DBMS > Databricks vs. MySQL vs. Rockset vs. Solr vs. SpatiaLite

System Properties Comparison Databricks vs. MySQL vs. Rockset vs. Solr vs. SpatiaLite

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

A scalable, reliable search and analytics service in the cloud, built on RocksDB

A widely used distributed, scalable search engine based on Apache Lucene

Spatial extension of SQLite

Primary database model

Document store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Document store

Search engine

Spatial DBMS

Secondary database models

Document store
Spatial DBMS

Relational DBMS
Search engine

Spatial DBMS

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	0.79
Rank	#211	Overall
	#35	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

docs.databricks.com

dev.mysql.com/doc

docs.rockset.com

solr.apache.org/resources.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Databricks

Oracle

since 2010, originally MySQL AB, then Sun

Rockset

Apache Software Foundation

Alessandro Furieri

Initial release

2013

1995

2019

2006

2008

Current release

8.4.0, April 2024

9.6.0, April 2024

5.0.0, August 2020

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

Apache Version 2

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

C++

Java

C++

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

server-less

Data scheme

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

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

dynamic typing

yes

supports customizable data types and automatic typing

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

ingestion from XML files supported

yes

Secondary indexes

yes

all fields are automatically indexed

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

with Databricks SQL

yes

with proprietary extensions

Read-only SQL queries, including JOINs

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

Java API
RESTful HTTP/JSON API

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

Go
Java
JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

proprietary syntax

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Automatic sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

optimistic locking

ACID

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

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and organizations can be defined via Rockset console

yes

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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