How Do You Optimize KQL Queries for High-Volume Real-Time Analytics?

Hi codeautomation​ ,

I’ve been dealing with exactly this over the last few months, so here’s what I’ve learned.

What makes the biggest difference?

Filtering on time as early as possible, no question. I always put the where on the datetime column right after the table name, before any extend or join. Kusto indexes datetime columns and can skip entire chunks of data that way. Sounds basic, but it has the biggest effect.

I also only filter on actual columns now, not on values I calculated with extend first. And wherever I can, I use has instead of contains and == instead of =~.

One thing I underestimated at first: anything expensive should happen once at ingestion, not in every query. Parsing JSON out of dynamic columns or converting Unix timestamps is something I now do with an update policy during ingest. After that, every query works with clean, typed columns. Microsoft recommends exactly this in their best practices, too.

High cardinality and large data volumes

For summarize or join on columns with a huge number of distinct values, hint.shufflekey helps. Microsoft’s rule of thumb is more than a million distinct values.

I’d be careful with the partitioning policy. It only pays off if almost all your queries filter on a column like that with == or in (TenantId, for example) and you have lots of concurrent queries, like from dashboards. According to Microsoft you usually don’t need it, and it costs extra processing.

In my experience, the caching policy gets you more. Raw data you rarely look at can go cold. The aggregations your dashboards actually read should stay in the hot cache for a long time.

When to use materialized views?

I mostly use them in three cases:

1. When I only ever need the latest state per key, so arg_max. That’s great when the source corrects values after the fact. The raw table just stays append-only, and the view always gives you the current version.
2. To remove duplicates with take_any, for example when retries or backfills send the same data twice.
3. To downsample, for example from per-second to per-minute values. My dashboards only read the downsampled data for longer time ranges.

For row-level transformations, though, an update policy is the right tool.

Dashboards under heavy ingestion

On Real-Time Dashboards I use Live Refresh instead of fixed intervals. The dashboard just runs a quick check with ingestion_time() to see if new data has arrived, and only then runs the actual query. With high-frequency ingestion I also set a refresh rate limit.

And my tiles never read straight from the raw tables, only from materialized views or stored functions. It’s faster, and all tiles calculate things the same way.

Here are the Microsoft docs on this:

• Best practices for KQL queries
• Materialized views use cases
• Caching policy (hot and cold cache)Partitioning policy
• Real-Time Dashboard live refresh

I hope this helps you understand it better.

Many greetings
spaceman127

Feel free to leave kudos or accept it as a solution. This will also help other community members.