[role="xpack"]
[testenv="basic"]
[[transform-examples]]
=== {transform-cap} examples
++++
<titleabbrev>Examples</titleabbrev>
++++
These examples demonstrate how to use {transforms} to derive useful
insights from your data. All the examples use one of the
{kibana-ref}/add-sample-data.html[{kib} sample datasets]. For a more detailed,
step-by-step example, see
<<ecommerce-transforms>>.
* <<example-best-customers>>
* <<example-airline>>
* <<example-clientips>>
[[example-best-customers]]
==== Finding your best customers
In this example, we use the eCommerce orders sample dataset to find the
customers who spent the most in our hypothetical webshop. Let's transform the
data such that the destination index contains the number of orders, the total
price of the orders, the amount of unique products and the average price per
order, and the total amount of ordered products for each customer.
[source,console]
----------------------------------
POST _transform/_preview
{
"source": {
"index": "kibana_sample_data_ecommerce"
},
"dest" : { <1>
"index" : "sample_ecommerce_orders_by_customer"
},
"pivot": {
"group_by": { <2>
"user": { "terms": { "field": "user" }},
"customer_id": { "terms": { "field": "customer_id" }}
},
"aggregations": {
"order_count": { "value_count": { "field": "order_id" }},
"total_order_amt": { "sum": { "field": "taxful_total_price" }},
"avg_amt_per_order": { "avg": { "field": "taxful_total_price" }},
"avg_unique_products_per_order": { "avg": { "field": "total_unique_products" }},
"total_unique_products": { "cardinality": { "field": "products.product_id" }}
}
}
}
----------------------------------
// TEST[skip:setup kibana sample data]
<1> This is the destination index for the {transform}. It is ignored by
`_preview`.
<2> Two `group_by` fields have been selected. This means the {transform} will
contain a unique row per `user` and `customer_id` combination. Within this
dataset both these fields are unique. By including both in the {transform} it
gives more context to the final results.
NOTE: In the example above, condensed JSON formatting has been used for easier
readability of the pivot object.
The preview {transforms} API enables you to see the layout of the
{transform} in advance, populated with some sample values. For example:
[source,js]
----------------------------------
{
"preview" : [
{
"total_order_amt" : 3946.9765625,
"order_count" : 59.0,
"total_unique_products" : 116.0,
"avg_unique_products_per_order" : 2.0,
"customer_id" : "10",
"user" : "recip",
"avg_amt_per_order" : 66.89790783898304
},
...
]
}
----------------------------------
// NOTCONSOLE
This {transform} makes it easier to answer questions such as:
* Which customers spend the most?
* Which customers spend the most per order?
* Which customers order most often?
* Which customers ordered the least number of different products?
It's possible to answer these questions using aggregations alone, however
{transforms} allow us to persist this data as a customer centric index. This
enables us to analyze data at scale and gives more flexibility to explore and
navigate data from a customer centric perspective. In some cases, it can even
make creating visualizations much simpler.
[[example-airline]]
==== Finding air carriers with the most delays
In this example, we use the Flights sample dataset to find out which air carrier
had the most delays. First, we filter the source data such that it excludes all
the cancelled flights by using a query filter. Then we transform the data to
contain the distinct number of flights, the sum of delayed minutes, and the sum
of the flight minutes by air carrier. Finally, we use a
{ref}/search-aggregations-pipeline-bucket-script-aggregation.html[`bucket_script`]
to determine what percentage of the flight time was actually delay.
[source,console]
----------------------------------
POST _transform/_preview
{
"source": {
"index": "kibana_sample_data_flights",
"query": { <1>
"bool": {
"filter": [
{ "term": { "Cancelled": false } }
]
}
}
},
"dest" : { <2>
"index" : "sample_flight_delays_by_carrier"
},
"pivot": {
"group_by": { <3>
"carrier": { "terms": { "field": "Carrier" }}
},
"aggregations": {
"flights_count": { "value_count": { "field": "FlightNum" }},
"delay_mins_total": { "sum": { "field": "FlightDelayMin" }},
"flight_mins_total": { "sum": { "field": "FlightTimeMin" }},
"delay_time_percentage": { <4>
"bucket_script": {
"buckets_path": {
"delay_time": "delay_mins_total.value",
"flight_time": "flight_mins_total.value"
},
"script": "(params.delay_time / params.flight_time) * 100"
}
}
}
}
}
----------------------------------
// TEST[skip:setup kibana sample data]
<1> Filter the source data to select only flights that were not cancelled.
<2> This is the destination index for the {transform}. It is ignored by
`_preview`.
<3> The data is grouped by the `Carrier` field which contains the airline name.
<4> This `bucket_script` performs calculations on the results that are returned
by the aggregation. In this particular example, it calculates what percentage of
travel time was taken up by delays.
The preview shows you that the new index would contain data like this for each
carrier:
[source,js]
----------------------------------
{
"preview" : [
{
"carrier" : "ES-Air",
"flights_count" : 2802.0,
"flight_mins_total" : 1436927.5130677223,
"delay_time_percentage" : 9.335543983955839,
"delay_mins_total" : 134145.0
},
...
