DBMS > Alibaba Cloud Table Store vs. IBM Db2 Event Store vs. RRDtool

System Properties Comparison Alibaba Cloud Table Store vs. IBM Db2 Event Store vs. RRDtool

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Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Distributed Event Store optimized for Internet of Things use cases

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

Wide column store

Event Store
Time Series DBMS

Time Series DBMS

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

Score	0.27
Rank	#315	Overall
	#12	Wide column stores

Score	0.19
Rank	#345	Overall
	#3	Event Stores
	#30	Time Series DBMS

Score	2.71
Rank	#116	Overall
	#7	Time Series DBMS

Website

www.alibabacloud.com/product/table-store

www.ibm.com/products/db2-event-store

oss.oetiker.ch/rrdtool

Technical documentation

www.alibabacloud.com/help/product/27278.htm

www.ibm.com/support/knowledgecenter/en/SSGNPV_2.0.0/welcome.html

oss.oetiker.ch/rrdtool/doc

Developer

Alibaba

IBM

Tobias Oetiker

Initial release

2016

2017

1999

Current release

2.0

1.8.0, 2022

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GPL V2 and FLOSS

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

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

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

HP-UX
Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

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

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

in-process shared library
Pipes

Supported programming languages

Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

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

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Active-active shard replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

by using the rrdcached daemon

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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 based on subaccounts and tokens

fine grained access rights according to SQL-standard

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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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