DBMS > atoti vs. EJDB vs. HEAVY.AI vs. Postgres-XL

System Properties Comparison atoti vs. EJDB vs. HEAVY.AI vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Primary database model

Object oriented DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

atoti.io

github.com/Softmotions/ejdb

github.com/heavyai/heavydb
www.heavy.ai

www.postgres-xl.org

Technical documentation

docs.atoti.io

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

docs.heavy.ai

www.postgres-xl.org/documentation

Developer

ActiveViam

Softmotions

HEAVY.AI, Inc.

Initial release

2012

2016

2014

since 2012, originally named StormDB

Current release

5.10, January 2022

10 R1, October 2018

License

Commercial or Open Source

commercial

free versions available

Open Source

GPLv2

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla public license

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

C++ and CUDA

Server operating systems

server-less

Linux

Linux
macOS

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

distributed, parallel query execution

APIs and other access methods

in-process shared library

JDBC
ODBC
Thrift
Vega

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

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

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

Server-side scripts

Stored procedures

Python

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding

Round robin

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

MVCC

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

fine grained access rights according to SQL-standard

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