Apache Hive key features

Cloudera Runtime (CR) services include Hive on Tez and Hive metastore. These services use Apache Hive 3.x, a SQL-based data warehouse system. The enhancements in Hive 3.x over previous versions can improve your query performance and comply with internet regulations.

ACID transaction processing

Hive 3 tables are ACID (Atomicity, Consistency, Isolation, and Durability)-compliant, which is critical to observing the right to be forgotten requirement of the GDPR (General Data Protection Regulation).

Shared metastore

Hive metastore (HMS) interoperates with multiple engines, Impala and Spark for example, simplifying interoperation between engines and user data access.

Low-latency analytical processing (CDP Public Cloud)

Hive processes transactions using low-latency analytical processing (LLAP) or the Apache Tez execution engine. The Hive LLAP service is not available in CDP Data Center.

Spark integration with Hive

You can use Hive to query data from Apache Spark applications without workarounds. The Hive Warehouse Connector supports reading and writing Hive tables from Spark.

Security improvements

Apache Ranger secures Hive data by default. To meet demands for concurrency improvements, ACID support for GDPR, render security, and other features, Hive tightly controls the location of the warehouse on a file system, or object store, and memory resources.

Workload management at the query level

You can configure who uses query resources, how much can be used, and how fast Hive responds to resource requests. Workload management can improve parallel query execution, cluster sharing for queries, and query performance.

Materialized views

Because multiple queries frequently need the same intermediate roll up or joined table, you can avoid costly, repetitious query portion sharing, by precomputing and caching intermediate tables into views.

Query result cache

Hive filters and caches similar or identical queries. Hive does not recompute the data that has not changed. Caching repetitive queries can reduce the load substantially when hundreds or thousands of users of BI tools and web services query Hive.

Information schema database

When launched, Hive creates two databases from JDBC data sources: information_schema and sys. All Metastore tables are mapped into your tablespace and available in sys. The information_schema data reveals the state of the system, similar to sys database data. You can query information_schema using SQL standard queries.

Unavailable or unsupported interfaces

  • Hive CLI (replaced by Beeline)
  • WebHCat
  • Hcat CLI
  • SQL Standard Authorization
  • MapReduce execution engine (replaced by Tez)