]
}
----------------------------------
// NOTCONSOLE
This {transform} makes it easier to answer questions such as:
* Which air carrier has the most delays as a percentage of flight time?
NOTE: This data is fictional and does not reflect actual delays
or flight stats for any of the featured destination or origin airports.
[[example-clientips]]
==== Finding suspicious client IPs by using scripted metrics
With {transforms}, you can use
{ref}/search-aggregations-metrics-scripted-metric-aggregation.html[scripted
metric aggregations] on your data. These aggregations are flexible and make
it possible to perform very complex processing. Let's use scripted metrics to
identify suspicious client IPs in the web log sample dataset.
We transform the data such that the new index contains the sum of bytes and the
number of distinct URLs, agents, incoming requests by location, and geographic
destinations for each client IP. We also use a scripted field to count the
specific types of HTTP responses that each client IP receives. Ultimately, the
example below transforms web log data into an entity centric index where the
entity is `clientip`.
[source,console]
----------------------------------
PUT _transform/suspicious_client_ips
{
"source": {
"index": "kibana_sample_data_logs"
},
"dest" : { <1>
"index" : "sample_weblogs_by_clientip"
},
"sync" : { <2>
"time": {
"field": "timestamp",
"delay": "60s"
}
},
"pivot": {
"group_by": { <3>
"clientip": { "terms": { "field": "clientip" } }
},
"aggregations": {
"url_dc": { "cardinality": { "field": "url.keyword" }},
"bytes_sum": { "sum": { "field": "bytes" }},
"geo.src_dc": { "cardinality": { "field": "geo.src" }},
"agent_dc": { "cardinality": { "field": "agent.keyword" }},
"geo.dest_dc": { "cardinality": { "field": "geo.dest" }},
"responses.total": { "value_count": { "field": "timestamp" }},
"responses.counts": { <4>
"scripted_metric": {
"init_script": "state.responses = ['error':0L,'success':0L,'other':0L]",
"map_script": """
def code = doc['response.keyword'].value;
if (code.startsWith('5') || code.startsWith('4')) {
state.responses.error += 1 ;
} else if(code.startsWith('2')) {
state.responses.success += 1;
} else {
state.responses.other += 1;
}
""",
"combine_script": "state.responses",
"reduce_script": """
def counts = ['error': 0L, 'success': 0L, 'other': 0L];
for (responses in states) {
counts.error += responses['error'];
counts.success += responses['success'];
counts.other += responses['other'];
}
return counts;
"""
}
},
"timestamp.min": { "min": { "field": "timestamp" }},
"timestamp.max": { "max": { "field": "timestamp" }},
"timestamp.duration_ms": { <5>
"bucket_script": {
"buckets_path": {
"min_time": "timestamp.min.value",
"max_time": "timestamp.max.value"
},
"script": "(params.max_time - params.min_time)"
}
}
}
}
}
----------------------------------
// TEST[skip:setup kibana sample data]
<1> This is the destination index for the {transform}.
<2> Configures the {transform} to run continuously. It uses the `timestamp` field
to synchronize the source and destination indices. The worst case
ingestion delay is 60 seconds.
<3> The data is grouped by the `clientip` field.
<4> This `scripted_metric` performs a distributed operation on the web log data
to count specific types of HTTP responses (error, success, and other).
<5> This `bucket_script` calculates the duration of the `clientip` access based
on the results of the aggregation.
After you create the {transform}, you must start it:
[source,console]
----------------------------------
POST _transform/suspicious_client_ips/_start
----------------------------------
// TEST[skip:setup kibana sample data]
Shortly thereafter, the first results should be available in the destination
index:
[source,console]
----------------------------------
GET sample_weblogs_by_clientip/_search
----------------------------------
// TEST[skip:setup kibana sample data]
The search result shows you data like this for each client IP:
[source,js]
----------------------------------
"hits" : [
{
"_index" : "sample_weblogs_by_clientip",
"_id" : "MOeHH_cUL5urmartKj-b5UQAAAAAAAAA",
"_score" : 1.0,
"_source" : {
"geo" : {
"src_dc" : 2.0,
"dest_dc" : 2.0
},
"clientip" : "0.72.176.46",
"agent_dc" : 2.0,
"bytes_sum" : 4422.0,
"responses" : {
"total" : 2.0,
"counts" : {
"other" : 0,
"success" : 2,
"error" : 0
}
},
"url_dc" : 2.0,
"timestamp" : {
"duration_ms" : 5.2191698E8,
"min" : "2019-11-25T07:51:57.333Z",
"max" : "2019-12-01T08:50:34.313Z"
}
}
}
]
----------------------------------
// NOTCONSOLE
NOTE: Like other Kibana sample data sets, the web log sample dataset contains
timestamps relative to when you installed it, including timestamps in the future.
The {ctransform} will pick up the data points once they are in the past. If you
installed the web log sample dataset some time ago, you can uninstall and
reinstall it and the timestamps will change.
This {transform} makes it easier to answer questions such as:
* Which client IPs are transferring the most amounts of data?
* Which client IPs are interacting with a high number of different URLs?
* Which client IPs have high error rates?
* Which client IPs are interacting with a high number of destination countries?