DBMS > Blueflood vs. Datomic vs. IRONdb vs. LeanXcale vs. MongoDB

System Properties Comparison Blueflood vs. Datomic vs. IRONdb vs. LeanXcale vs. MongoDB

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

Key-value store
Relational DBMS

Document store

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	0.02
Rank	#344	Overall
	#31	Time Series DBMS

Score	1.70
Rank	#138	Overall
	#68	Relational DBMS

Score	0.00
Rank	#354	Overall
	#33	Time Series DBMS

Score	0.18
Rank	#292	Overall
	#42	Key-value stores
	#136	Relational DBMS

Score	478.24
Rank	#5	Overall
	#1	Document stores

Website

blueflood.io

www.datomic.com

www.circonus.com/solutions/time-series-database

www.leanxcale.com

www.mongodb.com

Technical documentation

github.com/rackerlabs/blueflood/wiki

docs.datomic.com

docs.circonus.com/irondb

docs.mongodb.com/manual

Developer

Rackspace

Cognitect

Circonus LLC.

LeanXcale

MongoDB, Inc

Initial release

2013

2012

2017

2015

2009

Current release

1.0.6362, January 2022

V0.10.20, January 2018

5.0.8, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

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.

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

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Database as a Service

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Implementation language

Java

Java, Clojure

C and C++

C++

Server operating systems

Linux
OS X

All OS with a Java VM

Linux

Linux
OS X
Solaris
Windows

Data scheme

predefined scheme

yes

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.

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

through Apache Derby

Read-only SQL queries via the MongoDB Connector for BI

APIs and other access methods

HTTP REST

RESTful HTTP API

HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

proprietary protocol using JSON

Supported programming languages

Clojure
Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
Java
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

Server-side scripts

Stored procedures

yes

Transaction Functions

yes, in Lua

JavaScript

Triggers

By using transaction functions

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

But extensive use of caching in the application peers

Automatic, metric affinity per node

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.

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

But extensive use of caching in the application peers

configurable replication factor, datacenter aware

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

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

ACID

Multi-document ACID Transactions with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

optional, enabled by default

In-memory capabilities

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

yes

recommended only for testing and development

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

Full ACID and full SQL operational database with analytical queries over operational...
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MongoDB’s application data platform provides developers a unified interface to power...
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Competitive advantages

Linear scalability upto 100s of nodes (tested with 100 nodes and TPC-C).
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The MongoDB platform can be used by developers building transactional, operational,...
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Typical application scenarios

Hybrid Transactional-Analytical Processing (HTAP) requiring an operational database...
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Internet of Things and Time Series (Bosch, Jaguar Land Rover) Mobile (The Weather...
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Key customers

banking/finance/fintech, telco, insurance, traveltech, M2M/IoT, retail
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ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

15 beta testers
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200+ million downloads, over 1.5M Atlas database clusters under management handling...
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Licensing and pricing models

annual fee covering license and support based on number of physical cores
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MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
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