DBMS > Cassandra vs. Elasticsearch vs. SQLite vs. TerminusDB

System Properties Comparison Cassandra vs. Elasticsearch vs. SQLite vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Widely used embeddable, in-process RDBMS

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Vector DBMS

starting with release V5

Document store
Spatial DBMS
Vector DBMS

Document store
RDF store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

cassandra.apache.org

www.elastic.co/elasticsearch

www.sqlite.org

terminusdb.com

Technical documentation

cassandra.apache.org/doc/latest

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

www.sqlite.org/docs.html

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Elastic

Dwayne Richard Hipp

DataChemist Ltd.

Initial release

2008

2010

2000

2018

Current release

4.1.3, July 2023

8.6, January 2023

3.46.0 (23 May 2024), May 2024

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Elastic License

Open Source

Public Domain

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Prolog, Rust

Server operating systems

BSD
Linux
OS X
Windows

All OS with a Java VM

server-less

Linux

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like query language

yes

SQL-92 is not fully supported

SQL-like query language (WOQL)

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

yes

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

in-memory journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Memcached and Redis integration

yes

User concepts

Access control

Access rights for users can be defined per object

Role-based access control

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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News

An Introduction to David Jones-Gilardi, Developer Relations
11 June 2024

Introducing Tejas Kumar, Developer Relations Engineer
7 June 2024

Indexing All of Wikipedia on a Laptop
5 June 2024

An Introduction to Phil Nash, Developer Relations
3 June 2024

Focus on Creativity, Not Clusters: DataStax Mission Control in Action!
30 May 2024

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