# Install hive with Homebrew Hadoop-based data summarization, query, and analysis. Version 4.2.0 via Homebrew; verified 2026-06-22. ## Install ```sh sudo av install brew:hive ``` Additional install commands: ### macOS - Homebrew (100%): ```sh brew install hive ``` Evidence: local Homebrew formula metadata ## Package facts - **Package key:** brew:hive - **Package manager:** Homebrew - **Package manager page:** - **Version:** 4.2.0 - **Source summary:** Hadoop-based data summarization, query, and analysis - **Homepage:** - **Repository:** - **Upstream docs:** - **License:** Apache-2.0 - **Source archive:** - **Last updated:** 2026-06-22T14:03:43-07:00 - **Generated:** 2026-07-08T07:18:31+00:00 ## Executables - beeline (cli) - hive (cli) - hive-config.sh (cli) - hiveserver2 (cli) - hplsql (cli) - init-hive-dfs.sh (cli) - metatool (cli) - replstats.sh (cli) - schematool (cli) - beeline (alias) - hive (alias) - hive-config.sh (alias) - hiveserver2 (alias) - hplsql (alias) - init-hive-dfs.sh (alias) - metatool (alias) - replstats.sh (alias) - schematool (alias) ## Dependencies - hadoop - openjdk@21 ## Install behavior - Post-install hook: not defined - Caveats: If you want to use HCatalog with Pig, set $HCAT_HOME in your profile: export HCAT_HOME=$HOMEBREW_PREFIX/opt/hive/libexec/hcatalog - Bottle: available on all ## Freshness - Page generated: 2026-07-08 - Package-manager version: 4.2.0 - Package-manager updated: 2026-06-22 - Local data: ok - Upstream repository: https://hive.apache.org - info: Release/tag comparison is only available for GitHub repositories. ## Project history and usage Apache Hive is a distributed data warehouse system for reading, writing, managing, and querying large datasets in distributed storage using SQL. It began at Facebook as a way to make Hadoop usable by analysts and engineers who did not want to write MapReduce jobs for ordinary aggregation and reporting. As a package, Hive is historically important because it turned Hadoop clusters into SQL-addressable data warehouses. Installing the package gives users not just a CLI named `hive`, but Beeline, HiveServer2, metastore tooling, schema tooling, HPL/SQL, and the operational surface around Hadoop-era data warehousing. ### Project history Facebook engineers started building Hive after data growth exposed the limits of a commercial RDBMS-backed warehouse. The Meta engineering history says Facebook's data grew from a 15 TB dataset in 2007 to more than 2 PB by the time of the 2009 article, and that MapReduce was too low-level for many analysis tasks. Hive's design brought tables, columns, partitions, and a SQL subset to Hadoop while preserving Hadoop's extensibility. Facebook open sourced Hive in August 2008, and Apache Foundation milestones record Hive entering the Apache Incubator in 2008. ASF milestones record Hive becoming a top-level Apache project in 2010. The Apache Hive site describes the project as a distributed, fault-tolerant data warehouse at massive scale. It emphasizes SQL over distributed storage, the Hive Metastore as a central metadata repository, HiveServer2 for multi-client access, cost-based optimization, compaction, replication, security integrations, and support for modern storage systems and table formats. ### Adoption history Early adoption was intense inside Facebook. The Meta engineering post says Hive was popular with internal users from the start, regularly ran thousands of jobs, served hundreds of users, stored more than 2 PB of uncompressed data, and loaded 15 TB daily. Open-source adoption followed because Hive lowered the barrier to Hadoop analytics. It let analysts use a SQL-like language while Hadoop handled distributed execution. The Apache site later presents Hive as used by enterprises and cloud/data-platform vendors, and highlights integrations with S3, Azure Data Lake, Google Cloud Storage, Spark, Presto, Impala, Apache Ranger, Apache Atlas, Apache Iceberg, and other data-stack components. Hive also created a durable ecosystem surface: the metastore became a central catalog for other engines and data lake architectures, while Beeline and HiveServer2 became common access points for JDBC, ODBC, BI tools, and scripts. ### How it is used In package-manager terms, users install Hive to get command-line and service entry points. The Homebrew package exposes `beeline`, `hive`, `hive-config.sh`, `hiveserver2`, `hplsql`, `init-hive-dfs.sh`, `metatool`, `replstats.sh`, and `schematool`. Historically, users ran HiveQL through the `hive` CLI; Beeline and HiveServer2 became the preferred client/server model for multi-client and authenticated access. Operators configure XML files such as `hive-site.xml`, metastore and server configuration files, and Beeline connection files. Typical workloads include SQL analytics on distributed storage, ETL, table and partition management, metastore-backed data lake catalogs, batch reporting, compaction, replication, and integration with BI and JDBC/ODBC clients. ### Why package nerds care Hive is one of the packages that made the Hadoop ecosystem approachable to SQL users. It matters in package history because it bridged a low-level distributed-computing substrate and the familiar data-warehouse interface that enterprises already knew how to staff, script, and operate. The package also illustrates why some CLI packages are really ecosystems. The `hive` formula is not only a command; it packages