DBMS > Apache Druid vs. Apache Impala vs. GBase vs. Splice Machine vs. Yanza

System Properties Comparison Apache Druid vs. Apache Impala vs. GBase vs. Splice Machine 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

Analytic DBMS for Hadoop

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

yanza.com

Technical documentation

druid.apache.org/docs/latest/design

impala.apache.org/impala-docs.html

splicemachine.com/how-it-works

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

General Data Technology Co., Ltd.

Splice Machine

Yanza

Initial release

2012

2013

2004

2014

2015

Current release

29.0.1, April 2024

4.1.0, June 2022

GBase 8a, GBase 8s, GBase 8c

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

commercial

Open Source

AGPL 3.0, commercial license available

commercial

free version available

Cloud-based only

Only available as a cloud service

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

Java

C++

C, Java, Python

Java

Server operating systems

Linux
OS X
Unix

Linux

Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

schema-less columns are supported

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

SQL-like DML and DDL statements

Standard with numerous extensions

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

Java

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

yes, multi-version concurrency control (MVCC)

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

Access rights for users, groups and roles according to SQL-standard

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