261 lines
7.6 KiB
Plaintext
261 lines
7.6 KiB
Plaintext
[role="xpack"]
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[testenv="basic"]
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[[ecommerce-dataframes]]
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=== Transforming the eCommerce sample data
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beta[]
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<<ml-dataframes,{transforms-cap}>> enable you to retrieve information
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from an {es} index, transform it, and store it in another index. Let's use the
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{kibana-ref}/add-sample-data.html[{kib} sample data] to demonstrate how you can
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pivot and summarize your data with {transforms}.
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. If the {es} {security-features} are enabled, obtain a user ID with sufficient
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privileges to complete these steps.
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+
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--
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You need `manage_data_frame_transforms` cluster privileges to preview and create
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{transforms}. Members of the built-in `data_frame_transforms_admin`
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role have these privileges.
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You also need `read` and `view_index_metadata` index privileges on the source
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index and `read`, `create_index`, and `index` privileges on the destination
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index.
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For more information, see <<security-privileges>> and <<built-in-roles>>.
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--
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. Choose your _source index_.
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+
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--
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In this example, we'll use the eCommerce orders sample data. If you're not
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already familiar with the `kibana_sample_data_ecommerce` index, use the
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*Revenue* dashboard in {kib} to explore the data. Consider what insights you
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might want to derive from this eCommerce data.
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--
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. Play with various options for grouping and aggregating the data.
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+
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--
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_Pivoting_ your data involves using at least one field to group it and applying
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at least one aggregation. You can preview what the transformed data will look
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like, so go ahead and play with it!
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For example, you might want to group the data by product ID and calculate the
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total number of sales for each product and its average price. Alternatively, you
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might want to look at the behavior of individual customers and calculate how
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much each customer spent in total and how many different categories of products
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they purchased. Or you might want to take the currencies or geographies into
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consideration. What are the most interesting ways you can transform and
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interpret this data?
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Go to *Machine Learning* > *Data Frames* in {kib} and use the
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wizard to create a {transform}:
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[role="screenshot"]
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image::images/ecommerce-pivot1.jpg["Creating a simple {transform} in {kib}"]
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In this case, we grouped the data by customer ID and calculated the sum of
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products each customer purchased.
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Let's add some more aggregations to learn more about our customers' orders. For
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example, let's calculate the total sum of their purchases, the maximum number of
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products that they purchased in a single order, and their total number of orders.
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We'll accomplish this by using the
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{ref}/search-aggregations-metrics-sum-aggregation.html[`sum` aggregation] on the
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`taxless_total_price` field, the
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{ref}/search-aggregations-metrics-max-aggregation.html[`max` aggregation] on the
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`total_quantity` field, and the
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{ref}/search-aggregations-metrics-cardinality-aggregation.html[`cardinality` aggregation]
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on the `order_id` field:
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[role="screenshot"]
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image::images/ecommerce-pivot2.jpg["Adding multiple aggregations to a {transform} in {kib}"]
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TIP: If you're interested in a subset of the data, you can optionally include a
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{ref}/search-request-body.html#request-body-search-query[query] element. In this
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example, we've filtered the data so that we're only looking at orders with a
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`currency` of `EUR`. Alternatively, we could group the data by that field too.
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If you want to use more complex queries, you can create your {dataframe} from a
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{kibana-ref}/save-open-search.html[saved search].
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If you prefer, you can use the
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{ref}/preview-data-frame-transform.html[preview {transforms} API]:
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[source,console]
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--------------------------------------------------
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POST _data_frame/transforms/_preview
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{
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"source": {
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"index": "kibana_sample_data_ecommerce",
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"query": {
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"bool": {
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"filter": {
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"term": {"currency": "EUR"}
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}
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}
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}
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},
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"pivot": {
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"group_by": {
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"customer_id": {
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"terms": {
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"field": "customer_id"
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}
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}
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},
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"aggregations": {
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"total_quantity.sum": {
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"sum": {
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"field": "total_quantity"
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}
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},
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"taxless_total_price.sum": {
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"sum": {
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"field": "taxless_total_price"
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}
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},
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"total_quantity.max": {
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"max": {
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"field": "total_quantity"
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}
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},
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"order_id.cardinality": {
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"cardinality": {
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"field": "order_id"
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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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// TEST[skip:set up sample data]
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--
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. When you are satisfied with what you see in the preview, create the
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{transform}.
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+
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--
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.. Supply a job ID and the name of the target (or _destination_) index. If the
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target index does not exist, it will be created automatically.
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.. Decide whether you want the {transform} to run once or continuously.
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--
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+
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--
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Since this sample data index is unchanging, let's use the default behavior and
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just run the {transform} once.
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[role="screenshot"]
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image::images/ecommerce-batch.jpg["Specifying the {transform} options in {kib}"]
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If you want to try it out, however, go ahead and click on *Continuous mode*.
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You must choose a field that the {transform} can use to check which
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entities have changed. In general, it's a good idea to use the ingest timestamp
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field. In this example, however, you can use the `order_date` field.
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If you prefer, you can use the
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{ref}/put-data-frame-transform.html[create {transforms} API]. For
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example:
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[source,console]
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--------------------------------------------------
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PUT _data_frame/transforms/ecommerce-customer-transform
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{
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"source": {
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"index": [
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"kibana_sample_data_ecommerce"
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],
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"query": {
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"bool": {
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"filter": {
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"term": {
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"currency": "EUR"
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}
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}
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}
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}
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},
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"pivot": {
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"group_by": {
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"customer_id": {
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"terms": {
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"field": "customer_id"
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}
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}
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},
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"aggregations": {
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"total_quantity.sum": {
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"sum": {
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"field": "total_quantity"
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}
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},
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"taxless_total_price.sum": {
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"sum": {
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"field": "taxless_total_price"
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}
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},
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"total_quantity.max": {
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"max": {
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"field": "total_quantity"
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}
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},
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"order_id.cardinality": {
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"cardinality": {
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"field": "order_id"
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}
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}
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}
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},
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"dest": {
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"index": "ecommerce-customers"
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}
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}
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--------------------------------------------------
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// TEST[skip:setup kibana sample data]
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--
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. Start the {transform}.
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+
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--
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TIP: Even though resource utilization is automatically adjusted based on the
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cluster load, a {transform} increases search and indexing load on your
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cluster while it runs. If you're experiencing an excessive load, however, you
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can stop it.
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You can start, stop, and manage {transforms} in {kib}:
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[role="screenshot"]
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image::images/dataframe-transforms.jpg["Managing {transforms} in {kib}"]
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Alternatively, you can use the
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{ref}/start-data-frame-transform.html[start {transforms}] and
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{ref}/stop-data-frame-transform.html[stop {transforms}] APIs. For
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example:
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[source,console]
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--------------------------------------------------
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POST _data_frame/transforms/ecommerce-customer-transform/_start
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--------------------------------------------------
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// TEST[skip:setup kibana sample data]
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--
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. Explore the data in your new index.
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+
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--
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For example, use the *Discover* application in {kib}:
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[role="screenshot"]
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image::images/ecommerce-results.jpg["Exploring the new index in {kib}"]
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--
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TIP: If you do not want to keep the {transform}, you can delete it in
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{kib} or use the
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{ref}/delete-data-frame-transform.html[delete {transform} API]. When
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you delete a {transform}, its destination index and {kib} index
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patterns remain.
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