services, schema tools, metastore administration, connection clients, and configuration conventions. That makes it closer to a platform component than a simple executable. Hive's long tail is especially visible in the metastore. Even as newer engines evolved, the Hive Metastore remained a shared metadata layer in data-lake deployments, so package maintainers and operators continued to care about Hive compatibility beyond the original MapReduce execution model. ### Timeline - 2007: Facebook begins moving large-scale data analysis pressure toward Hadoop-backed infrastructure. - 2008-08: Hive is open sourced by Facebook. - 2008: ASF milestones record Hive entering the Apache Incubator. - 2009-06-10: Facebook publishes the Hive petabyte-scale Hadoop data warehouse article. - 2010: ASF milestones record Hive becoming a top-level Apache project. - 2010s: HiveServer2, Beeline, cost-based optimization, metastore use, and enterprise security integrations become part of common Hive deployments. - 2020s: Apache Hive site highlights data lake, cloud storage, Iceberg, compaction, replication, security, and metastore-centered use cases. ### Related projects - Apache Hadoop is the distributed storage and processing ecosystem Hive was built on. - Apache HCatalog graduated in 2013 to become part of Apache Hive, adding table and storage management services for Hadoop data. - Apache Spark, Presto, Impala, and other engines integrate with Hive concepts or the Hive Metastore in data lake environments. - Apache Ranger and Apache Atlas are documented by the Hive site as security, authorization, lineage, and governance integrations. - Apache Iceberg is highlighted by the Hive site as a modern table-format integration. ### Sources - Apache Hive version-control page for official repository URL. - Apache Hive website for project description, features, integrations, and adoption framing. - Apache Software Foundation milestones for Incubator and top-level-project dates. - Meta Engineering article for Facebook origin, open-sourcing, internal scale, and early architecture. ## Security Notes broad file, network, media, or database tool signal. generalized runtime or code generation signal. - **Geiger risk:** yellow / medium - broad file, network, media, or database tool signal - generalized runtime or code generation signal ## Configuration and credential file locations These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval. ## Configuration files - Unix: $HIVE_CONF_DIR/hive-site.xml, $HIVE_CONF_DIR/hivemetastore-site.xml, $HIVE_CONF_DIR/hiveserver2-site.xml, $HIVE_HOME/conf/hive-site.xml ## Credential files - Unix: ${user.home}/.beeline/beeline-hs2-connection.xml, $HIVE_CONF_DIR/beeline-hs2-connection.xml, /etc/hive/conf/beeline-hs2-connection.xml - Windows: ${user.home}\beeline\beeline-hs2-connection.xml ## Source Database Details - **Source Database:** Homebrew formula API - **Tap:** homebrew/core - **Full Name:** hive - **Version Scheme:** 0 - **Revision:** 0 - **Bottle Stable Root URL:** - **Deprecated:** no - **Disabled:** no - **Keg Only:** no - **URL Keys:** stable ## Related links - [Secret-risk packages](https://www.automicvault.com/pkg/secret-risk-packages/) - Has protected-tool coverage, approval-gate, or non-low Geiger security signals. - [Terminal utility packages](https://www.automicvault.com/pkg/terminal-utilities/) - Matched terminal and command-line workflow metadata. - [Text processing packages](https://www.automicvault.com/pkg/text-processing-tools/) - Matched text, document, or structured-data processing metadata. - [Networking and protocol packages](https://www.automicvault.com/pkg/networking-protocol-tools/) - Matched network, protocol, or remote-service metadata. - [hadoop](https://www.automicvault.com/pkg/brew/hadoop/) - Runtime dependency declared by Homebrew. - [openjdk@21](https://www.automicvault.com/pkg/brew/openjdk-21/) - Runtime dependency declared by Homebrew. - [prestodb](https://www.automicvault.com/pkg/brew/prestodb/) - Shares av.db curated category or tags: analytics, big-data, cli, data, sql. - [trino](https://www.automicvault.com/pkg/brew/trino/) - Shares av.db curated category or tags: analytics, big-data, cli, data, sql. - [apache-drill](https://www.automicvault.com/pkg/brew/apache-drill/) - Shares av.db curated category or tags: big-data, cli, data, sql. - [duckdb](https://www.automicvault.com/pkg/brew/duckdb/) - Shares av.db curated category or tags: analytics, cli, data, sql. - [hbase](https://www.automicvault.com/pkg/brew/hbase/) - Shares av.db curated category or tags: big-data, cli, data, hadoop. - [monetdb](https://www.automicvault.com/pkg/brew/monetdb/) - Shares av.db curated category or tags: analytics, cli, data, sql. - [pig](https://www.automicvault.com/pkg/brew/pig/) - Shares av.db curated category or tags: big-data, cli, data, hadoop. - [anyquery](https://www.automicvault.com/pkg/brew/anyquery/) - Shares av.db curated category or tags: cli, data, sql. ## Combined YAML source View the package source record on GitHub. [combined/hive.yml](https://github.com/automic-vault/db/blob/main/combined/hive.yml) ## Sources - Nucleus package database - Geiger risk classifier - package-page enrichment - curated configuration and credential file locations - curated package history - package version freshness - av.db category and tag curation - package relationship graph - cross-ecosystem install command graph