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[DOCS] Update ML tutorial for when security is not enabled
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‎docs/en/stack/ml/getting-started-data.asciidoc

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@@ -96,60 +96,59 @@ for the fields. Mappings divide the documents in the index into logical groups
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and specify a field's characteristics, such as the field's searchability or
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whether or not it's _tokenized_, or broken up into separate words.
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The sample data includes an `upload_server-metrics.sh` script, which you can use
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to create the mappings and load the data set. You can download it by clicking
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here: https://download.elastic.co/demos/machine_learning/gettingstarted/upload_server-metrics.sh[upload_server-metrics.sh]
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You can use scripts to create the mappings and load the data set. Download https://download.elastic.co/demos/machine_learning/gettingstarted/upload_server_metrics.sh[upload_server_metrics.sh] to the directory where you extracted the sample data.
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Before you run it, however, you must edit the USERNAME and PASSWORD variables
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with your actual user ID and password.
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with your actual user ID and password. If {security} is not enabled, use the
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https://download.elastic.co/demos/machine_learning/gettingstarted/upload_server_metrics_noauth.sh[upload_server_metrics_noauth.sh]
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script instead.
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The script runs a command similar to the following example, which sets up a
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mapping for the data set:
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The script runs a `curl` command that makes the following create index API
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request:
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[source,sh]
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----------------------------------
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curl -u elastic:x-pack-test-password -X PUT -H 'Content-Type: application/json'
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http://localhost:9200/server-metrics -d '{
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"settings":{
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"number_of_shards":1,
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"number_of_replicas":0
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},
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"mappings":{
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"metric":{
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"properties":{
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"@timestamp":{
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"type":"date"
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},
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"accept":{
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"type":"long"
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},
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"deny":{
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"type":"long"
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},
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"host":{
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"type":"keyword"
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},
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"response":{
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"type":"float"
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},
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"service":{
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"type":"keyword"
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},
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"total":{
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"type":"long"
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}
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}
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PUT server-metrics
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{
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"settings": {
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"number_of_shards":1,
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"number_of_replicas":0
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},
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"mappings": {
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"metric": {
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"properties":{
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"@timestamp": {
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"type":"date"
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},
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"accept": {
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"type":"long"
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},
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"deny": {
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"type":"long"
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},
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"host": {
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"type":"keyword"
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},
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"response": {
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"type":"float"
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},
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"service": {
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"type":"keyword"
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},
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"total": {
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"type":"long"
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}
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}
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}
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}'
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}
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}
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}
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----------------------------------
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// NOTCONSOLE
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// CONSOLE
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NOTE: If you run this command, you must replace `x-pack-test-password` with your
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actual password.
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To learn more about mappings and data types, see {ref}/mapping.html[Mapping].
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You can then use the {es} `bulk` API to load the data set. The
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`upload_server-metrics.sh` script runs commands similar to the following
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example, which loads the four JSON files:
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You can then use the bulk API to load the sample data set. The
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`upload_server_metrics.sh` script runs commands similar to the following example,
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which loads the four JSON files:
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[source,sh]
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----------------------------------
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----------------------------------
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// NOTCONSOLE
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TIP: This will upload 200MB of data. This is split into 4 files as there is a
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maximum 100MB limit when using the `_bulk` API.
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NOTE: If you want to try running these commands, you must specify a valid user
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ID and password in the `-u` parameter. If {security} is not enabled, you can
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omit the `-u` parameter.
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These commands might take some time to run, depending on the computing resources
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available.
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These commands upload 200MB of data and might take some time to run, depending
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on the computing resources available.
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You can verify that the data was loaded successfully with the following command:
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You can verify that the data was loaded successfully by running the cat indices
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API:
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[source,sh]
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----------------------------------
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curl 'http://localhost:9200/_cat/indices?v' -u elastic:x-pack-test-password
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GET _cat/indices?v
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----------------------------------
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// NOTCONSOLE
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// CONSOLE
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You should see output similar to the following:
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Next, you must define an index pattern for this data set:
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. Open {kib} in your web browser and log in. If you are running {kib}
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. Open {kib} in your web browser. If you are running {kib}
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locally, go to `http://localhost:5601/`.
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. Click the **Management** tab, then **{kib}** > **Index Patterns**.

