DBMS > Drizzle vs. Google Cloud Bigtable vs. IBM Db2 warehouse vs. JaguarDB

System Properties Comparison Drizzle vs. Google Cloud Bigtable vs. IBM Db2 warehouse vs. JaguarDB

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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.

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

Cloud-based data warehousing service

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Key-value store
Vector DBMS

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

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

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Website

cloud.google.com/bigtable

www.ibm.com/products/db2/warehouse

www.jaguardb.com

Technical documentation

cloud.google.com/bigtable/docs

www.jaguardb.com/support.html

Developer

Drizzle project, originally started by Brian Aker

Google

IBM

DataJaguar, Inc.

Initial release

2008

2015

2014

2015

Current release

7.2.4, September 2012

3.3 July 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

GPL V3.0

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++

the server part. Clients available in other languages

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC

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

.NET Client API
JDBC
ODBC
OLE DB

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

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

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

Server-side scripts

Stored procedures

PL/SQL, SQL PL

Triggers

hooks for callbacks inside the server can be used.

yes

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

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

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency

Eventual 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

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

fine grained access rights according to SQL-standard

rights management via user accounts

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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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