DBMS > Cachelot.io vs. InfluxDB vs. RavenDB vs. Riak TS vs. ToroDB

System Properties Comparison Cachelot.io vs. InfluxDB vs. RavenDB vs. Riak TS vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

In-memory caching system

DBMS for storing time series, events and metrics

Open Source Operational and Transactional Enterprise NoSQL Document Database

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Secondary database models

Spatial DBMS

with GEO package

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

cachelot.io

www.influxdata.com/products/influxdb-overview

ravendb.net

github.com/torodb/server

Technical documentation

docs.influxdata.com/influxdb

ravendb.net/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Hibernating Rhinos

Open Source, formerly Basho Technologies

8Kdata

Initial release

2015

2013

2010

2015

2016

Current release

2.7.6, April 2024

5.4, July 2022

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

MIT-License; commercial enterprise version available

Open Source

AGPL version 3, commercial license available

Open Source

AGPL-V3

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++

Erlang

Java

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X

through Homebrew

Linux
macOS
Raspberry Pi
Windows

Linux
OS X

All OS with a Java 7 VM

Data scheme

schema-free

Typing

predefined data types such as float or date

Numeric data and Strings

yes

string, integer, double, boolean, date, object_id

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

restricted

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

yes, limited

APIs and other access methods

Memcached protocol

HTTP API
JSON over UDP

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

in enterprise version only

Multi-source replication

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

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

Depending on used storage engine

User concepts

Access control

simple rights management via user accounts

Authorization levels configured per client per database

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

InfluxData is the creator of InfluxDB , the open source time series database. It...
» more

Competitive advantages

Time to Value InfluxDB is available in all the popular languages and frameworks,...
» more

Typical application scenarios

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
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Key customers

InfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more

Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more

Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
» more

News

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23 May 2024

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21 May 2024

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9 May 2024

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

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

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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