DBMS > MySQL vs. Snowflake vs. Solr vs. Spark SQL vs. SQream DB

System Properties Comparison MySQL vs. Snowflake vs. Solr vs. Spark SQL vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

Widely used open source RDBMS

Cloud-based data warehousing service for structured and semi-structured data

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

Technical documentation

dev.mysql.com/doc

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

spark.apache.org/docs/latest/sql-programming-guide.html

docs.sqream.com

Developer

Oracle

since 2010, originally MySQL AB, then Sun

Snowflake Computing Inc.

Apache Software Foundation

SQream Technologies

Initial release

1995

2014

2006

2014

2017

Current release

8.4.0, April 2024

9.6.1, May 2024

3.5.0 ( 2.13), September 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Java

Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

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)

Linux
OS X
Windows

Linux

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

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

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

yes, ANSI Standard SQL Types

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

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
Proprietary native API

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

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

JavaScript (Node.js)
Python

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

Java
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions

Java plugins

user defined functions in Python

Triggers

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Sharding

yes, utilizing Spark Core

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

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

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

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

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