DBMS > Infobright vs. IRONdb vs. Solr vs. Spark SQL vs. SQLite

System Properties Comparison Infobright vs. IRONdb vs. Solr vs. Spark SQL vs. SQLite

Editorial information provided by DB-Engines

Name

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

Description

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

ignitetech.com/softwarelibrary/infobrightdb

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

solr.apache.org

spark.apache.org/sql

www.sqlite.org

Technical documentation

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

solr.apache.org/resources.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

Developer

Ignite Technologies Inc.; formerly InfoBright Inc.

Circonus LLC.

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2005

2017

2006

2014

2000

Current release

V0.10.20, January 2018

9.6.0, April 2024

3.5.0 ( 2.13), September 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Public Domain

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++

Java

Scala

Server operating systems

Linux
Windows

Linux

All OS with a Java VM

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

Linux
OS X
Windows

server-less

Data scheme

yes

schema-free

yes

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

yes

dynamic column types

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

supports customizable data types and automatic typing

yes

not rigid because of 'dynamic typing' concept.

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

Knowledge Grid Technology used instead

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

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

Solr Parallel SQL Interface

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP API

Java API
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

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

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes, in Lua

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

configurable replication factor, datacenter aware

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

exploiting MySQL or PostgreSQL frontend capabilities

yes

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