DBMS > BaseX vs. Elasticsearch vs. Microsoft Azure Cosmos DB vs. RocksDB vs. Sequoiadb

System Properties Comparison BaseX vs. Elasticsearch vs. Microsoft Azure Cosmos DB vs. RocksDB vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

Globally distributed, horizontally scalable, multi-model database service

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

NewSQL database with distributed OLTP and SQL

Primary database model

Native XML DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Document store
Relational 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	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	132.83
Rank	#7	Overall
	#1	Search engines

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

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

basex.org

www.elastic.co/elasticsearch

azure.microsoft.com/services/cosmos-db

rocksdb.org

www.sequoiadb.com

Technical documentation

docs.basex.org

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

learn.microsoft.com/azure/cosmos-db

github.com/facebook/rocksdb/wiki

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

BaseX GmbH

Elastic

Microsoft

Facebook, Inc.

Sequoiadb Ltd.

Initial release

2007

2010

2014

2013

Current release

10.7, August 2023

8.6, January 2023

9.2.1, May 2024

License

Commercial or Open Source

Open Source

BSD license

Open Source

Elastic License

commercial

Open Source

BSD

Open Source

Server: AGPL; Client: Apache V2

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++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

hosted

Linux

Data scheme

schema-free

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

schema-free

Typing

predefined data types such as float or date

XQuery supports types

yes

JSON types

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

Java API
RESTful HTTP/JSON API

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

C++ API
Java API

proprietary protocol using JSON

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

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

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

C
C++
Go
Java
Perl
Python
Ruby

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes

via events

yes

by using the 'percolation' feature

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

multiple readers, single writer

Multi-item ACID transactions with snapshot isolation within a partition

yes

Document is locked during a transaction

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.

Memcached and Redis integration

yes

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

Access rights can be defined down to the item level

simple password-based access control

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