Hive metastore and Impala metastore are the tools that allow performing several SQL queries on data residing in the HBase/HDFS. These are easy to learn for anyone who has experience in developing SQL. Hive makes use of HiveQL and this converts the data into the Spark Jobs or MapReduce which runs on a Hadoop cluster. Impala makes use of a specialized and fast SQL engine.
Let us now understand what the Hive Metastore is.
Hive Metastore is a component in Hive that stores the catalog of the system that contains the metadata about Hive create columns, Hive table creation, and partitions. Metadata is mostly stored in the traditional form of RDBMS.
The Apache Hives make use of the Derby databases to store the metadata. Any of the JDBC compliant or Java database connectivity like MySQL can be used to create a Hive Metastore.
Many primary attributes should be configured for the Hive Metastore. Some of them are:
· URL Connection
· Driver Connection
· User ID Connection
· Password Connection
Hive and the Impala server make use of Metastore to get the data location and the table structure. The query gets sent to the Impala or the Hive server and then it catches the Hive Metastore to get the data location and the table structure. The query when gets received, the server then queries the actual data on the Hive table that is present in HDFS.
All the data by default gets stored in the /user/hive/warehouse. Every table is a directory in the default location and contains one or more than one file.
Here we will understand how to define, create and then delete the database. When you create a database in Hive it is managed using the DDL or the data definition language of the HiveQL or impala SQL.
Here you learn about the various data types in Hive. The data types are of three types. These are primitive, complex, and user-defined.
The table data is stored in the default warehouse location by default. The default tables are managed or internal where the data gets deleted when the table gets removed from any internal location.
The data either in Hive or Impala are applied to a schema or a plan and it gets pulled from a stored location rather than the data being pulled out directly. This is why both Hive and Impala are schema on.
In the following topic, you will learn about the HCatalog and its uses. HCatalog is a Hive sub-project that offers access to the Hive Metastore.
Along with the Namenode in Impala, there are two Impala daemons in the master node. These are the catalog daemon that will relay the metadata change to every Impala daemon in the cluster. The State Store daemon offers a lookup service for the Impala daemons and checks their status periodically.
In the Hive tutorial, we have learned that every table will map to a directory in the HDFS. The Hive Metastore will store the data about the data in the RDBMS. The tables get created and managed using the HiveQL DDL or the Impala SQL. SQOOP offers the support to import the data into the Hive and Impala from the RDBMS. HCatalog offers access to the Hive Metastore from the tools outside impala or hive.
Hadoop has grown and developed since it was introduced. Facebook introduces the Apache Hive to manage and process the large sets of data in a distributed storage in Hadoop.
Cloudera Impala was developed to solve the limitations that are posed by the low Hadoop SQL interactions.
Cloudera Impala and Apache Hive are competitors that are vying to get accepted in the space of database query and the debate of which among the two is the best continues.
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