HBase vs. Cassandra: Difference Between HBase and Cassandra [2023]

Introduction

While working with large datasets, it is crucial to have storage units and management systems that can handle such a vast amount of data. HBase and Cassandra are two such scalable databases. Both of them are capable of handling large amounts of data without compromising performance. Both of them are open-source databases and work on the concept of linear scalability. Additionally, both of the databases are non-relational. 

Cassandra is used by companies like Activision, Apple, Instagram, and Netflix, to name a few. 

HBase is used by companies like Siemens, Nvidia, and Pinterest, to name a few. Some of the implementations of Cassandra at Apple handles over 10PB of data. 

In our analysis of HBase vs. Cassandra, we discuss the differences and some shared functionalities between these systems. It would help you decide which database to use in your next big data project and help in bridging some of the knowledge gaps regarding these databases. 

Dissimilarities 

1. Base infrastructures 

While discussing HBase vs. Cassandra, it is first necessary to understand the base infrastructures. Although both are said to be on NoSQL databases, Cassandra is more of a pure implementation. Unlike Cassandra, HBase uses the NoSQL database developed on top of Hadoop. Moreover, Cassandra and HBase were both derived from Google’s BigTable, but in HBase, we find some traces of Amazon’s Dynamo.

2. Base nodes

In HBase, we use master nodes used to monitor and coordinate the local servers and the actions associated. Along with the master nodes, there are standby nodes. These are used in case a master node fails and also in inter-node communications. While in Cassandra, there are seed nodes rather than master nodes, for inter-node communications. To ensure the scalability and performance, in Cassandra, a single cluster contains multiple seed nodes. 

3. Internode communication 

In either of the databases, inter-node communication is crucial for driving the database’s performance on large datasets. In HBase, ZooKeeper protocol handles all the inter-node communications. This protocol is quite similar to a master-slave network protocol. In the ZooKeeper protocol, one node acts as the master, and other nodes are dependent on the master node for all the communication.

In Cassandra, a Gossip protocol is used. Just like the word suggests, communication is chained. The inter-node communication is implemented through message passing between the nodes. So, at each node, the data is replicated. 

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4. Query language

Although HBase and Cassandra are both based on JRuby and Shell, there is one critical point for HBase vs. Cassandra. Cassandra uses its specific query language known as CQL. Cassandra Query language or CQL is the primary language to communicate with a Cassandra database. To access Cassandra through CQL, the most common tool used is CQL Shell (CQLSH). Due to this, sometimes Cassandra is considered more feature-rich than HBase. 

5. Transaction mechanisms 

As both are non-relational databases, Cassandra and HBase do not offer the basic RDBMS ACID transactions using rollback and locks. Cassandra provides atomic, isolated, and durable transaction mechanisms with tunable consistency.

In HBase, a transaction server is used to handle transactions. For lightweight transactions, HBase uses Read-Check-Delete and Check-Put means. Cassandra, however, uses Compare-Set and Row-Write-Isolation for handling lightweight transactions.

6. Infrastructure

As discussed earlier, HBase is developed upon Hadoop. Hence, HBase uses the base infrastructure of Hadoop and provides similar functionalities like ZooKeeper, master nodes, etc. However, Cassandra is built using protocols such as Gossip, Snitch, Partitioner, Replica placement strategy, etc.

Cassandra uses elements like datacentres and string sorting and CQL tables. Cassandra is often used along with Hadoop or Storm, by keeping the infrastructure type as a single-node. There is only one drawback to doing so. The complexity increases with the usage of other systems in combination with Cassandra. 

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7. Partitioning and coprocessor 

HBase does not provide support for ordered partitioning. Although HBase allows for coprocessor functionality, it fails to support read-load balancing as a single row is considered one region server. Cassandra, however, provides support for ordered partitioning. With this, Cassandra can offer a row size of several MBs. But Cassandra neither supports the range-based row scans, nor it provides support for coprocessor functionality.  

These were some of the core differences in HBase vs. Cassandra. But few other differences should be looked at too.  

Bloom filters: A bloom filter is a data structure used for processing continuous streams of data. Bloom filters are efficient in terms of space and are probabilistic. HBase uses boom filters to check whether a particular row or cell is there in the StoreFile or not. Some consider boom filters as a sort of indexing for HBase. In Cassandra, boom filters are used in searching for some data in a particular file. 

Setup and documentation: Although HBase may seem easier to set up, the Cassandra cluster is much easier to tune and set up than the HBase cluster. Cassandra also offers somewhat better documentation that makes it easier for adapting and learning. HBase workings could be trickier to understand. Nevertheless, with proper sources, can be known effectively. 

Usage over WAN: Both the databases allows for data replication functionality over WAN (Wide Area Network). In Cassandra, it is done through row replications provided by a random partitioning module. In HBase, the reproduction is performed asynchronously by replicating the clusters as storage units. 

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Also Read: Hadoop Tutorial: Ultimate Guide to Learn Big Data Hadoop

Conclusion

So, these were some of the primary points of our analysis of HBase vs. Cassandra. As both the databases are based on NoSQL, they are sometimes confused as twins. But while looking at the core complexities, the differences surface. By looking at the analysis, selecting the useful one for your big data project is possible. Both databases have some merits and demerits. Hence, it is crucial to understand the usage of the database in your project and select accordingly. 

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