[DOCS] Update ML getting started docs with server-metrics sample data (elastic/x-pack-elasticsearch#1166)

* [DOCS] Add role info to ML getting started docs

* [DOCS] Getting started with sample ML data

* [DOCS] Getting started with server-metrics sample data

Original commit: elastic/x-pack-elasticsearch@2f268f87b4

docs/en/ml/getting-started.asciidoc

@@ -1,7 +1,6 @@
 [[ml-getting-started]]
 == Getting Started
 
-TBD.
 ////
 {xpack} {ml} features automatically detect:
 * Anomalies in single or multiple time series
@@ -10,29 +9,39 @@ TBD.
 
 This tutorial is focuses on an anomaly detection scenario in single time series.
 ////
+Ready to get some hands-on experience with the {xpack} {ml} features? This
+tutorial shows you how to:
+
+* Load a sample data set into Elasticsearch
+* Create a {ml} job
+* Use the results to identify possible anomalies in the data
+
+{nbsp}
+
+At the end of this tutorial, you should have a good idea of what {ml} is and
+will hopefully be inspired to use it to detect anomalies in your own data.
+
+You might also be interested in these video tutorials:
+
+* Getting started with machine learning (single metric)
+* Getting started with machine learning (multiple metric)
 
-In this tutorial, you will explore the {xpack} {ml} features by using sample
-data. You will create two simple jobs and use the results to identify possible
-anomalies in the data. You can also optionally create an alert. At the end of
-this tutorial, you should have a good idea of what {ml} is and will hopefully
-be inspired to use it to detect anomalies in your own data.
 
 [float]
 [[ml-gs-sysoverview]]
 === System Overview
 
-TBD.
-
 To follow the steps in this tutorial, you will need the following
 components of the Elastic Stack:
 
 * Elasticsearch {version}, which stores the data and the analysis results
 * {xpack} {version}, which provides the {ml} features
-* Kibana {version}, which provides a helpful user interface for creating
-and viewing jobs
+* Kibana {version}, which provides a helpful user interface for creating and
+viewing jobs +
+
 
 See the https://www.elastic.co/support/matrix[Elastic Support Matrix] for
-information about supported operating systems and product compatibility.
+information about supported operating systems.
 
 See {stack-ref}/installing-elastic-stack.html[Installing the Elastic Stack] for
 information about installing each of the components.
@@ -47,15 +56,26 @@ optionally dedicate nodes to specific purposes. If you want to control which
 nodes are _machine learning nodes_ or limit which nodes run resource-intensive
 activity related to jobs, see <<ml-settings>>.
 
-NOTE: This tutorial uses Kibana to create jobs and view results, but you can
-alternatively use APIs to accomplish most tasks.
-For API reference information, see <<ml-apis>>.
+[float]
+[[ml-gs-users]]
+==== Users, Roles, and Privileges
+
+The {xpack} {ml} features implement cluster privileges and built-in roles to
+make it easier to control which users have authority to view and manage the jobs,
+data feeds, and results.
+
+By default, you can perform all of the steps in this tutorial by using the
+built-in `elastic` user. If you are performing these steps in a production
+environment, take extra care because that user has the `superuser` role and you
+could inadvertently make significant changes to the system. You can
+alternatively assign the `machine_learning_admin` and `kibana_user` roles to a
+user ID of your choice.
+
+For more information, see <<built-in-roles>> and <<privileges-list-cluster>>.
 
 [[ml-gs-data]]
 === Identifying Data for Analysis
 
-TBD.
-
 For the purposes of this tutorial, we provide sample data that you can play with.
 When you consider your own data, however, it's important to take a moment
 and consider where the {xpack} {ml} features will be most impactful.
@@ -69,11 +89,10 @@ very efficient and occur in real-time.
 The second consideration, especially when you are first learning to use {ml},
 is the importance of the data and how familiar you are with it. Ideally, it is
 information that contains key performance indicators (KPIs) for the health or
-success of your business or system. It is information for which you want alarms
-to ring when anomalous behavior occurs. You might even have Kibana dashboards
-that you're already using to watch this data. The better you know the data,
-the quicker you will be able to create jobs that generate useful insights from
-{ml}.
+success of your business or system. It is information that you need to act on
+when anomalous behavior occurs. You might even have Kibana dashboards that
+you're already using to watch this data. The better you know the data,
+the quicker you will be able to create {ml} jobs that generate useful insights.
 
 //TBD: Talk about layering additional jobs?
 ////
@@ -102,84 +121,413 @@ future more-advanced scenario.)
 
