DBMS > Drizzle vs. XTDB vs. Yanza

System Properties Comparison Drizzle vs. XTDB vs. Yanza

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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.

A general purpose database with bitemporal SQL and Datalog and graph queries

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Document store

Time Series DBMS

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

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/xtdb/xtdb
www.xtdb.com

yanza.com

Technical documentation

www.xtdb.com/docs

Developer

Drizzle project, originally started by Brian Aker

Juxt Ltd.

Yanza

Initial release

2008

2019

2015

Current release

7.2.4, September 2012

1.19, September 2021

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

MIT License

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

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Database as a Service

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Implementation language

C++

Clojure

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java 8 (and higher) VM
Linux

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

HTTP REST
JDBC

HTTP API

Supported programming languages

C
C++
Java
PHP

Clojure
Java

any language that supports HTTP calls

Server-side scripts

Stored procedures

Triggers

hooks for callbacks inside the server can be used.

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, each node contains all data

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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