Add new track for tsdb based on k8s integration#373
Add new track for tsdb based on k8s integration#373martijnvg merged 97 commits intoelastic:masterfrom
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This is looking good, thank you. I left some README suggestions and have not executed the track yet.
Can you add track parameters for number_of_shards and number_of_replicas, if it makes sense to do so?
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Thanks for taking a look @inqueue. I updated the track.
I think it makes sense and I added the track params. |
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This is a great and awesome amount of work, thank you!
I have been able to run this benchmark (it's ongoing, will report when it succeeds).
Left a suggestion to clarify a little about what the touch-... tasks are doing and also clarify some sentences in the readme.
dliappis
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LGTM
Left two comments, one for a minor typo, and another for a suggestion to move all the variables in track.json for better discoverability. Up to you if you want to implement the suggestion.
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| {% set end_time = "2023-05-16T21:00:00.000Z" %} | |||
| {% set time_intervals = {"15_minutes": ["30s", "2023-05-16T20:45:00.000Z"], "2_hours": ["1m", "2023-05-16T19:00:00.000Z"], "24_hours": ["30m", "2023-05-15T21:00:00.000Z"]} %} | |||
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The above two seem to be used also in challenges/default.json; does it make more sense to move them, as well as the variables from challenges/default.json:
{% set search_iterations = 20 %}
{% set search_warmup_iterations = 50 %}
{% set target_interval = 4 %}
to a centralized place, e.g. at the top of track.json (right after {% import "rally.helpers" as rally with context %}) for better discoverability? If you choose to do that, remember to update the references in README.md.
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This makes sense to me. I will update this PR.
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This makes sense to me. I will update this PR.
LGTM also after 5ea2206
Co-authored-by: Dimitrios Liappis <dimitrios.liappis@gmail.com>
This track indexes generated k8s pod and k8s container data based on the k8s integration and then test performance of searches that are based on k8s integration visualisations. See README for more details.
This track indexes generated k8s pod and k8s container data based on the k8s integration and then test performance of searches that are based on k8s integration visualisations. See README for more details.