From 4669a823cc79b7b97da7d3def014c312e9b377fe Mon Sep 17 00:00:00 2001 From: Lisa Cawley Date: Fri, 28 Apr 2017 10:24:10 -0700 Subject: [PATCH] [DOCS] Added ML sample data URLs (elastic/x-pack-elasticsearch#1256) Original commit: elastic/x-pack-elasticsearch@528a32f26f7f74de8e63aab3c2a6667c5c30e372 --- docs/en/ml/getting-started.asciidoc | 30 +++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/docs/en/ml/getting-started.asciidoc b/docs/en/ml/getting-started.asciidoc index 98d415564ca..97530dc85f5 100644 --- a/docs/en/ml/getting-started.asciidoc +++ b/docs/en/ml/getting-started.asciidoc @@ -122,22 +122,21 @@ In this step we will upload some sample data to {es}. This is standard 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. +administrator might use this type of information to track 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. -Download this sample data from: https://github.com/elastic/examples -//Download this data set by clicking here: -//See https://download.elastic.co/demos/kibana/gettingstarted/shakespeare.json[shakespeare.json]. +Download this sample data by clicking here: +https://download.elastic.co/demos/machine_learning/gettingstarted/server_metrics.tar.gz[server_metrics.tar.gz] Use the following commands to extract the files: [source,shell] ---------------------------------- -tar xvf server_metrics.tar.gz +tar -zxvf server_metrics.tar.gz ---------------------------------- Each document in the server-metrics data set has the following schema: @@ -183,9 +182,10 @@ 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. Before you run it, however, you -must edit the USERNAME and PASSWORD variables with your actual user ID and -password. +to create the mappings and load the data set. You can download it by clicking +here: https://download.elastic.co/demos/machine_learning/gettingstarted/upload_server-metrics.sh[upload_server-metrics.sh] +Before you run it, however, you must edit the USERNAME and PASSWORD variables +with your actual user ID and password. The script runs a command similar to the following example, which sets up a mapping for the data set: @@ -456,7 +456,9 @@ the progress of {ml} as the data is processed. This view is only available whils job is running. TIP: The `create_single_metic.sh` script creates a similar job and data feed by -using the {ml} APIs. For API reference information, see <>. +using the {ml} APIs. You can download that script by clicking +here: https://download.elastic.co/demos/machine_learning/gettingstarted/create_single_metric.sh[create_single_metric.sh] +For API reference information, see <>. [[ml-gs-job1-manage]] === Managing Jobs