DBMS > Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. RavenDB vs. RocksDB

System Properties Comparison Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. RavenDB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Graph DBMS

Relational DBMS

Key-value store
Wide column store

Document store

Key-value store

Secondary database models

Graph DBMS
Spatial DBMS
Time Series 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	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	3.01
Rank	#101	Overall
	#17	Document stores

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Website

dgraph.io

www.esgyn.cn

cloud.google.com/bigtable

ravendb.net

rocksdb.org

Technical documentation

dgraph.io/docs

cloud.google.com/bigtable/docs

ravendb.net/docs

github.com/facebook/rocksdb/wiki

Developer

Dgraph Labs, Inc.

Esgyn

Google

Hibernating Rhinos

Facebook, Inc.

Initial release

2016

2015

2010

2013

Current release

5.4, July 2022

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

AGPL version 3, commercial license available

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java

C++

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux
macOS
Raspberry Pi
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

yes

SQL-like query language (RQL)

APIs and other access methods

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

ADO.NET
JDBC
ODBC

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

C++ API
Java API

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

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

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication between multi datacenters

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

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID, Cluster-wide transaction available

yes

Concurrency

Support for concurrent manipulation of data

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

Planned for future releases

fine grained access rights according to SQL-standard

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

Authorization levels configured per client per database

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