DBMS > Alibaba Cloud Table Store vs. Citus vs. Google BigQuery vs. MonetDB vs. ToroDB

System Properties Comparison Alibaba Cloud Table Store vs. Citus vs. Google BigQuery vs. MonetDB vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Wide column store

Relational DBMS

Document store

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

www.alibabacloud.com/product/table-store

www.citusdata.com

cloud.google.com/bigquery

www.monetdb.org

github.com/torodb/server

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.citusdata.com

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

Developer

Alibaba

Google

MonetDB BV

8Kdata

Initial release

2016

2010

2004

2016

Current release

8.1, December 2018

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

AGPL, commercial license also available

commercial

Open Source

Mozilla Public License 2.0

Open Source

AGPL-V3

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

Java

Server operating systems

hosted

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

SQL 2003 with some extensions

APIs and other access methods

HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

Java
Python

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

in JavaScript

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

other methods possible by using 3rd party extensions

none

Source-replica replication available in experimental status

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Since BigQuery is designed for querying data

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.

User concepts

Access control

Access rights based on subaccounts and tokens

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users and roles

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