DBMS > Google BigQuery vs. MonetDB vs. Sphinx vs. Splice Machine

System Properties Comparison Google BigQuery vs. MonetDB vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

Open source search engine for searching in data from different sources, e.g. relational databases

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Search engine

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	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

cloud.google.com/bigquery

www.monetdb.org

sphinxsearch.com

splicemachine.com

Technical documentation

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

sphinxsearch.com/docs

splicemachine.com/how-it-works

Developer

Google

MonetDB BV

Sphinx Technologies Inc.

Splice Machine

Initial release

2010

2004

2001

2014

Current release

Dec2023 (11.49), December 2023

3.5.1, February 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, 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

C++

Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

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.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

SQL-like query language (SphinxQL)

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in SQL, C, R

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding via remote tables

Sharding

Partitioning is done manually, search queries against distributed index is supported

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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 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, groups and roles according to SQL-standard

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