This example uses the eCommerce orders sample data set to find the customers who spent the most in a hypothetical webshop. Let’s use the pivot type of transform 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.

Alternatively, you can use the preview transform and the create transform API.

POST _transform/_preview
{
  "source": {
    "index": "kibana_sample_data_ecommerce"
  },
  "dest" : { 
    "index" : "sample_ecommerce_orders_by_customer"
  },
  "pivot": {
    "group_by": { 
      "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" }}
    }
  }
}

{
  "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
    },
    ...
    ]
  }

This transform makes it easier to answer questions such as:

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.

This example uses the Flights sample data set to find out which air carrier had the most delays. First, filter the source data such that it excludes all the cancelled flights by using a query filter. Then 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, use a bucket_script to determine what percentage of the flight time was actually delay.

POST _transform/_preview
{
  "source": {
    "index": "kibana_sample_data_flights",
    "query": { 
      "bool": {
        "filter": [
          { "term":  { "Cancelled": false } }
        ]
      }
    }
  },
  "dest" : { 
    "index" : "sample_flight_delays_by_carrier"
  },
  "pivot": {
    "group_by": { 
      "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": { 
        "bucket_script": {
          "buckets_path": {
            "delay_time": "delay_mins_total.value",
            "flight_time": "flight_mins_total.value"
          },
          "script": "(params.delay_time / params.flight_time) * 100"
        }
      }
    }
  }
}

The preview shows you that the new index would contain data like this for each carrier:

{
  "preview" : [
    {
      "carrier" : "ES-Air",
      "flights_count" : 2802.0,
      "flight_mins_total" : 1436927.5130677223,
      "delay_time_percentage" : 9.335543983955839,
      "delay_mins_total" : 134145.0
    },
    ...
  ]
}

This transform makes it easier to answer questions such as:

This example uses the web log sample data set to identify suspicious client IPs. It transforms 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. It also uses filter aggregations 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.

PUT _transform/suspicious_client_ips
{
  "source": {
    "index": "kibana_sample_data_logs"
  },
  "dest" : { 
    "index" : "sample_weblogs_by_clientip"
  },
  "sync" : { 
    "time": {
      "field": "timestamp",
      "delay": "60s"
    }
  },
  "pivot": {
    "group_by": {  
      "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" }},
      "success" : { 
         "filter": {
            "term": { "response" : "200"}}
        },
      "error404" : {
         "filter": {
            "term": { "response" : "404"}}
        },
      "error5xx" : {
         "filter": {
            "range": { "response" : { "gte": 500, "lt": 600}}}
        },
      "timestamp.min": { "min": { "field": "timestamp" }},
      "timestamp.max": { "max": { "field": "timestamp" }},
      "timestamp.duration_ms": { 
        "bucket_script": {
          "buckets_path": {
            "min_time": "timestamp.min.value",
            "max_time": "timestamp.max.value"
          },
          "script": "(params.max_time - params.min_time)"
        }
      }
    }
  }
}

After you create the transform, you must start it:

POST _transform/suspicious_client_ips/_start

Shortly thereafter, the first results should be available in the destination index:

GET sample_weblogs_by_clientip/_search

The search result shows you data like this for each client IP:

    "hits" : [
      {
        "_index" : "sample_weblogs_by_clientip",
        "_id" : "MOeHH_cUL5urmartKj-b5UQAAAAAAAAA",
        "_score" : 1.0,
        "_source" : {
          "geo" : {
            "src_dc" : 2.0,
            "dest_dc" : 2.0
          },
          "success" : 2,
          "error404" : 0,
          "error503" : 0,
          "clientip" : "0.72.176.46",
          "agent_dc" : 2.0,
          "bytes_sum" : 4422.0,
          "responses" : {
            "total" : 2.0
          },
          "url_dc" : 2.0,
          "timestamp" : {
            "duration_ms" : 5.2191698E8,
            "min" : "2020-03-16T07:51:57.333Z",
            "max" : "2020-03-22T08:50:34.313Z"
          }
        }
      }
    ]

This transform makes it easier to answer questions such as:

This example uses the web log sample data set to find the last log from an IP address. Let’s use the latest type of transform in continuous mode. It copies the most recent document for each unique key from the source index to the destination index and updates the destination index as new data comes into the source index.

Pick the clientip field as the unique key; the data is grouped by this field. Select timestamp as the date field that sorts the data chronologically. For continuous mode, specify a date field that is used to identify new documents, and an interval between checks for changes in the source index.

Let’s assume that we’re interested in retaining documents only for IP addresses that appeared recently in the log. You can define a retention policy and specify a date field that is used to calculate the age of a document. This example uses the same date field that is used to sort the data. Then set the maximum age of a document; documents that are older than the value you set will be removed from the destination index.

This transform creates the destination index that contains the latest login date for each client IP. As the transform runs in continuous mode, the destination index will be updated as new data that comes into the source index. Finally, every document that is older than 30 days will be removed from the destination index due to the applied retention policy.

