DBMS > Atos Standard Common Repository vs. Microsoft Azure Cosmos DB vs. Solr vs. Splunk vs. Sqrrl

System Properties Comparison Atos Standard Common Repository vs. Microsoft Azure Cosmos DB vs. Solr vs. Splunk vs. Sqrrl

Editorial information provided by DB-Engines

Name

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

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

Globally distributed, horizontally scalable, multi-model database service

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

Analytics Platform for Big Data

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

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

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

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

azure.microsoft.com/services/cosmos-db

solr.apache.org

www.splunk.com

sqrrl.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

solr.apache.org/resources.html

docs.splunk.com/Documentation/Splunk

Developer

Atos Convergence Creators

Microsoft

Apache Software Foundation

Splunk Inc.

Amazon

originally Sqrrl Data, Inc.

Initial release

2016

2014

2006

2003

2012

Current release

1703

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Limited free edition and free developer edition available

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

Server operating systems

Linux

hosted

All OS with a Java VM

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

Linux
OS X
Solaris
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

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

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

JSON types

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

All properties auto-indexed by default

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

Splunk Search Processing Language for search commands

APIs and other access methods

LDAP

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

Java API
RESTful HTTP/JSON API

HTTP REST

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

All languages with LDAP bindings

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

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

C#
Java
JavaScript
PHP
Python
Ruby

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

JavaScript

Java plugins

yes

Triggers

yes

JavaScript

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Multi-source replication

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

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

optimistic locking

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

Atomic updates per row, document, or graph entity

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

LDAP bind authentication

Access rights can be defined down to the item level

yes

Access rights for users and roles

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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