Apache Hive key features

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 Private Cloud Base.

Spark integration with Hive🔗

Spark and Hive ACID tables interoperate using the Hive Warehouse Connector. You can access external tables from Spark directly using SparkSQL. You do not need HWC to read or write Hive external tables. Spark users just read from or write to Hive directly. You can write Hive external tables in ORC format only. (See link below.)

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🔗