DBMS > Google BigQuery vs. MonetDB vs. ToroDB vs. YDB

System Properties Comparison Google BigQuery vs. MonetDB vs. ToroDB vs. YDB

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Editorial information provided by DB-Engines

Name

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

Description

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

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Document store

Document store
Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

cloud.google.com/bigquery

www.monetdb.org

github.com/torodb/server

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

ydb.tech/en/docs

Developer

Google

MonetDB BV

8Kdata

Yandex

Initial release

2010

2004

2016

2019

Current release

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

AGPL-V3

Open Source

Apache 2.0; commercial license 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

Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java 7 VM

Linux

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

SQL-like query language (YQL)

APIs and other access methods

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Source-replica replication

Active-passive shard 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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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 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

Access rights defined for Yandex Cloud users

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