DBMS > Apache Druid vs. OpenQM vs. Postgres-XL vs. Yaacomo

System Properties Comparison Apache Druid vs. OpenQM vs. Postgres-XL vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

QpenQM is a high-performance, self-tuning, multi-value DBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS
Time Series DBMS

Multivalue DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	0.28
Rank	#291	Overall
	#10	Multivalue DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Website

druid.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.postgres-xl.org

yaacomo.com

Technical documentation

druid.apache.org/docs/latest/design

www.postgres-xl.org/documentation

Developer

Apache Software Foundation and contributors

Rocket Software, originally Martin Phillips

Q2WEB GmbH

Initial release

2012

1993

2014

since 2012, originally named StormDB

2009

Current release

30.0.0, June 2024

3.4-12

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

GPLv2, extended commercial license available

Open Source

Mozilla public license

commercial

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

Linux
OS X
Unix

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
macOS

Android
Linux
Windows

Data scheme

yes

schema-less columns are supported

yes

with some exceptions

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

distributed, parallel query execution

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Basic
C
Java
Objective C
PHP
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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More resources

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