Getting Started with Apache HBase – The NoSQL Database for Big Data

Apache HBase is a powerful, scalable, distributed NoSQL database that runs on top of Hadoop and HDFS. It provides random, real-time read/write access to big data and is commonly used for applications that require low latency and high throughput access to large amounts of sparse data.

In this in-depth guide, we‘ll dive into what makes HBase unique, explore its architecture and data model, walk through getting started with HBase, and discuss important considerations for running HBase in production. By the end, you‘ll have a solid foundation in HBase to decide if it‘s the right fit for your use case and get up and running quickly.

What is Apache HBase?

HBase is an open source, non-relational, versioned database modeled after Google‘s Bigtable. It provides a fault-tolerant way of storing large quantities of sparse data using column-oriented compression and storage. HBase has been designed to provide quick random access to huge amounts of structured data and scales linearly to handle very large data sets with billions of rows and millions of columns.

Some of the key features and benefits of HBase include:

• Massive scalability – scales out horizontally by adding more nodes to the cluster
• Strictly consistent reads/writes – row-level consistency for simultaneous updates
• Automatic sharding – tables are dynamically partitioned into regions and distributed
• Low latency and high throughput – optimized for fast, random reads and writes
• Hadoop/HDFS integration – built to work seamlessly with Hadoop components
• Language support – Java API as well as REST, Thrift, Avro gateways
• Fault tolerance – auto-failover support if nodes go down

These capabilities make HBase well-suited for a variety of use cases:

• Content repositories for serving data to web and mobile apps
• Real-time queues for capturing streaming data
• Store for time series data like clickstreams, logs, and sensor data
• Storing large amounts of semi-structured data like JSON, XML
• Powering live dashboards and metrics applications
• Session store for storing user state and history
• Message and log storage and analysis

HBase is battled-tested and powers many large-scale applications. Notable users include Adobe, Airbnb, Alibaba, Bloomberg, Facebook, Netflix, Pinterest, Salesforce, Spotify, Twitter, and Yahoo. The active developer community and wide adoption make it a stable choice for large-scale, production NoSQL workloads.

HBase and the Hadoop Ecosystem

An important aspect of HBase is how it fits in and complements the Hadoop ecosystem. HBase typically runs on top of Hadoop as a real-time data store alongside a batch processing system like MapReduce or Spark.

While HDFS is good at storing large data sets, it‘s not viable for fast individual record lookups and updates. MapReduce provides high throughput batch processing, but not low latency access. HBase fills in these gaps, making it easy to store and query large semi-structured data sets in real-time.

In a typical big data architecture, raw data is collected and stored on HDFS. This historical data can be batch processed using tools like MapReduce, Hive, Pig, or Spark to compute views. The resulting aggregations can be stored in HBase tables to provide fast access for analytic and serving workloads. HBase can also injest streaming data and updates in real-time.

By leveraging HDFS, YARN, and Zookeeper, HBase serves as a scalable, transactional NoSQL database that integrates with the rest of the Hadoop ecosystem. This allows for a unified batch and real-time big data platform.

Under the Hood – HBase Architecture

Next let‘s examine the key architectural components that power HBase and how they work together under the covers.

Regions and Region Servers

HBase tables are divided horizontally by row key range into "regions". A region contains all rows in the table between a start key and end key. Regions are the units which get distributed across an HBase cluster. As data grows, regions are automatically split into smaller regions to be spread across more servers.

Regions are managed by RegionServers. A RegionServer serves data for reads and writes from regions. A single region is serviced by only one RegionServer, but a RegionServer may serve multiple regions. The mapping of regions to RegionServers is kept in ZooKeeper. When a RegionServer goes down, the regions it was serving are automatically reassigned to other RegionServers to maintain availability.

HMaster

The HMaster is responsible for monitoring all the RegionServer instances in a cluster. It is the interface for all metadata changes. The HMaster performs admin functions like assigning regions to RegionServers, load balancing of regions across RegionServers, and handling RegionServer failures. There is only one active master for a cluster.

Catalog Tables

There are special HBase tables used to maintain information about the table and region locations. This metadata is accessible through the HBase shell using special commands.

ZooKeeper

ZooKeeper is used for coordinating between the HMaster and RegionServers in a cluster. It maintains details of which servers are alive and which regions they are serving. The HMaster uses ZooKeeper to assign regions to RegionServers. All participating nodes communicate via maintaining transient status in ZooKeeper.

HFiles and MemStore

Regions are made up of "stores" which correspond to column families. Stores consist of two things – a MemStore and HFiles.

Incoming writes accumulate in the MemStore until they reach a limit and are flushed into read-only HFiles on disk in HDFS. This provides a persistent storage structure optimized for reads.

HBase Data Model Basics

Data in HBase is stored in tables, which are made up of rows and columns, like a traditional database. However, the format is very flexible and looks quite different than a SQL table.

