DBMS > Apache Druid vs. HarperDB vs. Kinetica vs. MongoDB vs. PostgreSQL

System Properties Comparison Apache Druid vs. HarperDB vs. Kinetica vs. MongoDB vs. PostgreSQL

Editorial information provided by DB-Engines

Name

Description

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Relational DBMS
Time Series DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

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

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	421.08
Rank	#5	Overall
	#1	Document stores

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

docs.harperdb.io/docs

docs.kinetica.com

www.mongodb.com/docs/manual

www.postgresql.org/docs

Developer

Apache Software Foundation and contributors

HarperDB

Kinetica

MongoDB, Inc

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2012

2017

2012

2009

1989

1989: Postgres, 1996: PostgreSQL

Current release

29.0.1, April 2024

3.1, August 2021

7.1, August 2021

6.0.7, June 2023

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free community edition available

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

BSD

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

Node.js

C, C++

C++

Server operating systems

Linux
OS X
Unix

Linux
OS X

Linux

Linux
OS X
Solaris
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

yes

schema-less columns are supported

dynamic schema

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

JSON data types

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

SQL-like data manipulation statements

SQL-like DML and DDL statements

Read-only SQL queries via the MongoDB Atlas SQL Interface

yes

standard with numerous extensions

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
RESTful HTTP API

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

user defined functions

JavaScript

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

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

yes

in MongoDB Atlas only

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

A table resides as a whole on one (or more) nodes in a cluster

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.

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

the nodes on which a table resides can be defined

Source-replica replication

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

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Immediate Consistency

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic execution of specific operations

Multi-document ACID Transactions with snapshot isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

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 rights for users and roles

Access rights for users and roles on table level

Access rights for users and roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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

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...
» 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

Licensing and pricing models

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

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.

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