DBMS > Apache Druid vs. Snowflake vs. Spark SQL vs. StarRocks

System Properties Comparison Apache Druid vs. Snowflake vs. Spark SQL vs. StarRocks

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Name

Description

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

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

docs.snowflake.net/manuals/index.html

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

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Apache Software Foundation and contributors

Snowflake Computing Inc.

Apache Software Foundation

The Linux Foundation

since Feb 2023

Initial release

2012

2014

2020

Current release

29.0.1, April 2024

3.5.0 ( 2.13), September 2023

3.3, June 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

C++, Java

Server operating systems

Linux
OS X
Unix

hosted

Linux
OS X
Windows

Linux

Data scheme

yes

schema-less columns are supported

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

SQL for querying

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

JDBC
ODBC

JDBC
MySQL protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

JavaScript (Node.js)
Python

Java
Python
R
Scala

Java

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

Sharding

manual/auto, time-based

yes

yes, utilizing Spark Core

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Role based access control and fine grained access rights

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