DBMS > 4D vs. Apache Impala vs. Kyligence Enterprise vs. ReductStore

System Properties Comparison 4D vs. Apache Impala vs. Kyligence Enterprise vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Analytic DBMS for Hadoop

A distributed analytics engine for big data, built on top of Apache Kylin

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

www.4d.com

impala.apache.org

kyligence.io/kyligence-enterprise

github.com/reductstore
www.reduct.store

Technical documentation

developer.4d.com

impala.apache.org/impala-docs.html

www.reduct.store/docs

Developer

4D, Inc

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Kyligence, Inc.

ReductStore LLC

Initial release

1984

2013

2016

2023

Current release

v20, April 2023

4.1.0, June 2022

1.9, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Business Source License 1.1

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

C++

Java

C++, Rust

Server operating systems

OS X
Windows

Linux

Docker
Linux
macOS
Windows

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

HTTP API

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

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

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

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.

User concepts

Access control

Users and groups

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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More resources

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Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

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28 November 2017, Datanami

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