DBMS > Apache Druid vs. Axibase vs. GridGain vs. Microsoft SQL Server vs. SiriDB

System Properties Comparison Apache Druid vs. Axibase vs. GridGain vs. Microsoft SQL Server vs. SiriDB

Editorial information provided by DB-Engines

Name

Description

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

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

GridGain is an in-memory computing platform, built on Apache Ignite

Microsofts flagship relational DBMS

Open Source Time Series DBMS

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
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	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

druid.apache.org

axibase.com/docs/atsd/finance

www.gridgain.com

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

siridb.com

Technical documentation

druid.apache.org/docs/latest/design

www.gridgain.com/docs/index.html

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

docs.siridb.com

Developer

Apache Software Foundation and contributors

Axibase Corporation

GridGain Systems, Inc.

Microsoft

Cesbit

Initial release

2012

2013

2007

1989

2017

Current release

29.0.1, April 2024

15585

GridGain 8.5.1

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

restricted free version is available

Open Source

MIT License

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

Java, C++, .Net

C++

Server operating systems

Linux
OS X
Unix

Linux

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

Numeric data

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

SQL

Support of SQL

SQL for querying

SQL-like query language

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Go
Java
PHP
Python
R
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

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

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

yes (replicated cache)

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

simple rights management via user accounts

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