DBMS > Google BigQuery vs. GridDB vs. SingleStore vs. SiteWhere

System Properties Comparison Google BigQuery vs. GridDB vs. SingleStore vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Scalable in-memory time series database optimized for IoT and Big Data

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

M2M integration platform for persisting/querying time series data

Primary database model

Secondary database models

Key-value store
Relational DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

cloud.google.com/bigquery

griddb.net

www.singlestore.com

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/bigquery/docs

docs.griddb.net

docs.singlestore.com

sitewhere1.sitewhere.io/index.html

Developer

Google

Toshiba Corporation

SingleStore Inc.

SiteWhere

Initial release

2010

2013

2010

Current release

5.1, August 2022

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

free developer edition available

Open Source

Common Public Attribution License Version 1.0

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++

C++, Go

Java

Server operating systems

hosted

Linux

64 bit version required

Linux
OS X
Windows

Data scheme

yes

predefined scheme

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL92, SQL-like TQL (Toshiba Query Language)

yes

but no triggers and foreign keys

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

hash partitioning

Sharding

based on HBase

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

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

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 at container level

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

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)

Access rights for users can be defined per database

Fine grained access control via users, groups and roles

Users with fine-grained authorization concept

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
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SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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SingleStore’s competitive advantages include: Easy and Simplified Architecture with...
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Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
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Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more

Market metrics

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

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

F ree Tier and Enterprise Edition
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.