DBMS > HEAVY.AI vs. MongoDB vs. OpenSearch vs. Riak KV vs. Solr

System Properties Comparison HEAVY.AI vs. MongoDB vs. OpenSearch vs. Riak KV vs. Solr

Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

Distributed, fault tolerant key-value store

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Search engine

Secondary database models

Spatial 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

Vector DBMS

Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	421.65
Rank	#5	Overall
	#1	Document stores

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

github.com/heavyai/heavydb
www.heavy.ai

www.mongodb.com

github.com/opensearch-project
opensearch.org

solr.apache.org

Technical documentation

docs.heavy.ai

www.mongodb.com/docs/manual

opensearch.org/docs/latest

www.tiot.jp/riak-docs/riak/kv/latest

solr.apache.org/resources.html

Developer

HEAVY.AI, Inc.

MongoDB, Inc

Amazon Web Services

OpenSource, formerly Basho Technologies

Apache Software Foundation

Initial release

2016

2009

2021

2009

2006

Current release

5.10, January 2022

6.0.7, June 2023

2.5.0, January 2023

3.2.0, December 2022

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

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

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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.

Aiven for OpenSearch: Fully managed open source search and analytics suite. Out-of-the-box integrations get you up and running in a couple of clicks.

Implementation language

C++ and CUDA

C++

Java

Erlang

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
OS X

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

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.

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

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

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

All search fields are automatically indexed

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

Read-only SQL queries via the MongoDB Atlas SQL Interface

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC
Thrift
Vega

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
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

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

JavaScript

yes

Erlang

Java plugins

Triggers

yes

in MongoDB Atlas only

yes

by using the 'percolation' feature

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

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

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

links between data sets can be stored

Transaction concepts

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

Multi-document ACID Transactions with snapshot isolation

optimistic locking

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

yes, using Riak Security

yes

More information provided by the system vendor

Specific characteristics

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

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

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

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
» more

Typical application scenarios

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

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» 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

The OpenSearch Project offers open source software licensed under Apache License...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.