elasticsearch/docs/reference/transform/ecommerce-tutorial.asciidoc

[role="xpack"]
[testenv="basic"]
[[ecommerce-transforms]]
= Tutorial: Transforming the eCommerce sample data

<<transforms,{transforms-cap}>> enable you to retrieve information
from an {es} index, transform it, and store it in another index. Let's use the
{kibana-ref}/add-sample-data.html[{kib} sample data] to demonstrate how you can
pivot and summarize your data with {transforms}.

. Verify that your environment is set up properly to use {transforms}. If the
{es} {security-features} are enabled, to complete this tutorial you need a user
that has authority to preview and create {transforms}. You must also have
specific index privileges for the source and destination indices. See
<<transform-setup>>.

. Choose your _source index_.
+
--
In this example, we'll use the eCommerce orders sample data. If you're not
already familiar with the `kibana_sample_data_ecommerce` index, use the
*Revenue* dashboard in {kib} to explore the data. Consider what insights you
might want to derive from this eCommerce data.
--

. Choose the `pivot` type of {transform} and play with various options for
grouping and aggregating the data.
+
--
There are two types of {transforms}, but first we'll try out _pivoting_ your
data, which involves using at least one field to group it and applying at least
one aggregation. You can preview what the transformed data will look
like, so go ahead and play with it! You can also enable histogram charts to get
a better understanding of the distribution of values in your data.

For example, you might want to group the data by product ID and calculate the
total number of sales for each product and its average price. Alternatively, you
might want to look at the behavior of individual customers and calculate how
much each customer spent in total and how many different categories of products
they purchased. Or you might want to take the currencies or geographies into
consideration. What are the most interesting ways you can transform and
interpret this data?

Go to *Management* > *Stack Management* > *Data* > *Transforms* in {kib} and use
the wizard to create a {transform}:

[role="screenshot"]
image::images/ecommerce-pivot1.png["Creating a simple {transform} in {kib}"]

Group the data by customer ID and add one or more aggregations to learn more
about each customer's orders. For example, let's calculate the sum of products
they purchased, the total price of their purchases, the maximum number of
products that they purchased in a single order, and their total number of orders. We'll accomplish this by using the
<<search-aggregations-metrics-sum-aggregation,`sum` aggregation>> on the
`total_quantity` and `taxless_total_price` fields, the
<<search-aggregations-metrics-max-aggregation,`max` aggregation>> on the
`total_quantity` field, and the
<<search-aggregations-metrics-cardinality-aggregation,`cardinality` aggregation>>
on the `order_id` field:

[role="screenshot"]
image::images/ecommerce-pivot2.png["Adding multiple aggregations to a {transform} in {kib}"]

TIP: If you're interested in a subset of the data, you can optionally include a
<<request-body-search-query,query>> element. In this
example, we've filtered the data so that we're only looking at orders with a
`currency` of `EUR`. Alternatively, we could group the data by that field too.
If you want to use more complex queries, you can create your {dataframe} from a
{kibana-ref}/save-open-search.html[saved search].

If you prefer, you can use the
<<preview-transform,preview {transforms} API>>.

.API example
[%collapsible]
====
[source,console]
--------------------------------------------------
POST _transform/_preview
{
  "source": {
    "index": "kibana_sample_data_ecommerce",
    "query": {
      "bool": {
        "filter": {
          "term": {"currency": "EUR"}
        }
      }
    }
  },
  "pivot": {
    "group_by": {
      "customer_id": {
        "terms": {
          "field": "customer_id"
        }
      }
    },
    "aggregations": {
      "total_quantity.sum": {
        "sum": {
          "field": "total_quantity"
        }
      },
      "taxless_total_price.sum": {
        "sum": {
          "field": "taxless_total_price"
        }
      },
      "total_quantity.max": {
        "max": {
          "field": "total_quantity"
        }
      },
      "order_id.cardinality": {
        "cardinality": {
          "field": "order_id"
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[skip:set up sample data]
====
--

. When you are satisfied with what you see in the preview, create the
{transform}.
+
--
.. Supply a {transform} ID, the name of the target (or _destination_) index and 
optionally a description. If the target index does not exist, it will be created 
automatically.

