DBMS > Google Cloud Datastore vs. HarperDB vs. Microsoft Azure Cosmos DB vs. SAP IQ

System Properties Comparison Google Cloud Datastore vs. HarperDB vs. Microsoft Azure Cosmos DB vs. SAP IQ

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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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Globally distributed, horizontally scalable, multi-model database service

Columnar RDBMS optimized for Big Data analytics

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

column-oriented

Secondary database models

Spatial DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Website

cloud.google.com/datastore

www.harperdb.io

azure.microsoft.com/services/cosmos-db

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

Technical documentation

cloud.google.com/datastore/docs

docs.harperdb.io/docs

learn.microsoft.com/azure/cosmos-db

help.sap.com/docs/SAP_IQ

Developer

Google

HarperDB

Microsoft

SAP, formerly Sybase

Initial release

2008

2017

2014

1994

Current release

3.1, August 2021

16.1 SPS04, April 2019

License

Commercial or Open Source

commercial

free community edition 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

Node.js

Server operating systems

hosted

Linux
OS X

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

JSON data types

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like data manipulation statements

SQL-like query language

yes

APIs and other access methods

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

Custom Functions

since release 3.1

JavaScript

yes

Triggers

Callbacks using the Google Apps Engine

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Implicit feature of the cloud service

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

the nodes on which a table resides can be defined

yes

Implicit feature of the cloud service

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Hadoop integration

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

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate 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

Serializable Isolation within Transactions, Read Committed outside of Transactions

Atomic execution of specific operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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 and roles

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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