DBMS > Apache Kylin vs. Sadas Engine vs. Spark SQL vs. VictoriaMetrics

System Properties Comparison Apache Kylin vs. Sadas Engine vs. Spark SQL vs. VictoriaMetrics

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Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Spark SQL is a component on top of 'Spark Core' for structured data processing

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Relational DBMS

Time Series DBMS

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

Technical documentation

kylin.apache.org/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

spark.apache.org/docs/latest/sql-programming-guide.html

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Apache Software Foundation, originally contributed from eBay Inc

SADAS s.r.l.

Apache Software Foundation

VictoriaMetrics

Initial release

2015

2006

2014

2018

Current release

3.1.0, July 2020

8.0

3.5.0 ( 2.13), September 2023

v1.91, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free trial version available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

Java

C++

Scala

Server operating systems

Linux

AIX
Linux
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

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.

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

.Net
C
C#
C++
Groovy
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

managed by 'Learn by Usage'

User concepts

Access control

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

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More resources

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