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

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

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

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

Document store
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	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

druid.apache.org

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

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

rockset.com

vitess.io

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

vitess.io/docs

Developer

Apache Software Foundation and contributors

Circonus LLC.

Microsoft

Rockset

The Linux Foundation, PlanetScale

Initial release

2012

2017

1989

2019

2013

Current release

29.0.1, April 2024

V0.10.20, January 2018

SQL Server 2022, November 2022

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

restricted free version is available

commercial

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

C++

Server operating systems

Linux
OS X
Unix

Linux

Linux
Windows

hosted

Docker
Linux
macOS

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

dynamic 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

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL for querying

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

yes

Read-only SQL queries, including JOINs

yes

with proprietary extensions

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP API

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

HTTP REST

ADO.NET
JDBC
MySQL protocol
ODBC

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes, in Lua

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

yes

proprietary syntax

Triggers

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

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