DBMS > IRONdb vs. PouchDB vs. RocksDB vs. Solr vs. Splice Machine

System Properties Comparison IRONdb vs. PouchDB vs. RocksDB vs. Solr vs. Splice Machine

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

JavaScript DBMS with an API inspired by CouchDB

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A widely used distributed, scalable search engine based on Apache Lucene

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

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

Technical documentation

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

pouchdb.com/guides

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

splicemachine.com/how-it-works

Developer

Circonus LLC.

Apache Software Foundation

Facebook, Inc.

Apache Software Foundation

Splice Machine

Initial release

2017

2012

2013

2006

2014

Current release

V0.10.20, January 2018

7.1.1, June 2019

8.11.4, April 2024

9.6.0, April 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2

Open Source

AGPL 3.0, commercial license available

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

C and C++

JavaScript

C++

Java

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

via views

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

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

Solr Parallel SQL Interface

yes

APIs and other access methods

HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

C++ API
Java API

Java API
RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

JavaScript

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes, in Lua

View functions in JavaScript

Java plugins

yes

Java

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

with a proxy-based framework, named couchdb-lounge

horizontal partitioning

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

yes

Access rights for users, groups and roles according to SQL-standard

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