DBMS > Amazon Redshift vs. Badger vs. Blueflood vs. ClickHouse vs. gStore

System Properties Comparison Amazon Redshift vs. Badger vs. Blueflood vs. ClickHouse vs. gStore

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Scalable TimeSeries DBMS based on Cassandra

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.

A native Graph DBMS to store and maintain very large RDF datasets.

Primary database model

Secondary database models

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	0.04
Rank	#359	Overall
	#37	Graph DBMS
	#18	RDF stores

Website

aws.amazon.com/redshift

github.com/dgraph-io/badger

blueflood.io

clickhouse.com

en.gstore.cn

Technical documentation

docs.aws.amazon.com/redshift

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

github.com/rax-maas/blueflood/wiki

clickhouse.com/docs

en.gstore.cn/#/enDocs

Developer

Amazon (based on PostgreSQL)

DGraph Labs

Rackspace

Clickhouse Inc.

Initial release

2012

2017

2013

2016

Current release

v24.4.1.2088-stable, May 2024

1.2, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Linux
OS X

FreeBSD
Linux
macOS

Linux

Data scheme

yes

schema-free

predefined scheme

yes

schema-free and OWL/RDFS-schema support

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

JDBC
ODBC

HTTP REST

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

HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

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)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

key based and custom

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

based on Cassandra

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

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

fine grained access rights according to SQL-standard

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.

Users, roles and permissions, Role-Based Access Control (RBAC) supported

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