Elasticsearch管道处理器：如何批量处理文档所有字段？

Absolutely no need to list every field manually—there's a far more efficient way to apply this logic to all fields in your document. Here's how you can adjust your pipeline:

Use a Painless Script Processor #

Instead of using the remove processor for each field, you can leverage a script processor to iterate over every field in the document and remove those that match your condition.

Here's the updated pipeline configuration:

{
  "description": "my pipeline that removes empty string and null strings from all fields",
  "processors": [
    {
      "script": {
        "source": """
          // Iterate over all key-value pairs in the document
          for (def entry : ctx.entrySet()) {
            def fieldName = entry.getKey();
            def fieldValue = entry.getValue();
            
            // Check if the value is an empty string or "null" string
            if (fieldValue == "" || fieldValue == "null") {
              ctx.remove(fieldName);
            }
          }
        """,
        "lang": "painless"
      }
    }
  ]
}

How this works: #

• The script loops through every top-level field in your document (ctx.entrySet() gives all key-value pairs).
• For each field, it checks if the value matches your condition (empty string or "null" string).
• If the condition is met, it removes the field from the document using ctx.remove(fieldName).

Note for nested fields: #

If your document has nested fields (like user.name), the above script will only check top-level fields. To handle nested fields, you'd need a recursive script to traverse all levels. Just let me know if you need that version!

内容的提问来源于stack exchange，提问作者user11725513