cassandra datastax - Wikitechy

What is the use of Cassandra database ?

Editor — Tue, 13 Jul 2021 05:36:30 +0000

Apache Cassandra is a high-performance provides database to manage large number of data among many commodity servers and then it is highly scalable.
It is a type of NoSQL database.
Apache Cassandra can be used for different applications.

In activity-tracking and monitoring applications:

Numerous entertainment and media organisations use Cassandra to observe user activity based on movies, music, albums, artists or other parameters.

In heavy write systems or in time-series-based applications:

Cassandra is best for heavy write systems .For example, data is logged for each request based on hits in web analytics, by type of browser, traffic sources, position, character, technology, tools, etc.

In social media analytics:

Cassandra is used by many social media providers to recognize the data and gives recommendations to their users.

In product catalogues and retail applications:

A very popular use case of Cassandra is to show fast product catalog inputs and lookup, and in retail applications.

Messaging:

Cassandra serves as the database backbone for countless mobile phone and message providers applications.

Apache Cassandra is a high-performance provides database to manage large number of data among many commodity servers and then it is highly scalable.

Editor — Tue, 13 Jul 2021 04:45:53 +0000

Amazon’s dynamo and Prashant malik are written by Avinash Lakshman at first succeed Cassandra at Facebook to capable the Facebook inbox search purpose.
In July 2008,Facebook published Cassandra as an open-source project on Google code.
In March 2009 it develop an Apache incubator project.
On february 17, 2010 it qualify to a top-ranking project.

Releases after graduation include

Added help for integrated caching, and Apache Hadoop MapReduce distributed 0.6 in Apr 12 2010.
Added secondary indexes and online schema changes distributed 0.7 in Jan 08 2011.
Added the Cassandra Query Language (CQL), self-tuning memTables, and help for zero-downtime upgrades distributed 0.8 in Jun 2 2011.
Added integrated compression, levelled compaction, and improved read-performance distributed 1.0 in Oct 17 2011,
Added self-tuning caches, row-level isolation, and support for mixed ssd/spinning disk deployments distributed 1.1 in Apr 23 2012,
Added clustering among virtual nodes, inter-node communication, atomic batches, and request tracing distributed 1.2 in Jan 2 2013,
Added lightweight transactions (based on the Paxos consensus protocol), triggers, improved compactions distributed 2.0 in Sep 4 2013,
In Sep 10 2014 publish 2.1
In July 20, 2015 publish 2.2
In November 11, 2015 publish 3.0
Monthly publish tick-tock like release model in 3.1 across 3.10. When even-numbered contain both bug fixes and new features while odd-numbered involve bug fixes only.
In June 23, 2017 publish 3.11 as a stable and it distributed series and bug fix from the last tick-tock characteristic publish.

Editor — Tue, 13 Jul 2021 04:40:35 +0000

Apache Cassandra is a simple and open-source provides NoSQL DBMS build to handle huge volume of data against more fabric servers.It providing high availability with no single point of failure.
The performance of Cassandra place a high value. In 2012, University of Toronto researchers studying NoSQL systems resulted that “Scalability defined there is a clear winner throughout our experiments.Cassandra achieves the largest throughput for the large number of nodes in all experiments” although “it determine at the price of high write and read latencies.”

Cluster contain every node has the same role.
There is no single point of failure.
To provides data across the cluster so every node have various data, but there is no master as every node can service if any request.

Replication strategies are configurable.
Cassandra is built as a distributed system, for deployment of maximum numbers of nodes across many data centers.
Key features of Cassandra’s distributed architecture are specifically change for multiple-data center deployment, for redundancy, for fail over and disaster recovery.

As new machines are added read and write throughput both increase linearly, with the achieve of no halt or interrupt to applications.

Data is automatically replicated to many nodes for fault-tolerance. Replication across multiple data centers is maintained. Failed nodes can be changed with no downtime.

Writes and reads gives a adjustable level of consistency, all the way from “writes never fail” to “block for all replicas to be readable”, with the quorum level in the middle.