DBMS > Google Cloud Bigtable vs. MonetDB vs. SQLite vs. Vitess

System Properties Comparison Google Cloud Bigtable vs. MonetDB vs. SQLite vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

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

A relational database management system that stores data in columns

Widely used embeddable, in-process RDBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

cloud.google.com/bigtable

www.monetdb.org

www.sqlite.org

vitess.io

Technical documentation

cloud.google.com/bigtable/docs

www.monetdb.org/Documentation

www.sqlite.org/docs.html

vitess.io/docs

Developer

Google

MonetDB BV

Dwayne Richard Hipp

The Linux Foundation, PlanetScale

Initial release

2015

2004

2000

2013

Current release

Dec2023 (11.49), December 2023

3.46.1 (13 August 2024), August 2024

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

Public Domain

Open Source

Apache Version 2.0, commercial licenses available

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

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

server-less

Docker
Linux
macOS

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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 2003 with some extensions

yes

SQL-92 is not fully supported

yes

with proprietary extensions

APIs and other access methods

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

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

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

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

yes, in SQL, C, R

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Source-replica replication available in experimental status

none

Multi-source replication
Source-replica 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)

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic single-row operations

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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