DBMS > Google BigQuery vs. HyperSQL vs. JaguarDB vs. Microsoft Azure Table Storage vs. SingleStore

System Properties Comparison Google BigQuery vs. HyperSQL vs. JaguarDB vs. Microsoft Azure Table Storage vs. SingleStore

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Performant, highly scalable DBMS for AI and IoT applications

A Wide Column Store for rapid development using massive semi-structured datasets

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

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Wide column store

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	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Website

cloud.google.com/bigquery

hsqldb.org

www.jaguardb.com

azure.microsoft.com/en-us/services/storage/tables

www.singlestore.com

Technical documentation

cloud.google.com/bigquery/docs

hsqldb.org/web/hsqlDocsFrame.html

www.jaguardb.com/support.html

docs.singlestore.com

Developer

Google

DataJaguar, Inc.

Microsoft

SingleStore Inc.

Initial release

2010

2001

2015

2012

2013

Current release

2.7.2, June 2023

3.3 July 2023

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

GPL V3.0

commercial

free developer edition 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.

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

Java

C++

the server part. Clients available in other languages

C++, Go

Server operating systems

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux

hosted

Linux

64 bit version required

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

but no triggers and foreign keys

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

JDBC
ODBC

RESTful HTTP API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC
Java

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

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

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

Eventual 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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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.

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

rights management via user accounts

Access rights based on private key authentication or shared access signatures

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more

Competitive advantages

SingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more

Typical application scenarios

Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more

Key customers

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

Market metrics

Customers in various industries worldwide including US and International Industry...
» more

Licensing and pricing models

F ree Tier and Enterprise Edition
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.