DBMS > Apache IoTDB vs. MongoDB vs. Sphinx

System Properties Comparison Apache IoTDB vs. MongoDB vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

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

Primary database model

Time Series DBMS

Document store

Search engine

Secondary database models

Spatial DBMS
Search engine

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

Time Series Collections introduced in Release 5.0

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

Score	0.74
Rank	#241	Overall
	#19	Time Series DBMS

Score	441.90
Rank	#5	Overall
	#1	Document stores

Score	6.80
Rank	#59	Overall
	#7	Search engines

Website

iotdb.apache.org

www.mongodb.com

sphinxsearch.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.mongodb.com/manual

sphinxsearch.com/docs

Social network pages

Developer

Apache Software Foundation

MongoDB, Inc

Sphinx Technologies Inc.

Initial release

2018

2009

2001

Current release

V1.0.1, February 2023

6.0.1, August 2022

3.4.1, July 2021

License

Commercial or Open Source

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

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)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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.

yes

Typing

predefined data types such as float or date

yes

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

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

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

Read-only SQL queries via the MongoDB Connector for BI

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
Native API

proprietary protocol using JSON

Proprietary protocol

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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
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

JavaScript

Triggers

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

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

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

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.

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

yes

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

Apache IoTDB (Internet of Things Database) is a time series data management system...
» more

Competitive advantages

Low Cost and High Performance Tens of million points write throughput High compression...
» more

Typical application scenarios

IoT / IIoT End-Edge-Cloud Collaboration Real-Time and Historian Time Series Analytics
» more

Key customers

Over 200 customers worldwide, including Bosch, Siemens, Komat'su, Alibaba, etc.
» more

Market metrics

240+ contributors and 10,000+ times monthly clones on GitHub Rank 2 by commits among...
» more

Licensing and pricing models

Open Source: Apache License v2.0 Pricing for commercial-/cloud-/edge-edition provided...
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