DBMS > Apache Impala vs. Datomic vs. RRDtool

System Properties Comparison Apache Impala vs. Datomic vs. RRDtool

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	2.14
Rank	#124	Overall
	#10	Time Series DBMS

Website

impala.apache.org

www.datomic.com

oss.oetiker.ch/rrdtool

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

oss.oetiker.ch/rrdtool/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

Tobias Oetiker

Initial release

2013

2012

1999

Current release

4.1.0, June 2022

1.0.6735, June 2023

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

GPL V2 and FLOSS

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, Clojure

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

Server operating systems

Linux

All OS with a Java VM

HP-UX
Linux

Data scheme

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

in-process shared library
Pipes

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

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

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

none

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

Immediate Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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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?
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