macOS
brew install hbaselocal Homebrew formula metadata
安装
brew install hbaselocal Homebrew formula metadata
nix profile install nixpkgs#hbasenixpkgs package indexes · hbase · 来源: raw.githubusercontent.com
概览
Hadoop database: a distributed, scalable, big data store
历史
Apache HBase is a distributed, scalable, Bigtable-like data store built on Hadoop and HDFS for random, real-time read/write access to very large sparse tables. In package-manager terms it is a heavyweight data-system package: a CLI, daemons, configuration directory, Java/Hadoop dependency stack, and operational database all in one.
The official reference guide's history appendix begins with Google's 2006 Bigtable paper, then places the start of HBase development at the end of 2006. The project was designed to bring Bigtable-like capabilities to the Hadoop ecosystem rather than to be a standalone relational database.
HBase became a Hadoop sub-project in 2008 and an Apache top-level project in 2010. That transition matters historically because it moved HBase from an add-on in the Hadoop orbit to an Apache project with its own project management committee, release process, community, and operational identity.
The HBase website describes the system as a distributed, scalable big-data store for random, real-time read/write access. It emphasizes billions of rows, millions of columns, consistent operations, automatic failover and sharding on Hadoop/HDFS, and APIs including Java, REST, Thrift, filters, and Bloom filters.
The reference guide is unusually central to the project history: it covers standalone quick starts, distributed modes, configuration files, shell usage, schema design, MapReduce integration, security, architecture, backup/restore, replication, APIs, performance tuning, operations, and development. That breadth reflects HBase's evolution from a storage engine into a production database system with a large operator surface.
The official Powered by Apache HBase page lists user-submitted deployments across companies, institutions, and projects. It includes early production or large-scale use cases from Adobe, Facebook Messages, Flurry, HubSpot, OCLC WorldCat, OpenLogic, Trend Micro, Twitter, WorldLingo, Yahoo, and others.
The adoption examples show why HBase became important in the Hadoop era: teams needed low-latency reads and updates, wide sparse rows, time-series or event data, search and analytics backends, and MapReduce/Spark-adjacent processing without abandoning the commodity-cluster model.
HBase's adoption was also ecosystem-driven. Its value came not only from the database itself, but from living near HDFS, Hadoop MapReduce, ZooKeeper, Java clients, REST/Thrift gateways, and later higher-level systems such as Trafodion or Spark integrations.
The quick start uses a standalone instance where the Master, RegionServer, and ZooKeeper daemon run in one JVM, then connects through the hbase shell to create, list, describe, put, and scan a table. Production usage moves the same model into distributed deployments with separate daemons and HDFS storage.
Administrators configure HBase through files such as conf/hbase-site.xml and conf/hbase-env.sh, start and stop services with scripts such as start-hbase.sh and stop-hbase.sh, and use the shell, Java API, REST, Thrift, MapReduce jobs, and operational tools to manage data and clusters.
HBase is a classic package-manager stress test because installing the package is only the beginning. The formula has to deliver shell commands and scripts, but the real system depends on Java compatibility, Hadoop/HDFS behavior, ZooKeeper coordination, configuration files, daemon management, and careful version matching.
For package nerds, HBase also represents the Hadoop-era pattern where a local package can be used for development or a single-node quick start, while production meaning lives in clusters, configuration, and service orchestration. That makes its package metadata deceptively small compared with its operational footprint.
安全态势
broad file, network, media, or database tool signal. formula declares a Homebrew service.
orange 风险 · 中 置信度 · infrastructure
在无人值守的代理使用前,请检查该工具是否读取明文凭据、写入远程状态、发布制品或调用插件。
local files
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.
Config paths the tool may read or write during local use.
conf/hbase-site.xmlconf/hbase-env.sh可执行文件
| 命令 | 类型 | 暴露范围 | 备注 |
|---|---|---|---|
hbase | cli | 全局可执行文件 | |
start-hbase.sh | cli | 全局可执行文件 | |
stop-hbase.sh | cli | 全局可执行文件 |
新鲜度
这些信号区分页生成时间、软件包管理器活动和上游发布比较。只有存在证据 URL 和可比较版本时,才会提示版本落后。
安装元数据
| 软件包键 | brew:hbase |
|---|---|
| 版本 | 2.6.6 |
| 软件包管理器 | Homebrew |
| 软件包管理器页面 | https://formulae.brew.sh/formula/hbase |
| 主页 | https://hbase.apache.org |
| 仓库 | https://gitbox.apache.org/repos/asf?p=hbase.git |
| 上游文档 | https://hbase.apache.org/book.html |
| 许可证 | Apache-2.0 AND GPL-3.0-or-later |
| 源码归档 | https://www.apache.org/dyn/closer.lua?path=hbase/2.6.6/hbase-2.6.6-bin.tar.gz |
| 最后更新 | 2026-06-22T14:03:42-07:00 |
| Pulse | updated |
| 依赖 | lzo, openjdk@17 |
| 构建依赖 | ant |
| Bottle | 可用 (于 arm64_linux, arm64_sequoia, arm64_sonoma, arm64_tahoe, sonoma, x86_64_linux) |
| Homebrew post-install | 未定义 |
| 服务 | declared |
注册表事实
| Source Database | Homebrew formula API |
|---|---|
| Tap | homebrew/core |
| Full Name | hbase |
| Version Scheme | 0 |
| Revision | 0 |
| Bottle Stable Root URL | https://ghcr.io/v2/homebrew/core |
| Deprecated | no |
| Disabled | no |
| Keg Only | no |
| URL Keys |
|
源数据库匹配
匹配项来自外部软件包管理器索引,并与本地 Automic Vault 软件包链接分开显示。
hbase
nix profile install nixpkgs#hbase来源线索
此页面由 av-web 从 scripts/generate-pkg-sqlite.py 生成的私有软件包 SQLite 工件提供。
View the package source record on GitHub.