Apache Hive 3 architectural overview

Understanding Apache Hive 3 major design features, such as default ACID transaction processing, can help you use Hive to address the growing needs of enterprise data warehouse systems.

Data storage and access control

One of the major architectural changes to support Hive 3 design gives Hive much more control over metadata memory resources and the file system, or object store. The following architectural changes from Hive 2 to Hive 3 provide improved security:

  • Tightly controlled file system and computer memory resources, replacing flexible boundaries: Definitive boundaries increase predictability. Greater file system control improves security.
  • Optimized workloads in shared files and YARN containers
Hive 3 is optimized for object stores in the following ways:
  • Hive uses ACID to determine which files to read rather than relying on the storage system.
  • In Hive 3, file movement is reduced from that in Hive 2.
  • Hive caches metadata and data agressively to reduce file system operations

The major authorization model for Hive is Ranger. Hive enforces access controls specified in Ranger. This model offers stronger security than other security schemes and more flexibility in managing policies.

This model permits only Hive to access the Hive warehouse.

Transaction processing

You can deploy new Hive application types by taking advantage of the following transaction processing characteristics:

  • Mature versions of ACID transaction processing:

    ACID tables are the default table type.

    ACID enabled by default causes no performance or operational overload.

  • Simplified application development, operations with strong transactional guarantees, and simple semantics for SQL commands

    You do not need to bucket ACID tables.

  • Materialized view rewrites
  • Automatic query cache
  • Advanced optimizations

Hive client changes

You can use the thin client Beeline for querying Hive from the command line. You can run Hive administrative commands from the command line. Beeline uses a JDBC connection to Hive to run commands. Hive parses, compiles, and runs operations. Beeline supports many of the command-line options that Hive CLI supported. Beeline does not support hive -e set key=value to configure the Hive Metastore.

You enter supported Hive CLI commands by invoking Beeline using the hive keyword, command option, and command. For example, hive -e set. Using Beeline instead of the thick client Hive CLI, which is no longer supported, has several advantages, including low overhead. Beeline does not use the entire Hive code base. A small number of daemons required to run queries simplifies monitoring and debugging.

Hive enforces allowlist and denylist settings that you can change using SET commands. Using the denylist, you can restrict memory configuration changes to prevent instability. Different Hive instances with different allowlists and denylists to establish different levels of stability.

Apache Hive Metastore sharing

Hive, Impala, and other components can share a remote Hive metastore.

Spark integration

Spark and Hive tables interoperate using the Hive Warehouse Connector.

You can access ACID and external tables from Spark using the Hive Warehouse Connector. You do not need the Hive Warehouse Connector to read Hive external tables from Spark and write Hive external tables from Spark. You do not need HWC to read or write Hive external tables. Spark users just read from or write to Hive directly. You can read Hive external tables in ORC or Parquet formats. You can write Hive external tables in ORC format only.

Query execution of batch and interactive workloads

You can connect to Hive using a JDBC command-line tool, such as Beeline, or using an JDBC/ODBC driver with a BI tool, such as Tableau. You configure the settings file for each instance to perform either batch or interactive processing.