DBMS > Apache Impala vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Sphinx

System Properties Comparison Apache Impala vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Globally distributed, horizontally scalable, multi-model database service

Oracles in-memory data grid solution

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Search engine

Secondary database models

Document store

Vector DBMS

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

impala.apache.org

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

azure.microsoft.com/services/cosmos-db

www.oracle.com/java/coherence

sphinxsearch.com

Technical documentation

impala.apache.org/impala-docs.html

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

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/en/middleware/standalone/coherence

sphinxsearch.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

Oracle

Sphinx Technologies Inc.

Initial release

2013

2015

2014

2007

2001

Current release

4.1.0, June 2022

14.1, August 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

hosted

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

full-text index on all search fields

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

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

JCache
JPA
RESTful HTTP API

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

C#
Java
JavaScript
Python

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

.Net
C++
Java

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

JavaScript

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

The original contents of fields are not stored in the Sphinx index.

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 Apache Sentry and Kerberos

yes

using Azure authentication

Access rights can be defined down to the item level

authentification to access the cache via certificates or http basic authentication

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