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Apache Hive

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by Apache Software Foundation · since 2010 · also known as Hive, Hive Metastore

The original SQL-on-Hadoop warehouse, now mostly valued for its Metastore and Iceberg support.

relationalcolumnar / olapdatabase engineopen source (permissive)legacy
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Overview

Apache Hive brought SQL to Hadoop at Facebook in 2008: HiveQL compiled to MapReduce, later Tez, over files in HDFS. Hive 4.x runs on Tez with LLAP caching, ACID ORC tables, materialized views with query rewrite and first-class Iceberg tables, and requires JDK 21 from 4.2. Its most enduring piece is the Hive Metastore, still the catalog behind countless Spark, Trino and Impala deployments, now with an Iceberg REST catalog. New projects rarely choose Hive as the query engine, but it remains embedded in Hadoop-era estates.

Data warehouseAnalytics (OLAP)LakehouseMetadata & config

Strengths & trade-offs

Strengths

  • +Hive Metastore is the de facto catalog for Spark, Trino and Impala
  • +Handles very large batch SQL jobs over HDFS and object storage
  • +ACID ORC tables and Iceberg tables with time travel and compaction
  • +Materialized views with automatic query rewrite

Trade-offs

  • −High query latency compared with modern MPP engines
  • −Heavy Hadoop-era dependencies (Tez, YARN, ZooKeeper)
  • −Shrinking community as users move to Spark, Trino and cloud warehouses
  • −Operationally complex to run and upgrade

Great for

  • +Existing Hadoop data warehouses
  • +Shared Metastore catalog for lakehouse engines
  • +Large overnight batch SQL

Think twice for

  • −Interactive BI
  • −New cloud-native platforms
  • −Streaming or real-time analytics

Capabilities

Native or official first-party support · 9 of 25 tracked

✓Transactions
✓Joins
✓JSON
✓Materialized views
✓Columnar storage
✓Durable persistence
✓Compression
✓Role-based access
✓Time travel
–Secondary indexes
–Full-text search
–Vector search
–Geospatial
–Change data capture
–Triggers
–Stored procedures
–TTL / expiry
–Pub/Sub
–In-memory
–Encryption at rest
–Branching
–Online schema changes
–Streaming ingest
–Offline sync
–Runs in WebAssembly

Scale & consistency

Comfortable data size
Massive · Multi-PB
Horizontal scaling
Native
Multi-region
No
Transactions
Limited ACID
Consistency
Strong
Schema
Strict
Architecture
Distributed, Client server
Query languages
HiveQL

Scales with the underlying Hadoop/Tez or Kubernetes cluster; historic multi-PB deployments.

Deployment

Self-hosted Vendor-managed cloud Serverless Embedded Edge Kubernetes operator

available on · AWS · Google Cloud · Azure

Amazon EMR (Hive)AWSCloudera Data WarehouseCloudera

Ecosystem

apis & protocolsHiveServer2 Thrift, JDBC, ODBC, Metastore Thrift API, Iceberg REST catalog
client languagesJava, Python, Go, JavaScript

Alternatives to Apache Hive

Compare all→
Apache Spark SQLRelational50.3

Spark's SQL and DataFrame engine for large-scale batch, streaming and lakehouse processing.

apache-hive vs apache-spark-sql →
TrinoRelational49.9

Fast distributed SQL query engine for federated analytics across lakes and databases.

apache-hive vs trino →
Apache ImpalaRelational29.0

Low-latency MPP SQL engine for Hadoop, Kudu and Iceberg tables, developed largely by Cloudera.

apache-hive vs apache-impala →
PrestoRelational37.7

Distributed SQL query engine from Meta for interactive queries over data lakes and many sources.

apache-hive vs presto →
Databricks SQLColumnar / OLAP20.8

Serverless SQL warehouses on the Databricks lakehouse, powered by the Photon C++ engine.

apache-hive vs databricks-sql →
Amazon AthenaRelational19.5

Serverless AWS query service running Trino-based SQL directly over data in S3.

apache-hive vs amazon-athena →

Apache Hive FAQ

What is Apache Hive?

Apache Hive is a relational database by Apache Software Foundation, first released in 2010. Apache Hive brought SQL to Hadoop at Facebook in 2008: HiveQL compiled to MapReduce, later Tez, over files in HDFS. Hive 4.x runs on Tez with LLAP caching, ACID ORC tables, materialized views with query rewrite and first-class Iceberg tables, and requires JDK 21 from 4.2. Its most enduring piece is the Hive Metastore, still the catalog behind countless Spark, Trino and Impala deployments, now with an Iceberg REST catalog. New projects rarely choose Hive as the query engine, but it remains embedded in Hadoop-era estates.

Is Apache Hive open source?

Yes. Apache Hive is open source. License: Apache License 2.0.

Does Apache Hive support ACID transactions?

Partly. Apache Hive offers limited ACID guarantees (for example, single-record or single-partition transactions), with strong consistency.

Does Apache Hive scale horizontally?

Apache Hive scales horizontally out of the box, with built-in sharding or partitioning. It is comfortable at massive scale (Multi-PB).

How can I run Apache Hive?

You can self-host it. Managed offerings include Amazon EMR (Hive) and Cloudera Data Warehouse. It is available on AWS, Google Cloud and Azure.

What is Apache Hive best for?

Apache Hive is a strong fit for existing Hadoop data warehouses, shared Metastore catalog for lakehouse engines and large overnight batch SQL. Think twice for interactive BI.

What are the best alternatives to Apache Hive?

Common alternatives to Apache Hive include Apache Spark SQL, Trino, Apache Impala, Presto and Databricks SQL. The right choice depends on your workload, scale and hosting; WhichDB's smart match ranks them against your requirements.

How popular is Apache Hive?

Apache Hive ranks #87 of 844 databases on the WhichDB Score (30.1/100), and #27 among relational databases. The score combines public adoption signals such as package downloads, GitHub activity, job posts and developer discussion.