provide a sample test dataset with instructions to use the plugin

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Provide sample dataset,about synapticielfactory/kibana_cohort

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ylasri commented on July 28, 2024

Here is example of setup Cohort plugin. Tested on Kibana 7.8.0, 7.8.1 and 7.9.0

Set mapping what defines '@timestamp' (when doc was created, in our example order_date) and 'first_order_date' (when user/item was created for active for the first time) fields as date type,
also you can define 'dayUntilReturn' if you'll not create it as scripted field (step 3) :

PUT food_relay
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 0
  },
  "mappings": {
    "properties": {
      "common_id": {
        "type": "keyword"
      },
      "dayUntilReturn": {
        "type": "integer"
      },
      "first_order_date": {
        "type": "date"
      },
      "order_date": {
        "type": "date"
      },
      "order_id": {
        "type": "keyword"
      },
      "pickup_date": {
        "type": "date"
      },
      "pup_id": {
        "type": "keyword"
      },
      "total_charges": {
        "type": "float"
      },
      "user_id": {
        "type": "keyword"
      }
    }
  }
}

2- Put some cohort similar data : use _bulk file food_relay_es_bulk.csv.json under dataset folder

POST _bulk
{"index": {"_index": "food_relay", "_id": 262}}
{"order_id": "262", "order_date": "2009-01-11", "user_id": "47", "total_charges": "50.67", "common_id": "TRQKD", "pup_id": "2", "pickup_date": "2009-01-12", "first_order_date": "2009-01-11", "dayUntilReturn": "0"}
{"index": {"_index": "food_relay", "_id": 278}}
{"order_id": "278", "order_date": "2009-01-20", "user_id": "47", "total_charges": "26.6", "common_id": "4HH2S", "pup_id": "3", "pickup_date": "2009-01-20", "first_order_date": "2009-01-11", "dayUntilReturn": "9"}
{"index": {"_index": "food_relay", "_id": 294}}
{"order_id": "294", "order_date": "2009-02-03", "user_id": "47", "total_charges": "38.71", "common_id": "3TRDC", "pup_id": "2", "pickup_date": "2009-02-04", "first_order_date": "2009-01-11", "dayUntilReturn": "23"}
{"index": {"_index": "food_relay", "_id": 301}}
{"order_id": "301", "order_date": "2009-02-06", "user_id": "47", "total_charges": "53.38", "common_id": "NGAZJ", "pup_id": "2", "pickup_date": "2009-02-09", "first_order_date": "2009-01-11", "dayUntilReturn": "26"}
{"index": {"_index": "food_relay", "_id": 302}}

3- Alternative to 'dayUntilReturn' is to create scripted field with same name and next value:
(doc['order_date'].value.toInstant().toEpochMilli() - doc['first_order_date'].value.toInstant().toEpochMilli()) / (1000 * 60 * 60* 24)
To calculate days diff on elastic side.

GET food_relay/_search
{
  "_source": ["first_order_date", "order_date", "dayUntilReturn"],  
  "script_fields": {
    "daysBetween": {
      "script": {
        "source": "(doc['order_date'].value.toInstant().toEpochMilli() - doc['first_order_date'].value.toInstant().toEpochMilli()) / params.factor",
        "params": {
          "factor": 86400000
        }
      }
    }
  }
}

4- Create new Cohort visualization, setup buckets :

set Cohort Date bucket: Aggregation - 'Date Histogram', Field - 'first_order_date', Interval - up to you;
set Cohort Period bucket: Sub Aggregation - 'Histogram', Field - 'dayUntilReturn', Minimum Interval - '0'

from kibana_cohort.

ylasri commented on July 28, 2024

from kibana_cohort.

Provide sample dataset about kibana_cohort HOT 2 CLOSED

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