DBMS > Drizzle vs. Heroic vs. Kinetica vs. OpenTSDB vs. Transwarp Hippo

System Properties Comparison Drizzle vs. Heroic vs. Kinetica vs. OpenTSDB vs. Transwarp Hippo

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

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Fully vectorized database across both GPUs and CPUs

Scalable Time Series DBMS based on HBase

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Score	0.05
Rank	#386	Overall
	#16	Vector DBMS

Website

github.com/spotify/heroic

www.kinetica.com

opentsdb.net

www.transwarp.cn/en/subproduct/hippo

Technical documentation

spotify.github.io/heroic

docs.kinetica.com

opentsdb.net/docs/build/html/index.html

Developer

Drizzle project, originally started by Brian Aker

Spotify

Kinetica

currently maintained by Yahoo and other contributors

Initial release

2008

2014

2012

2011

2023

Current release

7.2.4, September 2012

7.1, August 2021

1.0, May 2023

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

Open Source

LGPL

commercial

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

C++

Java

C, C++

Java

C++

Server operating systems

FreeBSD
Linux
OS X

Linux

Linux
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

Vector, Numeric and String

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

via Elasticsearch

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
RESTful HTTP API

HTTP API
Telnet API

RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

C++
Java
JavaScript (Node.js)
Python

Erlang
Go
Java
Python
R
Ruby

C++
Java
Python

Server-side scripts

Stored procedures

user defined functions

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

based on HBase

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.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles on table level

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

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Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

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21 March 2024, insideBIGDATA

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26 March 2024, Datanami

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18 March 2024, SiliconANGLE News

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18 March 2024, GlobeNewswire

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30 October 2023, InfoWorld

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10 February 2022, hackernoon.com

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28 June 2016, SiliconANGLE News

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2 April 2015, O'Reilly Radar

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