Cassandra Database

What is Cassandra?

The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages. Casandra is NoSql database.

The design goal of Cassandra is to handle big data workloads across multiple nodes without any single point of failure. Cassandra has peer-to-peer distributed system across its nodes, and data is distributed among all the nodes in a cluster.

• All the nodes in a cluster play the same role. Each node is independent and at the same time interconnected to other nodes.
• Each node in a cluster can accept read and write requests, regardless of where the data is actually located in the cluster.
• When a node goes down, read/write requests can be served from other nodes in the network.

Data Replication in Cassandra

In Cassandra, one or more of the nodes in a cluster act as replicas for a given piece of data. If it is detected that some of the nodes responded with an out-of-date value, Cassandra will return the most recent value to the client. After returning the most recent value, Cassandra performs a read repair in the background to update the stale values.
The following figure shows a schematic view of how Cassandra uses data replication among the nodes in a cluster to ensure no single point of failure.

Note − Cassandra uses the Gossip Protocol in the background to allow the nodes to communicate with each other and detect any faulty nodes in the cluster.

Components of Cassandra

The key components of Cassandra are as follows −

• Node − It is the place where data is stored.
• Data center − It is a collection of related nodes.
• Cluster − A cluster is a component that contains one or more data centers.
• Commit log − The commit log is a crash-recovery mechanism in Cassandra. Every write operation is written to the commit log.
• Mem-table − A mem-table is a memory-resident data structure. After commit log, the data will be written to the mem-table. Sometimes, for a single-column family, there will be multiple mem-tables.
• SSTable − It is a disk file to which the data is flushed from the mem-table when its contents reach a threshold value.
• Bloom filter − These are nothing but quick, nondeterministic, algorithms for testing whether an element is a member of a set. It is a special kind of cache. Bloom filters are accessed after every query.

Cassandra Query Language

Users can access Cassandra through its nodes using Cassandra Query Language (CQL). CQL treats the database (Keyspace) as a container of tables. Programmers use cqlsh: a prompt to work with CQL or separate application language drivers.
Clients approach any of the nodes for their read-write operations. That node (coordinator) plays a proxy between the client and the nodes holding the data.

Write Operations

Every write activity of nodes is captured by the commit logs written in the nodes. Later the data will be captured and stored in the mem-table. Whenever the mem-table is full, data will be written into the SStable data file. All writes are automatically partitioned and replicated throughout the cluster. Cassandra periodically consolidates the SSTables, discarding unnecessary data.

Read Operations

During read operations, Cassandra gets values from the mem-table and checks the bloom filter to find the appropriate SSTable that holds the required data.

How Does Cassandra Differ From a Relational Database?

Although the non-relational databases in the market today provide different features and benefits, a database like Cassandra differs from a typical relational database in the following ways:

Table 1. Table A quick comparison of RDBMS and a NoSQL database like Cassandra.

Relational Database	Cassandra
Handles moderate incoming data velocity	Handles high incoming data velocity
Data arriving from one/few locations	Data arriving from many locations
Manages primarily structured data	Manages all types of data
Supports complex/nested transactions	Supports simple transactions
Single points of failure with failover	No single points of failure; constant uptime
Supports moderate data volumes	Supports very high data volumes
Centralized deployments	Decentralized deployments
Data written in mostly one location	Data written in many locations
Supports read scalability (with consistency sacrifices)	Supports read and write scalability
Deployed in vertical scale up fashion	Deployed in horizontal scale out fashion

Key Cassandra Features and Benefits

Cassandra provides a number of key features and benefits for those looking to use it as the underlying database for modern online applications:

• Massively scalable architecture – a masterless design where all nodes are the same, which provides operational simplicity and easy scale-out.
  
• Active everywhere design – all nodes may be written to and read from.
  
• Linear scale performance – the ability to add nodes without going down produces predictable increases in performance.
  
• Continuous availability – offers redundancy of both data and node function, which eliminate single points of failure and provide constant uptime.
  
• Transparent fault detection and recovery – nodes that fail can easily be restored or replaced.
  
• Flexible and dynamic data model – supports modern data types with fast writes and reads.
  
• Strong data protection – a commit log design ensures no data loss and built in security with backup/restore keeps data protected and safe.
  
• Tunable data consistency – support for strong or eventual data consistency across a widely distributed cluster.
  
• Multi-data center replication – cross data center (in multiple geographies) and multi-cloud availability zone support for writes/reads.
  
• Data compression – data compressed up to 80% without performance overhead.
  
• CQL (Cassandra Query Language) – an SQL-like language that makes moving from a relational database very easy.
  

Top Use Cases

While Cassandra is a general purpose non-relational database that can be used for a variety of different applications, there are a number of use cases where the database excels over most any other option. These include:

• Internet of things applications – Cassandra is perfect for consuming lots of fast incoming data from devices, sensors and similar mechanisms that exist in many different locations.
  
• Product catalogs and retail apps – Cassandra is the database of choice for many retailers that need durable shopping cart protection, fast product catalog input and lookups, and similar retail app support.
  
• User activity tracking and monitoring – many media and entertainment companies use Cassandra to track and monitor the activity of their users’ interactions with their movies, music, website and online applications.
  
• Messaging – Cassandra serves as the database backbone for numerous mobile phone and messaging providers’ applications.
  
• Social media analytics and recommendation engines – many online companies, websites, and social media providers use Cassandra to ingest, analyze, and provide analysis and recommendations to their customers.
  
• Other time-series-based applications – because of Cassandra’s fast write capabilities, wide-row design, and ability to read only the columns needed to satisfy queries, it is well suited time series based applications.