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Platform/GFX/Telemetry: Difference between revisions
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[[File:GFX-Telemetry-Sample.png|240x140px|framed|center]] | [[File:GFX-Telemetry-Sample.png|240x140px|framed|center]] | ||
In addition, there are two analyses at the bottom of the file. The first asks, "How many people are using Intel devices with a driver less than 8.5.10.2622?" This is a question related to bug 1175366. Running this analysis on a 14-day window of 0. | In addition, there are two analyses at the bottom of the file. The first asks, "How many people are using Intel devices with a driver less than 8.5.10.2622?" This is a question related to bug 1175366. Running this analysis on a 14-day window of 0.1% of users, I get: | ||
<pre> | <pre> | ||
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* When debugging, use a very small sample set. Large samples can be very slow to analyze. | * When debugging, use a very small sample set. Large samples can be very slow to analyze. | ||
* Analysis objects are pipelined. You can observe a random object in the pipeline with the ".take()" function. For example, to observe a random ping, "pings.take(1)". | * Analysis objects are pipelined. You can observe a random object in the pipeline with the ".take()" function. For example, to observe a random ping, "pings.take(1)". | ||
* To execute | * To execute an entire pipeline and observe the full results as a Python object, use ".collect()". | ||
* You can write files as part of the analysis. They will appear in the "analyses" folder on your spark instance. | * You can write files as part of the analysis. They will appear in the "analyses" folder on your spark instance. | ||
* You can automate spark jobs via telemetry-dash; output files will appear in S3 and can be used from people.mozilla.org to build dashboards. | * You can automate spark jobs via telemetry-dash; output files will appear in S3 and can be used from people.mozilla.org to build dashboards. | ||