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

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

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

A widely used distributed, scalable search engine based on Apache Lucene

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

Primary database model

Secondary database models

Document store
Spatial DBMS

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.alibabacloud.com/product/table-store

cloud.google.com/bigquery

www.monetdb.org

solr.apache.org

github.com/torodb/server

Technical documentation

www.alibabacloud.com/help/en/tablestore

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

solr.apache.org/resources.html

Developer

Alibaba

Google

MonetDB BV

Apache Software Foundation

8Kdata

Initial release

2016

2010

2004

2006

2016

Current release

Dec2023 (11.49), December 2023

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

Apache Version 2

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

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

All OS with a Java 7 VM

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL 2003 with some extensions

Solr Parallel SQL Interface

APIs and other access methods

HTTP API

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in SQL, C, R

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

Since BigQuery is designed for querying data

ACID

optimistic locking

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

Access rights based on subaccounts and tokens

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

yes

Access rights for users and roles

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