[[getting-started]]
== Getting Started with Kibana

Now that you have Kibana <<setup,installed>>, you can step through this tutorial to get fast hands-on experience with 
key Kibana functionality. By the end of this tutorial, you will have:

* Loaded a sample data set into your Elasticsearch installation
* Defined at least one index pattern
* Used the <<discover, Discover>> functionality to explore your data
* Set up some <<visualize,_visualizations_>> to graphically represent your data
* Assembled visualizations into a <<dashboard,Dashboard>>

The material in this section assumes you have a working Kibana install connected to a working Elasticsearch install.

[float]
[[tutorial-load-dataset]]
=== Before You Start: Loading Sample Data

The tutorials in this section rely on the following data sets:

* The complete works of William Shakespeare, suitably parsed into fields. Download this data set by clicking here: 
https://www.elastic.co/guide/en/kibana/3.0/snippets/shakespeare.json[shakespeare.json].
* A set of fictitious accounts with randomly generated data. Download this data set by clicking here: 
https://github.com/bly2k/files/blob/master/accounts.zip?raw=true[accounts.json]
* A set of randomly generated log files. Download this data set by clicking here: [logstash.json.gz]

Two of the data sets are compressed. Use the following commands to extract the files:

[source,shell]
unzip accounts.zip
gunzip logstash.json.gz

The Shakespeare data set is organized in the following schema:

[source,json]
{
    "line_id": INT,
    "play_name": "String",
    "speech_number": INT,
    "line_number": "String",
    "speaker": "String",
    "text_entry": "String",
}

The accounts data set is organized in the following schema:

[source,json]
{
    "account_number": INT,
    "balance": INT,
    "firstname": "String",
    "lastname": "String",
    "age": INT,
    "gender": "M or F",
    "address": "String",
    "employer": "String",
    "email": "String",
    "city": "String",
    "state": "String"
}

The schema for the logs data set has 114 different fields, but the notable ones used in this tutorial are:

[source,json]
{
    "memory": INT,
    "geo.coordinates": "geo_point"
    "@timestamp": "date"
}

Before we load the Shakespeare data set, we need to set up a {ref}/mapping.html[_mapping_] for the fields. Mapping 
divides the documents in the index into logical groups and specifies a field's characteristics, such as the field's
searchability or whether or not it's _tokenized_, or broken up into separate words.

Use the following command to set up a mapping for the Shakespeare data set:

[source,shell]
curl -XPUT http://localhost:9200/shakespeare -d '
{
 "mappings" : {
  "_default_" : {
   "properties" : {
    "speaker" : {"type": "string", "index" : "not_analyzed" },
    "play_name" : {"type": "string", "index" : "not_analyzed" },
    "line_id" : { "type" : "integer" },
    "speech_number" : { "type" : "integer" }
   }
  }
 }
}
';

This mapping specifies the following qualities for the data set:

* The _speaker_ field is a string that isn't analyzed. The string in this field is treated as a single unit, even if
there are multiple words in the field.
* The same applies to the _play_name_ field.
* The line_id and speech_number fields are integers.

The accounts and logstash data sets don't require any mappings, so at this point we're ready to load the data sets into 
Elasticsearch with the following commands:

[source,shell]
curl -XPOST 'localhost:9200/bank/_bulk?pretty' --data-binary @accounts.json
curl -XPOST 'localhost:9200/shakespeare/_bulk?pretty' --data-binary @shakespeare.json
curl -XPOST 'localhost:9200/_bulk?pretty' --data-binary @logstash.json

These commands may take some time to execute, depending on the computing resources available.

Verify successful loading with the following command:

[source,shell]
curl 'localhost:9200/_cat/indices?v'

You should see output similar to the following:

[source,shell]
health status index               pri rep docs.count docs.deleted store.size pri.store.size
yellow open   bank                  5   1       1000            0    418.2kb        418.2kb
yellow open   shakespeare           5   1     111396            0     17.6mb         17.6mb
yellow open   logstash-2015.05.18   5   1       4631            0     15.6mb         15.6mb
yellow open   logstash-2015.05.19   5   1       4624            0     15.7mb         15.7mb
yellow open   logstash-2015.05.20   5   1       4750            0     16.4mb         16.4mb

[[tutorial-define-index]]
=== Defining Your Index Patterns

Each set of data loaded to Elasticsearch has an https://www.elastic.co/guide/en/kibana/current/settings.html#settings-create-pattern[index pattern]. In the previous section, the Shakespeare data set has an index named `shakespeare`, and the accounts 
data set has an index named `bank`. An _index pattern_ is a string with optional wildcards that can match multiple 
indices. For example, in the common logging use case, a typical index name contains the date in MM-DD-YYYY 
format, and an index pattern for May would look something like `logstash-2015.05*`.

