DBMS > GridDB vs. Microsoft Azure Data Explorer vs. MongoDB vs. Stardog vs. StarRocks

System Properties Comparison GridDB vs. Microsoft Azure Data Explorer vs. MongoDB vs. Stardog vs. StarRocks

Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

Fully managed big data interactive analytics platform

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

column oriented

Secondary database models

Key-value store
Relational DBMS

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	421.08
Rank	#5	Overall
	#1	Document stores

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

griddb.net

azure.microsoft.com/services/data-explorer

www.mongodb.com

www.stardog.com

www.starrocks.io

Technical documentation

docs.griddb.net

docs.microsoft.com/en-us/azure/data-explorer

www.mongodb.com/docs/manual

docs.stardog.com

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Toshiba Corporation

Microsoft

MongoDB, Inc

Stardog-Union

The Linux Foundation

since Feb 2023

Initial release

2013

2019

2009

2010

2020

Current release

5.1, August 2022

cloud service with continuous releases

6.0.7, June 2023

7.3.0, May 2020

3.3, June 2024

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also 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.

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

Apache Version 2.0

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.
MongoDB Flex @ STACKIT offers managed MongoDB Instances with adjustable CPU, RAM, storage amount and speed, in enterprise grade to perfectly match all application requirements. All services are 100% GDPR-compliant.

Implementation language

C++

Java

C++, Java

Server operating systems

Linux

hosted

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

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 and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-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

Import/export of XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

Kusto Query Language (KQL), SQL subset

Read-only SQL queries via the MongoDB Atlas SQL Interface

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

JDBC
MySQL protocol

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

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
Clojure
Groovy
Java
JavaScript
Python
Ruby

Java

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

user defined functions

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

in MongoDB Atlas only

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

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.

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Spark connector (open source): github.com/Azure/azure-kusto-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Eventual Consistency
Immediate Consistency

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

relationships in graphs

Transaction concepts

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

ACID at container level

Multi-document ACID Transactions with snapshot isolation

ACID

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

Access rights for users can be defined per database

Azure Active Directory Authentication

Access rights for users and roles

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
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MongoDB provides an integrated suite of cloud database and data services to accelerate...
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Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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Built around the flexible document data model and unified API, MongoDB is a developer...
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Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
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Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
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ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

GitHub trending repository
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Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
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Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.