Nowadays, enterprise database users have many choices when it comes to the selection of database management platforms. Choosing an apt one for your business application must be based on the platform features and the application’s requirements. Here in this article, we are trying to explain why many enterprise users choose Apache Cassandra and leverage its features best.

About Apache Cassandra

Apache Cassandra is a highly fault-tolerant database, which follows distributed, column-oriented data architecture. Cassandra falls under the category NoSQL databases and is highly scalable to meet the needs of advanced big data applications. When the need is to handle massive among of structured, semi-structured, or unstructured data, Apache Cassandra can handle it well. Structured data means segregated in columns, semi-structured means the table row need not populate all the columns, and unstructured means the data does not follow any specific structure.

 

As we have seen above, Cassandra is also a distributed storage system that can scale linearly by adding commodity servers and can survive database failures at any node as there is no single point of failure. Cassandra can also be scaled easily across many data centers and different regions to increase the database system’s resiliency.

Top features of Cassandra

Let us next explore some of Cassandra’s notable features, which makes it the ideal choice for enterprise database modeling.

• It is open-source – Being an open-source project of Apache, Cassandra can easily be integrated with many other open-source applications as Apache Pig, Apache Hive, and Hadoop, etc.

• Peer-to-Peer model – The peer-to-peer architecture of Cassandra allows all nodes in the cluster to communicate with others. There is no master-slave model followed in Cassandra DBs.

• Single point of Failure eradicated – Adding to the above point, all the nodes are created equal in a given cluster. The data gets distributed across all the nodes, each of which can handle the read-write requests independently. The advantage is there is no single point of failure and no downtime.

• Highly fault-tolerant and high availability – As there is data replication on all nodes equally, Cassandra is largely fault tolerance and always available.

• Globally distributed –Cassandra cluster can be deployed across many data centers that are distributed globally.

• Very flexible data model – Most of the features of BigTable and DynamoDB are built into the Cassandra DB, which allows us to practice data structures that are complex. Cassandra data model may work the best for a wide range of data modeling use cases.

• Linearly scalability – While additional nodes are added, the data gets distributed more evenly across the nodes, helping reduce the load of individual nodes.

• Tunable Consistency – Consistency level is the number of the nodes which need to agree on the for all reads and writes. The consistency level of a DB controls the behavior of read and writes operations based on the replication factor. Sometimes the consistency level may go down to zero and one, quorum, and local quorum et al.

For a more comprehensive understanding of Cassandra database features and installation, you may contact the RemoteDBA experts.

Cassandra cluster layers

Cassandra Cluster is the collection of many nodes which is formed in a ring format and work together. This may span across multiple locations globally. The data gets distributed across various nodes in a cluster by using a consistent hash-based function. Let us explore other components of Cassandra.

• Node -Node is a basic component of the Cassandra infrastructure where the data is stored. Each of the nodes hosts a replica of data.

• Keyspace – This is the column family collection, which is equivalent to the database in RDBMS. Keyspace also contains a Replication factor, Replication strategy as topology or simple, and the Column families.
• Column Family – it is the container of row collections. It is equivalent to a table in the RDBMS.
• Row – Each of the Cassandra rows is identified using a unique key. Each row may consist of different columns. Ros key is a unique string that can have any characters without any size limit.

• Column– As seen above, each row contains many columns, a primary construct with a name and value. There is also a user-defined timestamp attached to it. Each row will have several columns.

Single datacenter Cassandra cluster

In this approach, the client request is received by the coordinator node in the cluster. Any random node in the given cluster may act as the coordinator node. This coordinator will further find the corresponding nodes which hold a matching token in the range and persists the data in that node. This data getsreplicated to other nodes, too, based on the defined Replication Factor at the keyspace. On choosing SimpleStrategy, it may just select the consecutive nodes in the ring to do replication.

Cassandra Cluster in multiple data centers

In the multi-data center model of replication, you have to custom define the number of replications to be run per DC. Cassandra may automatically replicate as per the rule. Here, NetworkTopologyStrategy will be used as the replication strategy for multi-DC replications.

Before choosing Cassandra DB for your enterprise applications, you may want to evaluate whether Cassandra can be the right choice. As we have seen, writes are superfast in Cassandra with its log-structured design, and written data will be persisted with a Commit Log, which will be relayed to the Memtable and SSTables.

Cassandra could be an ideal choice in the below use cases.

• High write throughput with a comparatively smaller volume of reads.
• We need the ability to scale the database linearly by adding more servers.
• Multi data center, multi-region replication needs
• It can set the time-to-live tab on each row of records

 

In the below use cases, choosing Cassandra may not be ideal.

• If your application needs to have ACID properties from the database
• For the applications need JOINs (not supported)
• The applications which need many secondary indexes
• For queue like designs, where writing and deleting data at a scale may result in degrading the performance

A good understanding of the Cassandra DB architecture and knowledge of its querying patterns will help the DBAs and developers to create an optimized data model for their enterprise applications on this reliable database.