Comparing Hive with HBase is like comparing Google with Facebook - although they compete over the same turf (our private information), they don’t provide the same functionality. But things can get confusing for the Big Data beginner when trying to understand what Hive and HBase do and when to use each one of them. Let’s try and clear it up.
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What They Do
Apache Hive is a data warehouse infrastructure built on top of Hadoop. It allows for querying data stored on HDFS for analysis via HQL, an SQL-like language that gets translated to MapReduce jobs. Despite providing SQL functionality, Hive does not provide interactive querying yet - it only runs batch processes on Hadoop.
Apache HBase is a NoSQL key/value store which runs on top of HDFS. Unlike Hive, HBase operations run in real-time on its database rather than MapReduce jobs. HBase is partitioned to tables, and tables are further split into column families. Column families, which must be declared in the schema, group together a certain set of columns (columns don’t require schema definition). For example, the "message" column family may include the columns: "to", "from", "date", "subject", and "body". Each key/value pair in HBase is defined as a cell, and each key consists of row-key, column family, column, and time-stamp. A row in HBase is a grouping of key/value mappings identified by the row-key. HBase enjoys Hadoop’s infrastructure and scales horizontally using off the shelf servers.
Hive can help the SQL savvy to run MapReduce jobs. Since it’s JDBC compliant, it also integrates with existing SQL based tools. Running Hive queries could take a while since they go over all of the data in the table by default. Nonetheless, the amount of data can be limited via Hive’s partitioning feature. Partitioning allows running a filter query over data that is stored in separate folders, and only read the data which matches the query. It could be used, for example, to only process files created between certain dates, if the files include the date format as part of their name.
HBase works by storing data as key/value. It supports four primary operations: put to add or update rows, scan to retrieve a range of cells, get to return cells for a specified row, and delete to remove rows, columns or column versions from the table. Versioning is available so that previous values of the data can be fetched (the history can be deleted every now and then to clear space via HBase compactions). Although HBase includes tables, a schema is only required for tables and column families, but not for columns, and it includes increment/counter functionality.
Hive does not currently support update statements. Additionally, since it runs batch processing on Hadoop, it can take minutes or even hours to get back results for queries. Hive must also be provided with a predefined schema to map files and directories into columns and it is not ACID compliant.
HBase queries are written in a custom language that needs to be learned. SQL-like functionality can be achieved via Apache Phoenix, though it comes at the price of maintaining a schema. Furthermore, HBase isn’t fully ACID compliant, although it does support certain properties. Last but not least - in order to run HBase, ZooKeeper is required - a server for distributed coordination such as configuration, maintenance, and naming.
Hive should be used for analytical querying of data collected over a period of time - for instance, to calculate trends or website logs. Hive should not be used for real-time querying since it could take a while before any results are returned.
Hive and HBase are two different Hadoop based technologies - Hive is an SQL-like engine that runs MapReduce jobs, and HBase is a NoSQL key/value database on Hadoop. But hey, why not use them both? Just like Google can be used for search and Facebook for social networking, Hive can be used for analytical queries while HBase for real-time querying. Data can even be read and written from Hive to HBase and back again.
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