{"id":982,"date":"2017-06-29T07:46:30","date_gmt":"2017-06-29T07:46:30","guid":{"rendered":"https:\/\/www.bsetec.com\/blog\/?p=982"},"modified":"2017-06-29T07:46:30","modified_gmt":"2017-06-29T07:46:30","slug":"apache-hbase","status":"publish","type":"post","link":"https:\/\/www.bsetec.com\/blog\/apache-hbase\/","title":{"rendered":"Apache HBASE"},"content":{"rendered":"<p><b><a href=\"https:\/\/www.bsetec.com\/blog\/wp-content\/uploads\/2017\/06\/Hbase.png\"><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter size-full wp-image-983\" src=\"https:\/\/www.bsetec.com\/blog\/wp-content\/uploads\/2017\/06\/Hbase.png\" alt=\"Hbase\" width=\"674\" height=\"389\" srcset=\"https:\/\/www.bsetec.com\/blog\/wp-content\/uploads\/2017\/06\/Hbase.png 674w, https:\/\/www.bsetec.com\/blog\/wp-content\/uploads\/2017\/06\/Hbase-300x173.png 300w\" sizes=\"(max-width: 674px) 100vw, 674px\" \/><\/a><br \/>\nApache HBASE<\/b><span style=\"font-weight: 400;\"> is an Open Source, Scalable, Consistent, Low latency and Random Access database.<\/span> <span style=\"font-weight: 400;\">It\u2019s designed to store record-oriented data across a scalable cluster of machines.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It supports <\/span><b>ACID<\/b><b> (Atomicity, Consistency, Isolation, Durability ) <\/b><span style=\"font-weight: 400;\">properties of databases and very low time in query and output make it low latent. It can be randomly accessed and failure do not block the system.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A distributed, column oriented, <\/span><b>NoSQL<\/b><span style=\"font-weight: 400;\"> data store that is part of the Hadoop Ecosystem. It is an open source implementation of Google\u2019s BigTable and is used by a large number of enterprises in production environments, such as Facebook, Twitter and Yahoo.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">distributed<\/span><\/i><span style=\"font-weight: 400;\">\u200a\u2014\u200arows are spread over many machines;<\/span><\/li>\n<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">consistent<\/span><\/i><span style=\"font-weight: 400;\">\u200a\u2014\u200ait\u2019s strongly consistent (at a per-row level);<\/span><\/li>\n<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">persistent\u200a<\/span><\/i><span style=\"font-weight: 400;\">\u2014\u200ait stores data on disk with a log, so it sticks around;<\/span><\/li>\n<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">sorted\u200a<\/span><\/i><span style=\"font-weight: 400;\">\u2014\u200arows are stored in sorted order, making seeks very fast;<\/span><\/li>\n<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">sparse<\/span><\/i><span style=\"font-weight: 400;\">\u200a\u2014\u200aif a row has no value in a column, it doesn\u2019t use any space;<\/span><\/li>\n<li style=\"font-weight: 400;\"><i><span style=\"font-weight: 400;\">multi-dimensional<\/span><\/i><span style=\"font-weight: 400;\">\u200a\u2014\u200adata is addressable in several dimensions: tables, rows, columns, versions, etc.<\/span><\/li>\n<\/ul>\n<p><b>When do you need Hbase ?<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">High write throughput.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Low latency big data applications.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Fast read and write operations.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Operational Application\u200a\u2014\u200aIf you have large data operational application then Hbase it is a perfect choice but it you have any analytical application then Impala will be great.<\/span><\/li>\n<\/ul>\n<p><b>Hbase Data Model<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Data is stored in Big table.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Table consist rows and each rows has arbitrary number of columns.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Every cell value gets assigned by timestamp and it plays an important role during operations.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Row keys are lexicographically sorted and stored in Bytes [].<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Column name and values are also stored in Bytes [].<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Columns are grouped on the basis of properties as a column family.