Mindbreeze GmbH, A-4020 Linz, 2019
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With app.telemetry, you can create ad-hoc reports about the use of Mindbreeze in the context of search queries any time you want. For example, you can quickly get an overview of frequently searched queries (or alternatively, just queries without hits).
Now, by switching to the dimension "Query" and entering the desired time range, you can pull up a list of the most common search queries.
To pull up only the searches that didn’t result in hits, select the dimension "No Results" and then restrict to the value "TRUE" using "Add Filter."
If you switch back to the dimension "Query" and define the desired time range, you’ll receive a list of all searches without hits for this period.
By default, Mindbreeze already provides a predefined “Mindbreeze – Search Experience“ dashboard for statistics about search usage.
If the number of search queries in an installation is too high and the loading time of this dashboard chart is no longer acceptable (or even results in a timeout), the following two strategies can be used to counter this and to take advantage of the other possibilities of the analysis.
The default setting of these charts can either be edited using the Edit icon in the upper right corner of each chart, or you can use the “Configuration” view to find the configuration objects.
There you can shorten the loading times and also better utilize the background cache using the following two settings:
Increase the update interval of the chart to e.g. one hour:
Decrease the analysis period of 1000 intervals (10 min each − corresponds to approximately one week) to e.g. 300 intervals (10 min each − corresponds to about 2 days).
Or you can configure aggregations on a daily basis to precalculate the values at night - see next chapter.
For longer-term search query analyses, we recommend that you precalculate (aggregate) the data based on a suitable interval. To do this, you can define log pool statistics with the desired interval and the necessary columns. Since this procedure is very installation-specific, you need to consider which data/columns for which period of time are suitable for the aggregation. The following is an example of our best practice.
The statistics defined in the previous chapter can also be used to define a dashboard chart with the searches per day.
For problems with app.telemetry the log file "/var/log/app.telemetry/apptelemetryserverd.log" is helpful.
This file is already active on G7 appliances.
On G6 appliances, it must first be activated.
Please add the following directive to the file "/etc/app.telemetry/server.conf":
The app.telemetry server service must then be restarted:
service apptelemetry server restart