DBMS > Apache Impala vs. EJDB vs. Faircom EDGE vs. JanusGraph vs. openGemini

System Properties Comparison Apache Impala vs. EJDB vs. Faircom EDGE vs. JanusGraph vs. openGemini

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

Graph DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Website

impala.apache.org

github.com/Softmotions/ejdb

www.faircom.com/products/faircom-edge

janusgraph.org

www.opengemini.org
github.com/openGemini

Technical documentation

impala.apache.org/impala-docs.html

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

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

docs.janusgraph.org

docs.opengemini.org/guide

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Softmotions

FairCom Corporation

Linux Foundation; originally developed as Titan by Aurelius

Huawei and openGemini community

Initial release

2013

2012

1979

2017

2022

Current release

4.1.0, June 2022

V3, October 2020

0.6.3, February 2023

1.1, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPLv2

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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++

ANSI C, C++

Java

Server operating systems

Linux

server-less

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Unix
Windows

Linux
Windows

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes, ANSI Standard SQL Types

yes

Integer, Float, Boolean, String

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

SQL-like DML and DDL statements

yes

ANSI SQL queries

SQL-like query language

APIs and other access methods

JDBC
ODBC

in-process shared library

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Clojure
Java
Python

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

File partitioning

Customizable business rules for partitioning

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

when using SQL

yes

Relationships in graphs

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

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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, groups and roles

based on Apache Sentry and Kerberos

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

User authentification and security via Rexster Graph Server

Administrators and common users accounts

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