DBMS > Apache Doris vs. Apache Pinot vs. Microsoft SQL Server vs. Splice Machine

System Properties Comparison Apache Doris vs. Apache Pinot vs. Microsoft SQL Server vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Microsofts flagship relational DBMS

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

Primary database model

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

pinot.apache.org

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

splicemachine.com

Technical documentation

github.com/apache/doris/wiki

docs.pinot.apache.org

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

splicemachine.com/how-it-works

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation and contributors

Microsoft

Splice Machine

Initial release

2017

2015

1989

2014

Current release

1.2.2, February 2023

1.0.0, September 2023

SQL Server 2022, November 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Java

Server operating systems

Linux

All OS with a Java JDK11 or higher

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC
MySQL client

JDBC

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

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java

Go
Java
Python

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

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

Server-side scripts

Stored procedures

user defined functions

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

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

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

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

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

yes, multi-version concurrency control (MVCC)

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

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

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