DBMS > Amazon DocumentDB vs. Google Cloud Datastore vs. HugeGraph vs. IBM Db2 vs. Snowflake

System Properties Comparison Amazon DocumentDB vs. Google Cloud Datastore vs. HugeGraph vs. IBM Db2 vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

A fast-speed and highly-scalable Graph DBMS

Common in IBM host environments, 2 different versions for host and Windows/Linux

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store

Graph DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

aws.amazon.com/documentdb

cloud.google.com/datastore

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2

www.snowflake.com

Technical documentation

aws.amazon.com/documentdb/resources

cloud.google.com/datastore/docs

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2

docs.snowflake.net/manuals/index.html

Developer

Google

Baidu

IBM

Snowflake Computing Inc.

Initial release

2019

2008

2018

1983

host version

2014

Current release

0.9

12.1, October 2016

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free version is available

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

C and C++

Server operating systems

hosted

Linux
macOS
Unix

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

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

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

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

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

CLI Client
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

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

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

asynchronous Gremlin script jobs

yes

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

only with Windows/Unix/Linux Version

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication using Paxos

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

with separate tools (MQ, InfoSphere)

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

via hugegraph-spark

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.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

via ReferenceProperties or Ancestor paths

yes

edges in graph

yes

Transaction concepts

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

Atomic single-document operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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 and roles

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

Users, roles and permissions

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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