Apache HBase
Apache HBase is an open-source, distributed, column-oriented NoSQL database built on top of Apache Hadoop and modeled after Google Bigtable. It’s designed for real-time read/write access to large datasets stored in Hadoop. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed Filesystem), providing BigTable-like capabilities for Hadoop. That is, it provides a fault-tolerant way of storing large quantities of sparse data (small amounts of information caught within a large collection of empty or unimportant data, such as finding the 50 largest items in a group of 2 billion records, or finding the non-zero items representing less than 0.1% of a huge collection).
Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.
Why we use HBase?
• To get real-time access to data stored in Hadoop
• To handle very large datasets (billions of rows)
• To support random reads and writes (not just batch jobs)
• To store sparse data efficiently
• To scale horizontally across many machines
When should you use HBase?
HBase is a good fit when:
• You need low-latency reads/writes on big data
• Your data is huge and continuously growing
• You are already using the Hadoop ecosystem
• Your access pattern is based on row keys (fast lookups)
• You deal with time-series or versioned data
Not ideal when:
• You need complex SQL queries or joins
• You require full ACID transactions
• Your dataset is small or moderate
• You want a simple setup (HBase can be complex)
Key features of Apache HBase
• Column-family storage model
• Strong consistency (unlike many NoSQL systems)
• Horizontal scalability
• Automatic sharding (region splitting)
• Versioning (multiple versions of a cell)
• Integration with Hadoop ecosystem
• Random, real-time access
Key components of HBase
• HMaster: Coordinates the cluster and manages metadata
• RegionServer: Handles read/write requests and manages regions
• Region: A subset of a table (horizontal partition)
• HDFS: Underlying storage layer
• ZooKeeper (Apache ZooKeeper): Handles coordination and cluster state
• Write-Ahead Log (WAL): Ensures durability
• MemStore: In-memory write buffer
• HFiles: Immutable files stored on disk
Advantages of HBase
• Real-time read/write on big data
• Strong consistency
• Efficient for sparse datasets
• Scales to billions of rows
• Tight integration with Hadoop tools
Disadvantages of HBase
• Operational complexity (setup, tuning, maintenance)
• No native SQL support (requires layers like Apache Phoenix)
• Limited query flexibility (row-key based access)
• Not ideal for ad hoc analytics
• High latency compared to in-memory systems
Alternatives
Depending on your needs, these systems may be better:
Apache Cassandra
Better for write-heavy workloads and global distribution
MongoDB
Easier to use, flexible schema
Google Bigtable
Managed cloud version of HBase-like system
Amazon DynamoDB
Fully managed, highly scalable
Apache Accumulo
Similar to HBase but with added security features
Other Features of Apache HBase
• Linear and modular scalability.
• Strictly consistent reads and writes.
• Automatic and configurable sharding of tables
• Automatic failover support between RegionServers.
• Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables.
• Easy to use Java API for client access.
• Block cache and Bloom Filters for real-time queries.
• Query predicate push down via server side Filters
• Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options
• Extensible jruby-based (JIRB) shell
• Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX
