DBMS > Geode vs. Google BigQuery vs. GridGain vs. Quasardb vs. TimesTen

System Properties Comparison Geode vs. Google BigQuery vs. GridGain vs. Quasardb vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Large scale data warehouse service with append-only tables

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

Distributed, high-performance timeseries database

In-Memory RDBMS compatible to Oracle

Primary database model

Key-value store

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

geode.apache.org

cloud.google.com/bigquery

www.gridgain.com

quasar.ai

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

Technical documentation

geode.apache.org/docs

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

doc.quasar.ai/master

docs.oracle.com/database/timesten-18.1

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

GridGain Systems, Inc.

quasardb

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2002

2010

2007

2009

1998

Current release

1.1, February 2017

GridGain 8.5.1

3.14.1, January 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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

Java, C++, .Net

C++

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

SQL-like query language (OQL)

yes

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

SQL-like query language

yes

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

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

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

PL/SQL

Triggers

yes

Cache Event Listeners

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes (replicated cache)

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes, on a single node

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

Access rights per client and object definable

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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