DBMS > Google Cloud Datastore vs. Microsoft Azure AI Search vs. NSDb vs. RocksDB vs. SAP Adaptive Server

System Properties Comparison Google Cloud Datastore vs. Microsoft Azure AI Search vs. NSDb vs. RocksDB vs. SAP Adaptive Server

Editorial information provided by DB-Engines

Name

Description

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

Search-as-a-service for web and mobile app development

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Secondary database models

Vector DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Website

cloud.google.com/datastore

azure.microsoft.com/en-us/services/search

nsdb.io

rocksdb.org

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

cloud.google.com/datastore/docs

learn.microsoft.com/en-us/azure/search

nsdb.io/Architecture

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_ASE

Developer

Google

Microsoft

Facebook, Inc.

SAP, Sybase

Initial release

2008

2015

2017

2013

1987

Current release

9.2.1, May 2024

16.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD

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, Scala

C++

C and C++

Server operating systems

hosted

Linux
macOS

Linux

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

yes: int, bigint, decimal, string

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 fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

yes

APIs and other access methods

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

RESTful HTTP API

gRPC
HTTP REST
WebSocket

C++ API
Java API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

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

C#
Java
JavaScript
Python

Java
Scala

C
C++
Go
Java
Perl
Python
Ruby

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

Java and Transact-SQL

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

yes

Multi-source replication
Source-replica replication

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 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

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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)

yes

using Azure authentication

fine grained access rights according to SQL-standard

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