DBMS > Apache Drill vs. Apache Impala vs. HyperSQL vs. KairosDB

System Properties Comparison Apache Drill vs. Apache Impala vs. HyperSQL vs. KairosDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Analytic DBMS for Hadoop

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Distributed Time Series DBMS based on Cassandra or H2

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Website

drill.apache.org

impala.apache.org

hsqldb.org

github.com/kairosdb/kairosdb

Technical documentation

drill.apache.org/docs

impala.apache.org/impala-docs.html

hsqldb.org/web/hsqlDocsFrame.html

kairosdb.github.io

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Initial release

2012

2013

2001

2013

Current release

1.20.3, January 2023

4.1.0, June 2022

2.7.2, June 2023

1.2.2, November 2018

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2

Open Source

based on BSD license

Open Source

Apache 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

C++

Java

Server operating systems

Linux
OS X
Windows

Linux

All OS with a Java VM

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

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

SQL SELECT statement is SQL:2003 compliant

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

Supported programming languages

C++

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC
Java

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

simple password-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

MapR to Speak on Stream Processing Systems, Apache Spark and Drill at Industry Events in January
31 May 2024, Yahoo Movies UK

Apache Drill vs. Apache Spark — Which SQL query engine is better for you?
23 September 2019, Towards Data Science

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Apache Drill Poised to Crack Tough Data Challenges
19 May 2015, Datanami

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

provided by Google News

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

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

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

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

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

Real-Time Performance and Health Monitoring Using Netdata
2 September 2019, CNX Software

provided by Google News

Share this page