DBMS > Google Cloud Datastore vs. InfinityDB vs. Postgres-XL vs. STSdb

System Properties Comparison Google Cloud Datastore vs. InfinityDB vs. Postgres-XL vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

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

A Java embedded Key-Value Store which extends the Java Map interface

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

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	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

cloud.google.com/datastore

boilerbay.com

www.postgres-xl.org

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/datastore/docs

boilerbay.com/infinitydb/manual

www.postgres-xl.org/documentation

Developer

Google

Boiler Bay Inc.

STS Soft SC

Initial release

2008

2002

2014

since 2012, originally named StormDB

2011

Current release

4.0

10 R1, October 2018

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

Open Source

GPLv2, commercial license 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.

Implementation language

Java

Server operating systems

hosted

All OS with a Java VM

Linux
macOS

Windows

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes, details here

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

primitive types and user defined types (classes)

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

XML type, but no XML query functionality

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

distributed, parallel query execution

APIs and other access methods

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.NET Client API

Supported programming languages

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

Java

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C#
Java

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

manual creation possible, using inversions based on multi-value capability

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

Optimistic locking for transactions; no isolation for bulk loads

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

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

fine grained access rights according to SQL-standard

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