9.1 KiB
Mapper Attachments Type for Elasticsearch
The mapper attachments plugin lets Elasticsearch index file attachments in common 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).
Installation
In order to install the plugin, run:
bin/plugin install mapper-attachments
Hello, world
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.
Usage
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:
datetitlenameonly available if you set_namesee aboveauthorkeywordscontent_typecontent_lengthis 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.
Copy To feature
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.
Querying or accessing metadata
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"
]
}
}
]
}
}
Indexed Characters
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 ..."
}
}
Metadata parsing error handling
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.
Language Detection
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 ..."
}
}
Highlighting attachments
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"
]
}
}
]
}
}
Stand alone runner
If you want to run some tests within your IDE, you can use StandaloneRunner class.
It accepts arguments:
-u file://URL/TO/YOUR/DOC--sizeset extracted size (default to mapper attachment size)BASE64encoded 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