anchor.diff:  // 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 {dataframe}. It is ignored by 
  +<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 
  
positive.code: ----------------------------------
  // TEST[skip:setup kibana sample data]
  
  <1> This is the destination index for the {dataframe}. It is ignored by 
  `_preview`.
  <2> Two `group_by` fields have been selected. This means the {dataframe} will 
  contain a unique row per `user` and `customer_id` combination. Within this 
  dataset both these fields are unique. By including both in the {dataframe} 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 
  {dataframe} in advance, populated with some sample values. For example:
  
  [source,js]
  ----------------------------------
  
negative.code: 
  [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" } }
  