DBMS > Apache Impala vs. Apache Kylin vs. GridDB vs. RRDtool

System Properties Comparison Apache Impala vs. Apache Kylin vs. GridDB vs. RRDtool

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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

Scalable in-memory time series database optimized for IoT and Big Data

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Key-value 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	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

impala.apache.org

kylin.apache.org

griddb.net

oss.oetiker.ch/rrdtool

Technical documentation

impala.apache.org/impala-docs.html

kylin.apache.org/docs

docs.griddb.net

oss.oetiker.ch/rrdtool/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation, originally contributed from eBay Inc

Toshiba Corporation

Tobias Oetiker

Initial release

2013

2015

2013

1999

Current release

4.1.0, June 2022

3.1.0, July 2020

5.1, August 2022

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

GPL V2 and FLOSS

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++

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux

HP-UX
Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

ANSI SQL for queries (using Apache Calcite)

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

in-process shared library
Pipes

Supported programming languages

All languages supporting JDBC/ODBC

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

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID at container level

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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