DBMS > Atos Standard Common Repository vs. CrateDB vs. Geode vs. XTDB

System Properties Comparison Atos Standard Common Repository vs. CrateDB vs. Geode vs. XTDB

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Distributed Database based on Lucene

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

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

Primary database model

Document store
Key-value store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

Document store

Secondary database models

Relational DBMS

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

Score	0.73
Rank	#224	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

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

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

cratedb.com

geode.apache.org

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

Technical documentation

cratedb.com/docs

geode.apache.org/docs

www.xtdb.com/docs

Developer

Atos Convergence Creators

Crate

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

Juxt Ltd.

Initial release

2016

2013

2002

2019

Current release

1703

1.1, February 2017

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

Clojure

Server operating systems

Linux

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

the JDK (8 or later) is also required

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

Data scheme

Schema and schema-less with LDAP views

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

schema-free

Typing

predefined data types such as float or date

optional

yes

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

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language (OQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

LDAP

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

Java Client API
Memcached protocol
RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

All languages with LDAP bindings

.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

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

Clojure
Java

Server-side scripts

Stored procedures

user defined functions (Javascript)

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Configurable replication on table/partition-level

Multi-source replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Read-after-write consistency on record level

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

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

yes, on a single node

ACID

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

In-memory capabilities

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

yes

User concepts

Access control

LDAP bind authentication

rights management via user accounts

Access rights per client and object definable

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more

Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more

Licensing and pricing models

See CrateDB pricing >
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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