DBMS > Couchbase vs. EJDB vs. PouchDB vs. Splice Machine

System Properties Comparison Couchbase vs. EJDB vs. PouchDB vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Document store

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.couchbase.com

github.com/Softmotions/ejdb

pouchdb.com

splicemachine.com

Technical documentation

docs.couchbase.com

github.com/Softmotions/ejdb/blob/master/README.md

pouchdb.com/guides

splicemachine.com/how-it-works

Developer

Couchbase, Inc.

Softmotions

Apache Software Foundation

Splice Machine

Initial release

2011

2012

2014

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

7.1.1, June 2019

3.1, March 2021

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

GPLv2

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

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Implementation language

C, C++, Go and Erlang

JavaScript

Java

Server operating systems

Linux
OS X
Windows

server-less

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

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

in-process shared library

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

JavaScript

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

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

View functions in JavaScript

yes

Java

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

selectable on a per-operation basis

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

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

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