Some key things to understand about the HBase data model:

• Tables are sparsely populated and columns can vary between rows
• Columns are grouped into "column families"
• Column families must be declared up front and are stored together on disk
• Columns can be added to families on the fly and rows can have varying columns
• Rows are identified by a unique byte array "row key"
• Table cells are versioned using timestamps
• Versions are sorted in descending order, with the most recent on top

Here is an example of what data looks like in an HBase table:

              Column Family Content             Column Family Meta    
              col-1     col-2      col-3                  col-4
        ---------------     ---------------         ---------------
row-1   |    val-1   |       | val-2     |         |     val-3    | 
        ---------------     ---------------         ---------------  
row-2   |  val-1.1   | val-2 |           |         |     val-3    | 
        ---------------     ---------------         ---------------
row-3   |    val-1   |       |           |         |  val-3.1     |
        ---------------     ---------------         --------------- 

HBase supports a wide range of data types for values, including:

• byte array
• String
• Boolean
• short, int, long
• float, double
• BigDecimal
• Date
• UUID
• byte[], String, protobuf, Avro serializations

Proper row key and schema design are critical for getting good performance with HBase. There is much more depth to the HBase data model than can be covered here. The Definitive Guide to HBase is a great resource for learning advanced schema design techniques.

Basic HBase Operations

There are several different ways to interact with data in HBase:

• HBase Shell – an interactive Ruby-based command line interface
• Java API – native APIs for Create, Read, Update, Delete (CRUD) operations
• REST API – interact with HBase via HTTP endpoints
• Thrift API – an RPC library with bindings in several languages

Here are some basic examples of interacting with HBase from the HBase Shell:

Create a table with a column family:

create ‘test‘, ‘cf‘

Insert a row:

put ‘test‘, ‘row1‘, ‘cf:col1‘, ‘value1‘

Get a row:

get ‘test‘, ‘row1‘

Scan a table:

scan ‘test‘

Delete a table:

disable ‘test‘
drop ‘test‘

HBase also integrates with MapReduce for running distributed computations over HBase tables and Spark for in-memory distributed processing.

Getting Started with HBase

The easiest way to get started with HBase is to set up a standalone instance on your local machine. Download a stable HBase release and follow the Quickstart instructions in the reference guide.

You‘ll need to have Java installed. Start the HBase services and you can connect to HBase in your local instance using the HBase Shell.

Try the following steps:

1. Create a table with two column families, "cf1" and "cf2"
2. Put sample data in several rows
3. Get row by row key
4. Scan over table with a filter to return a subset of rows
5. Get a specific row/column value
6. Delete table

There are more advanced HBase setups like pseudo-distributed mode and fully-distributed mode for running on a cluster. Once you have a live cluster, you can bulk load data into tables using the completebulkload tool or integrate HBase with Flume or Storm for streaming data injestion.

HBase in Production

Running HBase in a production environment requires some extra considerations beyond the basics. Some key aspects to consider:

Cluster Sizing and Hardware

HBase is designed to run on commodity hardware. Typically RegionServers are provisioned with lots of memory and rotating disks in a replication factor of 3. The HMaster nodes generally have less intensive hardware requirements. Having a good monitoring setup is also critical for keeping an eye on cluster health.

Deployment Options

HBase can be deployed in public cloud or on-premise environments. There are also several vendors that offer managed HBase such as Amazon EMR, Cloudera CDH, Google Cloud Bigtable, and Microsoft Azure HBase. These can simplify the operational burden of scaling and maintaining HBase clusters.

Performance Tuning

HBase exposes many different configuration options and knobs for tuning performance. Areas to look into are block cache sizing, MemStore sizing, compaction strategies, Bloom filters, and compression. Proper capacity planning and monitoring are important for achieving consistent low latency and high throughput.

Backup and Disaster Recovery

As the source of truth for application data, it‘s important to have a solid backup and recovery strategy for HBase clusters. HBase supports shipping incremental backups to a backup cluster or to the cloud. The HBase cluster can also be snapshotted on a regular basis. There are also commercial tools that provide managed backup and recovery options.

HBase Drawbacks and Limitations

While HBase is a powerful tool, it‘s not the right fit for every use case. Some of the limitations to be aware of include:

• Aggregations are slow unless pre-defined with counters
• Can‘t do relational-style joins between tables
• No built-in indices or search features
• Requires careful capacity planning
• Configuration and tuning complexity

Additionally, HBase is very latency-sensitive as a random access database. This makes it difficult to run analytical workloads or large scans without affecting production SLAs.

HBase vs. Other Databases

There are many new NoSQL database options to choose from today, each with different strengths, scalability, and data models. HBase is often compared to databases like Apache Cassandra, MongoDB, and Amazon DynamoDB.

Some of the key differentiators of HBase are:

• Strong consistency vs eventual consistency
• Scale up on big nodes vs scale out on smaller nodes
• Deeper Hadoop/HDFS integration vs independent storage
• Automatic sharding vs manual
• Tight control vs fully managed service

Selecting the right database depends on your scalability, latency, throughput, data model, and operational requirements. While HBase is the clear choice for low-latency, high-throughput access to large amounts of sparse data in the Hadoop ecosystem, it‘s not the best fit for every use case.

Conclusion

HBase is a powerful tool for serving data-intensive applications that require real-time read/write access to large data sets. Assuming HBase is a good fit for your access patterns and use case, it offers massive scalability, fault tolerance, and Hadoop ecosystem integration.

In this guide, we took a deep dive into HBase, from the basics of what it is, to its architecture and data model, to getting started and running in production. While there is much more detail we could go into, you should now have a solid foundation for beginning to work with HBase.

HBase, like Hadoop, can require a steep learning curve to understand its complexities and how to use it effectively. However, it is continuing to evolve and improve with each new release. If you‘re looking to learn more, there are many great resources, including the official Apache HBase Reference Guide, HBase: The Definitive Guide (book), and an active user mailing list.