 The final consideration is where the data is located. If the data that you want
 to analyze is stored in Elasticsearch, you can define a _data feed_ that
-provides data to the job in real time. By having both the input data and the
-analytical results in Elasticsearch, you get performance benefits? (TBD)
-The alternative to data feeds is to upload batches of data to the job by
-using the <<ml-post-data,post data API>>.
+provides data to the job in real time. When you have both the input data and the
+analytical results in Elasticsearch, this data gravity provides performance
+benefits.
+
+IMPORTANT: If you want to create {ml} jobs in Kibana, you must use data feeds.
+That is to say, you must store your input data in Elasticsearch. When you create
+a job, you select an existing index pattern and Kibana configures the data feed
+for you under the covers.
+
+If your data is not stored in Elasticsearch, you can create jobs by using
+the <<ml-put-job,create job API>> and upload batches of data to the job by
+using the <<ml-post-data,post data API>>. That scenario is not covered in
+this tutorial, however.
+
 //TBD: The data must be provided in JSON format?
 
 [float]
 [[ml-gs-sampledata]]
-==== Obtaining a sample dataset
+==== Obtaining a Sample Data Set
 
-TBD.
+The sample data for this tutorial contains information about the requests that
+are received by various applications and services in a system. A system
+administrator might use this type of information to track the the total
+number of requests across all of the infrastructure. If the number of requests
+increases or decreases unexpectedly, for example, this might be an indication
+that there is a problem or that resources need to be redistributed. By using
+the {xpack} {ml} features to model the behavior of this data, it is easier to
+identify anomalies and take appropriate action.
 
-* Provide instructions for downloading the sample data from https://github.com/elastic/examples
-* Provide overview/context of the sample data
+* TBD: Provide instructions for downloading the sample data after it's made
+available publicly on https://github.com/elastic/examples
+//Download this data set by clicking here:
+//See  https://download.elastic.co/demos/kibana/gettingstarted/shakespeare.json[shakespeare.json].
+
+////
+Use the following commands to extract the files:
+
+[source,shell]
+gzip -d transactions.ndjson.gz
+////
+Each document in the server-metrics data set has the following schema:
+
+[source,json]
+----------------------------------
+
+{
+  "index":
+  {
+    "_index":"server-metrics",
+    "_type":"metric",
+    "_id":"AVuQL1eekrHQ5a9V5qre"
+  }
+}
+{
+  "deny":1783,
+  "service":"app_0",
+  "@timestamp":"2017-03-26T06:47:28.684926",
+  "accept":24465,
+  "host":"server_1",
+  "total":26248,
+  "response":1.8242486553275024
+}
+----------------------------------
+
+Before you load the data set, you need to set up {ref}/mapping.html[_mappings_]
+for the fields. Mappings divide the documents in the index into logical groups
+and specify a field's characteristics, such as the field's searchability or
+whether or not it's _tokenized_, or broken up into separate words.
+
+The sample data includes an `upload_server-metrics.sh` script, which you can use
+to create the mappings and load the data set. The script runs a command similar
+to the following example, which sets up a mapping for the data set:
+
+[source,shell]
+----------------------------------
+
+curl -u elastic:elasticpassword -X PUT -H 'Content-Type: application/json'
+http://localhost:9200/server-metrics -d '{
+  "settings": {
+    "number_of_shards": 1,
+    "number_of_replicas": 0
+  },
+  "mappings": {
+      "metric": {
+        "properties": {
+          "@timestamp": {
+            "type": "date"
+          },
+          "accept": {
+            "type": "long"
+          },
+          "deny": {
+            "type": "long"
+          },
+          "host": {
+            "type": "text",
+            "fields": {
+              "keyword": {
+                "type": "keyword",
+                "ignore_above": 256
+              }
+            }
+          },
+          "response": {
+            "type": "float"
+          },
+          "service": {
+            "type": "text",
+            "fields": {
+              "keyword": {
+                "type": "keyword",
+                "ignore_above": 256
+              }
+            }
+          },
+          "total": {
+            "type": "long"
+          }
+        }
+      }
+    }
+  }
+}'
+----------------------------------
+
+NOTE: If you run this command, you must replace `elasticpassword` with your
+actual password. Likewise, if you use the `upload_server-metrics.sh` script,
+you must edit the USERNAME and PASSWORD variables before you run it.
+
+////
+This mapping specifies the following qualities for the data set:
+
+* The _@timestamp_ field is a date.
+//that uses the ISO format `epoch_second`,
+//which is the number of seconds since the epoch.
+* The _accept_, _deny_, and _total_ fields are long numbers.
+* The _host
+////
+
+You can then use the Elasticsearch `bulk` API to load the data set. The
+`upload_server-metrics.sh` script runs commands similar to the following
+example, which loads the four JSON files:
+
+[source,shell]
+----------------------------------
+
+curl -u elastic:elasticpassword -X POST -H "Content-Type: application/json"
+http://localhost:9200/server-metrics/_bulk --data-binary "@server-metrics_1.json"
+
+curl -u elastic:elasticpassword -X POST -H "Content-Type: application/json"
+http://localhost:9200/server-metrics/_bulk --data-binary "@server-metrics_2.json"
+
+curl -u elastic:elasticpassword -X POST -H "Content-Type: application/json"
+http://localhost:9200/server-metrics/_bulk --data-binary "@server-metrics_3.json"
+
+curl -u elastic:elasticpassword -X POST -H "Content-Type: application/json"
+http://localhost:9200/server-metrics/_bulk --data-binary "@server-metrics_4.json"
+----------------------------------
+
+These commands might take some time to run, depending on the computing resources
+available.
+
+You can verify that the data was loaded successfully with the following command:
+
+[source,shell]
+----------------------------------
+
+curl 'http://localhost:9200/_cat/indices?v' -u elastic:elasticpassword
+----------------------------------
+
+You should see output similar to the following:
+
+[source,shell]
+----------------------------------
+
+health status index ... pri rep docs.count  docs.deleted  store.size ...
+green  open   server-metrics ... 1 0 907200  0 134.9mb ...
+----------------------------------
+
+Next, you must define an index pattern for this data set:
+
+. Open Kibana in your web browser and log in. If you are running Kibana
+locally, go to `http://localhost:5601/`.
+
+. Click the **Management** tab, then **Index Patterns**.
+
+. Click the plus sign (+) to define a new index pattern.
+
+. For this tutorial, any pattern that matches the name of the index you've
+loaded will work. For example, enter `server-metrics*` as the index pattern.
+
+. Verify that the **Index contains time-based events** is checked.
+
+. Select the `@timestamp` field from the **Time-field name** list.
+
+. Click **Create**.
+
+This data set can now be analyzed in {ml} jobs in Kibana.
+//Content based on https://www.elastic.co/guide/en/kibana/current/tutorial-load-dataset.html
 
