DBMS > BoltDB vs. Heroic vs. IBM Db2 warehouse vs. MongoDB vs. TDengine

System Properties Comparison BoltDB vs. Heroic vs. IBM Db2 warehouse vs. MongoDB vs. TDengine

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Cloud-based data warehousing service

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

Time Series DBMS and big data platform

Primary database model

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
Vector DBMS

currently available in the MongoDB Atlas cloud service only

Relational DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	421.65
Rank	#5	Overall
	#1	Document stores

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Website

github.com/boltdb/bolt

github.com/spotify/heroic

www.ibm.com/products/db2/warehouse

www.mongodb.com

github.com/taosdata/TDengine
tdengine.com

Technical documentation

spotify.github.io/heroic

www.mongodb.com/docs/manual

docs.tdengine.com

Developer

Spotify

IBM

MongoDB, Inc

TDEngine, previously Taos Data

Initial release

2013

2014

2009

2019

Current release

6.0.7, June 2023

3.0, August 2022

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

commercial

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 V3, also commercial editions available

Cloud-based only

Only available as a cloud service

yes

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

MongoDB Atlas: Global multi-cloud database with unmatched data distribution and mobility across AWS, Azure, and Google Cloud, built-in automation for resource and workload optimization, and so much more.

Implementation language

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

schema-free

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

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.

Import/export of XML data possible

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

Read-only SQL queries via the MongoDB Atlas SQL Interface

Standard SQL with extensions for time-series applications

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

.NET Client API
JDBC
ODBC
OLE DB

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

JDBC
RESTful HTTP API

Supported programming languages

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

Server-side scripts

Stored procedures

PL/SQL, SQL PL

JavaScript

Triggers

yes

in MongoDB Atlas only

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

yes

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

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

fine grained access rights according to SQL-standard

Access rights for users and roles

yes

More information provided by the system vendor

Specific characteristics

MongoDB provides an integrated suite of cloud database and data services to accelerate...
» more

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

Built around the flexible document data model and unified API, MongoDB is a developer...
» more

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
» more

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
» more

Market metrics

Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
» more

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

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

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

Compare InfluxDB vs. TDengine
19 April 2024

Why We Need the Next Generation Data Historian
15 April 2024

Is Closed-Source Software Really More Secure?
8 April 2024

Developers: Stop Donating Your Work to Cloud Service Providers!
28 March 2024

Compare AVEVA Data Hub vs. TDengine
19 March 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.