DBMS > Apache Doris vs. Snowflake vs. Spark SQL vs. Transwarp StellarDB

System Properties Comparison Apache Doris vs. Snowflake vs. Spark SQL vs. Transwarp StellarDB

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Cloud-based data warehousing service for structured and semi-structured data

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

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

Relational DBMS

Graph DBMS

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Website

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

www.snowflake.com

spark.apache.org/sql

www.transwarp.cn/en/product/stellardb

Technical documentation

github.com/apache/doris/wiki

docs.snowflake.net/manuals/index.html

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

Developer

Apache Software Foundation, originally contributed from Baidu

Snowflake Computing Inc.

Apache Software Foundation

Transwarp

Initial release

2017

2014

Current release

1.2.2, February 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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 DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
MySQL client

CLI Client
JDBC
ODBC

JDBC
ODBC

OpenCypher

Supported programming languages

Java

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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