README.md in metka-1.0.3 vs README.md in metka-2.0.0

- old
+ new

@@ -41,12 +41,11 @@ end ``` ```ruby class Song < ActiveRecord::Base - include Metka::Model(column: 'tags') - include Metka::Model(column: 'genres') + include Metka::Model(columns: %w[genres tags]) end @song = Song.new(title: 'Migrate tags in Rails to PostgreSQL') @song.tag_list = 'top, chill' @song.genre_list = 'rock, jazz, pop' @@ -91,11 +90,11 @@ Song.without_all_tags('top, 1990') => [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] Song.without_all_tags('') -=> [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] +=> [] Song.without_all_genres('rock, pop') => [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] Song.without_all_genres('rock') @@ -112,21 +111,69 @@ Song.without_any_genres('rock, pop') => [] Song.without_any_genres('') +=> [] +``` + +### .tagged_with +```ruby +Song.tagged_with('top') => [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] + +Song.tagged_with('top, 1990') +=> [] + +Song.tagged_with('') +=> [] + +Song.tagged_with('rock') +=> [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] + +Song.tagged_with('rock', join_operator: Metka::And) +=> [] + +Song.tagged_with('chill', any: true) +=> [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] + +Song.tagged_with('chill, 1980', any: true) +=> [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] + +Song.tagged_with('', any: true) +=> [] + +Song.tagged_with('rock, rap', any: true, on: ['genres']) +=> [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] + +Song.without_all_tags('top') +=> [] + +Song.tagged_with('top, 1990', exclude: true) +=> [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] + +Song.tagged_with('', exclude: true) +=> [] + +Song.tagged_with('top, 1990', any: true, exclude: true) +=> [] + +Song.tagged_with('1990, 1980', any: true, exclude: true) +=> [#<Song id: 1, title: 'Migrate tags in Rails to PostgreSQL', tags: ['top', 'chill'], genres: ['rock', 'jazz', 'pop']] + +Song.without_any_genres('rock, pop') +=> [] ``` ## Custom delimiter By default, a comma is used as a delimiter to create tags from a string. You can make your own custom separator: ```ruby -Metka.config.delimiter = [',', ' ', '\|'] +Metka.config.delimiter = '|' parsed_data = Metka::GenericParser.instance.call('cool, data|I have') parsed_data.to_a -=>['cool', 'data', 'I', 'have'] +=>['cool, data', 'I have'] ``` ## Tags with quote ```ruby parsed_data = Metka::GenericParser.instance.call("'cool, data', code") @@ -134,32 +181,51 @@ => ['cool, data', 'code'] ``` ## Custom parser By default we use [generic_parser](lib/metka/generic_parser.rb "generic_parser") -If you want use your custom parser you can do: +If you want to use your custom parser you can do: ```ruby class Song < ActiveRecord::Base - include Metka::Model(column: 'tags', parser: Your::Custom::Parser.instance) - include Metka::Model(column: 'genres') + include Metka::Model(columns: %w[genres tags], parser: Your::Custom::Parser.instance) end ``` Custom parser must be a singleton class that has a `.call` method that accepts the tag string ## Tag Cloud Strategies There are several strategies to get tag statistics +### ActiveRecord Strategy (Default) + +Data about taggings is accessible via class methods of your model with `Metka::Model` attached. You can calculate a cloud for a single tagged column or multiple columns, the latter case would return to you a sum of taggings from multiple tagged columns, that are provided as arguments, for each tag present. ActiveRecord Strategy is an easiest way to implement, since it wouldn't require any additional code, but it's the slowest one on SELECT. + +```ruby +class Book < ActiveRecord::Base + include Metka::Model(column: 'authors') + include Metka::Model(column: 'co_authors') +end + +tag_cloud = Book.author_cloud +=> [["L.N. Tolstoy", 3], ["F.M. Dostoevsky", 6]] +genre_cloud = Book.co_author_cloud +=> [["A.P. Chekhov", 5], ["N.V. Gogol", 8], ["L.N. Tolstoy", 2]] +summary_cloud = Book.metka_cloud('authors', 'co_authors') +=> [["L.N. Tolstoy", 5], ["F.M. Dostoevsky", 6], ["A.P. Chekhov", 5], ["N.V. Gogol", 8]] +``` + ### View Strategy -Data about taggings will be agregated in SQL View. The easiest way to implement but the most slow on SELECT. +Data about taggings will be agregated in SQL View. Performance-wise that strategy has no benefits over ActiveRecord Strategy, but if you need to store tags aggregations in a distinct model, that's an easiest way to achieve it. ```bash -rails g metka:strategies:view --source-table-name=NAME_OF_TABLE_WITH_TAGS +rails g metka:strategies:view --source-table-name=NAME_OF_TABLE_WITH_TAGS [--source-columns=NAME_OF_COLUMN_1 NAME_OF_COLUMN_2] [--view-name=NAME_OF_RESULTING_VIEW] ``` -The code above will generate a migration that creates view to store aggregated data about tag in `NAME_OF_TABLE_WITH_TAGS` table. +The code above will generate a migration that creates view with specified `NAME_OF_RESULTING_VIEW`, that would aggregate tags data from specified array of tagged columns [`NAME_OF_COLUMN_1`, `NAME_OF_COLUMN_2`, ...], that are present within specified table `NAME_OF_TABLE_WITH_TAGS`. +If `source-columns` option is not provided, then `tags` column would be used as defaults. If array of multiple values would be provided to the option, then the aggregation would be made with the tags from multiple tagged columns, so if a single tag would be found within multiple tagged columns, the resulting aggregation inside the view would have a single row for that tag with a sum of it's occurences across all stated tagged columns. +`view-name` option is also optional, it would just force the resulting view's name to the one of your choice. If it's not provided, then view name would be generated automatically, you could check it within generated migration. Lets take a look at real example. We have a `notes` table with `tags` column. | Column | Type | Default | |--------|---------------------|-----------------------------------| @@ -179,19 +245,22 @@ # frozen_string_literal: true class CreateTaggedNotesView < ActiveRecord::Migration[5.0] def up execute <<-SQL - CREATE OR REPLACE VIEW tagged_notes AS - - SELECT UNNEST - ( tags ) AS tag_name, - COUNT ( * ) AS taggings_count - FROM - notes - GROUP BY - name; + CREATE OR REPLACE VIEW tagged_notes AS + SELECT + tag_name, + COUNT ( * ) AS taggings_count + FROM ( + SELECT UNNEST + ( tags ) AS tag_name + FROM + view_posts + ) subquery + GROUP BY + tag_name; SQL end def down execute <<-SQL @@ -213,47 +282,163 @@ Now you can create `TaggedNote` model and work with the view like you usually do with Rails models. ### Materialized View Strategy -Similar to the strategy above, but the view will be Materialized and refreshed with the trigger +Data about taggings will be aggregated in SQL Materialized View, that would be refreshed with the trigger on each change of the tagged column's data. Except for the another type of view being used, that strategy behaves the same way, as a View Strategy above. ```bash -rails g metka:strategies:materialized_view --source-table-name=NAME_OF_TABLE_WITH_TAGS +rails g metka:strategies:materialized_view --source-table-name=NAME_OF_TABLE_WITH_TAGS --source-columns=NAME_OF_COLUMN_1 NAME_OF_COLUMN_2 --view-name=NAME_OF_RESULTING_VIEW ``` -The code above will generate a migration that creates view to store aggregated data about tag in `NAME_OF_TABLE_WITH_TAGS` table. +All of the options for that stategy's generation command are the same as for the View Strategy. -Lets take a look at real example. We have a `notes` table with `tags` column. +The migration template can be seen [here](spec/dummy/db/migrate/06_create_tagged_materialized_view_posts_materialized_view.rb "here") -| Column | Type | Default | -|--------|---------------------|-----------------------------------| -| id | integer | nextval('notes_id_seq'::regclass) | -| body | text | | -| tags | character varying[] | '{}'::character varying[] | +With the same `notes` table with `tags` column the resulting view would have the same two columns -Now lets generate a migration. +| tag_name | taggings_count | +|----------|----------------| +| Ruby | 124056 | +| React | 30632 | +| Rails | 28696 | +| Crystal | 6566 | +| Elixir | 3475 | -```bash -rails g metka:strategies:materialized_view --source-table-name=notes -``` +And you can also create `TaggedNote` model to work with the view as with a Rails model. -The migration code you can see [here](spec/dummy/db/migrate/05_create_tagged_materialized_view_Songs_materialized_view.rb "here") - -Now lets take a look at `tagged_notes` materialized view. - -Now you can create `TaggedNote` model and work with the view like you usually do with Rails models. - ### Table Strategy with Triggers TBD ## Inspired by 1. [ActsAsTaggableOn](https://github.com/mbleigh/acts-as-taggable-on) 2. [ActsAsTaggableArrayOn](https://github.com/tmiyamon/acts-as-taggable-array-on) 3. [TagColumns](https://github.com/hopsoft/tag_columns) + +## Benchmark Comparison + +There are some results of benchmarking a performance of write, read and find operations for different gems, that provide solution for tagging. Keep in mind, that those results can't be used