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@@ -5,38 +5,49 @@
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Delayed data are documents that are indexed late. That is to say, it is data
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related to a time that the {dfeed} has already processed.
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-When you create a datafeed, you can specify a {ref}/ml-datafeed-resource.html[`query_delay`] setting.
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-This setting enables the datafeed to wait for some time past real-time, which means any "late" data in this period
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-is fully indexed before the datafeed tries to gather it. However, if the setting is set too low, the datafeed may query
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-for data before it has been indexed and consequently miss that document. Conversely, if it is set too high,
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-analysis drifts farther away from real-time. The balance that is struck depends upon each use case and
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-the environmental factors of the cluster.
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+When you create a datafeed, you can specify a
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+{ref}/ml-datafeed-resource.html[`query_delay`] setting. This setting enables the
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+datafeed to wait for some time past real-time, which means any "late" data in
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+this period is fully indexed before the datafeed tries to gather it. However, if
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+the setting is set too low, the datafeed may query for data before it has been
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+indexed and consequently miss that document. Conversely, if it is set too high,
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+analysis drifts farther away from real-time. The balance that is struck depends
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+upon each use case and the environmental factors of the cluster.
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==== Why worry about delayed data?
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-This is a particularly prescient question. If data are delayed randomly (and consequently missing from analysis),
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-the results of certain types of functions are not really affected. It all comes out ok in the end
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-as the delayed data is distributed randomly. An example would be a `mean` metric for a field in a large collection of data.
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-In this case, checking for delayed data may not provide much benefit. If data are consistently delayed, however, jobs with a `low_count` function may
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-provide false positives. In this situation, it would be useful to see if data
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-comes in after an anomaly is recorded so that you can determine a next course of action.
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+This is a particularly prescient question. If data are delayed randomly (and
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+consequently are missing from analysis), the results of certain types of
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+functions are not really affected. In these situations, it all comes out okay in
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+the end as the delayed data is distributed randomly. An example would be a `mean`
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+metric for a field in a large collection of data. In this case, checking for
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+delayed data may not provide much benefit. If data are consistently delayed,
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+however, jobs with a `low_count` function may provide false positives. In this
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+situation, it would be useful to see if data comes in after an anomaly is
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+recorded so that you can determine a next course of action.
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==== How do we detect delayed data?
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In addition to the `query_delay` field, there is a
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-{ref}/ml-datafeed-resource.html#ml-datafeed-delayed-data-check-config[delayed data check config], which enables you to
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-configure the datafeed to look in the past for delayed data. Every 15 minutes or every `check_window`,
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-whichever is smaller, the datafeed triggers a document search over the configured indices. This search looks over a
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-time span with a length of `check_window` ending with the latest finalized bucket. That time span is partitioned into buckets,
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-whose length equals the bucket span of the associated job. The `doc_count` of those buckets are then compared with the
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-job's finalized analysis buckets to see whether any data has arrived since the analysis. If there is indeed missing data
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-due to their ingest delay, the end user is notified.
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+{ref}/ml-datafeed-resource.html#ml-datafeed-delayed-data-check-config[delayed data check config],
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+which enables you to configure the datafeed to look in the past for delayed data.
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+Every 15 minutes or every `check_window`, whichever is smaller, the datafeed
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+triggers a document search over the configured indices. This search looks over a
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+time span with a length of `check_window` ending with the latest finalized bucket.
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+That time span is partitioned into buckets, whose length equals the bucket span
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+of the associated job. The `doc_count` of those buckets are then compared with
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+the job's finalized analysis buckets to see whether any data has arrived since
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+the analysis. If there is indeed missing data due to their ingest delay, the end
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+user is notified. For example, you can see annotations in {kib} for the periods
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+where these delays occur.
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==== What to do about delayed data?
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-The most common course of action is to simply to do nothing. For many functions and situations ignoring the data is
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-acceptable. However, if the amount of delayed data is too great or the situation calls for it, the next course
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-of action to consider is to increase the `query_delay` of the datafeed. This increased delay allows more time for data to be
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-indexed. If you have real-time constraints, however, an increased delay might not be desirable.
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-In which case, you would have to {ref}/tune-for-indexing-speed.html[tune for better indexing speed.]
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+The most common course of action is to simply to do nothing. For many functions
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+and situations, ignoring the data is acceptable. However, if the amount of
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+delayed data is too great or the situation calls for it, the next course of
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+action to consider is to increase the `query_delay` of the datafeed. This
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+increased delay allows more time for data to be indexed. If you have real-time
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+constraints, however, an increased delay might not be desirable. In which case,
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+you would have to {ref}/tune-for-indexing-speed.html[tune for better indexing speed].
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