DBMS > Google BigQuery vs. SingleStore vs. Sphinx vs. SQream DB

System Properties Comparison Google BigQuery vs. SingleStore vs. Sphinx vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

cloud.google.com/bigquery

www.singlestore.com

sphinxsearch.com

sqream.com

Technical documentation

cloud.google.com/bigquery/docs

docs.singlestore.com

sphinxsearch.com/docs

docs.sqream.com

Developer

Google

SingleStore Inc.

Sphinx Technologies Inc.

SQream Technologies

Initial release

2010

2013

2001

2017

Current release

8.5, January 2024

3.5.1, February 2023

2022.1.6, December 2022

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GPL version 2, commercial licence available

commercial

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C++, Go

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux

64 bit version required

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

yes

but no triggers and foreign keys

SQL-like query language (SphinxQL)

yes

APIs and other access methods

RESTful HTTP/JSON API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Proprietary protocol

.Net
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Bash
C
C#
Java
JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

hash partitioning

Sharding

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

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

stores two copies of each physical data partition on two separate nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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)

yes

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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 control via users, groups and roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
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