DBMS > Databricks vs. HBase vs. MySQL vs. STSdb vs. VoltDB

System Properties Comparison Databricks vs. HBase vs. MySQL vs. STSdb vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

Widely used open source RDBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store
Relational DBMS

Wide column store

Relational DBMS

Key/Value like access via memcached API

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	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.databricks.com

hbase.apache.org

www.mysql.com

github.com/STSSoft/STSdb4

www.voltdb.com

Technical documentation

docs.databricks.com

hbase.apache.org/book.html

dev.mysql.com/doc

docs.voltdb.com

Developer

Databricks

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Oracle

since 2010, originally MySQL AB, then Sun

STS Soft SC

VoltDB Inc.

Initial release

2013

2008

1995

2011

2010

Current release

2.3.4, January 2021

8.4.0, April 2024

4.0.8, September 2015

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPLv2, commercial license available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

Java, C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

FreeBSD
Linux
OS X
Solaris
Windows

Windows

Linux
OS X

for development

Data scheme

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

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

primitive types and user defined types (classes)

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

with Databricks SQL

yes

with proprietary extensions

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

.NET Client API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Python
R
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
Java

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Coprocessors in Java

yes

proprietary syntax

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

ACID

not for MyISAM storage engine

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

Users with fine-grained authorization concept

no user groups or roles

Users and roles with access to stored procedures

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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