.. Decide whether you want the {transform} to run once or continuously. Since 
this sample data index is unchanging, let's use the default behavior and just 
run the {transform} once. If you want to try it out, however, go ahead and click 
on *Continuous mode*. You must choose a field that the {transform} can use to 
check which entities have changed. In general, it's a good idea to use the 
ingest timestamp field. In this example, however, you can use the `order_date` 
field.

.. Optionally, you can configure a retention policy that applies to your 
{transform}. Select a date field that is used to identify old documents 
in the destination index and provide a maximum age. Documents that are older 
than the configured value are removed from the destination index.

[role="screenshot"]
image::images/ecommerce-pivot3.png["Adding transfrom ID and retention policy to a {transform} in {kib}"]

If you prefer, you can use the
<<put-transform,create {transforms} API>>.

.API example
[%collapsible]
====
[source,console]
--------------------------------------------------
PUT _transform/ecommerce-customer-transform
{
  "source": {
    "index": [
      "kibana_sample_data_ecommerce"
    ],
    "query": {
      "bool": {
        "filter": {
          "term": {
            "currency": "EUR"
          }
        }
      }
    }
  },
  "pivot": {
    "group_by": {
      "customer_id": {
        "terms": {
          "field": "customer_id"
        }
      }
    },
    "aggregations": {
      "total_quantity.sum": {
        "sum": {
          "field": "total_quantity"
        }
      },
      "taxless_total_price.sum": {
        "sum": {
          "field": "taxless_total_price"
        }
      },
      "total_quantity.max": {
        "max": {
          "field": "total_quantity"
        }
      },
      "order_id.cardinality": {
        "cardinality": {
          "field": "order_id"
        }
      }
    }
  },
  "dest": {
    "index": "ecommerce-customers"
  },
  "retention_policy": {
    "time": {
      "field": "order_date",
      "max_age": "60d"
    }
  }  
}
--------------------------------------------------
// TEST[skip:setup kibana sample data]
====
--

. Start the {transform}.
+
--

TIP: Even though resource utilization is automatically adjusted based on the
cluster load, a {transform} increases search and indexing load on your
cluster while it runs. If you're experiencing an excessive load, however, you
can stop it.

You can start, stop, and manage {transforms} in {kib}:

[role="screenshot"]
image::images/manage-transforms.png["Managing {transforms} in {kib}"]

Alternatively, you can use the
<<start-transform,start {transforms}>> and
<<stop-transform,stop {transforms}>> APIs.

.API example
[%collapsible]
====
[source,console]
--------------------------------------------------
POST _transform/ecommerce-customer-transform/_start
--------------------------------------------------
// TEST[skip:setup kibana sample data]
====

TIP: If you chose a batch {transform}, it is a single operation that has a
single checkpoint. You cannot restart it when it's complete. {ctransforms-cap}
differ in that they continually increment and process checkpoints as new source
data is ingested.

--

. Explore the data in your new index.
+
--
For example, use the *Discover* application in {kib}:

[role="screenshot"]
image::images/ecommerce-results.png["Exploring the new index in {kib}"]

--

. Optional: Create another {transform}, this time using the `latest` method.
+
--

This method populates the destination index with the latest documents for each
unique key value. For example, you might want to find the latest orders (sorted
by the `order_date` field) for each customer or for each country and region.

[role="screenshot"]
image::images/ecommerce-latest1.png["Creating a latest {transform} in {kib}"]

.API example
[%collapsible]
====
[source,console]
--------------------------------------------------
POST _transform/_preview
{
  "source": {
    "index": "kibana_sample_data_ecommerce",
    "query": {
      "bool": {
        "filter": {
          "term": {"currency": "EUR"}
        }
      }
    }
  },
  "latest": {
    "unique_key": ["geoip.country_iso_code", "geoip.region_name"],
    "sort": "order_date"
  }
}
--------------------------------------------------
// TEST[skip:set up sample data]
====
--

. If you do not want to keep a {transform}, you can delete it in
{kib} or use the <<delete-transform,delete {transform} API>>. By default, when
you delete a {transform}, its destination index and {kib} index patterns remain.

Now that you've created simple {transforms} for {kib} sample data, consider
possible use cases for your own data. For more ideas, see
<<transform-usage>> and <<transform-examples>>.