{"ok": true, "database": "tils", "table": "til", "rows": [{"path": "datasette_search-all-columns-trick.md", "topic": "datasette", "title": "Searching all columns of a table in Datasette", "url": "https://github.com/simonw/til/blob/main/datasette/search-all-columns-trick.md", "body": "I came up with this trick today, when I wanted to run a `LIKE` search against every column in a table.\n\nThe trick is to generate a SQL query that does a `LIKE` search against every column of a table. We can generate that query using another query:\n\n```sql\nselect\n 'select * from \"' || :table || '\" where ' || group_concat(\n '\"' || name || '\" like ''%'' || :search || ''%''',\n ' or '\n )\nfrom\n pragma_table_info(:table)\n```\nHere's what you get when you [run that query](https://fivethirtyeight.datasettes.com/fivethirtyeight?sql=select%0D%0A++%27select+*+from+%22%27+%7C%7C+%3Atable+%7C%7C+%27%22+where+%27+%7C%7C+group_concat%28%0D%0A++++%27%22%27+%7C%7C+name+%7C%7C+%27%22+like+%27%27%25%27%27+%7C%7C+%3Asearch+%7C%7C+%27%27%25%27%27%27%2C%0D%0A++++%27+or+%27%0D%0A++%29%0D%0Afrom%0D%0A++pragma_table_info%28%3Atable%29&table=avengers%2Favengers) against the [avengers example table](https://fivethirtyeight.datasettes.com/fivethirtyeight/avengers%2Favengers) from FiveThirtyEight (pretty-printed):\n\n```sql\nselect\n *\nfrom\n \"avengers/avengers\"\nwhere\n \"URL\" like '%' || :search || '%'\n or \"Name/Alias\" like '%' || :search || '%'\n or \"Appearances\" like '%' || :search || '%'\n or \"Current?\" like '%' || :search || '%'\n or \"Gender\" like '%' || :search || '%'\n or \"Probationary Introl\" like '%' || :search || '%'\n or \"Full/Reserve Avengers Intro\" like '%' || :search || '%'\n or \"Year\" like '%' || :search || '%'\n or \"Years since joining\" like '%' || :search || '%'\n or \"Honorary\" like '%' || :search || '%'\n or \"Death1\" like '%' || :search || '%'\n or \"Return1\" like '%' || :search || '%'\n or \"Death2\" like '%' || :search || '%'\n or \"Return2\" like '%' || :search || '%'\n or \"Death3\" like '%' || :search || '%'\n or \"Return3\" like '%' || :search || '%'\n or \"Death4\" like '%' || :search || '%'\n or \"Return4\" like '%' || :search || '%'\n or \"Death5\" like '%' || :search || '%'\n or \"Return5\" like '%' || :search || '%'\n or \"Notes\" like '%' || :search || '%'\n```\nHere's [an example search](https://fivethirtyeight.datasettes.com/fivethirtyeight?sql=select%0D%0A++*%0D%0Afrom%0D%0A++%22avengers%2Favengers%22%0D%0Awhere%0D%0A++%22URL%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Name%2FAlias%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Appearances%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Current%3F%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Gender%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Probationary+Introl%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Full%2FReserve+Avengers+Intro%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Year%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Years+since+joining%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Honorary%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Death1%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Return1%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Death2%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Return2%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Death3%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Return3%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Death4%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Return4%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Death5%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Return5%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22Notes%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27&search=Grim+Reaper) using that generated query.\n\n## Same trick for the entire database\n\nHere's a query that generates a query that searches every column in every table in the database!\n\n```sql\nwith tables as (\n select\n name as table_name\n from\n sqlite_master\n where\n type = 'table'\n),\nqueries as (\n select\n 'select ''' || tables.table_name || ''' as _table, rowid from \"' || tables.table_name || '\" where ' || group_concat(\n '\"' || name || '\" like ''%'' || :search || ''%''',\n ' or '\n ) as query\n from\n pragma_table_info(tables.table_name),\n tables\n group by\n tables.table_name\n)\nselect\n group_concat(query, ' union all ')\nfrom\n queries\n```\nI tried this against the FiveThirtyEight database and the query it produced was way beyond the URL length limit for Cloud Run.