the ability to just upload any unstructured files to it without having to “schematize” them first. You can dump any type of data into Hadoop then the input record readers will abstract it out as if it was structured (i.e. schema on read vs on write)Open Source Software allows for innovation by partners and customers. It also enables third-party inspection of source code which provides assurances on security and product quality.1 HDD = 75 MB/sec, 1000 HDDs = 75 GB/sec, the “head of fileserver” bottleneck is eliminated.
• #7: http://developer.yahoo.net/blogs/hadoop/2009/05/hadoop_sorts_a_petabyte_in_162.html100s of deployments worldwide (http://wiki.apache.org/hadoop/PoweredBy)
• #8: Speculative Execution, Data rebalancing, Background Checksumming, etc.
• #9: Pool commodity servers in a single hierarchical namespace.Designed for large files that are written once and read many times.Example here shows what happens with a replication factor of 3, each data block is present in at least 3 separate data nodes.Typical Hadoop node is eight cores with 16GB ram and four 1TB SATA disks.Default block size is 64MB, though most folks now set it to 128MB
• #10: Differentiate between MapReduce the platform and MapReduce the programming model. The analogy is similar to the RDBMs which executes the queries, and SQL which is the language for the queries.MapReduce can run on top of HDFS or a selection of other storage systemsIntelligent scheduling algorithms for locality, sharing, and resource optimization.
• #11: Think: SELECT word, count(*) FROM documents GROUP BY wordCheckout ParBASH:http://cloud-dev.blogspot.com/2009/06/introduction-to-parbash.html
• #12: The Data Node slave and the Task Tracker slave can, and should, share the same server instance to leverage data locality whenever possible.The NameNode and JobTracker are currently SPOFs which can affect the availability of the system by around 15 mins (no data loss though, so the system is reliable, but can suffer from downtime occasionally). That issue is currently being addressed by the Apache Hadoop community using Zookeeper.
• #13: HBase: Low Latency Random-Access with per-row consistency for updates/inserts/deletesJava MapReduceGives the most flexibility and performance, but with a potentially longer development cycleStreaming MapReduceAllows you to develop in any language of your choice, but slightly slower performancePigA relatively new data-flow language (contributed by Yahoo), suitable for ETL like workloads (procedural multi-stage jobs)HiveA SQL warehouse on top of MapReduce (contributed by Facebook), translates SQL into MapReduceHive Features: A subset of SQL covering the most common statementsAgile data types: Array, Map, Struct, and JSON objectsUser Defined Functions and AggregatesRegular Expression supportMapReduce supportJDBC supportPartitions and Buckets (for performance optimization)In The Works: Indices, Columnar Storage, Views, Microstrategy compatibility, Explode/CollectMore details: http://wiki.apache.org/hadoop/HiveQuery: SELECT, FROM, WHERE, JOIN, GROUP BY, SORT BY, LIMIT, DISTINCT, UNION ALLJoin: LEFT, RIGHT, FULL, OUTER, INNERDDL: CREATE TABLE, ALTER TABLE, DROP TABLE, DROP PARTITION, SHOW TABLES, SHOW PARTITIONSDML: LOAD DATA INTO, FROM INSERTTypes: TINYINT, INT, BIGINT, BOOLEAN, DOUBLE, STRING, ARRAY, MAP, STRUCT, JSON OBJECTQuery:Subqueries in FROM, User Defined Functions, User Defined Aggregates, Sampling (TABLESAMPLE)Relational: IS NULL, IS NOT NULL, LIKE, REGEXPBuilt in aggregates: COUNT, MAX, MIN, AVG, SUMBuilt in functions: CAST, IF, REGEXP_REPLACE, …Other: EXPLAIN, MAP, REDUCE, DISTRIBUTE BYList and Map operators: array[i], map[k], struct.field
• #14: Sports car is refined, accelerates very fast, and has a lot of addons/features. But it is pricey on a per bit basis and is expensive to maintain.Cargo train is rough, missing a lot of “luxury”, slow to accelerate, but it can carry almost anything and once it gets going it can move a lot of stuff very economically.Hadoop:A data grid operating systemStores Files (Unstructured)Stores 10s of petabytesProcesses 10s of PB/jobWeak ConsistencyScan all blocks in all filesQueries & Data ProcessingBatch response (>1sec)Relational Databases:An ACID Database systemStores Tables (Schema)Stores 100s of terabytesProcesses 10s of TB/queryTransactional ConsistencyLookup rows using indexMostly queriesInteractive responseHadoop Myths:Hadoop MapReduce requires Rocket ScientistsHadoop has the benefit of both worlds, the simplicity of SQL and the power of Java (or any other language for that matter)Hadoop is not very efficient hardware wiseHadoop optimizes for scalability, stability and flexibility versus squeezing every tiny bit of hardware performance It is cost efficient to throw more “pizza box” servers to gain performance than hire more engineers to manage, configure, and optimize the system or pay 10x the hardware cost in softwareHadoop can’t do quick random lookupsHBase enables low-latency key-value pair lookups (no fast joins)Hadoop doesn’t support updates/inserts/deletesNot for multi-row transactions, but HBase enables transactions with row-level consistency semanticsHadoop isn’t highly availableThough Hadoop rarely loses data, it can suffer from down-time if the master NameNode goes down. This issue is currently being addressed, and there are HW/OS/VM solutions for itHadoop can’t be backed-up/recovered quicklyHDFS, like other file systems, can copy files very quickly. It also has utilities to copy data between HDFS clustersHadoop doesn’t have securityHadoop has Unix style user/group permissions, and the community is working on improving its security modelHadoop can’t talk to other systemsHadoop can talk to BI tools using JDBC, to RDBMSes using Sqoop, and to other systems using FUSE, WebDAV & FTP