DBMS > Dgraph vs. GeoSpock vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. MySQL

System Properties Comparison Dgraph vs. GeoSpock vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. MySQL

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Distributed and scalable native Graph DBMS

Spatial and temporal data processing engine for extreme data scale

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

Globally distributed, horizontally scalable, multi-model database service

Widely used open source RDBMS

Primary database model

Graph DBMS

Relational DBMS

Key-value store
Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Time Series DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

dgraph.io

geospock.com

cloud.google.com/bigtable

azure.microsoft.com/services/cosmos-db

www.mysql.com

Technical documentation

dgraph.io/docs

cloud.google.com/bigtable/docs

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

Developer

Dgraph Labs, Inc.

GeoSpock

Google

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2016

2015

2014

1995

Current release

2.0, September 2019

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Javascript

C and C++

Server operating systems

Linux
OS X
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

temporal, categorical

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

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

JDBC

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

JavaScript

yes

proprietary syntax

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Automatic sharding

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

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

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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)

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Atomic single-row operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights for users can be defined per table

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

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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