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[ML] Rate limit established model memory updates #31768
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droberts195
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droberts195:delay_est_model_memory_updates
Jul 4, 2018
Merged
[ML] Rate limit established model memory updates #31768
droberts195
merged 1 commit into
elastic:master
from
droberts195:delay_est_model_memory_updates
Jul 4, 2018
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There is at most one model size stats document per bucket, but during lookback a job can churn through many buckets very quickly. This can lead to many cluster state updates if established model memory needs to be updated for a given model size stats document. This change rate limits established model memory updates to one per job per 5 seconds. This is done by scheduling the updates 5 seconds in the future, but replacing the value to be written if another model size stats document is received during the waiting period. Updating the values in arrears like this means that the last value received will be the one associated with the job in the long term, whereas alternative approaches such as not updating the value if a new value was close to the old value would not.
Pinging @elastic/ml-core |
dimitris-athanasiou
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LGMT. I think we should plan refactoring AutodetectResultProcessor
as it has grown with a few different responsibilities now. But we should do that as a next step rather in this PR.
dnhatn
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Jul 4, 2018
* master: [ML] Rate limit established model memory updates (#31768) [Docs] Correct default window_size (#31582) S3 fixture should report 404 on unknown bucket (#31782) Detach Transport from TransportService (#31727) [ML] Limit ML filter items to 10K (#31731) [ML] Return statistics about forecasts as part of the jobsstats and usage API (#31647) Fixture for Minio testing (#31688) [DOCS] Add missing get mappings docs to HLRC (#31765) [DOCS] Starting Elasticsearch (#31701) Painless: Complete Removal of Painless Type (#31699) Fix not waiting for Netty ThreadDeathWatcher in IT (#31758) Consolidate watcher setting update registration (#31762) Build: re-enabled bwc (#31769) ingest: Introduction of a bytes processor (#31733) Fix coerce validation_method in GeoBoundingBoxQueryBuilder (#31747) Add analyze API to high-level rest client (#31577) [DOCS] Typos DOC: Add examples to the SQL docs (#31633) Add support for AWS session tokens (#30414) Watcher: Reenable start/stop yaml tests (#31754) Implemented XContent serialisation for GetIndexResponse (#31675) JDBC: Fix stackoverflow on getObject and timestamp conversion (#31735) resolveHasher defaults to NOOP (#31723) Account for XContent overhead in in-flight breaker Split CircuitBreaker-related tests (#31659) Add write*Blob option to replace existing blob (#31729) Painless: Add Context Docs (#31190) Watcher: Fix chain input toXcontent serialization (#31721) Docs: Match the examples in the description (#31710) rest-high-level: added get cluster settings (#31706) [Docs] Correct typos (#31720) Clean up double semicolon code typos (#31687) [DOCS] Check for Windows and *nix file paths (#31648) [ML] Validate ML filter_id (#31535) Revert long lines Fix TransportChangePasswordActionTests
droberts195
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There is at most one model size stats document per bucket, but during lookback a job can churn through many buckets very quickly. This can lead to many cluster state updates if established model memory needs to be updated for a given model size stats document. This change rate limits established model memory updates to one per job per 5 seconds. This is done by scheduling the updates 5 seconds in the future, but replacing the value to be written if another model size stats document is received during the waiting period. Updating the values in arrears like this means that the last value received will be the one associated with the job in the long term, whereas alternative approaches such as not updating the value if a new value was close to the old value would not.
droberts195
added a commit
that referenced
this pull request
Jul 4, 2018
There is at most one model size stats document per bucket, but during lookback a job can churn through many buckets very quickly. This can lead to many cluster state updates if established model memory needs to be updated for a given model size stats document. This change rate limits established model memory updates to one per job per 5 seconds. This is done by scheduling the updates 5 seconds in the future, but replacing the value to be written if another model size stats document is received during the waiting period. Updating the values in arrears like this means that the last value received will be the one associated with the job in the long term, whereas alternative approaches such as not updating the value if a new value was close to the old value would not.
dnhatn
added a commit
that referenced
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Jul 5, 2018
* 6.x: Test: Do not remove xpack templates when cleaning (#31642) SQL: Allow long literals (#31777) SQL: Fix incorrect message for aliases (#31792) Detach Transport from TransportService (#31727) 6.3.1 release notes (#31829) Add unreleased version 6.3.2 [ML][TEST] Use java 11 valid time format in DataDescriptionTests (#31817) [ML] Don't treat stale FAILED jobs as OPENING in job allocation (#31800) [ML] Fix calendar and filter updates from non-master nodes (#31804) Fix license header generation on Windows (#31790) mark XPackRestIT.test {p0=monitoring/bulk/10_basic/Bulk indexing of monitoring data} as AwaitsFix Add JDK11 support without enabling in CI (#31644) Watcher: Fix check for currently executed watches (#31137) [DOCS] Fixes 6.3.0 release notes (#31771) Watcher: Ensure correct method is used to read secure settings (#31753) [ML] Rate limit established model memory updates (#31768) SQL: Update CLI logo
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There is at most one model size stats document per bucket, but
during lookback a job can churn through many buckets very quickly.
This can lead to many cluster state updates if established model
memory needs to be updated for a given model size stats document.
This change rate limits established model memory updates to one
per job per 5 seconds. This is done by scheduling the updates 5
seconds in the future, but replacing the value to be written if
another model size stats document is received during the waiting
period. Updating the values in arrears like this means that the
last value received will be the one associated with the job in the
long term, whereas alternative approaches such as not updating the
value if a new value was close to the old value would not.