DBMS > Badger vs. HBase vs. Microsoft Azure SQL Database vs. RocksDB vs. Valentina Server

System Properties Comparison Badger vs. HBase vs. Microsoft Azure SQL Database vs. RocksDB vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Database as a Service offering with high compatibility to Microsoft SQL Server

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

Object-relational database and reports server

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

github.com/dgraph-io/badger

hbase.apache.org

azure.microsoft.com/en-us/products/azure-sql/database

rocksdb.org

www.valentina-db.net

Technical documentation

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

hbase.apache.org/book.html

docs.microsoft.com/en-us/azure/azure-sql

github.com/facebook/rocksdb/wiki

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

DGraph Labs

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Facebook, Inc.

Paradigma Software

Initial release

2017

2008

2010

2013

1999

Current release

2.3.4, January 2021

V12

8.11.4, April 2024

5.7.5

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

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

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

hosted

Linux

Linux
OS X
Windows

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC

C++ API
Java API

ODBC

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes, with always 3 replicas available

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

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