254 lines
7.4 KiB
Plaintext
254 lines
7.4 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[ml-get-snapshot]]
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= Get model snapshots API
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++++
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<titleabbrev>Get model snapshots</titleabbrev>
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++++
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Retrieves information about model snapshots.
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[[ml-get-snapshot-request]]
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== {api-request-title}
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`GET _ml/anomaly_detectors/<job_id>/model_snapshots` +
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`GET _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>`
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[[ml-get-snapshot-prereqs]]
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== {api-prereq-title}
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* If the {es} {security-features} are enabled, you must have `monitor_ml`,
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`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. See
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<<security-privileges>> and {ml-docs-setup-privileges}.
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[[ml-get-snapshot-path-parms]]
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== {api-path-parms-title}
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`<job_id>`::
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(Required, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`<snapshot_id>`::
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(Optional, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=snapshot-id]
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+
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--
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You can multiple snapshots for a single job in a single API request
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by using a comma-separated list of `<snapshot_id>` or a wildcard expression.
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You can get all snapshots for all calendars by using `_all`,
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by specifying `*` as the `<snapshot_id>`, or by omitting the `<snapshot_id>`.
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--
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[[ml-get-snapshot-request-body]]
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== {api-request-body-title}
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`desc`::
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(Optional, Boolean) If true, the results are sorted in descending order.
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`end`::
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(Optional, date) Returns snapshots with timestamps earlier than this time.
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`from`::
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(Optional, integer) Skips the specified number of snapshots.
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`size`::
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(Optional, integer) Specifies the maximum number of snapshots to obtain.
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`sort`::
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(Optional, string) Specifies the sort field for the requested snapshots. By
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default, the snapshots are sorted by their timestamp.
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`start`::
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(Optional, string) Returns snapshots with timestamps after this time.
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[role="child_attributes"]
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[[ml-get-snapshot-results]]
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== {api-response-body-title}
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The API returns an array of model snapshot objects, which have the following
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properties:
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`description`::
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(string) An optional description of the job.
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`job_id`::
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(string) A numerical character string that uniquely identifies the job that
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the snapshot was created for.
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`latest_record_time_stamp`::
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(date) The timestamp of the latest processed record.
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`latest_result_time_stamp`::
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(date) The timestamp of the latest bucket result.
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`min_version`::
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(string) The minimum version required to be able to restore the model snapshot.
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//Begin model_size_stats
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`model_size_stats`::
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(object) Summary information describing the model.
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+
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.Properties of `model_size_stats`
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[%collapsible%open]
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====
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`bucket_allocation_failures_count`:::
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(long) The number of buckets for which entities were not processed due to memory
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limit constraints.
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`categorized_doc_count`:::
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(long) The number of documents that have had a field categorized.
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`categorization_status`:::
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(string) The status of categorization for this job.
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Contains one of the following values.
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+
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--
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* `ok`: Categorization is performing acceptably well (or not being
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used at all).
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* `warn`: Categorization is detecting a distribution of categories
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that suggests the input data is inappropriate for categorization.
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Problems could be that there is only one category, more than 90% of
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categories are rare, the number of categories is greater than 50% of
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the number of categorized documents, there are no frequently
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matched categories, or more than 50% of categories are dead.
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--
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`dead_category_count`:::
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(long) The number of categories created by categorization that will
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never be assigned again because another category's definition
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makes it a superset of the dead category. (Dead categories are a
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side effect of the way categorization has no prior training.)
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`failed_category_count`:::
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(long)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=failed-category-count]
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`frequent_category_count`:::
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(long) The number of categories that match more than 1% of categorized
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documents.
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`job_id`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`log_time`:::
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(date) The timestamp that the `model_size_stats` were recorded, according to
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server-time.
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`memory_status`:::
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(string) The status of the memory in relation to its `model_memory_limit`.
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Contains one of the following values.
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+
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--
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* `hard_limit`: The internal models require more space that the configured
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memory limit. Some incoming data could not be processed.
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* `ok`: The internal models stayed below the configured value.
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* `soft_limit`: The internal models require more than 60% of the configured
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memory limit and more aggressive pruning will be performed in order to try to
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reclaim space.
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--
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`model_bytes`:::
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(long) An approximation of the memory resources required for this analysis.
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`model_bytes_exceeded`:::
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(long) The number of bytes over the high limit for memory usage at the last allocation failure.
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`model_bytes_memory_limit`:::
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(long) The upper limit for memory usage, checked on increasing values.
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`rare_category_count`:::
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(long) The number of categories that match just one categorized document.
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`result_type`:::
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(string) Internal. This value is always `model_size_stats`.
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`timestamp`:::
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(date) The timestamp that the `model_size_stats` were recorded, according to the
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bucket timestamp of the data.
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`total_by_field_count`:::
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(long) The number of _by_ field values analyzed. Note that these are counted
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separately for each detector and partition.
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`total_category_count`:::
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(long) The number of categories created by categorization.
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`total_over_field_count`:::
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(long) The number of _over_ field values analyzed. Note that these are counted
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separately for each detector and partition.
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`total_partition_field_count`:::
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(long) The number of _partition_ field values analyzed.
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====
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//End model_size_stats
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`retain`::
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(Boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=retain]
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`snapshot_id`::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=snapshot-id]
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`snapshot_doc_count`::
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(long) For internal use only.
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`timestamp`::
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(date) The creation timestamp for the snapshot.
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[[ml-get-snapshot-example]]
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== {api-examples-title}
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[source,console]
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--------------------------------------------------
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GET _ml/anomaly_detectors/high_sum_total_sales/model_snapshots
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{
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"start": "1575402236000"
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}
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--------------------------------------------------
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// TEST[skip:Kibana sample data]
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In this example, the API provides a single result:
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[source,js]
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----
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{
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"count" : 1,
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"model_snapshots" : [
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{
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"job_id" : "high_sum_total_sales",
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"min_version" : "6.4.0",
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"timestamp" : 1575402237000,
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"description" : "State persisted due to job close at 2019-12-03T19:43:57+0000",
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"snapshot_id" : "1575402237",
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"snapshot_doc_count" : 1,
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"model_size_stats" : {
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"job_id" : "high_sum_total_sales",
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"result_type" : "model_size_stats",
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"model_bytes" : 1638816,
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"model_bytes_exceeded" : 0,
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"model_bytes_memory_limit" : 10485760,
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"total_by_field_count" : 3,
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"total_over_field_count" : 3320,
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"total_partition_field_count" : 2,
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"bucket_allocation_failures_count" : 0,
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"memory_status" : "ok",
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"categorized_doc_count" : 0,
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"total_category_count" : 0,
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"frequent_category_count" : 0,
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"rare_category_count" : 0,
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"dead_category_count" : 0,
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"categorization_status" : "ok",
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"log_time" : 1575402237000,
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"timestamp" : 1576965600000
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},
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"latest_record_time_stamp" : 1576971072000,
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"latest_result_time_stamp" : 1576965600000,
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"retain" : false
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}
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]
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}
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----
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