\n\nHere's the result if [run against latest.datasette.io/fixtures](https://latest.datasette.io/fixtures?sql=with+tables+as+%28%0D%0A++select%0D%0A++++name+as+table_name%0D%0A++from%0D%0A++++sqlite_master%0D%0A++where%0D%0A++++type+%3D+%27table%27%0D%0A%29%2C%0D%0Aqueries+as+%28%0D%0A++select%0D%0A++++%27select+%27%27%27+%7C%7C+tables.table_name+%7C%7C+%27%27%27+as+_table%2C+rowid+from+%22%27+%7C%7C+tables.table_name+%7C%7C+%27%22+where+%27+%7C%7C+group_concat%28%0D%0A++++++%27%22%27+%7C%7C+name+%7C%7C+%27%22+like+%27%27%25%27%27+%7C%7C+%3Asearch+%7C%7C+%27%27%25%27%27%27%2C%0D%0A++++++%27+or+%27%0D%0A++++%29+as+query%0D%0A++from%0D%0A++++pragma_table_info%28tables.table_name%29%2C%0D%0A++++tables%0D%0A++group+by%0D%0A++++tables.table_name%0D%0A%29%0D%0Aselect%0D%0A++group_concat%28query%2C+%27+union+all+%27%29%0D%0Afrom%0D%0A++queries):\n\n```sql\nselect\n '123_starts_with_digits' as _table,\n rowid\nfrom\n \"123_starts_with_digits\"\nwhere\n \"content\" like '%' || :search || '%'\nunion all\nselect\n 'Table With Space In Name' as _table,\n rowid\nfrom\n \"Table With Space In Name\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\nunion all\nselect\n 'attraction_characteristic' as _table,\n rowid\nfrom\n \"attraction_characteristic\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"name\" like '%' || :search || '%'\nunion all\nselect\n 'binary_data' as _table,\n rowid\nfrom\n \"binary_data\"\nwhere\n \"data\" like '%' || :search || '%'\nunion all\nselect\n 'complex_foreign_keys' as _table,\n rowid\nfrom\n \"complex_foreign_keys\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"f1\" like '%' || :search || '%'\n or \"f2\" like '%' || :search || '%'\n or \"f3\" like '%' || :search || '%'\nunion all\nselect\n 'compound_primary_key' as _table,\n rowid\nfrom\n \"compound_primary_key\"\nwhere\n \"pk1\" like '%' || :search || '%'\n or \"pk2\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\nunion all\nselect\n 'compound_three_primary_keys' as _table,\n rowid\nfrom\n \"compound_three_primary_keys\"\nwhere\n \"pk1\" like '%' || :search || '%'\n or \"pk2\" like '%' || :search || '%'\n or \"pk3\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\nunion all\nselect\n 'custom_foreign_key_label' as _table,\n rowid\nfrom\n \"custom_foreign_key_label\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"foreign_key_with_custom_label\" like '%' || :search || '%'\nunion all\nselect\n 'facet_cities' as _table,\n rowid\nfrom\n \"facet_cities\"\nwhere\n \"id\" like '%' || :search || '%'\n or \"name\" like '%' || :search || '%'\nunion all\nselect\n 'facetable' as _table,\n rowid\nfrom\n \"facetable\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"created\" like '%' || :search || '%'\n or \"planet_int\" like '%' || :search || '%'\n or \"on_earth\" like '%' || :search || '%'\n or \"state\" like '%' || :search || '%'\n or \"city_id\" like '%' || :search || '%'\n or \"neighborhood\" like '%' || :search || '%'\n or \"tags\" like '%' || :search || '%'\n or \"complex_array\" like '%' || :search || '%'\n or \"distinct_some_null\" like '%' || :search || '%'\nunion all\nselect\n 'foreign_key_references' as _table,\n rowid\nfrom\n \"foreign_key_references\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"foreign_key_with_label\" like '%' || :search || '%'\n or \"foreign_key_with_blank_label\" like '%' || :search || '%'\n or \"foreign_key_with_no_label\" like '%' || :search || '%'\n or \"foreign_key_compound_pk1\" like '%' || :search || '%'\n or \"foreign_key_compound_pk2\" like '%' || :search || '%'\nunion all\nselect\n 'infinity' as _table,\n rowid\nfrom\n \"infinity\"\nwhere\n \"value\" like '%' || :search || '%'\nunion all\nselect\n 'no_primary_key' as _table,\n rowid\nfrom\n \"no_primary_key\"\nwhere\n \"content\" like '%' || :search || '%'\n or \"a\" like '%' || :search || '%'\n or \"b\" like '%' || :search || '%'\n or \"c\" like '%' || :search || '%'\nunion all\nselect\n 'primary_key_multiple_columns' as _table,\n rowid\nfrom\n \"primary_key_multiple_columns\"\nwhere\n \"id\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\n or \"content2\" like '%' || :search || '%'\nunion all\nselect\n 'primary_key_multiple_columns_explicit_label' as _table,\n