DBMS > EJDB vs. Hawkular Metrics vs. IRONdb vs. Manticore Search vs. Yaacomo

System Properties Comparison EJDB vs. Hawkular Metrics vs. IRONdb vs. Manticore Search vs. Yaacomo

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded 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 distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Multi-storage database for search, including full-text search.

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

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Website

github.com/Softmotions/ejdb

www.hawkular.org

www.circonus.com/solutions/time-series-database/

manticoresearch.com

yaacomo.com

Technical documentation

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

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

docs.circonus.com/irondb/category/getting-started

manual.manticoresearch.com

Developer

Softmotions

Community supported by Red Hat

Circonus LLC.

Manticore Software

Q2WEB GmbH

Initial release

2012

2014

2017

2009

Current release

V0.10.20, January 2018

6.0, February 2023

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

commercial

Open Source

GPL version 2

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

C and C++

C++

Server operating systems

server-less

Linux
OS X
Windows

Linux

FreeBSD
Linux
macOS
Windows

Android
Linux
Windows

Data scheme

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

text, numeric, histograms

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

yes

APIs and other access methods

in-process shared library

HTTP REST

HTTP API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JDBC
ODBC

Supported programming languages

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

Go
Java
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Server-side scripts

Stored procedures

yes, in Lua

user defined functions

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Automatic, metric affinity per node

Sharding

Partitioning is done manually, search queries against distributed index is supported

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

configurable replication factor, datacenter aware

Synchronous replication based on Galera library

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate consistency per node, eventual consistency across nodes

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

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