DBMS > Drizzle vs. Google Cloud Datastore vs. KairosDB vs. Kinetica vs. Yanza

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. KairosDB vs. Kinetica vs. Yanza

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.

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Distributed Time Series DBMS based on Cassandra or H2

Fully vectorized database across both GPUs and CPUs

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Website

cloud.google.com/datastore

github.com/kairosdb/kairosdb

www.kinetica.com

yanza.com

Technical documentation

cloud.google.com/datastore/docs

kairosdb.github.io

docs.kinetica.com

Developer

Drizzle project, originally started by Brian Aker

Google

Kinetica

Yanza

Initial release

2008

2013

2012

2015

Current release

7.2.4, September 2012

1.2.2, November 2018

7.1, August 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

C, C++

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
OS X
Windows

Linux

Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

yes

with proprietary extensions

SQL-like query language (GQL)

SQL-like DML and DDL statements

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Graphite protocol
HTTP REST
Telnet API

JDBC
ODBC
RESTful HTTP API

HTTP API

Supported programming languages

C
C++
Java
PHP

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

C++
Java
JavaScript (Node.js)
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Triggers

hooks for callbacks inside the server can be used.

Callbacks using the Google Apps Engine

yes

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

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control

Access rights for users and roles on table level

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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

show all

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