The mapper attachments plugin lets Elasticsearch index file attachments in over a thousand formats (such as PPT, XLS, PDF) using the Apache text extraction library Tika.
In practice, the plugin adds the attachment
type when mapping properties so that documents can be populated with file attachment contents (encoded as base64
).
If you are looking for another version documentation, please refer to the compatibility matrix.
Create a property mapping using the new type attachment
:
POST /trying-out-mapper-attachments
{
"mappings": {
"person": {
"properties": {
"cv": { "type": "attachment" }
}}}}
Index a new document populated with a base64
-encoded attachment:
POST /trying-out-mapper-attachments/person/1
{
"cv": "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0="
}
Search for the document using words in the attachment:
POST /trying-out-mapper-attachments/person/_search
{
"query": {
"query_string": {
"query": "ipsum"
}}}
If you get a hit for your indexed document, the plugin should be installed and working.
Using the attachment type is simple, in your mapping JSON, simply set a certain JSON element as attachment, for example:
PUT /test
PUT /test/person/_mapping
{
"person" : {
"properties" : {
"my_attachment" : { "type" : "attachment" }
}
}
}
In this case, the JSON to index can be:
PUT /test/person/1
{
"my_attachment" : "... base64 encoded attachment ..."
}
Or it is possible to use more elaborated JSON if content type, resource name or language need to be set explicitly:
PUT /test/person/1
{
"my_attachment" : {
"_content_type" : "application/pdf",
"_name" : "resource/name/of/my.pdf",
"_language" : "en",
"_content" : "... base64 encoded attachment ..."
}
}
The attachment
type not only indexes the content of the doc in content
sub field, but also automatically adds meta
data on the attachment as well (when available).
The metadata supported are:
date
title
name
only available if you set_name
see aboveauthor
keywords
content_type
content_length
is the original content_length before text extraction (aka file size)language
They can be queried using the "dot notation", for example: my_attachment.author
.
Both the meta data and the actual content are simple core type mappers (string, date, ...), thus, they can be controlled in the mappings. For example:
PUT /test/person/_mapping
{
"person" : {
"properties" : {
"file" : {
"type" : "attachment",
"fields" : {
"content" : {"index" : "no"},
"title" : {"store" : "yes"},
"date" : {"store" : "yes"},
"author" : {"analyzer" : "myAnalyzer"},
"keywords" : {"store" : "yes"},
"content_type" : {"store" : "yes"},
"content_length" : {"store" : "yes"},
"language" : {"store" : "yes"}
}
}
}
}
}
In the above example, the actual content indexed is mapped under fields
name content
, and we decide not to index it, so
it will only be available in the _all
field. The other fields map to their respective metadata names, but there is no
need to specify the type
(like string
or date
) since it is already known.
If you want to use copy_to feature, you need to define it on each sub-field you want to copy to another field:
PUT /test/person/_mapping
{
"person": {
"properties": {
"file": {
"type": "attachment",
"fields": {
"content": {
"type": "string",
"copy_to": "copy"
}
}
},
"copy": {
"type": "string"
}
}
}
}
In this example, the extracted content will be copy as well to copy
field.
If you need to query on metadata fields, use the attachment field name dot the metadata field. For example:
DELETE /test
PUT /test
PUT /test/person/_mapping
{
"person": {
"properties": {
"file": {
"type": "attachment",
"fields": {
"content_type": {
"type": "string",
"store": true
}
}
}
}
}
}
PUT /test/person/1?refresh=true
{
"file": "IkdvZCBTYXZlIHRoZSBRdWVlbiIgKGFsdGVybmF0aXZlbHkgIkdvZCBTYXZlIHRoZSBLaW5nIg=="
}
GET /test/person/_search
{
"fields": [ "file.content_type" ],
"query": {
"match": {
"file.content_type": "text plain"
}
}
}
Will give you:
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.16273327,
"hits": [
{
"_index": "test",
"_type": "person",
"_id": "1",
"_score": 0.16273327,
"fields": {
"file.content_type": [
"text/plain; charset=ISO-8859-1"
]
}
}
]
}
}
By default, 100000
characters are extracted when indexing the content. This default value can be changed by setting
the index.mapping.attachment.indexed_chars
setting. It can also be provided on a per document indexed using the
_indexed_chars
parameter. -1
can be set to extract all text, but note that all the text needs to be allowed to be
represented in memory:
PUT /test/person/1
{
"my_attachment" : {
"_indexed_chars" : -1,
"_content" : "... base64 encoded attachment ..."
}
}
While extracting metadata content, errors could happen for example when parsing dates. Parsing errors are ignored so your document is indexed.
You can disable this feature by setting the index.mapping.attachment.ignore_errors
setting to false
.
By default, language detection is disabled (false
) as it could come with a cost.
This default value can be changed by setting the index.mapping.attachment.detect_language
setting.
It can also be provided on a per document indexed using the _detect_language
parameter.
Note that you can force language using _language
field when sending your actual document:
{
"my_attachment" : {
"_language" : "en",
"_content" : "... base64 encoded attachment ..."
}
}
If you want to highlight your attachment content, you will need to set "store": true
and "term_vector":"with_positions_offsets"
for your attachment field. Here is a full script which does it:
DELETE /test
PUT /test
PUT /test/person/_mapping
{
"person": {
"properties": {
"file": {
"type": "attachment",
"fields": {
"content": {
"type": "string",
"term_vector":"with_positions_offsets",
"store": true
}
}
}
}
}
}
PUT /test/person/1?refresh=true
{
"file": "IkdvZCBTYXZlIHRoZSBRdWVlbiIgKGFsdGVybmF0aXZlbHkgIkdvZCBTYXZlIHRoZSBLaW5nIg=="
}
GET /test/person/_search
{
"fields": [],
"query": {
"match": {
"file.content": "king queen"
}
},
"highlight": {
"fields": {
"file.content": {
}
}
}
}
It gives back:
{
"took": 9,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.13561106,
"hits": [
{
"_index": "test",
"_type": "person",
"_id": "1",
"_score": 0.13561106,
"highlight": {
"file.content": [
"\"God Save the <em>Queen</em>\" (alternatively \"God Save the <em>King</em>\"\n"
]
}
}
]
}
}
If you want to run some tests within your IDE, you can use StandaloneRunner
class.
It accepts arguments:
-u file:https://URL/TO/YOUR/DOC
--size
set extracted size (default to mapper attachment size)BASE64
encoded binary
Example:
StandaloneRunner BASE64Text
StandaloneRunner -u /tmp/mydoc.pdf
StandaloneRunner -u /tmp/mydoc.pdf --size 1000000
It produces something like:
## Extracted text
--------------------- BEGIN -----------------------
This is the extracted text
---------------------- END ------------------------
## Metadata
- author: null
- content_length: null
- content_type: application/pdf
- date: null
- keywords: null
- language: null
- name: null
- title: null
This software is licensed under the Apache 2 license, quoted below.
Copyright 2009-2015 Elasticsearch <http:https://www.elasticsearch.org>
Licensed under the Apache License, Version 2.0 (the "License"); you may not
use this file except in compliance with the License. You may obtain a copy of
the License at
http:https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.