DBMS > Elasticsearch vs. Firebird vs. Microsoft Azure Cosmos DB vs. Quasardb

System Properties Comparison Elasticsearch vs. Firebird vs. Microsoft Azure Cosmos DB vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Firebird is an open source RDBMS forked from Borland's InterBase

Globally distributed, horizontally scalable, multi-model database service

Distributed, high-performance timeseries database

Primary database model

Search engine

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.elastic.co/elasticsearch

www.firebirdsql.org

azure.microsoft.com/services/cosmos-db

quasar.ai

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

www.firebirdsql.org/en/reference-manuals

learn.microsoft.com/azure/cosmos-db

doc.quasar.ai/master

Developer

Elastic

Firebird Foundation

Microsoft

quasardb

Initial release

2010

2000

As fork of Borland's InterBase

2014

2009

Current release

8.6, January 2023

5.0.0, January 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Initial Developer's Public License

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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 and C++

C++

Server operating systems

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

BSD
Linux
OS X
Windows

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

integer and binary

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 search fields are automatically indexed

yes

All properties auto-indexed by default

yes

with tags

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP/JSON API

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

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

HTTP API

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

PSQL

JavaScript

Triggers

yes

by using the 'percolation' feature

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Memcached and Redis integration

yes

Transient mode

User concepts

Access control

Users with fine-grained authorization concept

Access rights can be defined down to the item level

Cryptographically strong user authentication and audit trail

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