DBMS > Apache Druid vs. FoundationDB vs. HBase vs. Kinetica vs. MongoDB

System Properties Comparison Apache Druid vs. FoundationDB vs. HBase vs. Kinetica vs. MongoDB

Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Ordered key-value store. Core features are complimented by layers.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Fully vectorized database across both GPUs and CPUs

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

Relational DBMS
Time Series DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Wide column store

Relational DBMS

Document store

Secondary database models

Spatial DBMS
Time Series 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	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.06
Rank	#185	Overall
	#31	Document stores
	#28	Key-value stores
	#85	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	421.08
Rank	#5	Overall
	#1	Document stores

Website

druid.apache.org

github.com/apple/foundationdb

hbase.apache.org

www.kinetica.com

www.mongodb.com

Technical documentation

druid.apache.org/docs/latest/design

apple.github.io/foundationdb

hbase.apache.org/book.html

docs.kinetica.com

www.mongodb.com/docs/manual

Developer

Apache Software Foundation and contributors

FoundationDB

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Kinetica

MongoDB, Inc

Initial release

2012

2013

2008

2012

2009

Current release

29.0.1, April 2024

6.2.28, November 2020

2.3.4, January 2021

7.1, August 2021

6.0.7, June 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

Open Source

Apache version 2

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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Implementation language

Java

C++

Java

C, C++

C++

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

schema-free

some layers support schemas

schema-free, schema definition possible

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

some layers support typing

options to bring your own types, AVRO

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 for querying

supported in specific SQL layer only

SQL-like DML and DDL statements

Read-only SQL queries via the MongoDB Atlas SQL Interface

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
RESTful HTTP API

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
JavaScript (Node.js)
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

Server-side scripts

Stored procedures

in SQL-layer only

yes

Coprocessors in Java

user defined functions

JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

yes, via HDFS, S3 or other storage engines

yes

Multi-source replication
Source-replica replication

Source-replica replication

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

Immediate Consistency

Linearizable consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Foreign keys

Referential integrity

in SQL-layer only

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users and roles on table level

Access rights for users and roles

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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