HBase vs Cassandra: Assessing Two Popular NoSQL Databases
In the era of big data analytics and data science, the modern-day solution to handling and managing distributed databases is NoSQL databases. The salient features that NoSQL offers are the need of the hour. Some of the significant reasons NoSQL is most recommended are the ability to manage large volumes of data, speed, easy scalability, fail-over safety, great CPU and memory competencies, faster performance, and no database breakage. And, as we talk about NoSQL databases, the two reigning technologies are HBase vs Cassandra which are constantly being compared.
Both are parented by Apache and hence hold many similarities. Developers are sometimes unable to mark the difference between HBase and Cassandra. But they do have their own individualistic characteristics, which sets them apart. Before we delve deeper into the comparison between the two, let us have a look at them individually.
What Is HBase?
HBase is an open-source non-relational distributed database modeled after Google’s Bigtable and written in Java – Wikipedia
Apache HBase is best used when there is a requirement for random and real-time read/write access to Big Data databases and huge amounts of structured data. It offers Bigtable-like competencies on the top of HDFS and Hadoop – compression, in-memory operation, and Bloom filters being some of them. HBase is a scalable, column-dependent database for structured data that facilitates effective and accurate management of huge sets of data that are spread out amidst several servers. It offers data replication and the three major components of HBase are Zookeeper, Region Server, and HMaster.
HBase is built on Java and hence offers great support for APIs like REST, Scala, Jython, etc. It has a standalone versioned database that is utilized in the development scenario. It is mainly designed to handle real-time queries in huge tables that have multiple rows and columns and execute across a cluster of hardware. It executes on a four-dimensional data model and possesses scalability and fault tolerance.
Features Of HBase:
- Consistency in reads and writes
- Support to export metrics through the Hadoop metrics subsystem
- Automated failover support and sharding of tables
- Linear scalability and modularity
- Easily usable Java API for clients
- Data storage as key or values
- Best suited for range-based scan
Companies Using HBase:
- Tumblr
- Hubspot
- Vinted
- JVM Stack
- Adobe
- Flurry
- Celer Technologies
- Yahoo!
- Lorven Technologies
- Zendesk Inc
- ResearchGate
What Is Cassandra (Apache Cassandra)?
Cassandra is a free and open-source, distributed, wide-column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure – Wikipedia
Powered by Apache, Cassandra is a NoSQL database that can easily manage huge amounts of data in a quick manner hence it is popular among many organizations globally, for its accessibility, performance, and linear scalability. It has proven fault tolerance on cloud-based infrastructure or commodity hardware and proves best for mission-critical information. It can bear data center outages without facing any loss because of its replication across multiple data centers and low latency.
Since it has effective architectural choices, it has an edge over the rest for real-time applications. It is ideal for applications where data loss is not affordable and there are no network hassles. It is easy to add new machines with no downtime hassle since there is a linear increase in the read/write throughput. Cassandra can stream data within nodes while scaling processes are going on, making it a faster and more elastic environment.
Apache Cassandra Features:
- Low latency and masterless architecture
- Scalable, testable on multiple clusters
- High-end security and observability
- Zero Copy Streaming
- Works with a wide column store
- Fast performance reads/writes
- No multiple secondary indexes needed
Companies Using Cassandra:
- Uber
- Accenture
- Netflix
- Spotify
- Instacart
- Apple
- eBay
- Zalando
- Avito
Cassandra vs HBase: The Similarities
As we head towards understanding the differences between Apache Cassandra vs HBase, firstly, have a look at the similarities too:
Similarities:
- Parented by Apache
- NoSQL open-source, column-oriented databases
- Capability to manage large data sets and non-relational data
- Possess high-end linear scalability
- Replication mode helps safeguard loss of data even after failure
- Databases are column-oriented implementing comparable write paths
- Replication between clusters/data centers
- Good with time-series data, sensor analyses in IoT systems, stock exchange data, etc.
