ml_results_summary.json

POST _watcher/watch/_execute
{
  "watch": {
    "trigger": {
      "schedule": {
        "interval": "5m"
      }
    },
    "input": {
      "search": {
        "request": {
          "search_type": "query_then_fetch",
          "indices": [
            ".ml-anomalies-*"
          ],
          "body": {
            "size": 1,
            "query": {
              "bool": {
                "filter": [
                  {
                    "range": {
                      "timestamp": {
                        "gte": "now-{{ctx.metadata.watch_timespan}}"
                      }
                    }
                  },
                  {
                    "regexp": {
                      "job_id": "{{ctx.metadata.job_name_regex}}"
                    }
                  },
                  {
                    "range": {
                      "record_score": {
                        "gte": "{{ctx.metadata.job_min_record_score}}"
                      }
                    }
                  }
                ]
              }
            },
            "aggs": {
              "max_record_score": {
                "max": {
                  "field": "record_score"
                }
              },
              "jobs": {
                "terms": {
                  "field": "job_id"
                }
              }
            }
          }
        }
      }
    },
    "condition": {
      "script": {
        "source": "ctx.payload.hits.total > 0",
        "lang": "painless"
      }
    },
    "actions": {
      "log": {
        "transform": {
          "script": """
           ArrayList job_list=new ArrayList();
           for (def i=0; i< ctx.payload.aggregations.jobs.buckets.size(); i++) {
             job_list.add(ctx.payload.aggregations.jobs.buckets[i].key);
             
           }
            return ['job_list': job_list, 'max_record_score': Math.round(ctx.payload.aggregations.max_record_score.value), '@timestamp': ctx.execution_time, 'total_critical_anomalies':ctx.payload.hits.total] ;
"""
        },
        "logging": {
          "level": "info",
          "text": """
total_critical_anomalies: {{ctx.payload.total_critical_anomalies}} 
max_record_score: {{ctx.payload.max_record_score}}
@timestamp: {{ctx.payload.@timestamp}}
jobs: {{ctx.payload.job_list}}
"""
        }
      },
      "index_payload": {
        "transform": {
          "script": """
             ArrayList job_list=new ArrayList();
             for (def i=0; i< ctx.payload.aggregations.jobs.buckets.size(); i++) {
               job_list.add(ctx.payload.aggregations.jobs.buckets[i].key);
             }
            return ['job_list': job_list, 'max_record_score': Math.round(ctx.payload.aggregations.max_record_score.value), '@timestamp': ctx.execution_time, 'total_critical_anomalies':ctx.payload.hits.total] ;
"""
        },
        "index": {
          "index": "ml_summary"
        }
      }
    },
    "metadata": {
      "watch_timespan": "5y",
      "job_name_regex": "farequote.*",
      "job_min_record_score": 90
    }
  }
}

GET ml_summary/_search

yields:

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "ml_summary",
        "_type" : "_doc",
        "_id" : "EeLhTWwBEx7iVUUAXjCI",
        "_score" : 1.0,
        "_source" : {
          "@timestamp" : "2019-08-01T15:51:51.120Z",
          "job_list" : [
            "farequote_multi",
            "farequote_count_split",
            "farequote_median",
            "farequote_model",
            "farequote_responsetime",
            "farequote_responsetime_split",
            "farequote_scripted",
            "farequote_count_nosplit",
            "farequote_nosplit",
            "farequote_resp_by_airline"
          ],
          "total_critical_anomalies" : 26,
          "max_record_score" : 99
        }
      }
    ]
  }
}