DBMS > Apache Impala vs. openGemini vs. RRDtool vs. Transbase

System Properties Comparison Apache Impala vs. openGemini vs. RRDtool vs. Transbase

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

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.

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.01
Rank	#375	Overall
	#39	Time Series DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

impala.apache.org

www.opengemini.org
github.com/openGemini

oss.oetiker.ch/rrdtool

www.transaction.de/en/products/transbase.html

Technical documentation

impala.apache.org/impala-docs.html

docs.opengemini.org/guide

oss.oetiker.ch/rrdtool/doc

www.transaction.de/en/products/transbase/features.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Huawei and openGemini community

Tobias Oetiker

Transaction Software GmbH

Initial release

2013

2022

1999

1987

Current release

4.1.0, June 2022

1.1, July 2023

1.8.0, 2022

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

GPL V2 and FLOSS

commercial

free development license

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++

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

C and C++

Server operating systems

Linux

Linux
Windows

HP-UX
Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

HTTP REST

in-process shared library
Pipes

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

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

yes

none

Source-replica replication

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

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

Administrators and common users accounts

fine grained access rights according to SQL-standard

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

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

show all

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

About HUAWEI Open Source
9 February 2022, Huawei

provided by Google News

Cacti servers under attack by attackers exploiting CVE-2022-46169
16 January 2023, Help Net Security

Critical IP spoofing bug patched in Cacti
15 December 2022, The Daily Swig

How to install Cacti SNMP Monitor on Ubuntu
24 November 2017, TechRepublic

Installation Guide for Collectd and Collectd-Web to Monitor Server Resources in Linux
29 November 2017, Linux.com

SNMP: The Little Protocol That Could
7 November 2020, Techopedia

provided by Google News

Share this page