NameNode and DataNode are HDFS components that work in a master/slave mode. NameNode is a major component that controls HDFS whereas DataNodes does the block replications, read/write operations and drives the workloads for HDFS.
JobTracker and TaskTracker are also components that work in master/slave mode where JobTracker tasks control the mapping and reducing tasks at individual nodes among other tasks. The TaskTrackers run at the node levels and maintains communications with JobTracker for all nodes within the cluster.
The main components include:Hadoop. Java software framework to support data-intensive distributed applications ZooKeeper. A highly reliable distributed coordination system MapReduce. A flexible parallel data processing framework for large data sets HDFS. Hadoop Distributed File System Oozie. A MapReduce job scheduler HBase. Key-value database Hive. A high-level language built on top of MapReduce for analyzing large data sets Pig. Enables the analysis of large data sets using Pig Latin. Pig Latinis a high-level language compiled into MapReduce for parallel data processing.
4. hadoop גיא לבנברג
The Good, The Bad and the UglyHow to tame the Big Data BeastGuy LoewenbergMay 2013
Overview• Big Data: A collection of data sets so large and complex thatit becomes difficult to process using on-hand databasemanagement tools or traditional data processing applications• Hadoop: A framework that allows distributedprocessing of large data-sets across clusters ofcomputers using a simple programming model• 1000 Kilobytes = 1 Megabyte• 1000 Megabytes = 1 Gigabyte• 1000 Gigabytes = 1 Terabyte• 1000 Terabytes = 1 Petabyte• 1000 Petabytes = 1 Exabyte• 1000 Exabytes = 1 Zettabyte• 1000 Zettabytes = 1 Yottabyte• 1000 Yottabytes = 1 Brontobyte• 1000 Brontobytes = 1 GeopbyteMost US SME corporationsMost US large corporationsLeaders like Facebook & Google
Hadoop Basics• Designed to scale• Uses commodity hardware• Processes data in batches• Can process very large scale of data (PBs)
Core Hadoop• Core hadoop is built from two main systems:– Hadoop Clustered file system - HDFS– MapReduce programming framework
Hadoop architecture• Hadoop Distributed File System (HDFS):self-healing high-bandwidth clusteredstorage.– NameNode controls HDFSwhereas DataNodes does theblock replications, read/writeoperations and drives theworkloads for HDFS– Work in a master/slave mode.
Hadoop architecture• MapReduce: Distributed fault-tolerant resourcemanagement and scheduling coupled with ascalable data programming abstraction.– The JobTracker schedulesjobs and allocates activitiesto TaskTracker nodes whichexecute the map and reduceprocesses requested– Work in master/slave mode
Hadoop software architectureMapReduce: Parallel data processingframework for large data setsHDFS: Hadoopdistributed File SystemOozie: MapReducejob SchedulerHBase: Key-valuedatabasePig: Large data setsanalysis languageHive: High-level language foranalyzing large data setsZooKeeper: distributedcoordination systemSolr / Lucene searchengine, query engine library
What Hadoop can’t do• Hadoop lets you perform batch analysis on whateverdata you have stored within Hadoop. That data, doesnot have to be structured– Many solutions take advantage of the low storage expense ofHadoop to store structured data there instead of RDBMS. Butshifting data back and forth between Hadoop and an RDBMSwould be overkill.– Transactional data is highly complex, as a transaction on anecommerce site can generate many steps that all have to beimplemented quickly. That scenario is not ideal for Hadoop– Structured data sets that require very minimal latency
Comparing RDBMS to MapReduceRDBMS MapReduceData size Gigabytes PetabytesAccess Interactive and batch BatchStructure Fixed schema Unstructured schemaLanguage SQL Procedural (Java, C++, Ruby, etc)Integrity High LowScaling Nonlinear LinearUpdates Read and write Write once, read many timesLatency Low High
What Hadoop can do• High data volume, stored in Hadoop, and queried atlength later using MapReduce functions– index building– pattern recognitions– creating recommendation engines– sentiment analysis• Hadoop should be integrated within your existing ITinfrastructure in order to capitalize on the countlesspieces of data that flows into your organization.
Hadoop Maturity?!• Inaccessible to analysts without programming ability• clusters have no record of who changed which record and whenit was changed• storage functionality they have always depended on (snapshots,mirroring) are lacking in HDFS.• Incompatibility with existing tools• Data without structure has limited value and applying thestructure at query time requires a lot of Java code.• Limited documentation• Limited troubleshooting capabilities
Choosing your infrastructure• Define what you want to achieve– POC– Scale (few, tens, hundreds)– One-time, periodic, continuous• Infrastructure design– Servers, storage, network, rack-space– Define a joined team Hadoop App/Dev and infrastructurespecialist (facilities/server/network) when building a solution– Virtual machines vs. Physical machines (IO performance, HighCPU, Network)
Choosing your infrastructure• Network infrastructure– Data movement between nodes (rack-awareness,replication factor)– Data between sites (Hosting/Service)• Storage (architecture, disks)– Local disks, JBOD– Increase default block-size• Operations– Monitor– Backup (configuration files, journal, Checkpoint …)
Performance & Scale considerations• Consider running on a dedicated/standalone notshared with other Hadoop processes on the sameserver– Name Node, Secondary Name Node and/or CheckpointNode– Job Tracker and the HBASE (or any DB) Master• Consider a Physical dedicated environment
Thank you!Hadoop - The Good, The Bad and the UglyGuy Loewenberg
Improving RDBMS with Hadoop• Accelerating nightly batch business processes.• Storage of extremely high volumes of enterprise data• Creation of automatic redundant backups• Improving the scalability of applications• Use of Java for data processing instead of SQL.• Produce just-in-time feeds for dashboards and business intelligence• Handling urgent, ad hoc requests for data• Turning unstructured data into relational data• Taking on tasks that require massive parallelism• Moving existing algorithms, code, frameworks, and components toa highly distributed computing environment.