Slow Elasticsearch indexing using join datatype

We have an index with a join datatype and the indexing speed is very slow. At best we are indexing 100/sec, but mostly around 50/sec, the times is varying depending of the document size. We are using multiple threads with .NET Nest when indexing but both batching and single inserts are pretty slow. We have tested various batch sizes but still not getting any speed to talk about. Even with only small documents containing "metadata" it is slow, but speed will drop radically when the document size is increasing. Document size in this solution can vary from small up to 6 MB

What can we expect using the join datatype and indexing? How much penalty must we expect to get using it? We did of course try to avoid this when designing it, but we did not find any way around it. Any tips or tricks?

We are using a 3-node cluster in Azure, all with 32 GB of RAM and premium SSD disks. The Java Heap size is set to 16GB. Swapping is Disabled. Memory usage on the VM’s is stable about 60% of total, but the CPU is very low < 10 %. We are running Elasticsearch v. 6.2.3.

A short version of the mapping:

"mappings": {
  "log": {
    "_routing": {
      "required": true
    },
    "properties": {
      "body": {
        "type": "text"
      },
      "description": {
        "type": "text"
      },
        "headStepJoinField": {
        "type": "join",
        "eager_global_ordinals": true,
        "relations": {
          "head": "step"
        }
      },
      "id": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },       
      "statusId": {
        "type": "keyword"
      },
      "stepId": {
        "type": "keyword"
      }
    }
  }
}