DBMS > Apache Druid vs. Apache Phoenix vs. BigObject vs. MySQL vs. Yanza

System Properties Comparison Apache Druid vs. Apache Phoenix vs. BigObject vs. MySQL vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it 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

A scale-out RDBMS with evolutionary schema built on Apache HBase

Analytic DBMS for real-time computations and queries

Widely used open source RDBMS

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

yanza.com

Technical documentation

druid.apache.org/docs/latest/design

phoenix.apache.org

docs.bigobject.io

dev.mysql.com/doc

Developer

Apache Software Foundation and contributors

Apache Software Foundation

BigObject, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Yanza

Initial release

2012

2014

2015

1995

2015

Current release

29.0.1, April 2024

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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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Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Unix

Linux
Unix
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

schema-less columns are supported

yes

late-bound, schema-on-read capabilities

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

Lua

yes

proprietary syntax

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Users with fine-grained authorization concept

no user groups or roles

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