DBMS > EXASOL vs. Google Cloud Spanner vs. JaguarDB vs. MySQL vs. Snowflake

System Properties Comparison EXASOL vs. Google Cloud Spanner vs. JaguarDB vs. MySQL vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Performant, highly scalable DBMS for AI and IoT applications

Widely used open source RDBMS

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

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	2.89
Rank	#105	Overall
	#53	Relational DBMS

Score	0.09
Rank	#356	Overall
	#53	Key-value stores
	#13	Vector DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

www.exasol.com

cloud.google.com/spanner

www.jaguardb.com

www.mysql.com

www.snowflake.com

Technical documentation

www.exasol.com/resources

cloud.google.com/spanner/docs

www.jaguardb.com/support.html

dev.mysql.com/doc

docs.snowflake.net/manuals/index.html

Developer

Exasol

Google

DataJaguar, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Snowflake Computing Inc.

Initial release

2000

2017

2015

1995

2014

Current release

3.3 July 2023

8.3.0, January 2024

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++

the server part. Clients available in other languages

C and C++

Server operating systems

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

yes

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

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

Secondary indexes

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

with proprietary extensions

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

CLI Client
JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

Go
Java
JavaScript (Node.js)
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

Multi-source replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

not for MyISAM storage engine

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

Access rights for users, groups and roles according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

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

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