DBMS > Axibase vs. Machbase Neo vs. PouchDB vs. Sadas Engine vs. Splice Machine

System Properties Comparison Axibase vs. Machbase Neo vs. PouchDB vs. Sadas Engine vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

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

TimeSeries DBMS for AIoT and BigData

JavaScript DBMS with an API inspired by CouchDB

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

Primary database model

Time Series DBMS

Document store

Relational DBMS

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

Score	0.21
Rank	#308	Overall
	#25	Time Series DBMS

Score	0.07
Rank	#348	Overall
	#34	Time Series DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

axibase.com/docs/atsd/finance

Technical documentation

machbase.com/dbms

pouchdb.com/guides

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

splicemachine.com/how-it-works

Developer

Axibase Corporation

Machbase

Apache Software Foundation

SADAS s.r.l.

Splice Machine

Initial release

2013

2012

2006

2014

Current release

15585

V8.0, August 2023

7.1.1, June 2019

8.0

3.1, March 2021

License

Commercial or Open Source

commercial

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

commercial

free test version available

Open Source

commercial

free trial version available

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.

Implementation language

Java

JavaScript

C++

Java

Server operating systems

Linux

Linux
macOS
Windows

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

AIX
Linux
Windows

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

yes

via views

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC
Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Go
Java
PHP
Python
R
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

JavaScript

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

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

Foreign keys

Referential integrity

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)

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

volatile and lookup table

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

simple password-based access control

Access rights for users, groups and roles 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

show all

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