Last modified: September 5, 2017
HBase and Cassandra are both great additions to the Hadoop ecosystem. While both are NoSQL database solutions with similar characteristics, there are some key differences between HBase and Cassandra. In this article, we discuss the fundamental differences between Cassandra and HBase including which to use and why.
HBase is a NoSQL distributed database that runs on top of Hadoop's HDFS. It emphasizes high consistency, meaning any write that is performed is immediately reflected in sequential reads. Hbase is mainly used for scalable reads/writes on HDFS.
Cassandra is also a NoSQL distributed database. Unlike HBase, Cassandra emphasizes high availability and minimal administration. If certain nodes go down, Cassandra guarantees continual availability of data. Cassandra also emphasizes fast reads/writes in a distributed environment.
HBase and Cassandra are largely used for the same purposes. Both are NoSQL distributed data stores that emphasize scalable reads/writes. Both claim linear scalability meaning storage capacity and performance is directly related to the number of nodes operating in the cluster.
Hbase and Cassandra both emphasize replication, meaning data is replicated across different nodes to prevent data loss. Similarly, both are tolerant to network partitions, meaning things will still work even if certain nodes go down or fails to communicate over a network (fault tolerance).
While Cassandra and HBase share similar roles in the Hadoop ecosystem, there are a few key differences that separate the two.
With HBase, a master node is specified to handle administrative functions and assignments for regional servers. These regional servers manage data nodes that perform actual reads/writes on HDFS. HBase uses Zookeeper to coordinate server state and operations. HBase stores data in HDFS.
Cassandra stores data outside of HDFS and the Hadoop cluster. Unlike HBase, all nodes in Cassandra share a similar role. There is no concept of masters/slaves but rather multiple seed nodes. With this decentralized architecture, any node can perform any operation. Cassandra uses Gossip instead of Zookeeper to manage internode communication.
Use HBase if you are emphasizing consistency with large scale reads. Use Cassandra if high availability is desired. Cassandra requires minimal setup with little administration overhead, making it easier to get started.
If you work a lot with MapReduce and batch processing, HBase is preferred for its direct relationship with HDFS.
Cassandra is good for single row reads and is optimized for writes. Cassandra also offers more flexibility in the way of CAP theorem tradeoffs. For example, you can configure consistency levels with Cassandra whereas HBase lacks these explicit configurations.
HBase is more suitable for data warehousing and optimized reads. Use HBase when you want to perform aggregations and analysis on big data in HDFS. Use Cassandra when you want to emphasize real time transaction processing and interactive data.