DBMS > CrateDB vs. Ehcache vs. Oracle Coherence vs. Oracle NoSQL

System Properties Comparison CrateDB vs. Ehcache vs. Oracle Coherence vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

A widely adopted Java cache with tiered storage options

Oracles in-memory data grid solution

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

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

Document store
Key-value store
Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.73
Rank	#229	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	2.02
Rank	#131	Overall
	#22	Key-value stores

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Website

cratedb.com

www.ehcache.org

www.oracle.com/java/coherence

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

Technical documentation

cratedb.com/docs

www.ehcache.org/documentation

docs.oracle.com/en/middleware/standalone/coherence

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

Developer

Crate

Terracotta Inc, owned by Software AG

Oracle

Initial release

2013

2009

2007

2011

Current release

3.10.0, March 2022

14.1, August 2023

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

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

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

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

Linux
Solaris SPARC/x86

Data scheme

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

schema-free

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

Typing

predefined data types such as float or date

yes

optional

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.

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like DML and DDL statements

APIs and other access methods

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

JCache

JCache
JPA
RESTful HTTP API

RESTful HTTP API

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

Java

.Net
C++
Java

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

Server-side scripts

Stored procedures

user defined functions (Javascript)

Triggers

yes

Cache Event Listeners

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

by using Terracotta Server

yes, with selectable consistency level

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

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

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

yes

supports JTA and can work as an XA resource

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

optionally

yes

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

rights management via user accounts

authentification to access the cache via certificates or http basic authentication

Access rights for users and roles

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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