DBMS > Amazon DynamoDB vs. GeoSpock vs. Google Cloud Datastore vs. H2GIS vs. Rockset

System Properties Comparison Amazon DynamoDB vs. GeoSpock vs. Google Cloud Datastore vs. H2GIS vs. Rockset

Editorial information provided by DB-Engines

Name

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

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Spatial and temporal data processing engine for extreme data scale

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

Spatial extension of H2

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Document store
Key-value store

Secondary database models

Time Series DBMS

Relational DBMS

Relational DBMS
Search engine

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

aws.amazon.com/dynamodb

geospock.com

cloud.google.com/datastore

www.h2gis.org

rockset.com

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/datastore/docs

www.h2gis.org/docs/home

docs.rockset.com

Developer

Amazon

GeoSpock

Google

CNRS

Rockset

Initial release

2012

2008

2013

2019

Current release

2.0, September 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

LGPL 3.0

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.

Implementation language

Java, Javascript

Java

C++

Server operating systems

hosted

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

dynamic 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.

ingestion from XML files supported

Secondary indexes

yes

temporal, categorical

yes

all fields are automatically indexed

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like query language (GQL)

yes

Read-only SQL queries, including JOINs

APIs and other access methods

RESTful HTTP API

JDBC

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

HTTP REST

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

Java

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

based on H2

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

none

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

yes

based on H2

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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 and roles can be defined via the AWS Identity and Access Management (IAM)

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)

yes

based on H2

Access rights for users and organizations can be defined via Rockset console

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