HBase vs Cassandra: A Head-to-Head Comparison
| Parameters | HBase | Cassandra |
| Architecture | Master-based architecture with a single failure point. Supports only data management. | Masterless architecture with no single failure point. Supports both data storage and management. |
| Node Handling Capacity | Approximately handles 1000 nodes | Approximately handles 400 nodes |
| Query Language | Developers can work with JRuby-based HBase shells with other technologies like Hive etc. | Developers can work with their own query language – CQL which is the Cassandra Query Language |
| Architectural Components | Hmaster, Zookeeper, Hregionmaster, Hregions, HDFS | Node, Replication factor, SStable, Partitioner, Memtable Cluster, Commit Log |
| Supported Programming Languages | C, C#, C++, Java, Python, Scala, Groovy, PHP | C, C#, C++, Java, Python, Ruby, Scala, Go, Erlang, PHP, Perl |
| Data Models | Row key, table, column family, cell, timestamp, column qualifier | Partition key, column family, secondary indexes, cluster, keyspace, column |
| Nodes | Presence of master nodes to monitor and coordinate activities of region servers. Internode communication is through Zookeeper protocol. | Presence of seed nodes acting as points for inter-cluster communication. Internode communication is through Gossip protocol. |
| Database | A wide column store based on Hadoop and BigTable has a distributed database | Wide column, store based on BigTable and DynamoDB, has a decentralized database |
| Triggers | It supports triggers through coprocessor capability | It does not support triggers through coprocessor capability |
| Performance | HBase offers better performance than Cassandra | Cassandra offers less performance than HBase |
| Server OS | Linux, Unix, Windows | Linux, BSD, OS X, Windows |
| API and access methodologies | Java API, RESTful API, Thrift | Proprietary protocol, Thrift |
| Replication Method | Multisource and source replica replication | Selectable replication factor |
| Utility | Makes use of Hadoop infrastructure – Zookeeper, NameNode, HDFS | Makes use of Cassandra + Storm (Zookeeper) or Cassandra + Hadoop |
| Range Based Scans | Best suited for range-based scans | Does not support range-based row scans |
| CAP Theorem (Consistency, Availability, Partition Tolerance) | Consistency and availability | Availability and partition tolerance |
| Atomic Compare and Set | Supports atomic compare and set | Does not support atomic compare and set |
| Read Load Balancing | Does not support read load balancing against a single row | Supports read load balancing against a single row |
| Security | Provides cell level access to users | Provides row-level access to users |
| Ordered Partitioning | Does not support ordered partitioning | Offers support ordered partitioning |
| User Case Scenarios | Heavy applications, online log analytics, large volume apps, etc. Best for projects where getting analysis results is not time or mission-critical. | Real-time apps, messaging systems, sensor management, eCommerce apps, etc. Best for projects where getting analysis is time and mission-critical. |
| Write Paths | HBase does not write to the log and cache simultaneously | Cassandra is faster and better at writing than HBase |
| Read Capability | HBase offers faster and more consistent reads | Cassandra is good at reads but not as much as HBase |
| Transaction Mechanisms | A transaction server manages transactions with Read-Check-Delete and Check-Put | It offers an isolated, atomic, and long-lasting transaction mechanism with tuneable consistency |
| Use of Bloom Filters | It uses bloom filters to see if a certain row/cell is existing in the StoreFile or not | It uses bloom filters to search for some data in a particular file |
| Documentation and Learning | HBase documentation is not too detailed and hence has a larger learning curve | Cassandra documentation is detailed and hence developers grasp it faster |
Also Read: Cassandra vs MongoDB: Comparing Two Popular NoSQL Databases
HBase vs Cassandra: The Conclusion
At first glance, it is a tough call to analyze the difference between HBase and Cassandra, since they both are powered by Apache and hold similarities as mentioned above. Organizations must take a call on which one to choose based on a variety of parameters like accessibility to skillset, project schedules, timelines, budget, etc.
Apache Cassandra vs HBase is a comparison that will go on, but the underlying fact is that both look similar but are not. They have their own characteristics which must be understood and analyzed.
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