DBMS > Amazon Redshift vs. LevelDB vs. Microsoft Azure AI Search vs. Microsoft Azure Synapse Analytics vs. Solr

System Properties Comparison Amazon Redshift vs. LevelDB vs. Microsoft Azure AI Search vs. Microsoft Azure Synapse Analytics vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Primary database model

Secondary database models

Vector DBMS

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

github.com/google/leveldb

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

azure.microsoft.com/services/synapse-analytics

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

github.com/google/leveldb/blob/main/doc/index.md

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

docs.microsoft.com/azure/synapse-analytics

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Google

Microsoft

Apache Software Foundation

Initial release

2012

2011

2015

2016

2006

Current release

1.23, February 2021

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

BSD

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

C++

Java

Server operating systems

hosted

Illumos
Linux
OS X
Windows

hosted

All OS with a Java VM

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

Data scheme

yes

schema-free

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C#
Java
JavaScript
Python

C#
Java
PHP

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

Server-side scripts

Stored procedures

user defined functions

in Python

Transact SQL

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

yes

using Azure authentication

yes

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