For this tutorial, any pattern that matches either of the two indices we've loaded will work. Open a browser and 
navigate to `localhost:5601`. Click the *Settings* tab, then the *Indices* tab. Click *Add New* to define a new index 
pattern. Since these data sets don't contain time-series data, make sure the *Index contains time-based events* box is 
unchecked. Specify `shakes*`  as the index pattern for the Shakespeare data set and click *Create* to define the index 
pattern, then define a second index pattern named `ba*`.

[float]
[[tutorial-discovering]]
=== Discovering Your Data

Click the *Discover* tab to display Kibana's data discovery functions:

image::images/tutorial-discover.png[]

Right under the tab itself, there is a search box where you can search your data. Searches take a specific 
{ref}/query-dsl-query-string-query.html#query-string-syntax[query syntax] that enable you to create custom searches, 
which you can save and load by clicking the buttons to the right of the search box.

Beneath the search box, the current index pattern is displayed in a drop-down. You can change the index pattern by 
selecting a different pattern from the drop-down selector.

You can construct searches by using the field names and the values you're interested in. With numeric fields you can 
use comparison operators such as greater than (>), less than (<), or equals (=). You can link elements with the
logical operators AND, OR, and NOT, all in uppercase.

Try selecting the `ba*` index pattern and putting the following search into the search box:

[source,text]
account_number:<100 AND balance:>47500

This search returns all account numbers between zero and 99 with balances in excess of 47,500.

If you're using the linked sample data set, this search returns 5 results: Account numbers 8, 32, 78, 85, and 97.

image::images/tutorial-discover-2.png[]

To narrow the display to only the specific fields of interest, highlight each field in the list that displays under the 
index pattern and click the *Add* button. Note how, in this example, adding the `account_number` field changes the 
display from the full text of five records to a simple list of five account numbers:

image::images/tutorial-discover-3.png[]

[[tutorial-visualizing]]
=== Data Visualization: Beyond Discovery

The visualization tools available on the *Visualize* tab enable you to display aspects of your data sets in several 
different ways. 

Click on the *Visualize* tab to start:

image::images/tutorial-visualize.png[]

Click on *Pie chart*, then *From a new search*. Select the `ba*` index pattern. 

Visualizations depend on Elasticsearch {ref}/search-aggregations.html[aggregations] in two different types: _bucket_ 
aggregations and _metric_ aggregations. A bucket aggregation sorts your data according to criteria you specify. For 
example, in our accounts data set, we can establish a range of account balances, then display what proportions of the 
total fall into which range of balances.

The whole pie displays, since we  haven't specified any buckets yet.

image::images/tutorial-visualize-pie-1.png[]

Select *Split Slices* from the *Select buckets type* list, then select *Range* from the *Aggregation* drop-down 
selector. Select the *balance* field from the *Field* drop-down, then click on *Add Range* four times to bring the 
total number of ranges to six. Enter the following ranges:

[source,text]
0            1000
1000         3000
3000         7000
7000        15000
15000       31000
31000       50000

Click the green *Apply changes* to display the chart:

image::images/tutorial-visualize-pie-2.png[]

This shows you what proportion of the 1000 accounts fall in these balance ranges. To see another dimension of the data, 
we're going to add another bucket aggregation. We can break down each of the balance ranges further by the account 
holder's age.

Click *Add sub-buckets* at the bottom, then select the *Terms* aggregation and the *age* field from the drop-downs. 
Click the green *Apply changes* button to add an external ring with the new results.

image::images/tutorial-visualize-pie-3.png[]

Save this chart by clicking the *Save Visualization* button to the right of the search field. Name the visualization
_Pie Example_.

Next, we're going to make a bar chart. Click on *New Visualization*, then *Vertical bar chart*. Select *From a new 
search* and the `shakes*` index pattern. You'll see a single big bar, since we haven't defined any buckets yet:

image::images/tutorial-visualize-bar-1.png[]

For the Y-axis metrics aggregation, select *Unique Count*, with *speaker* as the field. For Shakespeare plays, it might 
be useful to know which plays have the lowest number of distinct speaking parts, if your theater company is short on 
actors. For the X-Axis buckets, select the *Terms* aggregation with the *play_name* field. For the *Order*, select
*Bottom*, leaving the *Size* at 5.