 [[ml-gs-jobs]]
-=== Working with Jobs
-
-TBD.
+=== Creating Jobs
 
 Machine learning jobs contain the configuration information and metadata
 necessary to perform an analytical task. They also contain the results of the
-analytical task. Each job ID must be unique in your cluster.
+analytical task.
+
+NOTE: This tutorial uses Kibana to create jobs and view results, but you can
+alternatively use APIs to accomplish most tasks.
+For API reference information, see <<ml-apis>>.
 
 To work with jobs in Kibana:
 
 . Open Kibana in your web browser and log in. If you are running Kibana
-locally, go to `http://localhost:5601/`. To use the {ml} features,
-you must log in as a user who has the `kibana_user`
-and `monitor_ml` roles (TBD).
+locally, go to `http://localhost:5601/`.
 
 . Click **Machine Learning** in the side navigation:
-image::images/ml.jpg["Job Management"]
+image::images/ml-kibana.jpg["Job Management"]
 
-You can choose to create single-metric, multi-metric, or advanced jobs in Kibana.
+You can choose to create single metric, multi-metric, or advanced jobs in
+Kibana. In this tutorial, the goal is to detect anomalies in the total requests
+received by your applications and services. The sample data contains a single
+key performance indicator to track this, which is the total requests over time.
+It is therefore logical to start by creating a single metric job for this KPI.
 
 [float]
 [[ml-gs-job1-create]]
 ==== Creating a Single Metric Job
 
-TBD.
+A single metric job contains a single _detector_. A detector defines the type of
+analysis that will occur (for example, `max`, `average`, or `rare` analytical
+functions) and the fields that will be analyzed.
 
