DBMS > Dgraph vs. EsgynDB vs. Geode vs. Google Cloud Bigtable vs. SQLite

System Properties Comparison Dgraph vs. EsgynDB vs. Geode vs. Google Cloud Bigtable vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Widely used embeddable, in-process RDBMS

Primary database model

Graph DBMS

Relational DBMS

Key-value store

Key-value store
Wide column store

Relational DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

dgraph.io

www.esgyn.cn

geode.apache.org

cloud.google.com/bigtable

www.sqlite.org

Technical documentation

dgraph.io/docs

geode.apache.org/docs

cloud.google.com/bigtable/docs

www.sqlite.org/docs.html

Developer

Dgraph Labs, Inc.

Esgyn

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Dwayne Richard Hipp

Initial release

2016

2015

2002

2015

2000

Current release

1.1, February 2017

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

Public Domain

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

Java

Server operating systems

Linux
OS X
Windows

Linux

All OS with a Java VM

the JDK (8 or later) is also required

hosted

server-less

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

SQL

Support of SQL

yes

SQL-like query language (OQL)

yes

SQL-92 is not fully supported

APIs and other access methods

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

ADO.NET
JDBC
ODBC

Java Client API
Memcached protocol
RESTful HTTP API

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

.Net
All JVM based languages
C++
Groovy
Java
Scala

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication between multi datacenters

Multi-source replication

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

none

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Planned for future releases

fine grained access rights according to SQL-standard

Access rights per client and object definable

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

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