DBMS > Apache Doris vs. Hive vs. Snowflake vs. Warp 10

System Properties Comparison Apache Doris vs. Hive vs. Snowflake vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Time Series DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

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

hive.apache.org

www.snowflake.com

www.warp10.io

Technical documentation

github.com/apache/doris/wiki

cwiki.apache.org/confluence/display/Hive/Home

docs.snowflake.net/manuals/index.html

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation

initially developed by Facebook

Snowflake Computing Inc.

SenX

Initial release

2017

2012

2014

2015

Current release

1.2.2, February 2023

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2

commercial

Open Source

Apache License 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

Server operating systems

Linux

All OS with a Java VM

hosted

Linux
OS X
Windows

Data scheme

yes

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

schema-free

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

yes

APIs and other access methods

JDBC
MySQL client

JDBC
ODBC
Thrift

CLI Client
JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

Java

C++
Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

user defined functions

yes

WarpScript

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

based on HBase

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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