Apache Hive Overview in CDH

Hive data warehouse software enables reading, writing, and managing large datasets in distributed storage. Using the Hive query language (HiveQL), which is very similar to SQL, queries are converted into a series of jobs that execute on a Hadoop cluster through MapReduce or Apache Spark.

Users can run batch processing workloads with Hive while also analyzing the same data for interactive SQL or machine-learning workloads using tools like Apache Impala (incubating) or Apache Spark—all within a single platform.

As part of CDH, Hive also benefits from:

  • Unified resource management provided by YARN
  • Simplified deployment and administration provided by Cloudera Manager
  • Shared security and governance to meet compliance requirements provided by Apache Sentry and Cloudera Navigator

Use Cases for Hive

Because Hive is a petabyte-scale data warehouse system built on the Hadoop platform, it is a good choice for environments experiencing phenomenal growth in data volume. The underlying MapReduce interface with HDFS is hard to program directly, but Hive provides an SQL interface, making it possible to use existing programming skills to perform data preparation.

Hive on MapReduce or Spark is best-suited for batch data preparation or ETL:

  • You must run scheduled batch jobs with very large ETL sorts with joins to prepare data for Hadoop. Most data served to BI users in Impala is prepared by ETL developers using Hive.

  • You run data transfer or conversion jobs that take many hours. With Hive, if a problem occurs partway through such a job, it recovers and continues.

  • You receive or provide data in diverse formats, where the Hive SerDes and variety of UDFs make it convenient to ingest and convert the data. Typically, the final stage of the ETL process with Hive might be to a high-performance, widely supported format such as Parquet.

Hive Components

Hive consists of the following components:

The Metastore Database

The metastore database is an important aspect of the Hive infrastructure. It is a separate database, relying on a traditional RDBMS such as MySQL or PostgreSQL, that holds metadata about Hive databases, tables, columns, partitions, and Hadoop-specific information such as the underlying data files and HDFS block locations.

The metastore database is shared by other components. For example, the same tables can be inserted into, queried, altered, and so on by both Hive and Impala. Although you might see references to the "Hive metastore", be aware that the metastore database is used broadly across the Hadoop ecosystem, even in cases where you are not using Hive itself.

The metastore database is relatively compact, with fast-changing data. Backup, replication, and other kinds of management operations affect this database. See Configuring the Hive Metastore for CDH for details about configuring the Hive metastore.

Cloudera recommends that you deploy the Hive metastore, which stores the metadata for Hive tables and partitions, in "remote mode." In this mode the metastore service runs in its own JVM process and other services, such as HiveServer2, HCatalog, and Apache Impala (incubating) communicate with the metastore using the Thrift network API.

See Starting the Hive Metastore in CDH for details about starting the Hive metastore service.


HiveServer2 is a server interface that enables remote clients to submit queries to Hive and retrieve the results. It replaces HiveServer1, which has been deprecated and will be removed in a future release of CDH. HiveServer2 supports multi-client concurrency, capacity planning controls, Sentry authorization, Kerberos authentication, LDAP, SSL, and provides better support for JDBC and ODBC clients.

HiveServer2 is a container for the Hive execution engine. For each client connection, it creates a new execution context that serves Hive SQL requests from the client. It supports JDBC clients, such as the Beeline CLI, and ODBC clients. Clients connect to HiveServer2 through the Thrift API-based Hive service.

See Configuring HiveServer2 for CDH for details on configuring HiveServer2 and see Starting, Stopping, and Using HiveServer2 in CDH for details on starting/stopping the HiveServer2 service and information about using the Beeline CLI to connect to HiveServer2. For details about managing HiveServer2 with its native web user interface (UI), see Using HiveServer2 Web UI in CDH.

How Hive Works with Other Components

Hive integrates with other components, which serve as query execution engines or as data stores:

Hive on Spark

Hive traditionally uses MapReduce behind the scenes to parallelize the work, and perform the low-level steps of processing a SQL statement such as sorting and filtering. Hive can also use Spark as the underlying computation and parallelization engine. See Running Apache Hive on Spark in CDH for details about configuring Hive to use Spark as its execution engine and see Tuning Apache Hive on Spark in CDH for details about tuning Hive on Spark.

Hive and HBase

Apache HBase is a NoSQL database that supports real-time read/write access to large datasets in HDFS. See Using Apache Hive with HBase in CDH for details about configuring Hive to use HBase. For information about running Hive queries on a secure HBase server, see Using Hive to Run Queries on a Secure HBase Server.

Hive on Amazon S3

Use the Amazon S3 filesystem to efficiently manage transient Hive ETL (extract-transform-load) jobs. For step-by-step instructions to configure Hive to use S3 and multiple scripting examples, see Configuring Transient Hive ETL Jobs to Use the Amazon S3 Filesystem. To optimize how Hive writes data to and reads data from S3-backed tables and partitions, see Tuning Hive Performance on the Amazon S3 Filesystem. For information about setting up a shared Amazon Relational Database Service (RDS) as your Hive metastore, see How To Set Up a Shared Amazon RDS as Your Hive Metastore for CDH.

Hive on Microsoft Azure Data Lake Store

In CDH 5.12 and higher, both Hive on MapReduce2 and Hive-on-Spark can access tables on Microsoft Azure Data Lake store (ADLS). In contrast to Amazon S3, ADLS more closely resembles native HDFS behavior, providing consistency, file directory structure, and POSIX-compliant ACLs. See Configuring ADLS Connectivity for information about configuring and using ADLS with Hive on MapReduce2.