DBMS > EJDB vs. Hawkular Metrics vs. MonetDB vs. Sqrrl vs. Yaacomo

System Properties Comparison EJDB vs. Hawkular Metrics vs. MonetDB vs. Sqrrl vs. Yaacomo

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

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

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A relational database management system that stores data in columns

Adaptable, secure NoSQL built on Apache Accumulo

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

Primary database model

Document store

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

github.com/Softmotions/ejdb

www.hawkular.org

www.monetdb.org

sqrrl.com

yaacomo.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

www.hawkular.org/hawkular-metrics/docs/user-guide

www.monetdb.org/Documentation

Developer

Softmotions

Community supported by Red Hat

MonetDB BV

Amazon

originally Sqrrl Data, Inc.

Q2WEB GmbH

Initial release

2012

2014

2004

2012

2009

Current release

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

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

server-less

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Android
Linux
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

APIs and other access methods

in-process shared library

HTTP REST

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

JDBC
ODBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Go
Java
Python
Ruby

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes, in SQL, C, R

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding via remote tables

Sharding

making use of Hadoop

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

none

Source-replica replication available in experimental status

selectable replication factor

making use of Hadoop

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

fine grained access rights according to SQL-standard

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

fine grained access rights according to SQL-standard

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