PUT _transform/last-log-from-clientip
{
  "source": {
    "index": [
      "kibana_sample_data_logs"
    ]
  },
  "latest": {
    "unique_key": [ 
      "clientip"
    ],
    "sort": "timestamp" 
  },
  "frequency": "1m", 
  "dest": {
    "index": "last-log-from-clientip"
  },
  "sync": { 
    "time": {
      "field": "timestamp",
      "delay": "60s"
    }
  },
  "retention_policy": { 
    "time": {
      "field": "timestamp",
      "max_age": "30d"
    }
  },
  "settings": {
    "max_page_search_size": 500
  }
}

POST _transform/last-log-from-clientip/_start

After the transform processes the data, search the destination index:

GET last-log-from-clientip/_search

The search result shows you data like this for each client IP:

{
  "_index" : "last-log-from-clientip",
  "_id" : "MOeHH_cUL5urmartKj-b5UQAAAAAAAAA",
  "_score" : 1.0,
  "_source" : {
    "referer" : "http://twitter.com/error/don-lind",
    "request" : "/elasticsearch",
    "agent" : "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)",
    "extension" : "",
    "memory" : null,
    "ip" : "0.72.176.46",
    "index" : "kibana_sample_data_logs",
    "message" : "0.72.176.46 - - [2018-09-18T06:31:00.572Z] \"GET /elasticsearch HTTP/1.1\" 200 7065 \"-\" \"Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)\"",
    "url" : "https://www.elastic.co/downloads/elasticsearch",
    "tags" : [
      "success",
      "info"
    ],
    "geo" : {
      "srcdest" : "IN:PH",
      "src" : "IN",
      "coordinates" : {
        "lon" : -124.1127917,
        "lat" : 40.80338889
      },
      "dest" : "PH"
    },
    "utc_time" : "2021-05-04T06:31:00.572Z",
    "bytes" : 7065,
    "machine" : {
      "os" : "ios",
      "ram" : 12884901888
    },
    "response" : 200,
    "clientip" : "0.72.176.46",
    "host" : "www.elastic.co",
    "event" : {
      "dataset" : "sample_web_logs"
    },
    "phpmemory" : null,
    "timestamp" : "2021-05-04T06:31:00.572Z"
  }
}

This transform makes it easier to answer questions such as:

This example uses the web log sample data set to find the client IP that sent the most bytes to the server in every hour. The example uses a pivot transform with a top_metrics aggregation.

Group the data by a date histogram on the time field with an interval of one hour. Use a max aggregation on the bytes field to get the maximum amount of data that is sent to the server. Without the max aggregation, the API call still returns the client IP that sent the most bytes, however, the amount of bytes that it sent is not returned. In the top_metrics property, specify clientip and geo.src, then sort them by the bytes field in descending order. The transform returns the client IP that sent the biggest amount of data and the 2-letter ISO code of the corresponding location.

POST _transform/_preview
{
  "source": {
    "index": "kibana_sample_data_logs"
  },
  "pivot": {
    "group_by": { 
      "timestamp": {
        "date_histogram": {
          "field": "timestamp",
          "fixed_interval": "1h"
        }
      }
    },
    "aggregations": {
      "bytes.max": { 
        "max": {
          "field": "bytes"
        }
      },
      "top": {
        "top_metrics": { 
          "metrics": [
            {
              "field": "clientip"
            },
            {
              "field": "geo.src"
            }
          ],
          "sort": {
            "bytes": "desc"
          }
        }
      }
    }
  }
}

The API call above returns a response similar to this:

{
  "preview" : [
    {
      "top" : {
        "clientip" : "223.87.60.27",
        "geo.src" : "IN"
      },
      "bytes" : {
        "max" : 6219
      },
      "timestamp" : "2021-04-25T00:00:00.000Z"
    },
    {
      "top" : {
        "clientip" : "99.74.118.237",
        "geo.src" : "LK"
      },
      "bytes" : {
        "max" : 14113
      },
      "timestamp" : "2021-04-25T03:00:00.000Z"
    },
    {
      "top" : {
        "clientip" : "218.148.135.12",
        "geo.src" : "BR"
      },
      "bytes" : {
        "max" : 4531
      },
      "timestamp" : "2021-04-25T04:00:00.000Z"
    },
    ...
  ]
}

This example uses the ecommerce sample data set to create an entity-centric index based on customer ID, and to get the customer name and email address by using the top_metrics aggregation.

Group the data by customer_id, then add a top_metrics aggregation where the metrics are the email, the customer_first_name.keyword, and the customer_last_name.keyword fields. Sort the top_metrics by order_date in descending order. The API call looks like this:

POST _transform/_preview
{
  "source": {
    "index": "kibana_sample_data_ecommerce"
  },
  "pivot": {
    "group_by": { 
      "customer_id": {
        "terms": {
          "field": "customer_id"
        }
      }
    },
    "aggregations": {
      "last": {
        "top_metrics": { 
          "metrics": [
            {
              "field": "email"
            },
            {
              "field": "customer_first_name.keyword"
            },
            {
              "field": "customer_last_name.keyword"
            }
          ],
          "sort": {
            "order_date": "desc"
          }
        }
      }
    }
  }
}

The API returns a response that is similar to this:

 {
  "preview" : [
    {
      "last" : {
        "customer_last_name.keyword" : "Long",
        "customer_first_name.keyword" : "Recip",
        "email" : "recip@long-family.zzz"
      },
      "customer_id" : "10"
    },
    {
      "last" : {
        "customer_last_name.keyword" : "Jackson",
        "customer_first_name.keyword" : "Fitzgerald",
        "email" : "fitzgerald@jackson-family.zzz"
      },
      "customer_id" : "11"
    },
    {
      "last" : {
        "customer_last_name.keyword" : "Cross",
        "customer_first_name.keyword" : "Brigitte",
        "email" : "brigitte@cross-family.zzz"
      },
      "customer_id" : "12"
    },
    ...
  ]
}