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I started exploring ADX a few days back. I imported my data from Azure SQL to ADX using ADF pipeline but when I query those data, it is taking a long time. To find out some workaround I researched for Table Data Partitioning and I am much clear on partition types and tricks.

The problem is, I couldn't find any sample (Kusto Syntax) that guide me to define Paritionging on ADX Database Tables. Can anyone please help me with this syntax?

2
You could take a look on this docsMd Farid Uddin Kiron
I checked that but there is no technical implementation note.DSA
You mean Azure Table Storage table partition tips and techniques?Md Farid Uddin Kiron
No, Azure Data Explorer Database Table Partition. I understand that there are 3 methods (Horizontal, Vertical, Functional) for partitioning but question is, how to technically implement them while creating tables? There is no guide for that.DSA
Everything is to maintain with partition key and row key. So while you design a table you should keep it mind that row key and partition key must me unique.Md Farid Uddin Kiron

2 Answers

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partition operator is probably what you are looking for:

T | partition by Col1 ( top 10 by MaxValue )

T | partition by Col1 { U | where Col2=toscalar(Col1) }
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ADX doesn't currently have the notion of partitioning a table, though it may be added in the future.

that said, with the lack of technical information currently provided, it's somewhat challenging to understand how you got to the conclusion that partitioning your table is required and is the appropriate solution, as opposed to other (many) directions that ADX does allow you pursue.

if you would be willing to detail what actions you're performing, the characteristics of your data & schema, and which parts are performing slower than expected, that may help in providing you a more meaningful and helpful answer.

[if you aren't keen on exposing that information publicly, it's ok to open a support ticket with these details (through the Azure portal)]

(update: the functionality is available for a while now. read more @ https://yonileibowitz.github.io/blog-posts/data-partitioning.html)