DBMS > GridGain vs. HyperSQL vs. Oracle Berkeley DB vs. SAP IQ vs. Snowflake

System Properties Comparison GridGain vs. HyperSQL vs. Oracle Berkeley DB vs. SAP IQ vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Widely used in-process key-value store

Columnar RDBMS optimized for Big Data analytics

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

column-oriented

Relational DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.gridgain.com

hsqldb.org

www.oracle.com/database/technologies/related/berkeleydb.html

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.snowflake.com

Technical documentation

www.gridgain.com/docs/index.html

hsqldb.org/web/hsqlDocsFrame.html

docs.oracle.com/cd/E17076_05/html/index.html

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SAP, formerly Sybase

Snowflake Computing Inc.

Initial release

2007

2001

1994

2014

Current release

GridGain 8.5.1

2.7.2, June 2023

18.1.40, May 2020

16.1 SPS04, April 2019

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

commercial license 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.

Implementation language

Java, C++, .Net

Java

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
OS X
Solaris
Windows

All OS with a Java VM

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

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC
Java

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Java, SQL

yes

user defined functions

Triggers

yes (cache interceptors and events)

yes

only for the SQL API

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

Source-replica replication

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Hadoop 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

ACID

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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