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      "body": "Successful adulting involves constantly trying to strike a balance between the new and the old, the personal and the professional, the heart and the mind.\nWith constant emphasis on breakfast being the most important meal of the day, adulting has been a lot about finding that perfect breakfast concoction that is high on nutrition and low on preparation time. It has to feel right to keep your energy levels high for the first and most active part of your day while also giving you the feeling that you’ve started your day on a positive note by pampering yourself.\n\n![pexels-photo-107999.jpeg](https://steemitimages.com/DQmWrzcGkbnZGAJe84TSx9ivQqKNmvWGsgGmAauVk7rLdru/pexels-photo-107999.jpeg)\n \nThe art of breakfast, as we know it, is all about a feel-good factor and the right mix of imagination and preparation. Here is food for thought to make your breakfast the meal you’d want to relish everyday:\n \n1. Put on the chef’s hat\nWhether you prepare your own breakfast or you are amongst the lucky few to have a cook do it for you, make sure to be actively involved in the preparation of your breakfast. The aroma will wake up your senses that are still brooding over the alarm.![pexels-photo-113758.jpeg](https://steemitimages.com/DQmbH3iDvtzhFRBrZhR9gt8E7W7xfGqaNYbDeN9Jgj6dtzv/pexels-photo-113758.jpeg)\n\n2. Color therapy\nSet up the table and add color to your food as well as to your tableware. Don’t underestimate the sight of bright and beautiful hues, they are known to perk up your energy levels and jumpstart your day.![pexels-photo-245513.jpeg](https://steemitimages.com/DQmWRV5V6BtFKJ99M4uaJ9xkDbshtFLgqPS5ZbuAHQheMy1/pexels-photo-245513.jpeg)\n\n3. Don’t aim too high!\nKeep your options simple and healthy – choose between a variety of egg preparations, pancakes, fruits, sandwich combinations, salad portions, or good old muesli and milk.\n\n4. Time for yourself\nTake out time in the morning for yourself. Don’t be afraid to start your day with things you love. Don’t feel guilty about that longish bath you took, or that extra time to relish your meal.![love-pen-bed-drinking.jpg](https://steemitimages.com/DQmWfSVJtZZCGZ3j7w1haN1RZq2GgfKgrat6rgF7JJqveBY/love-pen-bed-drinking.jpg)\n\n5. Weekend bliss\nAll that has been put off for the weekend needs to be on priority list. Try perfecting that dish you think will take up too much time on a weekday morning, arrange your cutlery for easy access when you are in a hurry, buy that beautiful platter you’ve been wanting to eat your cheese and cracker from!![pexels-photo-265186.jpeg](https://steemitimages.com/DQmRc6TzxkcUGUd37Vh3c9PWvbKRGxiDGzzNzMq21WAgni8/pexels-photo-265186.jpeg)\n\n\n\n Afterword:\n “Breakfast! My favorite meal - and you can be so creative. I think of bowls of sparkling\n berries and fresh cream, baskets of Popovers and freshly squeezed orange juice, thick\n country bacon, hot maple syrup, pancakes and French toast - even the nutty flavor of\n Irish oatmeal with brown sugar and cream. Breakfast is the place I splurge with\n calories, then I spend the rest of the day getting them off! I love to use my prettiest table\n settings - crocheted placemats with lace-edged napkins and old hammered silver. And\n whether you are inside in front of a fire, candles burning brightly on a wintery day - or\n outside on a patio enjoying the morning sun - whether you are having a group of friends\n and family, a quiet little brunch for two, or an even quieter little brunch just for yourself,\n breakfast can set the mood and pace of the whole day.” – Susan Branch, American\n Author.",
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      "body": "Now a days when you have data, search becomes an essential part of your tool. Doesn’t matter if you have website or not, search is an important feature, which comes to mind before even starting to build the tool itself.\n\nThe most popular platform in last 5 years for search that we have seen are ElasticSearch and SOLR, both are Lucene based. They are very powerful and great tools, solves almost all the tasks of search, but before going down to the route of adding these massive tools in the existing toolset, lets look at something which is bit lighter and should be good enough!\n\nWhen we build a basic search feature in our app, we would at least want:\n\n    JSON attributes support\n    Indexing\n    Stemming\n\nSince the postgres > 8.3 , postgres supports full text search with all of the above feature and much more.\n\nlets see how easy it is to setup a basic search feature for your app.