DBMS > CrateDB vs. GridGain vs. SWC-DB vs. Trafodion

System Properties Comparison CrateDB vs. GridGain vs. SWC-DB vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Distributed Database based on Lucene

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

A high performance, scalable Wide Column DBMS

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Relational DBMS

Time Series DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Website

cratedb.com

www.gridgain.com

github.com/kashirin-alex/swc-db
www.swcdb.org

trafodion.apache.org

Technical documentation

cratedb.com/docs

www.gridgain.com/docs/index.html

trafodion.apache.org/documentation.html

Developer

Crate

GridGain Systems, Inc.

Alex Kashirin

Apache Software Foundation, originally developed by HP

Initial release

2013

2007

2020

2014

Current release

5.8.1, August 2024

GridGain 8.5.1

0.5, April 2021

2.3.0, February 2019

License

Commercial or Open Source

Open Source

commercial, open source

Open Source

GPL V3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, C++, .Net, Python, REST, SQL

C++

C++, Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

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

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

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

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C#
C++
Java
PHP
Python
Ruby
Scala

C++

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions (Javascript)

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

Java Stored Procedures

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes (replicated cache)

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

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

rights management via user accounts

Role-based access control
Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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