Leave the other elements at their default values and click the green *Apply changes* button. Your chart should now look 
like this:

image::images/tutorial-visualize-bar-2.png[]

Notice how the individual play names show up as whole phrases, instead of being broken down into individual words. This 
is the result of the mapping we did at the beginning of the tutorial, when we marked the *play_name* field as 'not 
analyzed'.

Hovering on each bar shows you the number of speaking parts for each play as a tooltip. You can turn this behavior off, 
as well as change many other options for your visualizations, by clicking the *Options* tab in the top left.

Now that you have a list of the smallest casts for Shakespeare plays, you might also be curious to see which of these 
plays makes the greatest demands on an individual actor by showing the maximum number of speeches for a given part. Add 
a Y-axis aggregation with the *Add metrics* button, then choose the *Max* aggregation for the *speech_number* field. In 
the *Options* tab, change the *Bar Mode* drop-down to *grouped*, then click the green *Apply changes* button. Your 
chart should now look like this:

image::images/tutorial-visualize-bar-3.png[]

As you can see, _Love's Labours Lost_ has an unusually high maximum speech number, compared to the other plays, and 
might therefore make more demands on an actor's memory.

Save this chart with the name _Bar Example_.

Next, we're going to make a tile map chart to visualize some geographic data. Click on *New Visualization*, then 
*Tile map*. Select *From a new search* and the `logstash-*` index pattern. Define the time window for the events we're 
exploring by clicking the time selector at the top right of the Kibana interface. Click on *Absolute*, then set the 
end time for the range to May 20, 2015 and the start time to May 18, 2015:

image::images/tutorial-timepicker.png[]

Once you've got the time range set up, click the *Go* button, then close the time picker by clicking the small up arrow 
at the bottom. You'll see a map of the world, since we haven't defined any buckets yet:

image::images/tutorial-visualize-map-1.png[]

Select *Geo Coordinates* as the bucket, then click the green *Apply changes* button. Your chart should now look like 
this:

image::images/tutorial-visualize-map-2.png[]

You can navigate the map by clicking and dragging, zoom with the image:images/viz-zoom.png[] buttons, or hit the *Fit 
Data Bounds* image:images/viz-fit-bounds.png[] button to zoom to the lowest level that includes all the points. You can 
also create a filter to define a rectangle on the map, either to include or exclude, by clicking the 
*Latitude/Longitude Filter* image:images/viz-lat-long-filter.png[] button and drawing a bounding box on the map. 
A green oval with the filter definition displays right under the query box:

image::images/tutorial-visualize-map-3.png[]

Hover on the filter to display the controls to toggle, pin, invert, or delete the filter. Save this chart with the name 
_Bar Example_.

Finally, we're going to define a sample Markdown widget to display on our dashboard. Click on *New Visualization*, then 
*Markdown widget*, to display a very simple Markdown entry field:

image::images/tutorial-visualize-md-1.png[]

Write the following text in the field:

[source,markdown]
# This is a tutorial dashboard! 
The Markdown widget uses **markdown** syntax.
> Blockquotes in Markdown use the > character.

Click the green *Apply changes* button to display the rendered Markdown in the preview pane:

image::images/tutorial-visualize-md-2.png[]

Save this visualization with the name _Markdown Example_.

[[tutorial-dashboard]]
=== Putting it all Together with Dashboards

A Kibana dashboard is a collection of visualizations that you can arrange and share. To get started, click the 
*Dashboard* tab, then the *Add Visualization* button at the far right of the search box to display the list of saved 
visualizations. Select _Markdown Example_, _Pie Example_, _Bar Example_, and _Map Example_, then close the list of 
visualizations by clicking the small up-arrow at the bottom of the list. You can move the containers for each 
visualization by clicking and dragging the title bar. Resize the containers by dragging the lower right corner of a 
visualization's container. Your sample dashboard should end up looking roughly like this:

image::images/tutorial-dashboard.png[]

Click the *Save Dashboard* button, then name the dashboard _Tutorial Dashboard_. You can share a saved dashboard by 
clicking the *Share* button to display HTML embedding code as well as a direct link.

[float]
[[wrapping-up]]
=== Wrapping Up

Now that you've handled the basic aspects of Kibana's functionality, you're ready to explore Kibana in further detail. 
Take a look at the rest of the documentation for more details!