Add mostrecent aggregation to Gauge#967
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OK. Addressed the comments. Please take a look again. |
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Generally this is looking good to me, just one small comment. Thanks!
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The code looks good, thanks! I realized we should also update the "Metrics tuning (Gauge):" section in the README before merging, basically just adding a bullet point for 'recent'.
In the multiprocess mode, the process that exposes the metrics needs to aggregate the samples from other processes. Gauge metric allows users to choose the aggregation mode. This implements 'mostrecent' (and 'livemostrecent') mode where the last observed value is exposed. In order to support this, the file format is expanded to store the timestamps in addition to the values. The stored timestamps are read by the reader process and it's used to find the latest value. Closes prometheus#847 Consideration on the atomicity: Previously, mmap_dict.py had a comment saying "We assume that reading from an 8 byte aligned value is atomic". With this change, the value write becomes a 16 bytes 8-byte aligned write. The code author tried to find a basis on the original assumption, but couldn't find any. According to write(2), **if a file descriptor is shared**, the write becomes atomic. However, we do not share the file descriptors in the current architecture. Considering that Ruby implementation also does the same and hadn't seen an issue with it, this write atomicity problem might be practically not an issue. See also: * prometheus/client_ruby#172 The approach and naming are taken from client_ruby. * https://github.com/prometheus/client_golang/blob/v1.17.0/prometheus/metric.go#L149-L161 client_golang has an API for setting timestamp already. It explains the use case for the timestamp beyond the client-local aggregation. In order to support the same use case in Python, further changes are needed. Signed-off-by: Masaya Suzuki <draftcode@gmail.com>
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Done. |
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prometheus/client_python#967 jiggers with the format of multiprocess metrics in a way that breaks our custom aggregation. Ignore for now.
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prometheus/client_python#967 jiggers with the format of multiprocess metrics in a way that broke our custom aggregation.
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prometheus/client_python#967 jiggers with the format of multiprocess metrics in a way that broke our custom aggregation.
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* fix(deps): update minor updates * fix: support timestamps in prometheus 0.18 prometheus/client_python#967 jiggers with the format of multiprocess metrics in a way that broke our custom aggregation. --------- Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com> Co-authored-by: Jakob van Santen <jvansanten@gmail.com>
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In the multiprocess mode, the process that exposes the metrics needs to aggregate the samples from other processes. Gauge metric allows users to choose the aggregation mode. This implements 'mostrecent' (and 'livemostrecent') mode where the last observed value is exposed.
In order to support this, the file format is expanded to store the timestamps in addition to the values. The stored timestamps are read by the reader process and it's used to find the latest value.
Closes #847
Consideration on the atomicity:
Previously, mmap_dict.py had a comment saying "We assume that reading from an 8 byte aligned value is atomic". With this change, the value write becomes a 16 bytes 8-byte aligned write. The code author (draftcode) tried to find a basis on the original assumption, but couldn't find any. According to write(2), if a file descriptor is shared, the write becomes atomic. However, we do not share the file descriptors in the current architecture.
Considering that Ruby implementation also does the same and hadn't seen an issue with it, this write atomicity problem might be practically not an issue.
See also:
Add :most_recent aggregation to DirectFileStore client_ruby#172
The approach and naming are taken from client_ruby.
https://github.com/prometheus/client_golang/blob/v1.17.0/prometheus/metric.go#L149-L161
client_golang has an API for setting timestamp already. It explains the use case for the timestamp beyond the client-local aggregation. In order to support the same use case in Python, further changes are needed.