DBMS > Apache Impala vs. Atos Standard Common Repository vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Postgres-XL

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Postgres-XL

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

Globally distributed, horizontally scalable, multi-model database service

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Relational DBMS

Document store
Key-value store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

Vector DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

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

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

impala.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

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

azure.microsoft.com/services/cosmos-db

www.postgres-xl.org

Technical documentation

impala.apache.org/impala-docs.html

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

learn.microsoft.com/azure/cosmos-db

www.postgres-xl.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Atos Convergence Creators

Microsoft

Initial release

2013

2016

2015

2014

since 2012, originally named StormDB

Current release

4.1.0, June 2022

1703

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Mozilla public license

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

Server operating systems

Linux

hosted

Linux
macOS

Data scheme

yes

Schema and schema-less with LDAP views

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JDBC
ODBC

LDAP

RESTful HTTP API

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

C#
Java
JavaScript
Python

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

JavaScript

user defined functions

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

horizontal partitioning

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

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 or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

MVCC

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

LDAP bind authentication

yes

using Azure authentication

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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