DBMS > PouchDB vs. Riak TS vs. Splice Machine vs. Trafodion

System Properties Comparison PouchDB vs. Riak TS vs. Splice Machine vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

JavaScript DBMS with an API inspired by CouchDB

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store

Time Series DBMS

Relational DBMS

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

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

pouchdb.com

splicemachine.com

trafodion.apache.org

Technical documentation

pouchdb.com/guides

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

splicemachine.com/how-it-works

trafodion.apache.org/documentation.html

Developer

Apache Software Foundation

Open Source, formerly Basho Technologies

Splice Machine

Apache Software Foundation, originally developed by HP

Initial release

2012

2015

2014

Current release

7.1.1, June 2019

3.0.0, September 2022

3.1, March 2021

2.3.0, February 2019

License

Commercial or Open Source

Open Source

AGPL 3.0, commercial license available

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.

Implementation language

JavaScript

Erlang

Java

C++, Java

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X

Linux
OS X
Solaris
Windows

Linux

Data scheme

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

yes

via views

restricted

yes

SQL

Support of SQL

yes, limited

yes

APIs and other access methods

HTTP REST

only for PouchDB Server
JavaScript API

HTTP API
Native Erlang Interface

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

JavaScript

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

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

View functions in JavaScript

Erlang

yes

Java

Java Stored Procedures

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

selectable replication factor

Multi-source replication
Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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