Apache Cassandra and Apache HBase are pretty much similar to one another.
It's like while walking down a road you sight a friend from a distance, but when she comes nearby, you realize it's someone else. You comprehend that the similarity was only from a distance. When you study HBase vs Cassandra, there are similarities, but subtle differences too.
HBase does not have a query language. Therefore you would have to work with (JRuby-based) HBase shell. Over and above you would have to involve technologies such as Apache Hive, Apache Drill, or others.
Cassandra on the other hand boasts of CQL (Cassandra Query Language). There's a load of support for Cassandra specialists here.
Both Cassandra and HBase can be reliable databases for IoT solutions. The choice really boils down to the peculiarities of each system and the operations to perform. Like when there is high-volume sensor data from a smart, connected product with lots of sensors. Cassandra is more than capable of managing this humungous data flow due to its better write performance.
Data Model Comparison - HBase vs Cassandra
The terms that are used may be the same, but they differ in what they mean.
When it comes to a column: Cassandra's column is similar to a cell in HBase. In Cassandra a column family is like an HBase table. While the column qualifier in HBase is similar to a Super Column in Cassandra. But contains at least 2 subcolumns, while in HBase - only one.
Cassandra permits a primary key to contain multiple columns. While HBase has only 1-column row key. The row key design falls on the developer. Cassandra's primary key consists of a partition key and clustering columns. Here the partition key may contain multiple columns.
Despite the differences, both data models are quite similar. As they have no joins, they group related data together. Both permit no value in some cells or column and no storage space is lost. But both need column families specified with schema design. This cannot be changed later, but offers columns and column qualifiers flexibility. Both are good at storing data.
Data Model
HBase
The HBase table consist of cells arranged in row key and column families. Sometimes, a Column Family (CF) may have column qualifiers to better organize data in a column family.
A cell consists of a value and timestamp. While a column is a combination of cells with a common column qualifier and common column family.
Data is partitioned by 1-column row key within a table in alphabetical order where related data is stored nearby to enhance performance. Design of row key is vital which needs to be meticulously planned in algorithm by the developer to warrant efficient data search.
Cassandra
Here a column family is made up of columns structured by row keys. A column contains a name or key, its value and timestamp. In addition to a column, Cassandra contains super columns made up of two or more sub-columns. These are grouped into super columns but rarely employed.
In a cluster data is split by multi-column primary key with a hash value. Which is then sent to the node whose token is larger than the hash value.
The data is written to more number of nodes depending on the duplication factor set by developers. Choice of further nodes are condition to the location in the cluster.
2. Architecture
HBase is a master-based architecture and Cassandra masterless. This is similar to the difference between Cassandra and Hadoop Distributed File System. HDFS architecture is hierarchical with a master node and several slave nodes. While Cassandra's architecture is peer-to-peer nodes resembles a ring.
HBase can have a single point of failure but not Cassandra. HBase client communicates directly with the slave-server bypassing the master. This gives the cluster ample working time after the master goes down. But, the always-on Cassandra cluster is miles ahead. Therefore those who cannot afford downtime the choice is Cassandra hands down.
Cassandra replicates and duplicates data to guarantee its availability and this causes data inconsistency. Therefore Cassandra is not a good choice if your solution depends on data consistency. HBase strength is its consistency.
Cassandra's architecture supports data management and storage. HBase architecture sways towards data management. HBase relies on technologies such as HDFS for storage. For server status management and metadata on Apache Zookeeper. And of course other technologies for queries.
3. Performance
Write
Both Cassandra and HBase on-server write paths are similar. The only difference being the names for data structures. Cassandra writes log and cache simultaneously which makes it slower and HBase doesn't.
But HBase has more drawbacks on the architectural level:
- Before reaching a server the client needs to query Zookeeper regarding which server has hbase meta table with info on table location in clusters. The client then queries the meta table holding server who stores the actual table where it needs to be written. Only then the client writes the data wherever it needs to be.
- If the reading and writing is frequent, the info is cached. But the client needs to redo the full round if a table region is moved to another server. Cassandra's data distribution and segregation is based on unfailing hashing which is smarter and quicker.
- When the in-HBase write path ends, cached data gets cleared. HDFS takes time to physically store data.
True measurements of Cassandra's write performance in a 32-node cluster is 326,500 operations every second. HBase performs 297,000 proving Cassandra is better at writes.
Read
For quick and steady reads, that is random access to data / scans opt for HBase. Because it writes on a single server it evades comparison to various node-data versions. HBase servers lack many data structures to scour for your data. Don't assume HBase read is inefficient as the data is stored in HDFS and needs to be retrieved from there every time. This is because HBase has a block cache with frequently accessed data and bloom filters with approximate address of all other data to speed up data recovery. HBase and HDFS's index system is multi-layered making it more efficient than Cassandra's indexes.
In a 32-node cluster Cassandra handles 129,000 reads per second and HBase's 8,000. If the reads are targeted based on primary keys they could be inconsistent. Cassandra loses out speaking of scans and consistency.
4. Security
HBase and Cassandra have security issues like all NoSQL databases. The biggest flaw is securing data hurts performance. This makes the system weighty and rigid. But rest assured both databases offer data security be it for authentication or authorization in both and inter-node. Or even in client-to-node encryption in Cassandra.
HBase provides secure means of communication using other technologies. Both (Cassandra and HBase) provide database-wide access control with a certain level of granularity.
Cassandra offers row level access and HBase cell level. Cassandra defines user roles and conditions access to data. In HBase admin assigns a visibility label to data sets and informs users/groups access to some labels.
5. Application areas
Both (Cassandra and HBase) store and read values efficiently. Looking at how they organize data models we perceive their capabilities handling time series info such as stock exchange data, heights of ocean tides, counts of sunspots, website visits, etc. Both possess scalability: HBase, linear and modular ones, while Cassandra - linear.
HBase is better to scan voluminous data to search for small number of results as it doesn't have data duplication. Therefore HBase can handle text analysis of web pages, social media posts, thesaurus, etc. HBase is also good with data management platforms and data analysis like counting, additions as its coprocessors are in Java.
As Cassandra is efficient with write oriented database, it is good data breakdown of huge data. You can use it to build a reliable data store at easy reach. With Cassandra you can create data centers at various locations and sync the data. Combination of Cassandra with Spark achieves good scan performance.
Cassandra is self-sustaining tech for data management and storage, not HBase. HBase is intended for random data input/output for HDFS, where the data is stored. HBase uses Zookeeper as a server status manager that has all the metadata. This takes care of cluster failures when metadata-containing master fails.
Thereby, HBase complex inter-reliant system is difficult to configure, secure and maintain. Cassandra shines at writes and HBase at intensive reads. Cassandra's soft belly is data consistency and HBase - data availability. But both work to diminish serious consequences. Frequent data deletes and updates are not their cup of tea.
To choose which to rely on, this study on HBase vs Cassandra would have helped you. Comprehensively analyze your tasks and strengthen the database's weak spots without losing performance.
