DBMS > Atos Standard Common Repository vs. Brytlyt vs. Microsoft Azure Cosmos DB vs. XTDB

System Properties Comparison Atos Standard Common Repository vs. Brytlyt vs. Microsoft Azure Cosmos DB vs. XTDB

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Editorial information provided by DB-Engines

Name

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

Description

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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Globally distributed, horizontally scalable, multi-model database service

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

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

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

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

brytlyt.io

azure.microsoft.com/services/cosmos-db

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.brytlyt.io

learn.microsoft.com/azure/cosmos-db

www.xtdb.com/docs

Developer

Atos Convergence Creators

Brytlyt

Microsoft

Juxt Ltd.

Initial release

2016

2014

2019

Current release

1703

5.0, August 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

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, C++ and CUDA

Clojure

Server operating systems

Linux

Linux
OS X
Windows

hosted

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

JSON types

yes, extensible-data-notation format

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

LDAP

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

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

HTTP REST
JDBC

Supported programming languages

All languages with LDAP bindings

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

Implicit feature of the cloud service

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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