DBMS > Amazon CloudSearch vs. Apache IoTDB vs. Microsoft Access vs. Snowflake vs. Solr

System Properties Comparison Amazon CloudSearch vs. Apache IoTDB vs. Microsoft Access vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.81
Rank	#137	Overall
	#12	Search engines

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/cloudsearch

iotdb.apache.org

www.microsoft.com/en-us/microsoft-365/access

www.snowflake.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/cloudsearch

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

developer.microsoft.com/en-us/access

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Amazon

Apache Software Foundation

Microsoft

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2012

2018

1992

2014

2006

Current release

1.1.0, April 2023

1902 (16.0.11328.20222), March 2019

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

commercial

Open Source

Apache Version 2

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++

Java

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Windows

Not a real database server, but making use of DLLs

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic 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.

yes

Secondary indexes

yes

all search fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

but not compliant to any SQL standard

yes

Solr Parallel SQL Interface

APIs and other access methods

HTTP API

JDBC
Native API

ADO.NET
DAO
ODBC
OLE DB

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

user defined functions

Java plugins

Triggers

yes

since Access 2010 using the ACE-engine

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

authentication via encrypted signatures

yes

a simple user-level security was built in till version Access 2003

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

yes

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