DBMS > BigObject vs. GreptimeDB vs. InfluxDB vs. Riak KV

System Properties Comparison BigObject vs. GreptimeDB vs. InfluxDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

An open source Time Series DBMS built for increased scalability, high performance and efficiency

DBMS for storing time series, events and metrics

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Key-value store

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

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

bigobject.io

greptime.com

www.influxdata.com/products/influxdb-overview

Technical documentation

docs.bigobject.io

docs.greptime.com

docs.influxdata.com/influxdb

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

Developer

BigObject, Inc.

Greptime Inc.

OpenSource, formerly Basho Technologies

Initial release

2015

2022

2013

2009

Current release

2.7.6, April 2024

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2.0

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache version 2, commercial enterprise edition

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

Rust

Erlang

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Android
Docker
FreeBSD
Linux
macOS
Windows

Linux
OS X

through Homebrew

Linux
OS X

Data scheme

yes

schema-free, schema definition possible

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

gRPC
HTTP API
JDBC

HTTP API
JSON over UDP

HTTP API
Native Erlang Interface

Supported programming languages

C++
Erlang
Go
Java
JavaScript

.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

Server-side scripts

Stored procedures

Lua

Python

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

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

in enterprise version only

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

links between data sets can be stored

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

simple rights management via user accounts

yes, using Riak Security

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

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

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

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

Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

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

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

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

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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

News

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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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