DBMS > GeoSpock vs. Google Cloud Datastore vs. InfinityDB vs. Microsoft SQL Server

System Properties Comparison GeoSpock vs. Google Cloud Datastore vs. InfinityDB vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

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

Microsofts flagship relational DBMS

Primary database model

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#59	Key-value stores

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Website

geospock.com

cloud.google.com/datastore

boilerbay.com

www.microsoft.com/en-us/sql-server

Technical documentation

cloud.google.com/datastore/docs

boilerbay.com/infinitydb/manual

learn.microsoft.com/en-US/sql/sql-server

Developer

GeoSpock

Google

Boiler Bay Inc.

Microsoft

Initial release

2008

2002

1989

Current release

2.0, September 2019

4.0

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Javascript

Java

C++

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

Data scheme

yes

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

yes, details here

yes

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

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

temporal, categorical

yes

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

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC

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
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

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

Java

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

using Google App Engine

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

yes, but depending on the SQL-Server Edition

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

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

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.

yes

User concepts

Access control

Access rights for users can be defined per table

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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