DBMS > etcd vs. Google Cloud Datastore vs. GridGain vs. MySQL vs. Oracle Rdb

System Properties Comparison etcd vs. Google Cloud Datastore vs. GridGain vs. MySQL vs. Oracle Rdb

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

GridGain is an in-memory computing platform, built on Apache Ignite

Widely used open source RDBMS

Primary database model

Key-value store

Document store

Key-value store
Relational 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	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

etcd.io
github.com/etcd-io/etcd

cloud.google.com/datastore

www.gridgain.com

www.mysql.com

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

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.google.com/datastore/docs

www.gridgain.com/docs/index.html

dev.mysql.com/doc

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Google

GridGain Systems, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2008

2007

1995

1984

Current release

3.4, August 2019

GridGain 8.5.1

8.4.0, April 2024

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Java, C++, .Net

C and C++

Server operating systems

FreeBSD
Linux
Windows

experimental

hosted

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

HP Open VMS

Data scheme

schema-free

yes

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

Typing

predefined data types such as float or date

yes, details here

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

SQL-like query language (GQL)

ANSI-99 for query and DML statements, subset of DDL

yes

with proprietary extensions

yes

APIs and other access methods

gRPC
JSON over HTTP

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Java
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

Server-side scripts

Stored procedures

using Google App Engine

yes (compute grid and cache interceptors can be used instead)

yes

proprietary syntax

Triggers

yes, watching key changes

Callbacks using the Google Apps Engine

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication using Paxos

yes (replicated cache)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

not for MyISAM storage engine

yes, on a single node

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 based on Google Cloud Identity and Access Management (IAM)

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

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