DBMS > Dgraph vs. EsgynDB vs. Faircom DB vs. Google Cloud Bigtable vs. OpenEdge

System Properties Comparison Dgraph vs. EsgynDB vs. Faircom DB vs. Google Cloud Bigtable vs. OpenEdge

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Application development environment with integrated database management system

Primary database model

Graph DBMS

Relational DBMS

Key-value store
Relational DBMS

Key-value store
Wide column store

Relational DBMS

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

Score	1.48
Rank	#158	Overall
	#15	Graph DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.24
Rank	#311	Overall
	#44	Key-value stores
	#140	Relational DBMS

Score	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	3.75
Rank	#88	Overall
	#47	Relational DBMS

Website

dgraph.io

www.esgyn.cn

www.faircom.com/products/faircom-db

cloud.google.com/bigtable

www.progress.com/openedge

Technical documentation

dgraph.io/docs

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cloud.google.com/bigtable/docs

documentation.progress.com/output/ua/OpenEdge_latest

Developer

Dgraph Labs, Inc.

Esgyn

FairCom Corporation

Google

Progress Software Corporation

Initial release

2016

2015

1979

2015

1984

Current release

V12, November 2020

OpenEdge 12.2, March 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Restricted, free version available

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++, Java

ANSI C, C++

Server operating systems

Linux
OS X
Windows

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

schema free, schema optional, schema required, partial schema,

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

yes, ANSI SQL with proprietary extensions

yes

close to SQL 92

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

Java Stored Procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication between multi datacenters

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Internal replication in Colossus, and regional replication between two clusters in different zones

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
Tunable consistency per server, database, table, and transaction

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Planned for future releases

fine grained access rights according to SQL-standard

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users and groups

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