DBMS > Drizzle vs. EsgynDB vs. Google Cloud Bigtable vs. GridGain

System Properties Comparison Drizzle vs. EsgynDB vs. Google Cloud Bigtable vs. GridGain

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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.

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Relational DBMS

Key-value store
Wide column store

Key-value store
Relational DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/bigtable

www.gridgain.com

Technical documentation

cloud.google.com/bigtable/docs

www.gridgain.com/docs/index.html

Developer

Drizzle project, originally started by Brian Aker

Esgyn

Google

GridGain Systems, Inc.

Initial release

2008

2015

2007

Current release

7.2.4, September 2012

GridGain 8.5.1

License

Commercial or Open Source

Open Source

GNU GPL

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++

C++, Java

Java, C++, .Net

Server operating systems

FreeBSD
Linux
OS X

Linux

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/ADO.Net

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

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes (compute grid and cache interceptors can be used instead)

Triggers

hooks for callbacks inside the server can be used.

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

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

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

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)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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

Security Hooks for custom implementations

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Related products and services

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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