DBMS > Databricks vs. HBase vs. Lovefield vs. MySQL vs. Sequoiadb

System Properties Comparison Databricks vs. HBase vs. Lovefield vs. MySQL vs. Sequoiadb

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

Embeddable relational database for web apps written in pure JavaScript

Widely used open source RDBMS

NewSQL database with distributed OLTP and SQL

Primary database model

Document store
Relational DBMS

Wide column store

Relational DBMS

Key/Value like access via memcached API

Document store
Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

www.databricks.com

hbase.apache.org

google.github.io/lovefield

www.mysql.com

www.sequoiadb.com

Technical documentation

docs.databricks.com

hbase.apache.org/book.html

github.com/google/lovefield/blob/master/docs/spec_index.md

dev.mysql.com/doc

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Databricks

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Google

Oracle

since 2010, originally MySQL AB, then Sun

Sequoiadb Ltd.

Initial release

2013

2008

2014

1995

2013

Current release

2.3.4, January 2021

2.1.12, February 2017

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

JavaScript

C and C++

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

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

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

oid, date, timestamp, binary, regex

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

SQL-like query language

via JavaScript builder pattern

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

proprietary protocol using JSON

Supported programming languages

Python
R
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Coprocessors in Java

yes

proprietary syntax

JavaScript

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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

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

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

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

Document is locked during a transaction

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

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

simple password-based access control

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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