DBMS > Apache Kylin vs. Hazelcast vs. Spark SQL vs. STSdb

System Properties Comparison Apache Kylin vs. Hazelcast vs. Spark SQL vs. STSdb

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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

A widely adopted in-memory data grid

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

kylin.apache.org

hazelcast.com

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

kylin.apache.org/docs

hazelcast.org/imdg/docs

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

Developer

Apache Software Foundation, originally contributed from eBay Inc

Hazelcast

Apache Software Foundation

STS Soft SC

Initial release

2015

2008

2014

2011

Current release

3.1.0, July 2020

5.3.6, November 2023

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

Linux

All OS with a Java VM

Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC

.NET Client API

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

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

User concepts

Access control

Role-based access control

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