rowid\nfrom\n \"primary_key_multiple_columns_explicit_label\"\nwhere\n \"id\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\n or \"content2\" like '%' || :search || '%'\nunion all\nselect\n 'roadside_attraction_characteristics' as _table,\n rowid\nfrom\n \"roadside_attraction_characteristics\"\nwhere\n \"attraction_id\" like '%' || :search || '%'\n or \"characteristic_id\" like '%' || :search || '%'\nunion all\nselect\n 'roadside_attractions' as _table,\n rowid\nfrom\n \"roadside_attractions\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"name\" like '%' || :search || '%'\n or \"address\" like '%' || :search || '%'\n or \"latitude\" like '%' || :search || '%'\n or \"longitude\" like '%' || :search || '%'\nunion all\nselect\n 'searchable' as _table,\n rowid\nfrom\n \"searchable\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"text1\" like '%' || :search || '%'\n or \"text2\" like '%' || :search || '%'\n or \"name with . and spaces\" like '%' || :search || '%'\nunion all\nselect\n 'searchable_fts' as _table,\n rowid\nfrom\n \"searchable_fts\"\nwhere\n \"text1\" like '%' || :search || '%'\n or \"text2\" like '%' || :search || '%'\n or \"name with . and spaces\" like '%' || :search || '%'\nunion all\nselect\n 'searchable_fts_docsize' as _table,\n rowid\nfrom\n \"searchable_fts_docsize\"\nwhere\n \"docid\" like '%' || :search || '%'\n or \"size\" like '%' || :search || '%'\nunion all\nselect\n 'searchable_fts_segdir' as _table,\n rowid\nfrom\n \"searchable_fts_segdir\"\nwhere\n \"level\" like '%' || :search || '%'\n or \"idx\" like '%' || :search || '%'\n or \"start_block\" like '%' || :search || '%'\n or \"leaves_end_block\" like '%' || :search || '%'\n or \"end_block\" like '%' || :search || '%'\n or \"root\" like '%' || :search || '%'\nunion all\nselect\n 'searchable_fts_segments' as _table,\n rowid\nfrom\n \"searchable_fts_segments\"\nwhere\n \"blockid\" like '%' || :search || '%'\n or \"block\" like '%' || :search || '%'\nunion all\nselect\n 'searchable_fts_stat' as _table,\n rowid\nfrom\n \"searchable_fts_stat\"\nwhere\n \"id\" like '%' || :search || '%'\n or \"value\" like '%' || :search || '%'\nunion all\nselect\n 'searchable_tags' as _table,\n rowid\nfrom\n \"searchable_tags\"\nwhere\n \"searchable_id\" like '%' || :search || '%'\n or \"tag\" like '%' || :search || '%'\nunion all\nselect\n 'select' as _table,\n rowid\nfrom\n \"select\"\nwhere\n \"group\" like '%' || :search || '%'\n or \"having\" like '%' || :search || '%'\n or \"and\" like '%' || :search || '%'\n or \"json\" like '%' || :search || '%'\nunion all\nselect\n 'simple_primary_key' as _table,\n rowid\nfrom\n \"simple_primary_key\"\nwhere\n \"id\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\nunion all\nselect\n 'sortable' as _table,\n rowid\nfrom\n \"sortable\"\nwhere\n \"pk1\" like '%' || :search || '%'\n or \"pk2\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\n or \"sortable\" like '%' || :search || '%'\n or \"sortable_with_nulls\" like '%' || :search || '%'\n or \"sortable_with_nulls_2\" like '%' || :search || '%'\n or \"text\" like '%' || :search || '%'\nunion all\nselect\n 'table/with/slashes.csv' as _table,\n rowid\nfrom\n \"table/with/slashes.csv\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"content\" like '%' || :search || '%'\nunion all\nselect\n 'tags' as _table,\n rowid\nfrom\n \"tags\"\nwhere\n \"tag\" like '%' || :search || '%'\nunion all\nselect\n 'units' as _table,\n rowid\nfrom\n \"units\"\nwhere\n \"pk\" like '%' || :search || '%'\n or \"distance\" like '%' || :search || '%'\n or \"frequency\" like '%' || :search || '%'\n```\n[It works!](https://latest.datasette.io/fixtures?sql=select%0D%0A++%27123_starts_with_digits%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22123_starts_with_digits%22%0D%0Awhere%0D%0A++%22content%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27Table+With+Space+In+Name%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22Table+With+Space+In+Name%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22content%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27attraction_characteristic%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22attraction_characteristic%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22name%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27binary_data%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22binary_data%22%0D%0Awhere%0D%0A++%22data%