<\/span><\/li>\n<\/ul>\n<p><b>Google<\/b><span style=\"font-weight: 400;\"> was among the first companies to move in this direction, because they were operating at the scale of the entire web. So, they long ago built their infrastructure on top of a new kind of system (Big Table, which is the direct intellectual ancestor of HBase). It grew from there, with dozens of Open Source variants on the theme emerging in the space of a few years: <\/span><i><span style=\"font-weight: 400;\">Cassandra, MongoDB, Riak, Redis, CouchDB<\/span><\/i><span style=\"font-weight: 400;\">, etc.<br \/>\n<\/span><\/p>\n<p><span style=\"font-weight: 400;\">HBase is a <\/span><b>strongly consistent <\/b><span style=\"font-weight: 400;\">store. In the <\/span><b><i>CAP Theorem<\/i><\/b><b><i>,<\/i><\/b> <b>consistency<\/b> <i><span style=\"font-weight: 400;\">(C)<\/span><\/i><span style=\"font-weight: 400;\">, <\/span><b>availabilit<\/b><span style=\"font-weight: 400;\">y <\/span><i><span style=\"font-weight: 400;\">(A)<\/span><\/i><span style=\"font-weight: 400;\">, and <\/span><b>partition tolerance<\/b> <i><span style=\"font-weight: 400;\">(P)<\/span><\/i><span style=\"font-weight: 400;\">, that means it\u2019s a <\/span><i><span style=\"font-weight: 400;\">(CP)<\/span><\/i><span style=\"font-weight: 400;\"> store, not an <\/span><i><span style=\"font-weight: 400;\">(AP)<\/span><\/i><span style=\"font-weight: 400;\"> store. Eventual consistency is great when used for the right purpose, but it can tend to push challenges up to the application developer. We didn\u2019t think we\u2019d be able to absorb that extra complexity, for general use in a product with such a large surface area.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It\u2019s a<\/span><b> high quality<\/b><span style=\"font-weight: 400;\"> project. It did well in our benchmarks and tests, and is well respected in the community. Facebook built their entire Messaging infrastructure on HBase (as well as many other things), and the Open Source community is active and friendly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">HBase can use <\/span><b>Hadoop\u2019s distributed filesystem<\/b><span style=\"font-weight: 400;\"> for persistence and offers first class integration with MapReduce.<\/span><\/p>\n<p><b>Region servers<\/b><span style=\"font-weight: 400;\">: Serve data for reads and writes. DDL Statements are handled via HMaster. Colocated with Data Nodes (data locality)<\/span><\/p>\n<p><b>Hadoop Data Nod<\/b><span style=\"font-weight: 400;\">e: Stores the data that the Region servers are managing as HDFS files. The crucial thing here is the data locality.<\/span><\/p>\n<p><b>NameNode<\/b><span style=\"font-weight: 400;\">: Maintains meta information for all the stored data that compose files.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In HBase, data is physically sharded into what are known as regions. A single region server hosts each region, and each region server is responsible for one or more regions. When data is added to HBase, it\u2019s first written to a write-ahead log (WAL) known as the HLog. Once written to the HLog, the data is then stored in an in-memory MemStore. Once the data in memory exceeds a certain threshold, it\u2019s flushed as an HFile to disk. As the number of HFiles increases with MemStore flushes, HBase will merge several smaller files into a few larger ones, to reduce the overhead of reads. This is known as compaction.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When a region server fails, all the regions hosted by that region server will migrate to another region server, providing automatic fail-over. Due to the nature of how failover is architected in HBase, this entails splitting and replaying the contents of the WAL associated with each region, which lengthens the failover time.<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Apache HBASE is an Open Source, Scalable, Consistent, Low latency and Random Access database. It\u2019s designed to store record-oriented data across a scalable cluster of machines. It supports ACID (Atomicity, Consistency, Isolation, Durability ) properties of databases and very low time in query and output make it low latent. It can be randomly accessed and [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":983,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-982","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Apache HBASE | BSEtec<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.bsetec.com\/blog\/apache-hbase\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apache HBASE | BSEtec\" \/>\n<meta property=\"og:description\" content=\"Apache HBASE is an Open Source, Scalable, Consistent, Low latency and Random Access database. 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