# Elasticsearch machine learning datafeed configuration error

**URL:** <https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484>\
**Category:** Elasticsearch\
**Tags:** elastic-stack-machine-learning, runtime-fields\
**Created:** [May 24, 2022, 9:54am UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484 "2022-05-24T09:54:40Z")\
**Posts on this page:** 8\
**Page:** 1

<div class="post-metadata">

**Author:** ![zerojin63](https://avatars.discourse-cdn.com/v4/letter/z/97f17d/32.png) [@zerojin63](https://discuss.elastic.co/u/zerojin63)\
**Post date:** [May 24, 2022, 9:54am UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/1 "2022-05-24T09:54:40Z")

</div>

I'm trying to alter datafeed for ml jobs but the same errror keep occurs.  
What I want to do is alter data like 'abc/def/ghi' into 'abc' so based on [this](https://www.elastic.co/guide/en/machine-learning/7.17/ml-configuring-transform.html#ml-configuring-transform3) i wrote script as follows

```auto
  "runtime_mappings": {
    "my_runtime_field": {
      "type": "keyword",
      "script": {
        "source": "def m = /^\/(\w+|_)\//.matcher(doc['tokenstring3'].value); emit(m.find() ? m.group(1): '');"
      }
    }
  }

```

But every time I try to write any escape letter, automatically two sets of quotation marks are added around my code, making it like this.

```auto
      "script": {
        "source": """def m = /^\/(\w+|_)\//.matcher(doc['tokenstring3'].value); emit(m.find() ? m.group(1): ''"");

```

And when i delete the quotation marks, the screen goes black.

Does anyone know how to fix this error?

---

<div class="post-metadata">

**Author:** ![Peter\_Harverson](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/peter_harverson/32/21057_2.png) [@Peter\_Harverson](https://discuss.elastic.co/u/Peter_Harverson)\
**Post date:** [May 25, 2022, 12:11pm UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/2 "2022-05-25T12:11:18Z")

</div>

@zerojin63 I have been able to configure a runtime field similar to the one you are trying to use in your datafeed. The double quotes at the end of the second snippet you posted just need editing.

I am using the Advanced wizard in the Machine Learning page in Kibana, and then edit the JSON used in the datafeed to this for the data view (index pattern) I am using:

```auto
"runtime_mappings": {
    "uri_path_first": {
      "type": "keyword",
      "script": {
        "source": """def m = /^\/(\w+|_)\//.matcher(doc['uri'].value); emit(m.find() ? m.group(1): '');"""
      }
    }
  }

```

You can see in the datafeed preview that the runtime field is getting populated (most of my entries are empty, but there are non-empty ones too), and I can use the field in a job:

 ![image](https://us1.discourse-cdn.com/elastic/original/3X/e/e/ee2e170e10651b72f03e51e1fa7457f00a8bf70b.png)

Note we have an [issue](https://github.com/elastic/kibana/issues/107082) open to switch to a new JSON editor throughout the Machine Learning UI, which would allow you to define the script using the same syntax you posted in your first snippet. Depending on which version of the stack you have, you can also define this field in the data view (index pattern) itself, using that simpler syntax, and it will be available to use in the anomaly detection job.

 ![image](https://us1.discourse-cdn.com/elastic/original/3X/e/1/e189fc62f344e8525cc5fdd5b896ada11c3e3a5f.png)

Hope this works for you.  
Pete

---

<div class="post-metadata">

**Author:** ![zerojin63](https://avatars.discourse-cdn.com/v4/letter/z/97f17d/32.png) [@zerojin63](https://discuss.elastic.co/u/zerojin63)\
**Post date:** [May 26, 2022, 5:05am UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/3 "2022-05-26T05:05:15Z")

</div>

The first one (datafeed configuration) still didn't work but the second one did!

This is the code I used.

```auto
if (doc["field_name.keyword"].size()!=0){
    def path = doc["field_name.keyword"].value;
    if (path != null) {
        int firstSlashIndex = path.indexOf('/');
        if (firstSlashIndex > 0) {
            emit(path.substring(0,firstSlashIndex));
        return;
        }
    }
}
emit("");

```

Thank you so much for your help 👍

---

<div class="post-metadata">

**Author:** ![zerojin63](https://avatars.discourse-cdn.com/v4/letter/z/97f17d/32.png) [@zerojin63](https://discuss.elastic.co/u/zerojin63)\
**Post date:** [June 3, 2022, 5:05am UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/4 "2022-06-03T05:05:08Z")

</div>

I've run into another problem..

