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 vs 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.
Why do you need a database management system?
Before looking at HBase vs Cassandra in detail, first let’s see why you need a reliable database management system in the first place:
- A centralized data repository: with a database management system, businesses can rest assured that all the data is kept in one place, is being monitored, and that everyone has access to it.
- Easier data analysis and decision-making: sine the data can be easier analyzed, it facilitates visualization. This, in turn, promotes more accurate decision-making since business owners get access to all needed insights in a suitable format.
- Better customer relationship management: with more accurate and structured data, businesses are able to better manage their customers since all the information is present, up to date, and stored in one place.
- Great data organization and consistency: a database management system allows storing the data in a structured and consistent format. This leads to higher data accuracy and impacts the ease of its use.
As you can see, an organization can benefit a lot from using a database management system. But considering there is quite a variety of options to choose from, it might be confusing to select the perfect one. With HBase and Cassandra being one of the most popular database management systems, it’s worth having a detailed look at both.
A bit on SQL and NoSQL database management systems
Since both HBase and Cassandra belong to NoSQL database management systems, let’s refresh our knowledge on what NoSQL systems are and how they differ from SQL ones.
Database management systems differ by various factors, such as the type of the data that they store, the way of data organization, distribution, etc. Today, there are two main DBMS types: relational (known as SQL) and NoSQL.
Relational database management systems work with relational (aka structured) data. In such systems, the data is stored in rows and columns, where rows represent data records and columns represent data attributes. The biggest benefits of SQL DBMS solutions are that they are very scalable and that the changes in the structure do not impact access to the data.
Non-relational DBMS systems were introduced as an alternative to SQL systems and they imply storage and processing of both structured, semi-structured, and unstructured data. Their name NoSQL stands for “not only SQL” and these database management systems work with such data as graphs, documents, and column-family databases. The biggest benefit of a NoSQL DBMS solution is that it’s highly versatile and shows a very impressive performance.
If you want to compare SQL and NoSQL solutions, it will be incorrect to say that one is better than the other. Both systems are used for various purposes and various data types and hence, both are great for their specific use cases.
You might find these articles interesting as well:
- Database Management Systems (DBMS): to SQL or NoSQL, That Is the Question
- 5 Best Pieces of Advice for Securing Database
Now, back to the topic.
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
There are a few interesting HBase components worth mentioning. The first is Avro - an open-source service for data serialization and data exchange. In HBase, Avro supports a set of primitive data types (i.e., binary data, strings) and complex data types (i.e., maps, records).
Another component is Apache ZooKeeper - a service for maintaining configuration information, providing distributed synchronization, providing group services, and naming. In HBase, ZooKeeper ensures high-performance coordination.
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.
The data in Cassandra is managed by using CQL - the Cassandra Query Language. It is a flexible API (similar to SQL) that allows developers to execute data definition language and data manipulation language statements. Another interesting thing to know about Cassandra is its elastic scalability which allows adding more hardware in order to accommodate more data and customers (if needed).
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. This is due to the fact that Cassandra performs impressively fast writes and is able to store an overwhelming amount of data while maintaining the read efficiency.
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.
Difference between HBase and Cassandra: an overview
Now that we’ve observed HBase vs 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, if comparing Cassandra vs HBase, the first 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. So in this case, when considering Cassandra vs HBase, Cassandra comes as a clear winner.
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.
When to choose HBase and when to choose Cassandra
Both Cassandra and HBase are designed to handle BigData (TBs/PBs of data) but each database has its own use cases that work the best for this specific database management system.
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.
Cassandra use cases:
- Transaction logging;
- Development of messengers and messaging systems;
- E-commerce development;
- Storage of time-series data;
- Storage of real-time sensors data (i.e. the data from health trackers)’
Note though that when working with Cassandra, it is critical to choose the right partition keys and to use it with a corresponding database. Let’s elaborate a bit more on that.
You need proper partition keys due to the principle of Cassandra’s work. Since it distributes the data across multiple nodes, it hashes a partition key (a part of every table’s primary key) and assignes tokens to specific nodes. Hence, when choosing partition keys, consider the following:
- There are enough partition key values to spread the data evenly across the nodes;
- The data that you might want to retrieve should be kept in single read (and within a single partition);
- Avoid making partitions too big.
As for the use of a corresponding database, Cassandra does not work well with databases that have:
- Tables with multiple access paths;
- Updates (and deletes).
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.
HBase use cases:
- Online log analytics;
- Large-volumed apps;
- Write-heavy applications;
- Data query with millisecond latency.
But same as Cassandra, HBase is not perfect and has its limitations that you should be aware of. They are:
- No support for transactions;
- HBase is indexed and sorted only on key;
- Lack of built-in authentication;
- No support for the SQL structure.
Of course, these are not all limitations that HBase has but some of the most significant ones. Hence, when choosing between the HBase vs Cassandra, we highly recommend basing your decision on the type of the database that you use and the type of data as well.
Bonus: tips on choosing the right DBMS solution
Whether you are choosing between an SQL and NoSQL solution or between HBase vs Cassandra, there are some tips on how to select a perfect database management solution. Here are the main things for you to consider that might help in making the right choice:
The type and the volume of the data that you expect to manage and store;
All needed integrations that a DBMS will have to support;
Requirements for future DBMS scaling;
Physical hosting of the system.
Note that in some cases, it will be a better idea to delegate the DBMS system maintenance to a knowledgeable software development team, since the process may be quite intricate.
Alexander StalmakovView all articles by this author.
Very well written blog and highly informative.
Great work, excellent content as usual.