[pull] master from DataDog:master#530
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* add(datadog_cluster_agent): add autoscaling metrics Added new metrics related to autoscaling conditions and constraints for the DatadogPodAutoscaler. those metrics were introduced in the cluster-agent by DataDog/datadog-agent#47138 * Apply suggestion from @clamoriniere
* ci(release): gate release-trigger on the release environment The prepare job in release-dispatch.yml creates tags before reaching the environment: release gate on the dispatch job. Adding environment: release to the calling dispatch job in release-trigger.yml ensures GitHub's deployment protection runs before the reusable workflow's jobs start, so tagging requires manual approval. Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> * ci(release): gate release-trigger on the release environment Add environment: release to the dispatch job that calls the reusable release-dispatch.yml workflow. GitHub's deployment protection now runs before any of the reusable workflow's jobs start, so the prepare step (which creates tags) requires manual approval. The inner environment: release on release-dispatch.yml's dispatch job is removed in integrations-core — a single gate at the trigger level is sufficient. Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> * ci(release): gate release-trigger via intermediate approve job environment: release cannot be used on a job that calls a reusable workflow (uses:). Instead, add an explicit approve job that holds the environment gate; the dispatch job depends on it, so the reusable workflow's prepare step (which creates tags) cannot run until a reviewer approves the deployment. Remove the previously-added environment: release from the dispatch job (invalid) and the inner environment: release from release-dispatch.yml (redundant — a single gate is sufficient). Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
…isolate its failures (#23580) * [kafka_consumer] Use AdminClient.list_offsets for earliest fetch and isolate its failures Replace the LOW_WATERMARK fetch in `_collect_topic_metadata` (which went through `Consumer.offsets_for_times(timestamp=0)` and forced the broker to walk `.timeindex` segment files) with `AdminClient.list_offsets` and `OffsetSpec.earliest()`. The broker services this from in-memory `logStartOffset` instead, eliminating the time-index scan that was timing out for clusters with many segments x multi-broker fan-out. Wrap the call so a failure no longer aborts the entire topic-metadata collection. When the earliest fetch fails (or returns errors per partition), only the metrics that genuinely depend on it are skipped: `partition.beginning_offset`, `partition.size`, and `topic.size`. Everything else - `topic.message_rate`, `topic.partitions`, `partition.isr/replicas/under_replicated/offline`, `topic.config.*`, and all consumer-side metrics - keeps emitting. Verified locally on a 20k-topic / 40k-partition / 20k-consumer-group cluster: with the failure simulated, sample volume drops from 480k to 380k (only the 100k earliest-dependent samples lost) instead of the prior 300k+ loss when the whole `_collect_topic_metadata` aborted. * Use actual PR number for changelog file * Drop dead lowwater code path and rename get_watermark_offsets -> get_highwater_offsets
Co-authored-by: dkirov-dd <166512750+dkirov-dd@users.noreply.github.com> Co-authored-by: Steven Yuen <steven.yuen@datadoghq.com>
Co-authored-by: dkirov-dd <166512750+dkirov-dd@users.noreply.github.com> Co-authored-by: Steven Yuen <steven.yuen@datadoghq.com>
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