‎docs/en/stack/ml/getting-started-multi.asciidoc

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To create a multi-metric job in {kib}:
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. Open {kib} in your web browser and log in. If you are running {kib} locally,
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. Open {kib} in your web browser. If you are running {kib} locally,
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go to `http://localhost:5601/`.
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. Click **Machine Learning** in the side navigation, then click **Create new job**.
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overwhelming and there is a small overhead to the analysis.
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========================
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//TBD: Is this something you can determine later from looking at results and
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//update your job with if necessary? Is it all post-processing or does it affect
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//the ongoing modeling?
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--
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. Click **Use full server-metrics* data**. Two graphs are generated for each
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real-time, and create a watch. In this tutorial, we will proceed to view the
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results.
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TIP: The `create_multi_metic.sh` script creates a similar job and {dfeed} by
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TIP: The `create_multi_metric.sh` script creates a similar job and {dfeed} by
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using the {ml} APIs. You can download that script by clicking
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here: https://download.elastic.co/demos/machine_learning/gettingstarted/create_multi_metric.sh[create_multi_metric.sh]
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For API reference information, see {ref}/ml-apis.html[Machine Learning APIs].
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here: https://download.elastic.co/demos/machine_learning/gettingstarted/create_multi_metric.sh[create_multi_metric.sh] Before you run it, you must edit the USERNAME and PASSWORD variables
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with your actual user ID and password. If {security} is not enabled, use the
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https://download.elastic.co/demos/machine_learning/gettingstarted/create_multi_metric_noauth.sh[create_multi_metric_noauth.sh]
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script instead. For API reference information, see {ref}/ml-apis.html[Machine Learning APIs].
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[[ml-gs-job2-analyze]]
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=== Exploring Multi-metric Job Results

‎docs/en/stack/ml/getting-started-single.asciidoc

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To create a single metric job in {kib}:
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. Open {kib} in your web browser and log in. If you are running {kib} locally,
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. Open {kib} in your web browser. If you are running {kib} locally,
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go to `http://localhost:5601/`.
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. Click **Machine Learning** in the side navigation.
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in real-time, and create a watch. In this tutorial, we will look at how to
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manage jobs and {dfeeds} before we view the results.
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TIP: The `create_single_metic.sh` script creates a similar job and {dfeed} by
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TIP: The `create_single_metric.sh` script creates a similar job and {dfeed} by
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using the {ml} APIs. You can download that script by clicking
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here: https://download.elastic.co/demos/machine_learning/gettingstarted/create_single_metric.sh[create_single_metric.sh]
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For API reference information, see {ref}/ml-apis.html[Machine Learning APIs].
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here: https://download.elastic.co/demos/machine_learning/gettingstarted/create_single_metric.sh[create_single_metric.sh] Before you run it, you must edit the USERNAME and PASSWORD variables
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with your actual user ID and password. If {security} is not enabled, use the
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https://download.elastic.co/demos/machine_learning/gettingstarted/create_single_metric_noauth.sh[create_single_metric_noauth.sh]
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script instead. For API reference information, see {ref}/ml-apis.html[Machine Learning APIs].
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[[ml-gs-job1-manage]]
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=== Managing Jobs

‎docs/en/stack/ml/getting-started-wizards.asciidoc

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To see the job creation wizards:
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. Open {kib} in your web browser and log in. If you are running {kib} locally,
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. Open {kib} in your web browser. If you are running {kib} locally,
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go to `http://localhost:5601/`.
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. Click **Machine Learning** in the side navigation.

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