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27complex_foreign_keys%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22complex_foreign_keys%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22f1%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22f2%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22f3%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27compound_primary_key%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22compound_primary_key%22%0D%0Awhere%0D%0A++%22pk1%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22pk2%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22content%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27compound_three_primary_keys%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22compound_three_primary_keys%22%0D%0Awhere%0D%0A++%22pk1%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22pk2%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22pk3%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22content%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27custom_foreign_key_label%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22custom_foreign_key_label%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22foreign_key_with_custom_label%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27facet_cities%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22facet_cities%22%0D%0Awhere%0D%0A++%22id%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22name%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27facetable%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22facetable%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22created%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22planet_int%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22on_earth%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22state%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22city_id%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22neighborhood%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22tags%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22complex_array%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22distinct_some_null%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27foreign_key_references%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22foreign_key_references%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22foreign_key_with_label%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22foreign_key_with_blank_label%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22foreign_key_with_no_label%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22foreign_key_compound_pk1%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22foreign_key_compound_pk2%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27infinity%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22infinity%22%0D%0Awhere%0D%0A++%22value%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27no_primary_key%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22no_primary_key%22%0D%0Awhere%0D%0A++%22content%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22a%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22b%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22c%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27primary_key_multiple_columns%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A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%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22sortable_with_nulls%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22sortable_with_nulls_2%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22text%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27table%2Fwith%2Fslashes.csv%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22table%2Fwith%2Fslashes.csv%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22content%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27tags%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22tags%22%0D%0Awhere%0D%0A++%22tag%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0Aunion+all%0D%0Aselect%0D%0A++%27units%27+as+_table%2C%0D%0A++rowid%0D%0Afrom%0D%0A++%22units%22%0D%0Awhere%0D%0A++%22pk%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22distance%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27%0D%0A++or+%22frequency%22+like+%27%25%27+%7C%7C+%3Asearch+%7C%7C+%27%25%27&search=museum&_hide_sql=1)", "html": "