\n\nLets assume following schema and data:\n\n\n\nCREATE TABLE profiles (\n\nid SERIAL PRIMARY KEY,\n\nname TEXT\n\n);\n\nCREATE TABLE accounts (\n\nid SERIAL PRIMARY KEY,\n\naccount_id INTEGER NOT NULL,\n\nprofile_id INTEGER NOT NULL\n\n);\n\n\nCREATE TABLE ads (\n\naccount_id INTEGER NOT NULL,\n\nid SERIAL PRIMARY KEY,\n\ndata jsonb\n\n);\n\nINSERT INTO profiles (id, name)\n\nVALUES (1, 'United Federation of Planets'),\n\n(2, 'Wayne Industries');\n\nINSERT INTO accounts (id, account_id, profile_id)\n\nVALUES (1, 999, 1), (2, 998, 1), (3, 666, 2);\n\nINSERT INTO ads (id, account_id, data)\n\nVALUES (1, 999, '{\"name\": \"Plane Accessories on sale\", \"ad_id\": 18976, \"type\": \"google\"}'),\n\n(2, 999, '{\"name\": \"Plane Taxi Service for free\", \"ad_id\": 76859, \"type\": \"twitter\"}'),\n\n(3, 998, '{\"name\": \"Plane on rent for wedding\", \"ad_id\": 34568, \"type\": \"youtube\"}'),\n\n(4, 666, '{\"name\": \"blow up plane training\", \"ad_id\": 98576, \"type\": \"Facebook\"}');\n\nThe setup is for the any advertising platform, a profile, which has many accounts and each accounts has many ads.\n\nWhen we talk about full text search, we usually talk about  a document, simply put a document is like an article or content which is the subject area of search and may be a logical entity. It may be across multiple tables.\n\n“In text retrieval, full text search refers to techniques for searching a single computer-stored document or a collection in a full text database.“\n\nDocument is not related to table schema but to data,  for our use case the we could use any/all of the following as a document:\n\n    profiles.name\n    ads.data.name\n    ads.data.ad_id\n    ads.data.type\n\nSo lets create them.\n\n\nSELECT profiles.name || ' ' || cast(ads.data->>'name' as text) || ' ' || cast(ads.data->>'ad_id' as text) || ' ' || cast(ads.data->>'type' as text) as document\n\nFROM  ads\n\nJOIN accounts on accounts.account_id = ads.account_id\n\nJOIN profiles on profiles.id = accounts.profile_id;\n\ndocument\n\n------------------------------------------------------------------\n\nWayne Industries plane training 98576 Facebook\n\nUnited Federation of Planets Plane on rent for wedding 34568 youtube\n\nUnited Federation of Planets Plane Accessories on sale 18976 google\n\nUnited Federation of Planets Plane Taxi Service for free 76859 twitter\n\n(4 rows)\n\nYou can also use string_agg() function with coalesce() function in order to aggregate many strings and remove nulls.\n\nNow our document is just a simple long string, and doesn’t really optimised to be searched, we need to format this in lexemes, which is more suited and understood  by postgres for full text search.\n\n“A lexeme is a sequence of characters in the source program that matches the pattern for a token and is identified by the lexical analyzer as an instance of that token”\n\nTo convert the above document to lexemes postgres provides us with to_tsvector().\n\n\n\nSELECT to_tsvector(profiles.name) ||\n\nto_tsvector(cast(ads.data->>'name' as text)) ||\n\nto_tsvector(cast(ads.data->>'ad_id' as text)) ||\n\nto_tsvector(cast(ads.data->>'type' as text))\n\nas document\n\nFROM ads\n\nJOIN accounts on accounts.account_id = ads.account_id\n\nJOIN profiles on profiles.id = accounts.profile_id;\n\ndocument\n\n------------------------------------------------------------------\n\n'98576':7 'blow':3 'facebook':8 'industries':2 'plane':5 'training':6 'up':4 'wayne':1\n\n'34568':10 'federation':2 'for':8 'of':3 'on':6 'plane':5 'planets':4 'rent':7 'united':1 'wedding':9 'youtube':11\n\n'18976':9 'accessories':6 'federation':2 'google':10 'of':3 'on':7 'plane':5 'planets':4 'sale':8 'united':1\n\n'76859':10 'federation':2 'for':8 'free':9 'of':3 'plane':5 'planets':4 'service':7 'taxi':6 'twitter':11 'united':1\n\n(4 rows)\n\n\nNow the result is slightly different.\n\nA tsvector value is a sorted list of distinct lexemes which are words that have been normalized to make different variants of the same word look alike. For example, normalization almost always includes folding upper-case letters to lower-case and often involves removal of suffixes (such as ‘s’, ‘es’ or ‘ing’ in English). This allows searches to find variant forms of the same word without tediously entering all the possible variants. The number  represents the occurance of the word in the original string.\n\nBy default, Postgres uses ‘english’ as text search configuration for the function to_tsvector and it will also ignore english stopwords. That explains why the tsvector results have fewer elements than the ones in our sentence. We see later a bit more about languages and text search configuration.\n\nNow in order to query a tsvector document, lets try following sql:\n\n\n\nselect to_tsvector('If you can dream it, you can do it') @@ 'dream';\n\n?column?