DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Couchbase vs. EJDB vs. JaguarDB vs. Kinetica

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Couchbase vs. EJDB vs. JaguarDB vs. Kinetica

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

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)

Performant, highly scalable DBMS for AI and IoT applications

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Document store

Key-value store
Vector DBMS

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

Spatial DBMS
Time Series DBMS

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

Score	0.76
Rank	#220	Overall
	#102	Relational DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

www.couchbase.com

github.com/Softmotions/ejdb

www.jaguardb.com

www.kinetica.com

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

docs.couchbase.com

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

www.jaguardb.com/support.html

docs.kinetica.com

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Couchbase, Inc.

Softmotions

DataJaguar, Inc.

Kinetica

Initial release

2016

2011

2012

2015

2012

Current release

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

3.3 July 2023

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

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

Open Source

GPLv2

Open Source

GPL V3.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Go and Erlang

C++

the server part. Clients available in other languages

C, C++

Server operating systems

hosted

Linux
OS X
Windows

server-less

Linux

Data scheme

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

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

in-process shared library

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

C
Java
Perl
Python
R

.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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

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

user defined functions

Triggers

yes

via the TAP protocol

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Automatic Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication

including cross data center replication
Source-replica replication

none

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

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

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

Ephemeral buckets

yes

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

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

rights management via user accounts

Access rights for users and roles on table level

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