DBMS > Google Cloud Datastore vs. Hypertable vs. InterSystems Caché vs. LokiJS vs. MySQL

System Properties Comparison Google Cloud Datastore vs. Hypertable vs. InterSystems Caché vs. LokiJS vs. MySQL

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

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

An open source BigTable implementation based on distributed file systems such as Hadoop

A multi-model DBMS and application server

In-memory JavaScript DBMS

Widely used open source RDBMS

Primary database model

Document store

Wide column store

Key-value store
Object oriented DBMS
Relational DBMS

Document store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

Document store
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

Website

cloud.google.com/datastore

www.intersystems.com/products/cache

github.com/techfort/LokiJS

www.mysql.com

Technical documentation

cloud.google.com/datastore/docs

docs.intersystems.com

techfort.github.io/LokiJS

dev.mysql.com/doc

Developer

Google

Hypertable Inc.

InterSystems

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2008

2009

1997

2014

1995

Current release

0.9.8.11, March 2016

2018.1.4, May 2020

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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.

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Implementation language

C++

JavaScript

C and C++

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

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

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

depending on used data model

schema-free

yes

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

restricted

only exact value or prefix value scans

yes

via views

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

with proprietary extensions

APIs and other access methods

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

C++ API
Thrift

.NET Client API
JDBC
ODBC
RESTful HTTP API

JavaScript API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

C++
Java
Perl
PHP
Python
Ruby

C#
C++
Java

JavaScript

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

View functions in JavaScript

yes

proprietary syntax

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor on file system level

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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.

Immediate Consistency

none

Immediate 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

ACID

atomic operations within a single collection possible

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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)

Access rights for users, groups and roles

Users with fine-grained authorization concept

no user groups or roles

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