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HBase Architecture: Everything That You Need to Know [2025]

HBase Architecture: Everything That You Need to Know [2025]

Q: 1. What are the roles performed by HMaster in HBase?

HMaster plays an essential role in terms of performance. It maintains nodes in the cluster. Admin performance is provided by HMaster, and it distributes services and assigns regions to different region servers. HMaster focuses on controlling load balancing and failover to handle the load over nodes present in the cluster. It takes responsibility when a client wants to change any metadata operations. HMaster also checks the health status of region servers and runs several background threads.

Q: 2. How does HBase work?

HBase is a high-reliability, high performance, column-oriented storage system that uses HBase technology to build large-scale structured storage clusters on PC servers. HBase is made to handle large amounts of data consisting of thousands of rows and columns. HBase is responsible for dividing the logical table into multiple data blocks, HRegion, and stores them in HRegionServer. HMaster manages all HRegionServers. It stores the mappings of data to HRegionServer. HBase is the perfect choice for high-scale, real-time applications. It does not require a fixed schema, and developers can add new data as and when required without having to conform to a predefined model.

Q: 3. What is the difference between HBase and Hadoop?

The Hadoop Distributed File System is a distributed file system designed to store and run on multiple machines that are connected to each other as nodes and provide data reliability. HBase, on the other hand, is a top-level Apache project written in Java which fulfills the need to read and write data in real-time. HDFS is highly fault-tolerant and cost-effective, while HBase is partially tolerant and highly consistent. HDFS provides only sequential read/write operations, whereas HBase supports random read and write operations in a file system. HDFS gives high latency to access operations while HBase provides low latency access to small amounts of data.

Q: 4. What are the key characteristics of HBase?

HBase is a distributed, column-oriented NoSQL database that is designed for high scalability and real-time access to large datasets. Its key characteristics are providing strong consistency, automatic sharding (region splitting), and integrating tightly with Hadoop for big data storage and processing as well as utilizing HDFS for durable storage.

Q: 5. What is the structure of the HBase table?

An HBase table consists of rows and column families. Each row is uniquely identified by a row key. Within a column family, data is stored in columns, and each column can hold multiple versions of data, which are timestamped. The schema is flexible and dynamic.

Q: 6. What is HBase architecture in big data?

HBase is designed as a distributed, fault-tolerant, and scalable database that operates on top of HDFS for storage. It uses RegionServers to handle data, an HMaster to coordinate the cluster, and Zookeeper for managing metadata and cluster health, making it suitable for big data applications.

Q: 7. What are the applications and use cases of HBase?

HBase is used in real-time applications requiring fast access to large datasets, such as online recommendation systems, time-series data analysis, log aggregation, IoT data storage, and social media platforms. It is ideal for write-heavy workloads and large-scale data processing applications.

Q: 8. How can we store big data using HBase?

Big data can be stored in HBase by creating tables with multiple column families and distributing the data across RegionServers. HBase leverages HDFS for storage, ensuring scalability and fault tolerance by automatically splitting and balancing regions as data grows, allowing for efficient data storage and retrieval.

Q: 9. How is data integrity ensured in HBase?

Data integrity in HBase is ensured by using Write-Ahead Logs (WAL) for durability, preventing data loss during failures. It also uses checksums to verify data integrity during read/write operations and ensures consistency within regions. Strong consistency is maintained within a region for atomic operations.

Q: 10. How many types of databases exist in NoSQL?

NoSQL databases can be broadly classified into four types: Document Stores (e.g., MongoDB), Key-Value Stores (e.g., Redis), Column-Family Stores (e.g., HBase, Cassandra), and Graph Databases (e.g., Neo4j). Each type serves different use cases based on data structure and query requirements.

By Mayank Sahu

Updated on Mar 07, 2025 | 10 min read | 14.3k views

Table of Contents

Both structured and unstructured data are growing exponentially, and Apache Hadoop has proven its excellence in handling such vast data. The Apache Hadoop has, therefore, gained much traction in the big data world. However, there are certain limitations to Hadoop’s HDFS architecture.

HDFS outputs high latency operations and cannot handle a large volume of the read and write requests simultaneously. Another limitation is that HDFS is a write-once read many times architecture, meaning that it has to rewrite a file completely to alter a data set. These limitations of HDFS architecture raised the need for HBase architecture.

In this article, we shall take a closer look at the HBase architecture, discussing its components, the request handling process as well as the data recovery process. Read along to explore HBase architecture in-depth!

Also Read: HBase vs. Cassandra: Difference Between HBase and Cassandra

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What is HBase?

HBase is a column-oriented data storage architecture that is formed on top of HDFS to overcome its limitations. It leverages the basic features of HDFS and builds upon it to provide scalability by handling a large volume of the read and write requests in real-time. Although the HBase architecture is a NoSQL database, it eases the process of maintaining data by distributing it evenly across the cluster. This makes accessing and altering data in the HBase data model quick.