\n\n----------\n\nt\n\n(1 row)\n\n> select to_tsvector('It''s kind of fun to do the impossible') @@ 'impossible';\n\n?column?\n\n----------\n\nf\n\n\nThe second result is false because we need to build this using lexemes and so we need two things to_tsquery() function and operator @@.\n\nThe following query shows the difference.\n\n\n\nSELECT 'impossible'::tsquery, to_tsquery('impossible');\n\ntsquery | to_tsquery\n\n--------------+------------\n\n'impossible' | 'imposs'\n\n(1 row)\n\nSELECT 'dream'::tsquery, to_tsquery('dream');\n\ntsquery | to_tsquery\n\n--------------+------------\n\n'dream' | 'dream'\n\n(1 row)\n\nIn the above case of ‘dream’ the stem is also dream but ‘impossible’ stem is ‘imposs’ and thats why our to_tsvector query returned false.\n\nNow if we run the above query using ts_query we get\n\n\n\nSELECT to_tsvector('It''s kind of fun to do the impossible') @@ to_tsquery('impossible');\n\n?column?\n\n----------\n\nt\n\n(1 row)\n\n\nts_query function can take all the boolean operators  & (AND), | (OR), and ! (NOT)\n\nLets come back to the main problem and construct the final query, which should look like the following.\n\n\n\nSELECT profile_name, ad_name, ad_id, vendor\n\nFROM (SELECT profiles.name as profile_name,\n\nads.data->>'name' as ad_name,\n\nads.data->>'ad_id' as ad_id,\n\nads.data->>'type' as vendor,\n\nto_tsvector(profiles.name) ||\n\nto_tsvector(cast(ads.data->>'name' as text)) ||\n\nto_tsvector(cast(ads.data->>'ad_id' as text)) ||\n\nto_tsvector(cast(ads.data->>'type' as text))\n\nas document\n\nFROM ads\n\nJOIN accounts on accounts.account_id = ads.account_id\n\nJOIN profiles on profiles.id = accounts.profile_id) as ad_search\n\nWHERE ad_search.document @@ to_tsquery('United');\n\nprofile_name | ad_name | ad_id | vendor\n\n------------------------------+-----------------------------+-------+---------\n\nUnited Federation of Planets | Plane on rent for wedding | 34568 | youtube\n\nUnited Federation of Planets | Plane Accessories on sale | 18976 | google\n\nUnited Federation of Planets | Plane Taxi Service for free | 76859 | twitter\n\n(3 rows)\n\n\nThis returns our results with united lexemes.\n\nNow lets do some optimizations.Since the document is spread across multiple tables, its necessary to denormalize the data or create materialized view.\nAs per Postgres: “Materialized views in PostgreSQL use the rule system like views do, but persist the results in a table-like form”.\n\nThe main differences between:\n\n\nCREATE MATERIALIZED VIEW mymatview AS SELECT * FROM mytab;\n\nand:\n\nCREATE TABLE mymatview AS SELECT * FROM myth;\n\nis that the materialized view cannot subsequently be directly updated and that the query used to create the materialized view is stored in exactly the same way that a view’s query is stored, so that fresh data can be generated for the materialized view with:\n\n\n\nREFRESH MATERIALIZED VIEW mymatview;\n\nSo lets create one for our search.\n\n\n\nCREATE MATERIALIZED VIEW ad_search\n\nAS SELECT profiles.name as profile_name,\n\nads.data->>'name' as ad_name,\n\nads.data->>'ad_id' as ad_id,\n\nads.data->>'type' as vendor,\n\nto_tsvector(profiles.name) ||\n\nto_tsvector(cast(ads.data->>'name' as text)) ||\n\nto_tsvector(cast(ads.data->>'ad_id' as text)) ||\n\nto_tsvector(cast(ads.data->>'type' as text))\n\nas document\n\nFROM ads\n\nJOIN accounts on accounts.account_id = ads.account_id\n\nJOIN profiles on profiles.id = accounts.profile_id;\n\nThen reindexing the search engine will be as simple as periodically running REFRESH MATERIALIZED VIEW search_index;.\n\nWe can now add an index on the materialized view.\n\n\n\nCREATE INDEX idx_ad_search ON ad_search USING gin(document);\n\nNow our query also becomes much simpler.\n\n\n\nSELECT profile_name, ad_name, ad_id, vendor\n\nFROM ad_search\n\nWHERE document @@ to_tsquery('United');\n\n\nThere are so many other things that we can do with postgres, like:\n\n    Ranking – providing weight to results and then ordering on the basis of that using ts_rank() function\n    Similarities search – covers misspelling and fuzzy search behaviours using similarity() function\n    Support for multiple languages\n    Support for accented characters\n\n \n\nConclusion\n\nThis article aims to give you a brief insight into building your own search with postgres and we have seen how easy is to build a document based search using it . Postgres might not be as advanced as ElasticSearch and SOLR, but these two are dedicated full-text search tools whereas full-text search is only a feature of PostgreSQL and a pretty good one.",
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