HBase vs HDFS

Difference Between HBase vs HDFS

In the article HBase vs HDFS, the volume of data is increasing every day and it is most important for organizations to store and process this huge volume of data. HBase, as well as HDFS, are one of the important components of the Hadoop ecosystem which help in storing as well as processing the huge datasets. The data might be structured, semi-structured or unstructured but it can be handled well with HDFS and HBase. HDFS stands for the Hadoop Distributed File System which manages the storage of data across a network of machines and the processing of the huge datasets is done using MapReduce. HDFS is suitable for storing large files with data having a streaming access pattern i.e. write the data once to files and read as many times required. In Hadoop, HBase is the NoSQL database that runs on top of HDFS. HBase stores the data in a column-oriented form and is known as the Hadoop database. HBase provides consistent read and writes in real-time and horizontal scalability.

Head to Head Comparison between HBase vs HDFS (Infographics)

Below is the top 4 Comparison between HBase vs HDFS:

Key Differences between HBase vs HDFS

Let’s discuss the top comparison between HBase vs HDFS:

• HDFS is designed specifically and suits best to perform batch processing. But when it comes to real-time analysis, HDFS is not suitable for such cases. Whereas HBase is not appropriate for performing batch processing but it handles the large datasets to perform read/write data in real-time.
• HDFS is suitable for writing files once and reading them many times. Whereas HBase is suitable for writing and reading data in a random manner which gets stored in HDFS.
• HDFS provides high latency operations for large datasets whereas HBase has a low latency for small datasets within the large datasets.
• HDFS stores large datasets in a distributed environment by splitting the files into blocks and uses MapReduce to process the huge datasets. Whereas HBase stores the data in the column-oriented database where columns are stored together so that the reading becomes faster in real-time.
• MapReduce jobs are executed to access HDFS generally. HBase can be accessed via Thrift, Avro, REST API or shell commands.

Comparison Table of HBase vs HDFS

The table below summarizes the comparisons between HBase vs HDFS:

HBase	HDFS
It is a NoSQL (Not Only SQL), column-oriented, distributed database which is built on top of HDFS. It is used when real-time writes and reads for random access of large datasets is required.	It supports batch processing where the data is stored as independent units called blocks. The files are split into different blocks and the data gets stored in them. The minimum block size in HDFS is 128 MB by default (in Hadoop 2.x).
HBase hosts sparsely populated but large tables. A table in HBase consists of rows, row is grouped into column families. A column family consists of columns. As part of schema definition, a table’s column families must be specified but a new column family can be added whenever required.	HDFS cluster has two types of nodes to store the data using NameNodes and DataNodes. The NameNodes are the master nodes which store the metadata whereas the DataNodes are the slave nodes that store the blocks of data (files split into blocks).
The tables in HBase are horizontally partitioned into Regions and each region consists of the subset of the rows of a table. Initially, a table consists of a single region. But as the region grows, it eventually surpasses the configurable threshold size, and then it gets split into more regions of approximately the same size. With the help of Zookeeper which provides configuration information, distributed synchronization, the client communicates with the Region servers.	The NameNode is the single point of failure as, without the metadata, the file system will not work. So the machine running the NameNode must have high availability. The processing of data is done through MapReduce. In Hadoop 1.x there used to be Job Tracker and Task Tracker for processing the data. But in Hadoop 2.x, this is performed through YARN where a Resource Manager and Scheduler do the same.
HBase has a similar data model as Google’s Big Table which provides very fast random access to the huge datasets. It has low latency of accessing single rows across billion of records and it uses Hash tables internally and for large tables uses fast lookups.	HDFS works best for very large files which may be of hundreds of terabytes or petabytes in size but working with a lot of small files is not recommended in HDFS as with more files, the NameNode requires more memory to store the metadata. The application requiring a low latency in accessing the data, will not work well with HDFS. Also in HDFS, the writes are done in an append-only manner and arbitrary file modifications are not possible.

Conclusion

In HDFS, the files get split into blocks and the blocks are efficient to use the remaining space after the file is stored in it. Also with HDFS, we get the bonus of fault-tolerant systems where it provides replication to keep back up of files in case any network disruption occurs. Also with the usage of commodity hardware, we get cheaper costs for a robust system. HBase as a database provides many advantages which a traditional RDBMS is not able to. With HBase, there is no fixed schema as we need to only define column families. Also, HBase is good for semi-structured data. In the Hadoop environment, where data is processed sequentially and in batches, HBase gives the advantage of real-time read and writes so that one does not have to search the entire dataset even for a single record. Both HDFS and HBase solve many of the issues related to storage and processing of a huge volume of data. However one needs to analyze the requirement to have a robust but efficient system.

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