DBMS > GridGain vs. Oracle NoSQL vs. XTDB vs. Yanza vs. YottaDB

System Properties Comparison GridGain vs. Oracle NoSQL vs. XTDB vs. Yanza vs. YottaDB

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

A general purpose database with bitemporal SQL and Datalog and graph queries

Time Series DBMS for IoT Applications

A fast and solid embedded Key-value store

Primary database model

Key-value store
Relational DBMS

Document store
Key-value store
Relational DBMS

Document store

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

www.gridgain.com

www.oracle.com/database/nosql/technologies/nosql

github.com/xtdb/xtdb
www.xtdb.com

yanza.com

yottadb.com

Technical documentation

www.gridgain.com/docs/index.html

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.xtdb.com/docs

yottadb.com/resources/documentation

Developer

GridGain Systems, Inc.

Oracle

Juxt Ltd.

Yanza

YottaDB, LLC

Initial release

2007

2011

2019

2015

2001

Current release

GridGain 8.5.1

24.1, May 2024

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

MIT License

commercial

free version available

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

Java

Clojure

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Solaris SPARC/x86

All OS with a Java 8 (and higher) VM
Linux

Windows

Docker
Linux

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

optional

yes, extensible-data-notation format

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

SQL

Support of SQL

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

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

by using the Octo plugin

APIs and other access methods

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

RESTful HTTP API

HTTP REST
JDBC

HTTP API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

Clojure
Java

any language that supports HTTP calls

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

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

Triggers

yes (cache interceptors and events)

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

yes, each node contains all data

none

yes

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

yes

In-memory capabilities

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

yes

off heap cache

yes

User concepts

Access control

Security Hooks for custom implementations

Access rights for users and roles

Users and groups based on OS-security mechanisms

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