DBMS > Badger vs. ClickHouse vs. HEAVY.AI

System Properties Comparison Badger vs. ClickHouse vs. HEAVY.AI

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Column-oriented Relational DBMS powering Yandex

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Primary database model

Key-value store

Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	0.30
Rank	#330	Overall
	#50	Key-value stores

Score	16.96
Rank	#39	Overall
	#24	Relational DBMS

Score	3.01
Rank	#111	Overall
	#55	Relational DBMS

Website

github.com/dgraph-io/badger

clickhouse.com

github.com/heavyai/heavydb
www.heavy.ai

Technical documentation

godoc.org/github.com/dgraph-io/badger

clickhouse.com/docs

docs.heavy.ai

Developer

DGraph Labs

Clickhouse Inc.

HEAVY.AI, Inc.

Initial release

2017

2016

Current release

v23.7.4.5-stable, August 2023

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition 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.

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

C++ and CUDA

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

Linux

Data scheme

schema-free

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, when using the MergeTree engine

SQL

Support of SQL

Close to ANSI SQL

yes

APIs and other access methods

HTTP REST
JDBC
ODBC
Proprietary protocol

JDBC
ODBC
Thrift
Vega

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
Scala

3rd party library

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

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

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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