DBMS > Amazon DocumentDB vs. Greenplum vs. JaguarDB vs. LeanXcale vs. Postgres-XL

System Properties Comparison Amazon DocumentDB vs. Greenplum vs. JaguarDB vs. LeanXcale vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Performant, highly scalable DBMS for AI and IoT applications

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Document store

Relational DBMS

Key-value store
Vector DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

aws.amazon.com/documentdb

Technical documentation

aws.amazon.com/documentdb/resources

docs.greenplum.org

www.jaguardb.com/support.html

www.postgres-xl.org/documentation

Developer

Pivotal Software Inc.

DataJaguar, Inc.

LeanXcale

Initial release

2019

2005

2015

2014

since 2012, originally named StormDB

Current release

7.0.0, September 2023

3.3 July 2023

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3.0

commercial

Open Source

Mozilla public license

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++

the server part. Clients available in other languages

Server operating systems

hosted

Linux

Linux
macOS

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.

yes

since Version 4.2

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

through Apache Derby

yes

distributed, parallel query execution

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
Java
Perl
Python
R

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

C
Java
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

rights management via user accounts

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

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20 May 2024, AWS Blog

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