DBMS > Elasticsearch vs. HugeGraph vs. MongoDB vs. SiteWhere vs. Sphinx

System Properties Comparison Elasticsearch vs. HugeGraph vs. MongoDB vs. SiteWhere vs. Sphinx

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

A fast-speed and highly-scalable Graph DBMS

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

M2M integration platform for persisting/querying time series data

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0
Vector DBMS

currently available in the MongoDB Atlas cloud service only

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	421.65
Rank	#5	Overall
	#1	Document stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.elastic.co/elasticsearch

github.com/hugegraph
hugegraph.apache.org

www.mongodb.com

github.com/sitewhere/sitewhere

sphinxsearch.com

Technical documentation

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

hugegraph.apache.org/docs

www.mongodb.com/docs/manual

sitewhere1.sitewhere.io/index.html

sphinxsearch.com/docs

Developer

Elastic

Baidu

MongoDB, Inc

SiteWhere

Sphinx Technologies Inc.

Initial release

2010

2018

2009

2010

2001

Current release

8.6, January 2023

0.9

6.0.7, June 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache Version 2.0

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Open Source

Common Public Attribution License Version 1.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Java

C++

Server operating systems

All OS with a Java VM

Linux
macOS
Unix

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

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

yes

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

predefined scheme

yes

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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.

Secondary indexes

yes

All search fields are automatically indexed

yes

also supports composite index and range index

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

Read-only SQL queries via the MongoDB Atlas SQL Interface

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

HTTP REST

Proprietary protocol

Supported programming languages

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

Groovy
Java
Python

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Kotlin
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

JavaScript

Triggers

yes

by using the 'percolation' feature

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

Sharding

based on HBase

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

via hugegraph-spark

yes

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

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

ACID

Multi-document ACID Transactions with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional, enabled by default

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

Memcached and Redis integration

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

Users, roles and permissions

Access rights for users and roles

Users with fine-grained authorization concept

More information provided by the system vendor

Specific characteristics

MongoDB provides an integrated suite of cloud database and data services to accelerate...
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Competitive advantages

Built around the flexible document data model and unified API, MongoDB is a developer...
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Typical application scenarios

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
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Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
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Licensing and pricing models

MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
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