as a proof, that some solution is better than the others, since each of the benchmarked gems has their unique features. You could run the benchmarks yourself or check, what exact operations has been used for benchmarking, with [MetkaBench application](https://github.com/jetrockets/metka_bench). + +```bash +$ rake bench:all +Deleted all MetkaSong +Deleted all ActsAsTaggableOn::Tagging +Deleted all ActsAsTaggableOn::Tag +Deleted all ActsAsTaggableSong +Deleted all ActsAsTaggableArraySong +Deleted all TagColumnsSong +Finished to clean + +################################################################### + +bench:write + +Time measurements: + +Rehearsal ---------------------------------------------------------- +Metka: 2.192410 0.161092 2.353502 ( 2.754766) +ActsAsTaggableOn: 13.769918 0.554951 14.324869 ( 16.990127) +ActsAsTaggableOnArray: 2.150441 0.154127 2.304568 ( 2.700022) +TagColumns: 2.202647 0.156162 2.358809 ( 2.753400) +------------------------------------------------ total: 21.341748sec + + user system total real +Metka: 2.137315 0.154046 2.291361 ( 2.643363) +ActsAsTaggableOn: 11.302848 0.448674 11.751522 ( 14.019458) +ActsAsTaggableOnArray: 2.143134 0.128655 2.271789 ( 2.670797) +TagColumns: 2.133780 0.125749 2.259529 ( 2.653404) + +Memory measurements: + +Calculating ------------------------------------- +Metka: 179.064M memsize ( 0.000 retained) + 1.689M objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) +ActsAsTaggableOn: 843.949M memsize ( 0.000 retained) + 8.550M objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) +ActsAsTaggableOnArray: 178.807M memsize ( 0.000 retained) + 1.684M objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) +TagColumns: 180.009M memsize ( 0.000 retained) + 1.699M objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) + +################################################################### + +bench:read + +Time measurements: + +Rehearsal ---------------------------------------------------------- +Metka: 0.479695 0.044399 0.524094 ( 0.590616) +ActsAsTaggableOn: 2.436328 0.140581 2.576909 ( 3.096142) +ActsAsTaggableOnArray: 0.515198 0.042127 0.557325 ( 0.623205) +TagColumns: 0.518363 0.042661 0.561024 ( 0.626968) +------------------------------------------------- total: 4.219352sec + + user system total real +Metka: 0.446751 0.041886 0.488637 ( 0.554018) +ActsAsTaggableOn: 2.395166 0.164500 2.559666 ( 3.069655) +ActsAsTaggableOnArray: 0.439608 0.041682 0.481290 ( 0.544679) +TagColumns: 0.435404 0.041623 0.477027 ( 0.540359) + +Memory measurements: + +Calculating ------------------------------------- +Metka: 42.291M memsize ( 0.000 retained) + 388.694k objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) +ActsAsTaggableOn: 178.664M memsize ( 0.000 retained) + 1.812M objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) +ActsAsTaggableOnArray: 42.173M memsize ( 0.000 retained) + 383.003k objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) +TagColumns: 41.948M memsize ( 0.000 retained) + 383.003k objects ( 0.000 retained) + 50.000 strings ( 0.000 retained) + +################################################################### + +bench:find_by_tag + +Time measurements: + +Rehearsal ---------------------------------------------------------- +Metka: 0.029961 0.000059 0.030020 ( 0.030052) +ActsAsTaggableOn: 0.067095 0.000068 0.067163 ( 0.067205) +ActsAsTaggableOnArray: 0.043156 0.000133 0.043289 ( 0.043440) +TagColumns: 0.056475 0.000143 0.056618 ( 0.056697) +------------------------------------------------- total: 0.197090sec + + user system total real +Metka: 0.028291 0.000019 0.028310 ( 0.028321) +ActsAsTaggableOn: 0.065925 0.000036 0.065961 ( 0.065989) +ActsAsTaggableOnArray: 0.043214 0.000079 0.043293 ( 0.043361) +TagColumns: 0.056390 0.000160 0.056550 ( 0.056666) + +Memory measurements: + +Calculating ------------------------------------- +Metka: 4.752M memsize ( 0.000 retained) + 43.000k objects ( 0.000 retained) + 1.000 strings ( 0.000 retained) +ActsAsTaggableOn: 8.967M memsize ( 0.000 retained) + 81.002k objects ( 0.000 retained) + 9.000 strings ( 0.000 retained) +ActsAsTaggableOnArray: 5.211M memsize ( 0.000 retained) + 57.003k objects ( 0.000 retained) + 6.000 strings ( 0.000 retained) +TagColumns: 6.696M memsize ( 0.000 retained) + 94.003k objects ( 0.000 retained) + 8.000 strings ( 0.000 retained) + +Finished all benchmarks +``` ## Development After checking out the repo, run `bin/setup` to install dependencies. Then, run `rake spec` to run the tests. You can also run `bin/console` for an interactive prompt that will allow you to experiment.