DBMS > Apache Druid vs. Drizzle vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Druid vs. Drizzle vs. HEAVY.AI vs. Kinetica vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

druid.apache.org

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

azure.microsoft.com/services/cosmos-db

Technical documentation

druid.apache.org/docs/latest/design

docs.heavy.ai

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation and contributors

Drizzle project, originally started by Brian Aker

HEAVY.AI, Inc.

Kinetica

Microsoft

Initial release

2012

2008

2016

2012

2014

Current release

29.0.1, April 2024

7.2.4, September 2012

5.10, January 2022

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

GNU GPL

Open Source

Apache Version 2; enterprise edition available

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

C++

C++ and CUDA

C, C++

Server operating systems

Linux
OS X
Unix

FreeBSD
Linux
OS X

Linux

hosted

Data scheme

yes

schema-less columns are supported

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL for querying

yes

with proprietary extensions

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C++
Java
PHP

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

user defined functions

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Round robin

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication
Source-replica replication

Multi-source replication

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

GPU vRAM or System RAM

User concepts

Access control

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Access rights can be defined down to the item level

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