How to index dump of html files to elasticsearch?

Question

I am totaly new in elastic so my knowledge is only from elasticsearch site and I need to help. My task is to index large row data in html format into elastic search. I already c

Answer 1

@javanna's suggestion to look at the Bulk API will definitely lead you in the right direction. If you are using NEST, you can store all your objects in a list which you can then serialize JSON objects for indexing the content.

Specifically, if you want to strip the html tags out prior to indexing and storing the content as is, you can use the mapper attachment plugin - in which when you define the mapping, you can categorize the content_type to be "html."

The mapper attachment is useful for many things especially if you are handling multiple document types, but most notably - I believe just using this for the purpose of stripping out the html tags is sufficient enough (which you cannot do with the html_strip char filter).

Just a forewarning though - NONE of the html tags will be stored. So if you do need those tags somehow, I would suggest defining another field to store the original content. Another note: You cannot specify multifields for mapper attachment documents, so you would need to store that outside of the mapper attachment document. See my working example below.

You'll need to result in this mapping:

{
  "html5-es" : {
    "aliases" : { },
    "mappings" : {
      "document" : {
        "properties" : {
          "delete" : {
            "type" : "boolean"
          },
          "file" : {
            "type" : "attachment",
            "fields" : {
              "content" : {
                "type" : "string",
                "store" : true,
                "term_vector" : "with_positions_offsets",
                "analyzer" : "autocomplete"
              },
              "author" : {
                "type" : "string",
                "store" : true,
                "term_vector" : "with_positions_offsets"
              },
              "title" : {
                "type" : "string",
                "store" : true,
                "term_vector" : "with_positions_offsets",
                "analyzer" : "autocomplete"
              },
              "name" : {
                "type" : "string"
              },
              "date" : {
                "type" : "date",
               "format" : "strict_date_optional_time||epoch_millis"
              },
              "keywords" : {
                "type" : "string"
              },
              "content_type" : {
                "type" : "string"
              },
          "content_length" : {
                "type" : "integer"
              },
              "language" : {
                "type" : "string"
              }
            }
          },
          "hash_id" : {
            "type" : "string"
          },
          "path" : {
            "type" : "string"
          },
          "raw_content" : {
            "type" : "string",
            "store" : true,
            "term_vector" : "with_positions_offsets",
            "analyzer" : "raw"
          },
          "title" : {
            "type" : "string"
          }
        }
      }
    },
    "settings" : { //insert your own settings here },
    "warmers" : { }
  }
}

Such that in NEST, I will assemble the content as such:

Attachment attachment = new Attachment();
attachment.Content =   Convert.ToBase64String(File.ReadAllBytes("path/to/document"));
attachment.ContentType = "html";

Document document = new Document();
document.File = attachment;
document.RawContent = InsertRawContentFromString(originalText);

I have tested this in Sense - results are as follows:

"file": {
    "_content": "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",
    "_content_length": 0,
    "_content_type": "html",
    "_date": "0001-01-01T00:00:00",
    "_title": "Topic10"
},
"delete": false,
"raw_content": "<h1>Topic10</h1><p>Delete this text and replace it with your own content. Check your mailbox.</p><p> </p><p>asdf</p><p> </p><p>10</p><p> </p><p>Lavender.</p><p> </p><p>10/6 12:03</p><p> </p><p>5 09</p><p> </p><p>11 47</p><p> </p><p>Halloween is in October.</p><p> </p><p>jog</p>"
},
"highlight": {
"file.content": [
    "\n    <em>Topic10</em>\n\n    Delete this text and replace it with your own content. Check your mailbox.\n\n     \n\n    asdf\n\n     \n\n    10\n\n     \n\n    Lavender.\n\n     \n\n    10/6 12:03\n\n     \n\n    5 09\n\n     \n\n    11 47\n\n     \n\n    Halloween is in October.\n\n     \n\n    jog\n\n  "
    ]
}

Answer 2

I'd look at the bulk api that allows you to send more than document in a single request, in order to speed up your indexing process. You can send batch of 10, 20 or more documents, depending on how big they are.

Depending on what you want to index you might need to parse the html, unless you want to index the whole html as a single field (you might want to use the html strip char filter in that case to strip out the html tags from the indexed text).

After indexing I'd suggest to make sure the mapping is correct and you can find what you're looking for. You can always reindex using the _source special field that elasticsearch stores under the hood, but if you already wrote your indexer code you might want to use it again to reindex when needed (of course with the same html documents). In practice, you never index your data once... so be careful :) even though elasticsearch always helps you out with the _source field), it's just a matter of querying the existing index and reindex all its documents on another index.