Choosing a Database Management System: HBase vs Cassandra
A database management system is a key to the efficient functioning of a software product and security of the processed data. It can be challenging though to choose the right solution due to a big number of available options in the market.
In this article, we are going to overview the two most popular database management systems for Big Data projects: HBase and Cassandra. Despite sharing certain similarities, these two solutions also have some differences that need to be considered in order to choose the best option for your specific product.
HBase is a database management system built atop the Hadoop file system. HBase can be integrated with Hadoop both as a source and a destination.
HBase is a distributed and column-oriented system and it provides low latency batch processing. Other features of HBase are:
- The system has linear scalability
- Consistent reads and writes
- Data replication across clusters
- Automatic failure support
Speaking about HBase pros, the first one will be its ability to manage and store large datasets which makes HBase great for heavy applications with massive amounts of data. As well, HBase has quite fast processing and good consistency of reads and writes.
There are certain cons though that make developers doubt the use of HBase:
- No transaction support
- No handling of JOINS
- No built-in authentication
- Possibility of failure (if only one HMaster is used)
Despite these cons, HBase is a reliable and good choice for certain software products. So let’s have a look at Cassandra and see the main features and pros that it offers.
Cassandra is a distributed database management system, much like HBase. Cassandra was first developed by Facebook and became open-sourced in 2008. The main purpose of Cassandra is to handle massive data sets and to offer high availability. These and other features of Cassandra make this system favored by many developers and businesses.
Some of the Cassandra features are:
- Ability to process massive data volumes
- No single point of failure
- Horizontal scalability
- Operational simplicity
As for the benefits, Cassandra is praised for its flexible data distribution, ACID support, and flexible data storage. As well, Cassandra displays a fast and reliable performance which is great for e-commerce and real-time sensor data handling.
The similarities between HBase and Cassandra
As we said, HBase and Cassandra have certain differences - but they also share several similarities.
First, the database: both HBase and Cassandra are open-source NoSQL databases. This means both systems can efficiently handle Big Data and non-relational data and have similarities in the database structure.
Second, the two systems have high linear scalability which is also a great benefit for working with massive datasets. So if you need to increase the amount of data, you just increase the number of nodes in a cluster.
As well, both HBase and Cassandra have special features that help prevent data loss. This is possible due to the replication mode.
Now that we’ve observed HBase and Cassandra and their similar features, it’s time to see how they differ.
Both Cassandra and HBase are column family based stores, which are based on Google BigTable principles.
HBase does not really have data types, all the data is considered as bytes, thus when working with HBase records developers should rely on business logic, which defines data types.
If we compare the components of both data models, they will have quite a difference: for instance, a column in Cassandra is more like a cell in HBase. As well, Cassandra allows its primary key to contain multiple columns while HBase has a one-column row key.
As for the similarities, the databases of both Cassandra and HBase include a lack of joins and the possibility of having no value in a column or a cell for better storage usage.
Data Storage and Infrastructure
The key point to remember here is that HBase utilizes Hadoop cluster infrastructure and it is built on top of the HDFS filesystem. It means that HBase should be a choice for those systems that already have Hadoop infrastructure. On the contrary, Cassandra uses its own nodes and clusters, which makes it independent from the system infrastructure. Also, Cassandra provides better support for geographically distributed applications.
Query Language and APIs
HBase and Cassandra are open-source, implemented in Java, and have Java APIs. Unlike Cassandra, HBase does not have its own query language and that means that you will have to deploy the JRuby-based HBase shell alongside additional technologies such as Apache Drill or Apache Hive. These technologies allow making SQL queries to the HBase.
Cassandra, on the other hand, has its own query language which is CQL (Cassandra Query Language). As well, C* has better documentation which is often essential for software development.
The architecture of HBase is master-based, meaning that it has a single failure point. This type of architecture implies that the HBase client can communicate directly with the slave-server with no need to contact the master. Such an approach provides a working time if the master is down.
Cassandra has a masterless architecture and does not have a single point of failure. And though the HBase architecture displays good performance, the constant availability of Cassandra’s cluster makes it a really significant advantage.
Scalability and Replication
Both Cassandra and HBase are very efficient in horizontal scaling. If a system developer wants to increase the number of nodes, they just need to adjust the cluster’s configuration. In this way, both Cassandra and HBase are a perfect fit for a big data project.
Also, by default in the datastores, all the data is replicated several times for redundancy. Cassandra and HBase both use a configurable Replication Factor which allows to control the number of replicas.
CAP theorem and application areas
According to the CAP Theorem (Consistency, Availability, Partition Tolerance), HBase provides more Consistency with its mechanisms of transaction support (though there are no full ACID transactions). Cassandra provides more Availability, and both of them for sure are partition-tolerant as all the scalable NoSQL databases.
Cassandra is designed to support a massive number of writes, which are faster than reads. It is widely used in the IoT systems, real-time monitoring, and collection of massive amounts of analytics data. HBase is a solution mostly for processing huge volumes of data as the Hadoop cluster has MapReduce functionality. HBase has good support for random reads and writes as well. All this makes HBase highly suitable for such use cases as online analytics systems or user recommendation engines.
Both Cassandra and HBase are designed to handle BigData (TBs/PBs of data). Cassandra suites better for write-oriented (the fewer updates the better, deletes are also updates in C*), geographically distributed large scale systems, in which availability and performance are a preference and no full transactions needed. HBase will be a good choice for existing Hadoop infrastructure and for systems that require extensive reads, along with random reads and scans/querying of row ranges, random updates for processing large sets of consistent data.
Alexander StalmakovView all articles by this author.
Very well written blog and highly informative.
Great work, excellent content as usual.