What are the Components of the HBase Data Model?

Since the HBase data model is a NoSQL database, developers can easily read and write data as and when required, making it faster than the HDFS architecture. It consists of the following components:

1. HBase Tables: HBase architecture is column-oriented; hence the data is stored in tables that are in table-based format.

2. RowKey: A RowKey is assigned to every set of data that is recorded. This makes it easy to search for specific data in HBase tables.

3. Columns: Columns are the different attributes of a dataset. Each RowKey can have unlimited columns.

4. Column Family: Column families are a combination of several columns. A single request to read a column family gives access to all the columns in that family, making it quicker and easier to read data.

5. Column Qualifiers: Column qualifiers are like column titles or attribute names in a normal table.

6. Cell: It is a row-column tuple that is identified using RowKey and column qualifiers.

7. Timestamp: Whenever a data is stored in the HBase data model, it is stored with a timestamp.

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Read: Components of Hadoop Ecosystem

What are the Components of HBase Architecture?

The HBase architecture comprises three major components, HMaster, Region Server, and ZooKeeper.

1. HMaster

HMaster operates similarly to its name. It is the master that assigns regions to Region Server (slave). HBase architecture uses an Auto Sharding process to maintain data. In this process, whenever an HBase table becomes too long, it is distributed by the system with the help of HMaster. Some of the typical responsibilities of HMaster include:

Control the failover
Manage the Region Server and Hadoop cluster
Handle the DDL operations such as creating and deleting tables
Manage changes in metadata operations
Manage and assign regions to Region Servers
Accept requests and sends it to the relevant Region Server

2. Region Server

Region Servers are the end nodes that handle all user requests. Several regions are combined within a single Region Server. These regions contain all the rows between specified keys. Handling user requests is a complex task to execute, and hence Region Servers are further divided into four different components to make managing requests seamless.

Write-Ahead Log (WAL): WAL is attached to every Region Server and stores sort of temporary data that is not yet committed to the drive.
Block Cache: It is a read request cache; all the recently read data is stored in block cache. Data that is not used often is automatically removed from the stock when it is full.
MemStore: It is a write cache responsible for storing data not written to the disk yet.
HFile: The HFile stores all the actual data after the commitment.

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3. ZooKeeper

ZooKeeper acts as the bridge across the communication of the HBase architecture. It is responsible for keeping track of all the Region Servers and the regions that are within them. Monitoring which Region Servers and HMaster are active and which have failed is also a part of ZooKeeper’s duties. When it finds that a Server Region has failed, it triggers the HMaster to take necessary actions. On the other hand, if the HMaster itself fails, it triggers the inactive HMaster that becomes active after the alert. Every user and even the HMaster need to go through ZooKeeper to access Region Servers and the data within. ZooKeeper stores a.Meta file, which contains a list of all the Region Servers. ZooKeeper’s responsibilities include:

Establishing communication across the Hadoop cluster
Maintaining configuration information
Tracking Region Server and HMaster failure
Maintaining Region Server information

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How are Requests Handled in HBase architecture?

Now that we know the major components of the HBase architecture and their function, let’s delve deep into how requests are handled throughout the HBase architecture.

1. Commence the Search in HBase Architecture

The steps to initialize the search are:

The user retrieves the Meta table from ZooKeeper and then requests for the location of the relevant Region Server.
Then the user will request the exact data from the Region Server with the help of RowKey.

2. Write Mechanism in HBase Architecture

The steps to write in the HBase architecture are:

The client will first have to find the Region Server and then the data’s location for altering it. (This step is involved only for converting data and not for writing fresh information)
The actual write request begins at the WAL, where the client writes the data.
WAL transfers the data to MemStore and sends an acknowledgment to the user.
When MemStore is filled with data, it commits the data to HFile, where it is stored.

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3. Read Mechanism in HBase Architecture

To read any data, the user will first have to access the relevant Region Server. Once the Region Server is known, the other process includes:

The first scan is made at the read cache, which is the Block cache.
The next scan location is MemStore, which is the write cache.
If the data is not found in block cache or MemStore, the scanner will retrieve the data from HFile.

Also Read: How to Become a Hadoop Administrator: Everything You Need to Know

How Does Data Recovery Operate in HBase Architecture?

The Hbase architecture breaks data through compaction and region split to reduce the data load in the cluster. However, if there is a crash and recovery is needed, this is how it is done:

The ZooKeeper triggers HMaster when a server failure occurs.
HMaster distributes crashed regions and WAL to active Region Servers.
These Region Servers re-executes WAL and builds the MemStore.
When all the Region Servers re-executes WAL, all the data along with the column families are recovered.

Also Read: Hadoop Ecosystem & Components

Conclusion

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