DBMS > BigObject vs. ClickHouse vs. Kinetica vs. TDengine

System Properties Comparison BigObject vs. ClickHouse vs. Kinetica vs. TDengine

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Fully vectorized database across both GPUs and CPUs

Time Series DBMS and big data platform

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series DBMS

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	2.48
Rank	#107	Overall
	#9	Time Series DBMS

Website

bigobject.io

clickhouse.com

www.kinetica.com

github.com/taosdata/TDengine
tdengine.com

Technical documentation

Developer

BigObject, Inc.

Clickhouse Inc.

Kinetica

TDEngine, previously Taos Data

Initial release

2015

2016

2012

2019

Current release

v24.6.2.17-stable, July 2024

7.1, August 2021

3.0, August 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

Open Source

AGPL V3, also commercial editions available

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.

ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

C, C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
macOS

Linux

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like DML and DDL statements

Standard SQL with extensions for time-series applications

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JDBC
ODBC
RESTful HTTP API

JDBC
RESTful HTTP API

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

Server-side scripts

Stored procedures

Lua

yes

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

none

key based and custom

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

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

GPU vRAM or System RAM

User concepts

Access control

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users and roles on table level

yes

More information provided by the system vendor

Specific characteristics

TDengine™ is a time-series database designed to help traditional industries overcome...
» more

Competitive advantages

High Performance at Any Scale: With its distributed scalable architecture that grows...
» more

Typical application scenarios

TDengine is purpose-built for Industry 4.0 and the Industrial IoT (IIoT) and particularly...
» more

Market metrics

TDengine has garnered over 23,000 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is free, open-source software released under the AGPLv3. TDengine Enterprise...
» more

News

Optimize Data Consolidation for Multi-Site Renewable Energy Operations
9 September 2024

Connect Power BI to TDengine
6 September 2024

Using Streams and Subscriptions for Alerting
5 September 2024

Solar Panel Monitoring: OPC UA Ingestion
2 September 2024

Time-Series Databases: A Game Changer for the Renewable Energy Industry
30 August 2024

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