DBMS > Axibase vs. Cassandra vs. Riak TS vs. Splice Machine

System Properties Comparison Axibase vs. Cassandra vs. Riak TS vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

axibase.com/docs/atsd/finance

cassandra.apache.org

splicemachine.com

Technical documentation

cassandra.apache.org/doc/latest

www.tiot.jp/riak-docs/riak/ts/latest

splicemachine.com/how-it-works

Developer

Axibase Corporation

Apache Software Foundation

Apache top level project, originally developped by Facebook

Open Source, formerly Basho Technologies

Splice Machine

Initial release

2013

2008

2015

2014

Current release

15585

4.1.3, July 2023

3.0.0, September 2022

3.1, March 2021

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

Apache version 2

Open Source

AGPL 3.0, commercial license available

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

Erlang

Java

Server operating systems

Linux

BSD
Linux
OS X
Windows

Linux
OS X

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

restricted

yes

SQL

Support of SQL

SQL-like query language

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

yes, limited

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

Proprietary protocol

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

HTTP API
Native Erlang Interface

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Go
Java
PHP
Python
R
Ruby

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

Erlang

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

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

yes, multi-version concurrency control (MVCC)

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.

yes

User concepts

Access control

Access rights for users can be defined per object

Access rights for users, groups and roles according to SQL-standard

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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