Big data is big business in the modern world, and without the proper data model, your business could be misplacing or even losing data stored in its servers.
Structured and unstructured data are growing exponentially, and while the Hadoop Distributed File System (HDFS) architecture is adept at handling vast amounts of data, there are limitations to HDFS, which has necessitated the need for HBase architecture.
This post will thoroughly explain what HBase architecture is and its various components. Data models and processes can seem complicated, but they are more straightforward than they appear.
An Introduction to HBase
HBase is a data storage architecture designed to run on Hadoop Distributed File System (HDFS), which is the storage component of the big data tool Hadoop. HBase was built to overcome the limitations of HDFS. It is a column-oriented storage architecture written in Java.
Plus, HBase is open-source. HDFS’s main limitation is that it cannot handle a large amount of simultaneous read and write requests. In addition, HDFS is a write-once-read-many-times architecture, which means HDFS has to rewrite a file completely to alter a data set.
As a result, HBase was developed to be highly scalable and handle a massive volume of read and write requests in real time. While HBase is a column-oriented NoSQL database, it simplifies maintaining data by evenly distributing it across the Hadoop cluster.
As a result, accessing and altering data in the HBase data model is simple and quick.
The Components of the HBase Data Model
Now that we know more about what HBase is, it is helpful to understand the parts that make up the HBase data model. Many of these components will seem familiar if you are familiar with NoSQL databases and data tables.
HBase Tables
HBase architecture is column-oriented. As a result, HBase data is stored in tables. Table-based data formats are commonly used. If you have ever used Microsoft Excel or a similar program, you know what table-based data looks like.
RowKey
In HBase, a RowKey gets assigned to every set of entered data. When you need to find a specific piece of data in the HBase cluster, all you have to do is enter the unique RowKey. RowKeys makes it easy for users to find data within HBase tables.
Columns
Columns represent the different facets and attributes of a set of data. There can be unlimited columns associated with a single RowKey in HBase.
Column Family
In HBase, columns can be grouped together to form column families. A read request for a column family grants access to all columns in the column family, which makes reading data simpler and quicker.
Column Qualifiers
Column qualifiers are names or unique identifiers that can be given to individual columns. Qualifiers make identifying columns in the same column families or tables easier.
Cell
Cells are individual areas specific to a particular row and column. They can be identified by using RowKey and column qualifiers. The cell is the smallest unit of data within an HBase table.
Timestamp
All data entries in HBase are time-stamped at the moment of entry. This makes it easy for users to look for data from specific time periods. In addition, it gives users more visibility over when data is being entered.
The Architecture of HBase
Now that we better understand the column-focused nature of the HBase data model, examining the primary parts of HBase architecture in greater detail will be helpful. The main three components of HBase that we should consider include:
- Region servers
- HMaster
- ZooKeeper
Region Servers
In HBase, a region server is the end node that handles user requests. Typically, several regions are combined within a single HBase region server. Each unique region contains all of the rows between two specified keys.
Since many complexities are associated with executing user requests, region servers are divided into four sub-components to make managing requests more efficient. The components of a region server include:
- Write ahead log
- Block cache
- MemStore
- HFile
Write Ahead Log (WAL)
The write ahead log is attached to every region server. WAL stores the temporary data in the different region servers that have yet to be committed to the drive. If there is a region server failure, WAL is responsible for recovering the data from its corresponding region server.
Block Cache
Block cache is a read request cache. Recently read data from all the region servers is stored in block cache. When the block cache is full, the least used data is automatically removed to make room for new data in the block cache.
MemStore
MemStore is a cache in region server instances that stores data not yet written to the disk. You might think that MemStore sounds a lot like WAL. While WAL recovers data when a region server fails, MemStore is used as temporary storage before data is written to HFile.
HFile
The HFile stores all data from a region server that has been committed to the disk. HFile is the unit of storage for HBase.
HMaster
HMaster functions as the master that assigns regions to region servers. HBase utilizes an auto-sharding process to maintain data. However, at times, when using auto-sharding, an HBase table can become too long. In these situations, the HMaster distributes the table across the system.
The HMaster monitors the region servers and maintains performance levels by controlling load balancing across all region server nodes in the HBase cluster. In addition, any time a user wants to change schema or metadata operations, the HMaster is responsible for these operations.
ZooKeeper
The ZooKeeper is the centralized monitoring server that administers the entire HBase cluster. The ZooKeeper maintains configuration data and distributed synchronization across region server nodes.
In addition, the Zookeeper monitors the active region servers and the regions within them. When a server region fails, the ZooKeeper triggers the HMaster to perform its duties. If the HMaster fails, the ZooKeeper triggers the inactive HMaster.
Every user or HMaster must go through the ZooKeeper to access region servers and their data.
Final Thoughts
HBase is not as complicated as it might seem from the outside. Of course, to properly configure and implement this tool, you must be proficient with Hadoop, HDFS, and big data applications. If you want to learn more about HBase architecture, contact a big data expert like Koombea.