-* Walk through creation of a simple single metric job.
-* Provide overview of:
-** aggregations
-** detectors (define which fields to analyze)
-*** The detectors define what type of analysis needs to be done
-(e.g. max, average, rare) and upon which fields (e.g. IP address, Host name, Num bytes).
-** bucket spans (define time intervals to analyze across)
-*** Take into account the granularity at which you want to analyze,
-the frequency of the input data, and the frequency at which alerting is required.
-*** When we analyze data, we use the concept of a bucket to divide up a continuous
-stream of data into batches for processing. For example, if you were monitoring the
-average response time of a system and received a data point every 10 minutes,
-using a bucket span of 1 hour means that at the end of each hour we would calculate
-the average (mean) value of the data for the last hour and compute the
-anomalousness of that average value compared to previous hours.
-*** The bucket span has two purposes: it dictates over what time span to look for
-anomalous features in data, and also determines how quickly anomalies can be detected.
-Choosing a shorter bucket span allows anomalies to be detected more quickly but
-at the risk of being too sensitive to natural variations or noise in the input data.
-Choosing too long a bucket span however can mean that interesting anomalies are averaged away.
-** analysis functions
-*** Some of the analytical functions look for single anomalous data points, e.g. max,
-which identifies the maximum value seen within a bucket.
-Others perform some aggregation over the length of the bucket, e.g. mean,
-which calculates the mean of all the data points seen within the bucket,
-or count, which calculates the total number of data points within the bucket.
-There is the possibility that the aggregation might smooth out some anomalies
-based on when the bucket starts in time.
-**** To avoid this, you can use overlapping buckets (how/where?).
-We analyze the data points in two buckets simultaneously, one starting half a bucket
-span later than the other. Overlapping buckets are only beneficial for
-aggregating functions, and should not be used for non-aggregating functions.
+To create a single metric job in Kibana:
+
+. Click **Machine Learning** in the side navigation,
+then click **Create new job**.
+
+. Click **Create single metric job**.
+image::images/ml-create-jobs.jpg["Create a new job"]
+
+. Click the `server-metrics` index. +
++
+--
+image::images/ml-gs-index.jpg["Select an index"]
+--
+
+. Configure the job by providing the following information:
+image::images/ml-gs-single-job.jpg["Create a new job from the server-metrics index"]
+
+.. For the **Aggregation**, select `Sum`. This value specifies the analysis
+function that is used.
++
+--
+Some of the analytical functions look for single anomalous data points. For
+example, `max` identifies the maximum value that is seen within a bucket.
+Others perform some aggregation over the length of the bucket. For example,
+`mean` calculates the mean of all the data points seen within the bucket.
+Similarly, `count` calculates the total number of data points within the bucket.
+In this tutorial, you are using the `sum` function, which calculates the sum of
+the specified field's values within the bucket.
+--
+
+.. For the **Field**, select `total`. This value specifies the field that
+the detector uses in the function.
++
+--
+NOTE: Some functions such as `count` and `rare` do not require fields.
+--
+
+.. For the **Bucket span**, enter `600s`. This value specifies the size of the
+interval that the analysis is aggregated into.
++
+--
+The {xpack} {ml} features use the concept of a bucket to divide up a continuous
+stream of data into batches for processing. For example, if you are monitoring
+the total number of requests in the system,
+//and receive a data point every 10 minutes
+using a bucket span of 1 hour would mean that at the end of each hour, it
+calculates the sum of the requests for the last hour and computes the
+anomalousness of that value compared to previous hours.
+
+The bucket span has two purposes: it dictates over what time span to look for
+anomalous features in data, and also determines how quickly anomalies can be
+detected. Choosing a shorter bucket span allows anomalies to be detected more
+quickly. However, there is a risk of being too sensitive to natural variations
+or noise in the input data. Choosing too long a bucket span can mean that
+interesting anomalies are averaged away. There is also the possibility that the
+aggregation might smooth out some anomalies based on when the bucket starts
+in time.
+
+The bucket span has a significant impact on the analysis. When you're trying to
+determine what value to use, take into account the granularity at which you
+want to perform the analysis, the frequency of the input data, and the frequency
+at which alerting is required.
+//TBD: Talk about overlapping buckets? "To avoid this, you can use overlapping
+//buckets (how/where?). We analyze the data points in two buckets simultaneously,
+//one starting half a bucket span later than the other. Overlapping buckets are
+//only beneficial for aggregating functions, and should not be used for
+//non-aggregating functions.
+--
+
+. Click **Use full transaction_counts data**.
++
+--
+A graph is generated, which represents the total number of requests over time.
+//TBD: What happens if you click the play button instead?
+--
+
+. Provide a name for the job, for example `total-requests`. The job name must
+be unique in your cluster. You can also optionally provide a description of the
+job.
+
+. Click **Create Job**.
+image::images/ml-gs-job1.jpg["A graph of the total number of requests over time"]
+
+As the job is created, the graph is updated to give a visual representation of
+the {ml} that occurs as the data is processed.
+//To explore the results, click **View Results**.
+//TBD: image::images/ml-gs-job1-results.jpg["The total-requests job is created"]
+
+[[ml-gs-job1-managa]]
+=== Managing Jobs
+
+After you create a job, you can see its status in the **Job Management** tab:
+image::images/ml-gs-job1-manage.jpg["Status information for the total-requests job"]
+
+The following information is provided for each job:
+
+Job ID::
+The unique identifier for the job.
+
+Description::
+The optional description of the job.
+
+Processed records::
+The number of records that have been processed by the job.
++
+--
+NOTE: Depending on how you send data to the job, the number of processed
+records is not always equal to the number of input records. For more information,
+see the `processed_record_count` description in <<ml-datacounts,Data Counts Objects>>.
+
+--
+
+Memory status::
+The status of the mathematical models. When you create jobs by using the APIs or
+by using the advanced options in Kibana, you can specify a `model_memory_limit`.
+That value is the maximum amount of memory, in MiB, that the mathematical models
+can use. Once that limit is approached, data pruning becomes more aggressive.
+Upon exceeding that limit, new entities are not modeled.
+The default value is `4096`. The memory status field reflects whether you have
+reached or exceeded the model memory limit. It can have one of the following
+values: +
+`ok`::: The models stayed below the configured value.
+`soft_limit`::: The models used more than 60% of the configured memory limit
+and older unused models will be pruned to free up space.
+`hard_limit`::: The models used more space than the configured memory limit.
+As a result, not all incoming data was processed.
+
+Job state::
+The status of the job, which can be one of the following values: +
+`open`::: The job is available to receive and process data.
+`closed`::: The job finished successfully with its model state persisted.
+The job must be opened before it can accept further data.
+`closing`::: The job close action is in progress and has not yet completed.
+A closing job cannot accept further data.
+`failed`::: The job did not finish successfully due to an error.
+This situation can occur due to invalid input data.
+If the job had irrevocably failed, it must be force closed and then deleted.
+If the data feed can be corrected, the job can be closed and then re-opened.
+
+Datafeed state::
+The status of the data feed, which can be one of the following values: +
+started::: The data feed is actively receiving data.
+stopped::: The data feed is stopped and will not receive data until it is re-started.
+//TBD: How to restart data feeds in Kibana?
+
+Latest timestamp::
+The timestamp of the last processed record.
+//TBD: Is that right?
+
+If you click the arrow beside the name of job, you can show or hide additional
+information, such as the settings, configuration information, or messages for
+the job.
+
+You can also click one of the **Actions** buttons to start the data feed, edit
+the job or data feed, and clone or delete the job, for example.
+
+* TBD: Demonstrate how to re-open the data feed and add additional data
+
+
+[[ml-gs-jobresults]]
+=== Exploring Job Results
+
+After you create a job, you can use the **Anomaly Explorer** or the
+**Single Metric Viewer** in Kibana to view the analysis results.
+
+Anomaly Explorer::
+TBD
+
+Single Metric Viewer::
+TBD
 
