DBMS > Databricks vs. MySQL vs. Oracle vs. Oracle Berkeley DB vs. QuestDB

System Properties Comparison Databricks vs. MySQL vs. Oracle vs. Oracle Berkeley DB vs. QuestDB

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.

Widely used open source RDBMS

Widely used RDBMS

Widely used in-process key-value store

A high performance open source SQL database for time series data

Primary database model

Document store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Relational 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	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Website

www.databricks.com

www.mysql.com

www.oracle.com/database

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

questdb.io

Technical documentation

docs.databricks.com

dev.mysql.com/doc

docs.oracle.com/en/database

docs.oracle.com/cd/E17076_05/html/index.html

questdb.io/docs

Developer

Databricks

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

originally developed by Sleepycat, which was acquired by Oracle

QuestDB Technology Inc

Initial release

2013

1995

1980

1994

2014

Current release

8.3.0, January 2024

23c, September 2023

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

restricted free version is available

Open Source

commercial license available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

C and C++

C, Java, C++ (depending on the Berkeley DB edition)

Java (Zero-GC), C++, Rust

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS
Windows

Data scheme

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

yes

Schemaless in JSON and XML columns

schema-free

yes

schema-free via InfluxDB Line Protocol

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

with proprietary extensions

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

SQL with time-series extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

Supported programming languages

Python
R
Scala

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

proprietary syntax

PL/SQL

also stored procedures in Java possible

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding, horizontal partitioning

none

horizontal partitioning (by timestamps)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Source-replica replication

Source-replica replication with eventual consistency

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

isolation level can be parameterized

ACID

ACID for single-table writes

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

through memory mapped files

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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