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https://gitlab.com/SIGBUS/nyaa.git
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0b78428abc
* Optimize Elasticsearch fullword field Since the main display_name field ngrams words up to 15 characters, anything to and under that will already be indexed - the fullword field (which we have for words longer than 15 characters) needs to index only words longer than that. * Preprocess ES terms for better literal matching This commit adds a new .exact subfield to display_name, which holds a barely-filtered version of the original title we can do "literal" matching against. This is not real substring matching, but quoting terms now actually does something! Implements a simple preprocessor for the search terms to extract quoted parts from the search terms, optionally prefixed with - to negate them. The preprocessor will create a query that'll join all three query-types: the simple_query_string, must-phrases and must-not-phrases.
138 lines
3.6 KiB
YAML
138 lines
3.6 KiB
YAML
---
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# CREATE DTABASE/TABLE equivalent for elasticsearch, in yaml
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# fo inline comments.
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settings:
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analysis:
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analyzer:
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my_search_analyzer:
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type: custom
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tokenizer: standard
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char_filter:
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- my_char_filter
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filter:
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- standard
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- lowercase
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my_index_analyzer:
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type: custom
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tokenizer: standard
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char_filter:
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- my_char_filter
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filter:
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- resolution
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- lowercase
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- my_ngram
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- word_delimit
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- trim_zero
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# For exact matching - simple lowercase + whitespace delimiter
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exact_analyzer:
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tokenizer: whitespace
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filter:
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- lowercase
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# For matching full words longer than the ngram limit (15 chars)
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my_fullword_index_analyzer:
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type: custom
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tokenizer: standard
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char_filter:
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- my_char_filter
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filter:
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- lowercase
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- word_delimit
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# Skip tokens shorter than N characters,
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# since they're already indexed in the main field
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- fullword_min
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filter:
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my_ngram:
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type: edgeNGram
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min_gram: 1
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max_gram: 15
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fullword_min:
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type: length
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# Remember to change this if you change the max_gram below!
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min: 16
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resolution:
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type: pattern_capture
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patterns: ["(\\d+)[xX](\\d+)"]
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trim_zero:
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type: pattern_capture
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patterns: ["0*([0-9]*)"]
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word_delimit:
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type: word_delimiter
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preserve_original: true
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split_on_numerics: false
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char_filter:
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my_char_filter:
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type: mapping
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mappings: ["-=>_", "!=>_", "_=>\\u0020"]
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index:
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# we're running a single es node, so no sharding necessary,
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# plus replicas don't really help either.
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number_of_shards: 1
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number_of_replicas : 0
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mapper:
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# disable elasticsearch's "helpful" autoschema
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dynamic: false
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# since we disabled the _all field, default query the
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# name of the torrent.
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query:
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default_field: display_name
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mappings:
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torrent:
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# don't want everything concatenated
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_all:
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enabled: false
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properties:
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id:
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type: long
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display_name:
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# TODO could do a fancier tokenizer here to parse out the
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# the scene convention of stuff in brackets, plus stuff like k-on
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type: text
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analyzer: my_index_analyzer
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fielddata: true # Is this required?
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fields:
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# Multi-field for full-word matching (when going over ngram limits)
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# Note: will have to be queried for, not automatic
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fullword:
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type: text
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analyzer: my_fullword_index_analyzer
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# Stored for exact phrase matching
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exact:
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type: text
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analyzer: exact_analyzer
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created_time:
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type: date
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# Only in the ES index for generating magnet links
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info_hash:
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enabled: false
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filesize:
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type: long
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anonymous:
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type: boolean
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trusted:
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type: boolean
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remake:
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type: boolean
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complete:
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type: boolean
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hidden:
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type: boolean
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deleted:
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type: boolean
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has_torrent:
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type: boolean
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download_count:
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type: long
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leech_count:
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type: long
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seed_count:
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type: long
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comment_count:
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type: long
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# these ids are really only for filtering, thus keyword
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uploader_id:
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type: keyword
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main_category_id:
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type: keyword
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sub_category_id:
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type: keyword |