DBMS > Google Cloud Datastore vs. LokiJS vs. MySQL vs. Netezza vs. Solr

System Properties Comparison Google Cloud Datastore vs. LokiJS vs. MySQL vs. Netezza vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

In-memory JavaScript DBMS

Widely used open source RDBMS

Data warehouse and analytics appliance part of IBM PureSystems

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

Primary database model

Document store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.43
Rank	#264	Overall
	#42	Document stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

cloud.google.com/datastore

github.com/techfort/LokiJS

www.mysql.com

www.ibm.com/products/netezza

solr.apache.org

Technical documentation

cloud.google.com/datastore/docs

techfort.github.io/LokiJS

dev.mysql.com/doc

solr.apache.org/resources.html

Developer

Google

Oracle

since 2010, originally MySQL AB, then Sun

IBM

Apache Software Foundation

Initial release

2008

2014

1995

2000

2006

Current release

8.4.0, April 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

C and C++

Java

Server operating systems

hosted

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

FreeBSD
Linux
OS X
Solaris
Windows

Linux

included in appliance

All OS with a Java VM

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

Data scheme

schema-free

yes

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

Typing

predefined data types such as float or date

yes, details here

yes

supports customizable data types and automatic typing

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

via views

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language (GQL)

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

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

JavaScript API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
OLE DB

Java API
RESTful HTTP/JSON API

Supported programming languages

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

JavaScript

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

C
C++
Fortran
Java
Lua
Perl
Python
R

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

Server-side scripts

Stored procedures

using Google App Engine

View functions in JavaScript

yes

proprietary syntax

yes

Java plugins

Triggers

Callbacks using the Google Apps Engine

yes

User configurable commands triggered on index changes

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

Multi-source replication using Paxos

none

Multi-source replication
Source-replica replication

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

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.

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

atomic operations within a single collection possible

ACID

not for MyISAM storage engine

ACID

optimistic locking

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

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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)

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept

yes

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