I came up with this trick today, when I wanted to run a LIKE search against every column in a table.

\n

The trick is to generate a SQL query that does a LIKE search against every column of a table. We can generate that query using another query:

\n
select\n  'select * from \"' || :table || '\" where ' || group_concat(\n    '\"' || name || '\" like ''%'' || :search || ''%''',\n    ' or '\n  )\nfrom\n  pragma_table_info(:table)
\n

Here's what you get when you run that query against the avengers example table from FiveThirtyEight (pretty-printed):

\n
select\n  *\nfrom\n  \"avengers/avengers\"\nwhere\n  \"URL\" like '%' || :search || '%'\n  or \"Name/Alias\" like '%' || :search || '%'\n  or \"Appearances\" like '%' || :search || '%'\n  or \"Current?\" like '%' || :search || '%'\n  or \"Gender\" like '%' || :search || '%'\n  or \"Probationary Introl\" like '%' || :search || '%'\n  or \"Full/Reserve Avengers Intro\" like '%' || :search || '%'\n  or \"Year\" like '%' || :search || '%'\n  or \"Years since joining\" like '%' || :search || '%'\n  or \"Honorary\" like '%' || :search || '%'\n  or \"Death1\" like '%' || :search || '%'\n  or \"Return1\" like '%' || :search || '%'\n  or \"Death2\" like '%' || :search || '%'\n  or \"Return2\" like '%' || :search || '%'\n  or \"Death3\" like '%' || :search || '%'\n  or \"Return3\" like '%' || :search || '%'\n  or \"Death4\" like '%' || :search || '%'\n  or \"Return4\" like '%' || :search || '%'\n  or \"Death5\" like '%' || :search || '%'\n  or \"Return5\" like '%' || :search || '%'\n  or \"Notes\" like '%' || :search || '%'
\n

Here's an example search using that generated query.

\n

\nSame trick for the entire database

\n

Here's a query that generates a query that searches every column in every table in the database!

\n
with tables as (\n  select\n    name as table_name\n  from\n    sqlite_master\n  where\n    type = 'table'\n),\nqueries as (\n  select\n    'select ''' || tables.table_name || ''' as _table, rowid from \"' || tables.table_name || '\" where ' || group_concat(\n      '\"' || name || '\" like ''%'' || :search || ''%''',\n      ' or '\n    ) as query\n  from\n    pragma_table_info(tables.table_name),\n    tables\n  group by\n    tables.table_name\n)\nselect\n  group_concat(query, ' union all ')\nfrom\n  queries
\n

I tried this against the FiveThirtyEight database and the query it produced was way beyond the URL length limit for Cloud Run.

\n

Here's the result if run against latest.datasette.io/fixtures:

\n
select\n  '123_starts_with_digits' as _table,\n  rowid\nfrom\n  \"123_starts_with_digits\"\nwhere\n  \"content\" like '%' || :search || '%'\nunion all\nselect\n  'Table With Space In Name' as _table,\n  rowid\nfrom\n  \"Table With Space In Name\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\nunion all\nselect\n  'attraction_characteristic' as _table,\n  rowid\nfrom\n  \"attraction_characteristic\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"name\" like '%' || :search || '%'\nunion all\nselect\n  'binary_data' as _table,\n  rowid\nfrom\n  \"binary_data\"\nwhere\n  \"data\" like '%' || :search || '%'\nunion all\nselect\n  'complex_foreign_keys' as _table,\n  rowid\nfrom\n  \"complex_foreign_keys\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"f1\" like '%' || :search || '%'\n  or \"f2\" like '%' || :search || '%'\n  or \"f3\" like '%' || :search || '%'\nunion all\nselect\n  'compound_primary_key' as _table,\n  rowid\nfrom\n  \"compound_primary_key\"\nwhere\n  \"pk1\" like '%' || :search || '%'\n  or \"pk2\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\nunion all\nselect\n  'compound_three_primary_keys' as _table,\n  rowid\nfrom\n  \"compound_three_primary_keys\"\nwhere\n  \"pk1\" like '%' || :search || '%'\n  or \"pk2\" like '%' || :search || '%'\n  or \"pk3\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\nunion all\nselect\n  'custom_foreign_key_label' as _table,\n  rowid\nfrom\n  \"custom_foreign_key_label\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"foreign_key_with_custom_label\" like '%' || :search || '%'\nunion all\nselect\n  'facet_cities' as _table,\n  rowid\nfrom\n  \"facet_cities\"\nwhere\n  \"id\" like '%' || :search || '%'\n  or \"name\" like '%' || :search || '%'\nunion all\nselect\n  'facetable' as _table,\n  rowid\nfrom\n  \"facetable\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"created\" like '%' || :search || '%'\n  or \"planet_int\" like '%' || :search || '%'\n  or \"on_earth\" like '%' || :search || '%'\n  or \"state\" like '%' || :search || '%'\n  or \"city_id\" like '%' || :search || '%'\n  or \"neighborhood\" like '%' || :search || '%'\n  or \"tags\" like '%' || :search || '%'\n  or \"complex_array\" like '%' || :search || '%'\n  or \"distinct_some_null\" like '%' || :search || '%'\nunion all\nselect\n  'foreign_key_references' as _table,\n  rowid\nfrom\n  \"foreign_key_references\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"foreign_key_with_label\" like '%' || :search || '%'\n  or \"foreign_key_with_blank_label\" like '%' || :search || '%'\n  or \"foreign_key_with_no_label\" like '%' || :search || '%'\n  or \"foreign_key_compound_pk1\" like '%' || :search || '%'\n  or \"foreign_key_compound_pk2\" like '%' || :search || '%'\nunion all\nselect\n  'infinity' as _table,\n  rowid\nfrom\n  \"infinity\"\nwhere\n  \"value\" like '%' || :search || '%'\nunion all\nselect\n  'no_primary_key' as _table,\n  rowid\nfrom\n  \"no_primary_key\"\nwhere\n  \"content\" like '%' || :search || '%'\n  or \"a\" like '%' || :search || '%'\n  or \"b\" like '%' || :search || '%'\n  or \"c\" like '%' || :search || '%'\nunion all\nselect\n  'primary_key_multiple_columns' as _table,\n  rowid\nfrom\n  \"primary_key_multiple_columns\"\nwhere\n  \"id\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\n  or \"content2\" like '%' || :search || '%'\nunion all\nselect\n  'primary_key_multiple_columns_explicit_label' as _table,\n  rowid\nfrom\n  \"primary_key_multiple_columns_explicit_label\"\nwhere\n  \"id\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\n  or \"content2\" like '%' || :search || '%'\nunion all\nselect\n  'roadside_attraction_characteristics' as _table,\n  rowid\nfrom\n  \"roadside_attraction_characteristics\"\nwhere\n  \"attraction_id\" like '%' || :search || '%'\n  or \"characteristic_id\" like '%' || :search || '%'\nunion all\nselect\n  'roadside_attractions' as _table,\n  rowid\nfrom\n  \"roadside_attractions\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"name\" like '%' || :search || '%'\n  or \"address\" like '%' || :search || '%'\n  or \"latitude\" like '%' || :search || '%'\n  or \"longitude\" like '%' || :search || '%'\nunion all\nselect\n  'searchable' as _table,\n  rowid\nfrom\n  \"searchable\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"text1\" like '%' || :search || '%'\n  or \"text2\" like '%' || :search || '%'\n  or \"name with . and spaces\" like '%' || :search || '%'\nunion all\nselect\n  'searchable_fts' as _table,\n  rowid\nfrom\n  \"searchable_fts\"\nwhere\n  \"text1\" like '%' || :search || '%'\n  or \"text2\" like '%' || :search || '%'\n  or \"name with . and spaces\" like '%' || :search || '%'\nunion all\nselect\n  'searchable_fts_docsize' as _table,\n  rowid\nfrom\n  \"searchable_fts_docsize\"\nwhere\n  \"docid\" like '%' || :search || '%'\n  or \"size\" like '%' || :search || '%'\nunion all\nselect\n  'searchable_fts_segdir' as _table,\n  rowid\nfrom\n  \"searchable_fts_segdir\"\nwhere\n  \"level\" like '%' || :search || '%'\n  or \"idx\" like '%' || :search || '%'\n  or \"start_block\" like '%' || :search || '%'\n  or \"leaves_end_block\" like '%' || :search || '%'\n  or \"end_block\" like '%' || :search || '%'\n  or \"root\" like '%' || :search || '%'\nunion all\nselect\n  'searchable_fts_segments' as _table,\n  rowid\nfrom\n  \"searchable_fts_segments\"\nwhere\n  \"blockid\" like '%' || :search || '%'\n  or \"block\" like '%' || :search || '%'\nunion all\nselect\n  'searchable_fts_stat' as _table,\n  rowid\nfrom\n  \"searchable_fts_stat\"\nwhere\n  \"id\" like '%' || :search || '%'\n  or \"value\" like '%' || :search || '%'\nunion all\nselect\n  'searchable_tags' as _table,\n  rowid\nfrom\n  \"searchable_tags\"\nwhere\n  \"searchable_id\" like '%' || :search || '%'\n  or \"tag\" like '%' || :search || '%'\nunion all\nselect\n  'select' as _table,\n  rowid\nfrom\n  \"select\"\nwhere\n  \"group\" like '%' || :search || '%'\n  or \"having\" like '%' || :search || '%'\n  or \"and\" like '%' || :search || '%'\n  or \"json\" like '%' || :search || '%'\nunion all\nselect\n  'simple_primary_key' as _table,\n  rowid\nfrom\n  \"simple_primary_key\"\nwhere\n  \"id\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\nunion all\nselect\n  'sortable' as _table,\n  rowid\nfrom\n  \"sortable\"\nwhere\n  \"pk1\" like '%' || :search || '%'\n  or \"pk2\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\n  or \"sortable\" like '%' || :search || '%'\n  or \"sortable_with_nulls\" like '%' || :search || '%'\n  or \"sortable_with_nulls_2\" like '%' || :search || '%'\n  or \"text\" like '%' || :search || '%'\nunion all\nselect\n  'table/with/slashes.csv' as _table,\n  rowid\nfrom\n  \"table/with/slashes.csv\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"content\" like '%' || :search || '%'\nunion all\nselect\n  'tags' as _table,\n  rowid\nfrom\n  \"tags\"\nwhere\n  \"tag\" like '%' || :search || '%'\nunion all\nselect\n  'units' as _table,\n  rowid\nfrom\n  \"units\"\nwhere\n  \"pk\" like '%' || :search || '%'\n  or \"distance\" like '%' || :search || '%'\n  or \"frequency\" like '%' || :search || '%'
\n

It works!

\n", "shot": {"$base64": true, "encoded": 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"}, "created": "2021-08-23T11:48:22-07:00", "created_utc": "2021-08-23T18:48:22+00:00", "updated": "2021-08-23T12:05:47-07:00", "updated_utc": "2021-08-23T19:05:47+00:00", "shot_hash": "e24510529260e6312f52947cb05723d0", "slug": "search-all-columns-trick"}], "primary_keys": ["path"], "primary_key_values": ["datasette_search-all-columns-trick.md"], "query_ms": 2.445450001687277, "truncated": false}