Elasticsearch ingest pipeline script processor fails to cast

I am trying to reindex data and do some calculations based on fields in the source document. I have used ingest pipelines to enrich the document with geo_point and want to calculate some other values as well.

The issue that I have is that the source data throws an error saying it can't be casted. Here the details:

Raw (from ML csv input):

"_source": {
"Time": "18.06.2017 17:37:32",
"Weight (kg)": 286000,
"People": 2,
"Seats": "2"}

However, the import done with ML clearly stated the following:

{
  "convert": {
    "field": "Seats",
    "type": "long",
    "ignore_missing": true
  }
},{
  "convert": {
    "field": "People",
    "type": "long",
    "ignore_missing": true
  }
}

The incoming raw data is consistent in a way that all values are strictly numbers, no quotes etc (the first 3 are the weight, the seats and the people:

66990;189;172;0;0;0;0;0

For clarification also the mapping/mapping template for the index later on which also shows the correct type:

"People": {
                "type": "long"
              },
"Seats": {
                "type": "long"
              },

Now, when I use a Kibana scripted field I can calculate as follows:

if (doc['Seats'].value == 0)

{ return 0 } else

{
long utilization = (doc["People"].value * 100)/doc["Seats"].value;
return utilization
}

everything works fine and I get a calculated utilization.

When I try to do th same with scripts in the ingest pipeline like this:

"caused_by" : {
          "type" : "class_cast_exception",
          "reason" : "cannot explicitly cast float [java.lang.String] to byte"
 }

The code I use is as follows:

"script": {
        "if": "!(ctx.Seats=0) && !(ctx.Seats==null)",
        "lang": "painless",
        "source": "ctx.utilization = (float)ctx.People*100.0/(float)ctx.Seats"
}

My questions are:

1. why does the ML ingest behave differently (the raw data from the csv is absolutely the same, only ints)
2. what can I do in the ingest pipeline to get it done
3. is the kibana index pattern way as performant as the ingest pipeline, or should I stick with the ingest pipeline in terms of load etc.

Thanks for your help and hints.

Chibisuke

In the ingest pipeline, ctx.Seats will still be a string because it's a string in the source document. You either need to parse it in your script or convert it just before the script.

Option without conversion and simply parsing the value in the script:

"script": {
    "if": "!(ctx.Seats=="0") && !(ctx.Seats==null)",
    "lang": "painless",
    "source": "ctx.utilization = 100.0 * ctx.People / Float.parseFloat(ctx.Seats)"
}

Option with conversion before running the script:

{
  "convert" : {
    "field" : "Seats",
    "type": "float",
    "ignore_missing": true
  }
},
{
  "script": {
    "if": "!(ctx.Seats==0) && !(ctx.Seats==null)",
    "lang": "painless",
    "source": "ctx.utilization = 100.0 * ctx.People / ctx.Seats"
  }
}