An error occurs on job validation process when I use the runtime field I added as a detector field on anomaly detection job.

This is the error that keeps occuring.

![이미지](data:image/png;base64,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)

I've checked that the runtime field I added works fine on 'Discover' section.  
I've also checked that if I use other fields (the original fields, except for the one I added) as detector, no error occurs.  
So it must have something to do with rhe field I added.. But I don't get what's wrong with it.  
Do you happen to have any idea regarding this situation?

---

<div class="post-metadata">

**Author:** ![Peter\_Harverson](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/peter_harverson/32/21057_2.png) [@Peter\_Harverson](https://discuss.elastic.co/u/Peter_Harverson)\
**Post date:** [June 16, 2022, 1:17pm UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/5 "2022-06-16T13:17:10Z")

</div>

Apologies for the delay in replying @zerojin63. The error you are seeing when trying to use the field in an ML job looks like it is being caused by the datafeed preview step which validates the job configuration taking too long. The fact that the field shows up in Discover would imply that the configuration of the field is ok.

What version of the stack do you have, and which of the anomaly detection job wizards are you using? If you use the advanced job wizard, add the detector using the runtime field, then hit 'Edit JSON' and take a look in the datafeeed preview panel. Do you see values for your runtime field in there, like I see here:

 ![image](https://us1.discourse-cdn.com/elastic/original/3X/d/0/d03005c12f6b0e4f883a08b74ea7cadced1fcb56.png)

You could also create the job directly in Kibana Dev Tools, and try running the datafeed preview from there. For example, with my config:

```auto
PUT _ml/anomaly_detectors/test1
{
  "analysis_config": {
    "bucket_span": "4h",
    "detectors": [
      {
        "function": "sum",
        "field_name": "bytes",
        "partition_field_name": "uri_first_path",
        "detector_description": "sum(bytes) partitionfield=uri_first_path"
      }
    ],
    "influencers": [
      "uri_first_path"
    ]
  },
  "data_description": {
    "time_field":"@timestamp"
  },
    "datafeed_config":{
    "datafeed_id": "datafeed-test1",
    "indices": ["gallery-*"],
    "runtime_mappings": {
      "uri_first_path": {
        "type": "keyword",
        "script": {
          "source": """def m = /^\/(\w+|_)\//.matcher(doc['uri'].value); emit(m.find() ? m.group(1): '');"""
        }
      }
    }
  }
}

GET _ml/datafeeds/datafeed-test1/_preview

```

---

<div class="post-metadata">

**Author:** ![zerojin63](https://avatars.discourse-cdn.com/v4/letter/z/97f17d/32.png) [@zerojin63](https://discuss.elastic.co/u/zerojin63)\
**Post date:** [June 17, 2022, 12:26am UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/6 "2022-06-17T00:26:48Z")

</div>

Thanks for your reply.  
Here's the answer to your questions.

1. What version of the stack do you have?  
-\> I'm using v 7.17.3
2. Which of the anomaly detection job wizatd I am using?  
-\> I used advanced job as well. Also I was able to see values for my runtime field.

So I don't think there's anything wrong with my runtime field script, but still the error occurs on job validation process.  
I'm using 'rare' as my anomaly detection job detector. Can this be the reason for this error?

---

<div class="post-metadata">

**Author:** ![Peter\_Harverson](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/peter_harverson/32/21057_2.png) [@Peter\_Harverson](https://discuss.elastic.co/u/Peter_Harverson)\
**Post date:** [June 17, 2022, 10:35am UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/7 "2022-06-17T10:35:31Z")

</div>

You shouldn't have any problems using the runtime field in a 'rare' detector. I have stepped through the advanced wizard and the validation step succeeded. My job and datafeed config and preview look like this (I have two runtime fields defined in my data view, but the job is only using one of them):

 ![image](https://us1.discourse-cdn.com/elastic/original/3X/3/e/3e7b36c925ac54fb63ef30a0ab5a84b86afdb15b.png)

Are you able to share your job and datafeed config?

Did you try creating the job and running the datafeed preview inside Kibana Dev Tools as shown [above](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/5)?

---

<div class="post-metadata">

**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\
**Post date:** [July 15, 2022, 10:36am UTC](https://discuss.elastic.co/t/elasticsearch-machine-learning-datafeed-configuration-error/305484/8 "2022-07-15T10:36:28Z")

</div>

This topic was automatically closed 28 days after the last reply. New replies are no longer allowed.