 [float]
 [[ml-gs-job1-analyze]]
-===== Viewing Single Metric Job Results
+==== Exploring Single Metric Job Results
 
 TBD.
 
@@ -213,6 +561,21 @@ view the detailed anomaly records which are the significant causal factors.
 * Provide general overview of management of jobs (when/why to start or
   stop them).
 
+Integrate the following images:
+
+. Single Metric Viewer: All
+image::images/ml-gs-job1-analysis.jpg["Single Metric Viewer for total-requests job"]
+
+. Single Metric Viewer: Anomalies
+image::images/ml-gs-job1-anomalies.jpg["Single Metric Viewer Anomalies for total-requests job"]
+
+. Anomaly Explorer: All
+image::images/ml-gs-job1-explorer.jpg["Anomaly Explorer for total-requests job"]
+
+. Anomaly Explorer: Selected a red area from the heatmap
+image::images/ml-gs-job1-explorer-anomaly.jpg["Anomaly Explorer details for total-requests job"]
+
+////
 [float]
 [[ml-gs-job2-create]]
 ==== Creating a Multi-Metric Job
@@ -259,11 +622,3 @@ TBD.
 * Walk through creation of simple alert for anomalous data?
 
 ////
-To start exploring anomalies in your data:
-
-. Open Kibana in your web browser and log in. If you are running Kibana
-locally, go to `http://localhost:5601/`.
-
-. Click **ML** in the side navigation ...
-////
-//image::images/graph-open.jpg["Accessing Graph"]

docs/en/ml/index.asciidoc

@@ -16,7 +16,7 @@ easily answer these types of questions.
 
 include::introduction.asciidoc[]
 include::getting-started.asciidoc[]
-include::ml-scenarios.asciidoc[]
+// include::ml-scenarios.asciidoc[]
 include::api-quickref.asciidoc[]
 
 //include::troubleshooting.asciidoc[]  Referenced from x-pack/docs/public/xpack-troubleshooting.asciidoc