DBMS > InfluxDB vs. Riak KV vs. Sadas Engine vs. StarRocks

System Properties Comparison InfluxDB vs. Riak KV vs. Sadas Engine vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

DBMS for storing time series, events and metrics

Distributed, fault tolerant key-value store

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

www.influxdata.com/products/influxdb-overview

www.sadasengine.com

www.starrocks.io

Technical documentation

docs.influxdata.com/influxdb

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

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

OpenSource, formerly Basho Technologies

SADAS s.r.l.

The Linux Foundation

since Feb 2023

Initial release

2013

2009

2006

2020

Current release

2.7.6, April 2024

3.2.0, December 2022

8.0

2.5.3, March 2023

License

Commercial or Open Source

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache version 2, commercial enterprise edition

commercial

free trial version available

Open Source

Apache Version 2.0

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

Erlang

C++

C++, Java

Server operating systems

Linux
OS X

through Homebrew

Linux
OS X

AIX
Linux
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

Numeric data and Strings

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

HTTP API
JSON over UDP

HTTP API
Native Erlang Interface

JDBC
ODBC
Proprietary protocol

JDBC
MySQL protocol

Supported programming languages

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

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

Java

Server-side scripts

Stored procedures

Erlang

user defined functions

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

Sharding

no "single point of failure"

horizontal partitioning

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

in enterprise version only

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

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

yes

managed by 'Learn by Usage'

User concepts

Access control

simple rights management via user accounts

yes, using Riak Security

Access rights for users, groups and roles according to SQL-standard

Role based access control and fine grained access rights

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...
» more

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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6 June 2024

Deadman Alerts with Grafana and InfluxDB Cloud 3.0
5 June 2024

Chasing the Skies: Monitoring Flights with InfluxDB
4 June 2024

Monitoring Your Cloud Environments and Applications with InfluxDB
30 May 2024

Webinar Recap: Unleash the Full Potential of Your Time Series Data with InfluxDB and AWS
29 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

show all

Recent citations in the news

Amazon Timestream for InfluxDB is now generally available
15 March 2024, AWS Blog

Apache Doris for Log and Time Series Data Analysis in NetEase: Why Not Elasticsearch and InfluxDB?
5 June 2024, hackernoon.com

Amazon Timestream: Managed InfluxDB for Time Series Data
14 March 2024, The New Stack

InfluxData Collaborating with AWS to Bring InfluxDB and Time Series Analytics to Developers Around the World
14 March 2024, Business Wire

How the FDAP Stack Gives InfluxDB 3.0 Real-Time Speed, Efficiency
15 March 2024, Datanami

provided by Google News

Basho Revamps Riak Open-Source Database
22 September 2023, InformationWeek

A Critique of Resizable Hash Tables: Riak Core & Random Slicing
26 August 2018, InfoQ.com

Riak NoSQL snapped up by Bet365
12 September 2017, ComputerWeekly.com

Basho to Bolster Riak with DB Plug-Ins
5 May 2014, Datanami

Basho Advances NoSQL Riak Enterprise 2.0 With Search, Advanced Data Types
15 September 2014, Data Center Knowledge

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CelerData: How This Company Reduces Data Migration And Direct Analysis Costs In Data Lakes
21 December 2023, Pulse 2.0

StarRocks analytical DB heads to Linux Foundation
14 February 2023, VentureBeat

StarRocks Brings Speedy OLAP Database to the Cloud
14 July 2022, Datanami

StarRocks launches managed DBaaS for real-time analytics
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StarRocks Launches Beta of Cloud Service for Its Analytics Engine
15 July 2022, The New Stack

provided by Google News

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