Documentation

REST Catalog Integration Table of Contents Hive + Gravitino + Keycloak Architecture Overview Prerequisites Quickstart Configuration Keyclock Gravitino Hive Networking Notes Hive + Polaris Architecture Overview Prerequisites Quickstart Configuration Polaris Hive Networking Notes Hive + Gravitino + Keycloak The code for this setup is located in the Hive repository in packaging/src/docker/thirdparties/gravitino folder. It contains a docker-compose-based setup integrating Apache Hive, Gravitino Iceberg REST server, and Keycloak for OAuth2 authentication.

Apache Hive : ChangeLog Release 4.0.0 - 2024-03-29 NEW FEATURES: JIRA Summary Priority Component Reporter Contributor HIVE-27850 Iceberg: Major QB Compaction Major Iceberg integration Dmitriy Fingerman Dmitriy Fingerman HIVE-26222 Native GeoSpatial Support in Hive Major Hive, HiveServer2 mahesh kumar behera Ayush Saxena HIVE-27980 Hive Iceberg Compaction: add support for OPTIMIZE TABLE syntax Major . Dmitriy Fingerman Dmitriy Fingerman HIVE-26435 Add method for collecting HMS meta summary Major .

Hive 4.0 - Overview of Major Changes Iceberg Integration Advanced Snapshot management Branches & Tags support DML (insert/update/delete/merge) COW & MOR modes Vectorised Reads & Writes Table migration command LOAD DATA statements support Partition-level operations support Improved statistics (column stats support) Hive ACID Use sequences for TXN_ID generation (performance) Read-only transactions optimization Zero-wait readers Optimistic and Pessimistic concurrency control Lockless reads Compaction Rebalance compaction (Hive ACID) Compaction requests prioritization (compaction pooling) Iceberg compaction (Major) Hive Metastore API optimization (performance) Dynamic leader election External data sources support HMS support for Thrift over HTTP JWT authentication for Thrift over HTTP HMS metadata summary Use Zookeeper for service discovery HiveServer2 Support SAML 2.

The Apache Hive™ data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage and queried using SQL syntax. Built on top of Apache Hadoop™, Hive provides the following features: Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis. A mechanism to impose structure on a variety of data formats Access to files stored either directly in Apache HDFS™ or in other data storage systems such as Apache HBase™ Query execution via Apache Tez™ or MapReduce Procedural language with HPL-SQL Sub-second query retrieval via Hive LLAP, Apache YARN and Apache Slider.