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

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

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

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

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Wide column store

Search engine

Secondary database models

Spatial 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	42.91
Rank	#24	Overall
	#3	Search engines

Website

cloud.google.com/bigquery

hsqldb.org

www.jaguardb.com

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

solr.apache.org

Technical documentation

cloud.google.com/bigquery/docs

hsqldb.org/web/hsqlDocsFrame.html

www.jaguardb.com/support.html

solr.apache.org/resources.html

Developer

Google

DataJaguar, Inc.

Microsoft

Apache Software Foundation

Initial release

2010

2001

2015

2012

2006

Current release

2.7.2, June 2023

3.3 July 2023

9.5.0, February 2024

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

GPL V3.0

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

the server part. Clients available in other languages

Java

Server operating systems

hosted

All OS with a Java VM

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

Linux

hosted

All OS with a Java VM

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

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

JDBC
ODBC

RESTful HTTP API

Java API
RESTful HTTP/JSON API

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

.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

Java, SQL

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

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.

yes

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

Immediate Consistency

Eventual 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

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 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

yes

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