DBMS > Apache Druid vs. Hive

System Properties Comparison Apache Druid vs. Hive

Please select another system to include it in the comparison.

Our visitors often compare Apache Druid and Hive with Snowflake, Amazon Redshift and ClickHouse.

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

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

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

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

Score	2.73
Rank	#111	Overall
	#57	Relational DBMS
	#6	Time Series DBMS

Score	78.66
Rank	#17	Overall
	#11	Relational DBMS

Website

druid.apache.org

hive.apache.org

Technical documentation

druid.apache.org/docs/latest/design

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

Developer

Apache Software Foundation and contributors

Apache Software Foundation

initially developed by Facebook

Initial release

2012

Current release

0.22.1, December 2021

3.1.2, August 2019

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

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

Server operating systems

Linux
OS X
Unix

All OS with a Java VM

Data scheme

yes

schema-less columns are supported

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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

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

Foreign keys

Referential integrity

Transaction concepts

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

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

Access rights for users, groups and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.