DBMS > Microsoft Azure Data Explorer vs. OpenQM vs. OpenSearch vs. TerarkDB

System Properties Comparison Microsoft Azure Data Explorer vs. OpenQM vs. OpenSearch vs. TerarkDB

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Editorial information provided by DB-Engines

Name

Description

Fully managed big data interactive analytics platform

QpenQM is a high-performance, self-tuning, multi-value DBMS

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

column oriented

Secondary database models

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

Vector DBMS

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

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

azure.microsoft.com/services/data-explorer

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

github.com/opensearch-project
opensearch.org

github.com/bytedance/terarkdb

Technical documentation

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

opensearch.org/docs/latest

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Microsoft

Rocket Software, originally Martin Phillips

Amazon Web Services

ByteDance, originally Terark

Initial release

2019

1993

2021

2016

Current release

cloud service with continuous releases

3.4-12

2.5.0, January 2023

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

Open Source

Apache Version 2.0

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java

C++

Server operating systems

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

All OS with a Java VM

Data scheme

Fixed schema with schema-less datatypes (dynamic)

yes

with some exceptions

schema-free

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

schema-free

Typing

predefined data types such as float or date

yes

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

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

Secondary indexes

all fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

SQL-like query language

APIs and other access methods

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

Java API
RESTful HTTP/JSON API

C++ API
Java API

Supported programming languages

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

.Net
Basic
C
Java
Objective C
PHP
Python

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C++
Java

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

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

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

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

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Azure Active Directory Authentication

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

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

Typical application scenarios

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

Licensing and pricing models

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

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