DBMS > Datomic vs. InfluxDB vs. RocksDB

System Properties Comparison Datomic vs. InfluxDB vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

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

DBMS for storing time series, events and metrics

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

Secondary database models

Spatial DBMS

with GEO package

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

Score	1.57
Rank	#146	Overall
	#66	Relational DBMS

Score	23.59
Rank	#28	Overall
	#1	Time Series DBMS

Score	2.89
Rank	#97	Overall
	#16	Key-value stores

Website

www.datomic.com

www.influxdata.com/products/influxdb-overview

rocksdb.org

Technical documentation

docs.datomic.com

docs.influxdata.com/influxdb

github.com/facebook/rocksdb/wiki

Developer

Cognitect

Facebook, Inc.

Initial release

2012

2013

Current release

1.0.7075, December 2023

2.7.6, April 2024

9.4.0, June 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

MIT-License; commercial enterprise version available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

C++

Server operating systems

All OS with a Java VM

Linux
OS X

through Homebrew

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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-like query language

APIs and other access methods

RESTful HTTP API

HTTP API
JSON over UDP

C++ API
Java API

Supported programming languages

Clojure
Java

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

in enterprise version only

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

selectable replication factor

in enterprise version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

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

Depending on used storage engine

yes

User concepts

Access control

simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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