DBMS > EJDB vs. HEAVY.AI vs. LeanXcale vs. RocksDB

System Properties Comparison EJDB vs. HEAVY.AI vs. LeanXcale vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

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

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store

Relational DBMS

Key-value store
Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

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

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

github.com/Softmotions/ejdb

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

www.leanxcale.com

rocksdb.org

Technical documentation

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

docs.heavy.ai

github.com/facebook/rocksdb/wiki

Developer

Softmotions

HEAVY.AI, Inc.

LeanXcale

Facebook, Inc.

Initial release

2012

2016

2015

2013

Current release

5.10, January 2022

9.2.1, May 2024

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

BSD

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

C++ and CUDA

C++

Server operating systems

server-less

Linux

Data scheme

schema-free

yes

schema-free

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

SQL

Support of SQL

yes

through Apache Derby

APIs and other access methods

in-process shared library

JDBC
ODBC
Thrift
Vega

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

C++ API
Java API

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

C
Java
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

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

yes

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

User concepts

Access control

fine grained access rights according to SQL-standard

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