[GitHub] [incubator-pinot] reallocf edited a comment on issue #5268: Filter during ingestion

Sun, 19 Apr 2020 19:20:14 -0700 


reallocf edited a comment on issue #5268:
URL: 
https://github.com/apache/incubator-pinot/issues/5268#issuecomment-616272779


   Hey @npawar - I'm interested in helping out on this one. First initiative, 
so bare with me if my understanding is wrong about everything 😄. A question on 
the requirements:
   
   The idea is that we should be able to filter out data during ingestion, so 
if we have a `pinotSchema.json` like
   ```
   {
     "schemaName": "events",
     "dimensionFieldSpecs": [
       {
         "name": "userId",
         "dataType": "LONG",
         “transformFunction”: “Groovy({userID}, userID)”
       },
       {
         "name": "fullName",
         "dataType": "STRING",
         “transformFunction”: “Groovy({firstName+' '+lastName}, firstName, 
lastName)”
       },
      {
         "name": "bids",
         "dataType": "INT",
         "singleValueField": false
       },
       {
         "name": "maxBid",
         "dataType": "INT",
         "transformFunction": "Groovy({bids.max{ it.toBigDecimal() }}, bids)"
       }
     ],
     "metricFieldSpecs": [
       {
         "name": "impressions",
         "dataType": "LONG",
         “transformFunction”: “Groovy({eventType == 'IMPRESSION' ? 1: 0}, 
eventType)”
       },
       {
         "name": "clicks",
         "dataType": "LONG",
         “transformFunction”: “Groovy({eventType == CLICK ? 1: 0}, eventType)”
       },
       {
         "name": "cost",
         "dataType": "double"
       },
       {
         "name": "daysSinceEpoch",
         "dataType": "INT",
         “transformFunction”: “Groovy({timestamp/(1000*60*60*24)}, timestamp)”
       }
     ],
      "timeFieldSpec": {
       "incomingGranularitySpec": {
         "name": "hoursSinceEpoch",
         "dataType": "LONG",
         "timeFormat" : "EPOCH",
         "timeType": "HOURS",
         “transformFunction”: “Groovy({timestamp/(1000*60*60)}, timestamp)”
       }
     }
   }
   ```
   we would want to add a new top-level element in that json with a 
transformFunction  like
   ```
   {
     "schemaName": "events",
     "filter": "Groovy({cost > 42}, cost)",
     "dimensionFieldSpecs": [
       {
         "name": "userId",
         "dataType": "LONG",
         “transformFunction”: “Groovy({userID}, userID)”
       },
       ...
     ],
     ...
   }
   ```
   Then we would apply a row-based filter based on that transformFunction either
   a) On source columns - applying the rest of the transformations AFTER 
filtering
   or
   b) On transformed columns - applying the filtering after the rest of the 
transformations.
   
   Is that all right? Am I on the right track?


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