DBMS > Apache Impala vs. Apache Kylin vs. RocksDB vs. SAP SQL Anywhere

System Properties Comparison Apache Impala vs. Apache Kylin vs. RocksDB vs. SAP SQL Anywhere

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

impala.apache.org

kylin.apache.org

rocksdb.org

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

impala.apache.org/impala-docs.html

kylin.apache.org/docs

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation, originally contributed from eBay Inc

Facebook, Inc.

SAP

formerly Sybase

Initial release

2013

2015

2013

1992

Current release

4.1.0, June 2022

3.1.0, July 2020

9.2.1, May 2024

17, July 2015

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

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