Remove duplicate custom metric events from application insights before plotting in Azure portal - azure-application-insights

Question

I'm logging some custom metrics in Application insights using the TelemetryClient.TrackMetric method in .NET, and I've noticed that occasionally some of the events are duplicated when I view them in the Azure portal.
I've drilled into the data, and the duplicate events have the same itemId and timestamp, but if I show the ingestion time by adding | extend ingestionTime = ingestion_time() to the query then I can see that the ingestion times are different.
This GitHub issue indicates that this behavior is expected, as AI uses at-least-once delivery.
I plot these metrics in charts in the Azure portal using a sum aggregation, however these duplicates are creating trust issues with the charts as the duplicates are simply treated as two separate events.
Is there a way to de-dupe the events based on itemId before plotting the data in the Azure portal?
Update
A more specific example:
I'm running an algorithm, triggered by an event, which results in a reward. The algorithm may be triggered several dozen times a day, and the reward is a positive or negative floating point value. It logs the reward each time to Application Insights as a custom metric (called say custom-reward), along with some additional properties for data splitting.
In the Azure portal I'm creating a simple chart by going to Application Insights -> Metrics and customising the chart. I select my custom-reward metric in the Metric dropdown, and select Sum as the aggregation. I may or may not apply splitting. I save the chart to my dashboard.
This simple chart gives me a nice way of monitoring the system to make sure nothing unexpected is happening, and the Sum value in the bottom left of the chart allows me to quickly see whether the sum of the rewards is positive or negative over the chart's range, and by how much.
However, on occasion I've been surprised by the result (say over the last 12 hours the sum of the rewards was surprisingly negative), and on closer inspection I discovered that a few large negative results have been duplicated. Further investigation shows this has been happening with other events, but with smaller results I tend not to notice.
I'm not that familiar with the advanced querying bit of Application Insights, I actually just used it for the first time today to dig into the events. But it does sound like there might be something I can do there to create a query that I can then plot, with the results deduped?
Update 2
I've managed to make progress with this thanks to the tips by #JohnGardner, so I'll mark that as the answer. I've deduped and plotted the results by adding the following line to the query:
| summarize timestamp=any(timestamp), value=any(value), name=any(name), customDimensions=any(customDimensions) by itemId
Update 3
Adding the following line to the query allowed me to split on custom data (in this case splitting by algorithm ID):
| extend algorithmId = tostring(customDimensions.["algorithm-id"])
With that line added, when you select "Chart" in the query results, algorithmId now shows up as an option in the split dropdown. After that you can click "Pin to dashboard". You lose the handy "sum over the time period" indicator in the bottom left of the chart which you get via the simple "Metrics" chart, however I'm sure I'll be able to recreate that in other ways.

Answer 1

if you are doing your own queries, you would generally be using something like summarize or makeseries to do this deduping for a chart. you wouldn't generally plot individual items unless you are looking at a very small time range?
so instead of something like
summarize count() ...
you could do
summarize dcount(itemId) ...
or you might add a "fake" summarize to a query that didn't need it before with by itemId to coalesce multiple rows into just one, using any(x) to grab any individual row's value for each column for each itemId.
but it really depends on what you are doing in your specific query. if you were using something like sum(itemCount) to also deal with sampling, you have other odd cases now, where the at-least-once delivery might have duplicated sampled items? (updating your question to add a specific query and hypothetical result would possibly lead to a more specific answer).