DBMS > Cassandra vs. EsgynDB vs. Hive vs. Memcached vs. Spark SQL

System Properties Comparison Cassandra vs. EsgynDB vs. Hive vs. Memcached vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

data warehouse software for querying and managing large distributed datasets, built on Hadoop

In-memory key-value store, originally intended for caching

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Vector DBMS

starting with release V5

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

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

cassandra.apache.org/doc/latest

cwiki.apache.org/confluence/display/Hive/Home

github.com/memcached/memcached/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Esgyn

Apache Software Foundation

initially developed by Facebook

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Apache Software Foundation

Initial release

2008

2015

2012

2003

2014

Current release

4.1.3, July 2023

3.1.3, April 2022

1.6.27, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache Version 2

Open Source

BSD license

Open Source

Apache 2.0

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

C++, Java

Java

Scala

Server operating systems

BSD
Linux
OS X
Windows

Linux

All OS with a Java VM

FreeBSD
Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

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

yes

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol

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

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift

Proprietary protocol

JDBC
ODBC

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
PHP
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication between multi datacenters

selectable replication factor

none

Repcached, a Memcached patch, provides this functionallity

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Eventual Consistency

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

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users can be defined per object

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

yes

using SASL (Simple Authentication and Security Layer) protocol

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

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

News

An Introduction to Phil Nash, Developer Relations
3 June 2024

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

How Concide Built its Chat Platform with Enhanced Document Management for Legal Tech Professionals
23 May 2024

Astra Vectorize: Generate Vector Embeddings with 1 Line of Code
22 May 2024

GitHub Copilot + DataStax Astra DB: Build GenAI Apps 100x Faster
21 May 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.