DBMS > EsgynDB vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. Rockset vs. SAP HANA

System Properties Comparison EsgynDB vs. Google Cloud Datastore vs. Microsoft Azure Cosmos DB vs. Rockset vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Globally distributed, horizontally scalable, multi-model database service

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

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS
Search engine

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

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

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/datastore

azure.microsoft.com/services/cosmos-db

rockset.com

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/datastore/docs

learn.microsoft.com/azure/cosmos-db

docs.rockset.com

help.sap.com/hana

Developer

Esgyn

Google

Microsoft

Rockset

SAP

Initial release

2015

2008

2014

2019

2010

Current release

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

C++

Server operating systems

Linux

hosted

Appliance or cloud-service

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

JSON types

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

SQL-like query language

Read-only SQL queries, including JOINs

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

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

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

HTTP REST

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

using Google App Engine

JavaScript

SQLScript, R

Triggers

Callbacks using the Google Apps Engine

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Automatic sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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.

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual 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

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Access rights can be defined down to the item level

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

yes

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