DBMS > Apache Impala vs. GBase vs. HyperSQL vs. Kdb

System Properties Comparison Apache Impala vs. GBase vs. HyperSQL vs. Kdb

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

High performance Time Series DBMS

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Secondary database models

Document store

Relational DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

hsqldb.org/web/hsqlDocsFrame.html

code.kx.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

General Data Technology Co., Ltd.

Kx Systems, a division of First Derivatives plc

Initial release

2013

2004

2001

2000

kdb was released 2000, kdb+ in 2003

Current release

4.1.0, June 2022

GBase 8a, GBase 8s, GBase 8c

2.7.2, June 2023

3.6, May 2018

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

based on BSD license

commercial

free 32-bit version

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++

C, Java, Python

Java

Server operating systems

Linux

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X
Solaris
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

table attribute 'grouped'

SQL

Support of SQL

SQL-like DML and DDL statements

Standard with numerous extensions

yes

SQL-like query language (q)

APIs and other access methods

JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC
Java

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Java, SQL

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

similar paradigm used for internal processing

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

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

yes

fine grained access rights according to SQL-standard

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

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

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

provided by Google News

HyperSQL DataBase flaw leaves library vulnerable to RCE
24 October 2022, The Daily Swig

Introduction to JDBC with HSQLDB tutorial
14 November 2022, TheServerSide.com

provided by Google News

Turbocharging the Engine: KX Unleashes AI-First Transformation with kdb+
28 February 2024, Business Wire

McLaren Applied and KX partner to enhance ATLAS software analytics capabilities
9 August 2023, Professional Motorsport World

Introducing Amazon FinSpace with Managed kdb Insights, a fully managed analytics engine, commonly used by capital ...
18 May 2023, AWS Blog

KX ANNOUNCES KDB INSIGHTS AS FULLY MANAGED SERVICE ON AMAZON FINSPACE
18 May 2023, PR Newswire

KX Brings the Power and Performance of kdb+ to Python Developers with PyKX
7 June 2023, Datanami

provided by Google News

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