DBMS > Apache Druid vs. IRONdb vs. Microsoft SQL Server vs. Rockset vs. Solr

System Properties Comparison Apache Druid vs. IRONdb vs. Microsoft SQL Server vs. Rockset vs. Solr

Editorial information provided by DB-Engines

Name

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

Description

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

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

Microsofts flagship relational DBMS

A scalable, reliable search and analytics service in the cloud, built on RocksDB

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

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

Spatial DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

druid.apache.org

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

www.microsoft.com/en-us/sql-server

rockset.com

solr.apache.org

Technical documentation

druid.apache.org/docs/latest/design

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

learn.microsoft.com/en-US/sql/sql-server

docs.rockset.com

solr.apache.org/resources.html

Developer

Apache Software Foundation and contributors

Circonus LLC.

Microsoft

Rockset

Apache Software Foundation

Initial release

2012

2017

1989

2019

2006

Current release

29.0.1, April 2024

V0.10.20, January 2018

SQL Server 2022, November 2022

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

restricted free version is available

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

C++

Java

Server operating systems

Linux
OS X
Unix

Linux

Linux
Windows

hosted

All OS with a Java VM

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

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

dynamic typing

yes

supports customizable data types and automatic typing

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

ingestion from XML files supported

yes

Secondary indexes

yes

all fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL for querying

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

yes

Read-only SQL queries, including JOINs

Solr Parallel SQL Interface

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

Java API
RESTful HTTP/JSON API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes, in Lua

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Automatic, metric affinity per node

tables can be distributed across several files (horizontal partitioning); sharding through federation

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

configurable replication factor, datacenter aware

yes, but depending on the SQL-Server Edition

yes

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

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

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

fine grained access rights according to SQL-standard

Access rights for users and organizations can be defined via Rockset console

yes

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