DBMS > CrateDB vs. Ehcache vs. Heroic vs. mSQL vs. Warp 10

System Properties Comparison CrateDB vs. Ehcache vs. Heroic vs. mSQL vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

A widely adopted Java cache with tiered storage options

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

mSQL (Mini SQL) is a simple and lightweight RDBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Secondary database models

Relational 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	4.79
Rank	#66	Overall
	#8	Key-value stores

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	1.26
Rank	#163	Overall
	#74	Relational DBMS

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

cratedb.com

www.ehcache.org

github.com/spotify/heroic

hughestech.com.au/products/msql

www.warp10.io

Technical documentation

cratedb.com/docs

www.ehcache.org/documentation

spotify.github.io/heroic

www.warp10.io/content/02_Getting_started

Developer

Crate

Terracotta Inc, owned by Software AG

Spotify

Hughes Technologies

SenX

Initial release

2013

2009

2014

1994

2015

Current release

5.8.1, August 2024

3.10.0, March 2022

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

Apache License 2.0

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.

Implementation language

Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

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

Linux
OS X
Windows

Data scheme

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

schema-free

yes

schema-free

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

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

JCache

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC

HTTP API
Jupyter
WebSocket

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

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

WarpScript

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

by using Terracotta Server

yes

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

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

none

Immediate Consistency

based on HBase

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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