DBMS > Amazon Redshift vs. Badger vs. BoltDB vs. RocksDB vs. TDSQL for MySQL

System Properties Comparison Amazon Redshift vs. Badger vs. BoltDB vs. RocksDB vs. TDSQL for MySQL

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.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	0.70
Rank	#225	Overall
	#31	Key-value stores

Score	2.84
Rank	#97	Overall
	#16	Key-value stores

Score	0.96
Rank	#192	Overall
	#88	Relational DBMS

Website

aws.amazon.com/redshift

github.com/dgraph-io/badger

github.com/boltdb/bolt

rocksdb.org

www.tencentcloud.com/products/dcdb

Technical documentation

docs.aws.amazon.com/redshift

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

github.com/facebook/rocksdb/wiki

www.tencentcloud.com/document/product/1042

Developer

Amazon (based on PostgreSQL)

DGraph Labs

Facebook, Inc.

Tencent

Initial release

2012

2017

2013

Current release

9.4.0, June 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT License

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

Data scheme

yes

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

C++ API
Java API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Java
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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

ACID

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

Users with fine-grained authorization concept

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