DBMS > Badger vs. ClickHouse vs. Oracle Rdb vs. Riak KV vs. Spark SQL

System Properties Comparison Badger vs. ClickHouse vs. Oracle Rdb vs. Riak KV vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

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.

Distributed, fault tolerant key-value store

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Key-value store

Relational DBMS

Key-value store

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

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/dgraph-io/badger

clickhouse.com

www.oracle.com/database/technologies/related/rdb.html

spark.apache.org/sql

Technical documentation

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

clickhouse.com/docs

www.oracle.com/database/technologies/related/rdb-doc.html

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

DGraph Labs

Clickhouse Inc.

Oracle, originally developed by Digital Equipment Corporation (DEC)

OpenSource, formerly Basho Technologies

Apache Software Foundation

Initial release

2017

2016

1984

2009

2014

Current release

v24.4.1.2088-stable, May 2024

7.4.1.1, 2021

3.2.0, December 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache 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.

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Implementation language

C++

Erlang

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

HP Open VMS

Linux
OS X

Linux
OS X
Windows

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

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

restricted

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

SQL-like DML and DDL statements

APIs and other access methods

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

HTTP API
Native Erlang Interface

JDBC
ODBC

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

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

Java
Python
R
Scala

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

key based and custom

Sharding

no "single point of failure"

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

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

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

yes, on a single node

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

yes, using Riak Security

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