Does anyone know if there's an efficient way to extract all the words from a single column and count the frequency of each word in SQL Server? I only have read-only access to my database so I can't create a self-defined function to do this.
Here's a reproducible example:
CREATE TABLE words
(
id INT PRIMARY KEY,
text_column VARCHAR(1000)
);
INSERT INTO words (id, text_column)
VALUES
(1, 'SQL Server is a popular database management system'),
(2, 'It is widely used for data storage and retrieval'),
(3, 'SQL Server is a powerful tool for data analysis');
I have found this code but it's not working correctly, and I think it's too complicated to understand:
WITH E1(N) AS
(
SELECT 1
FROM (VALUES
(1),(1),(1),(1),(1),(1),(1),(1),(1),(1)
) t(N)
),
E2(N) AS (SELECT 1 FROM E1 a CROSS JOIN E1 b),
E4(N) AS (SELECT 1 FROM E2 a CROSS JOIN E2 b)
SELECT
LOWER(x.Item) AS [Word],
COUNT(*) AS [Counts]
FROM
(SELECT * FROM words) a
CROSS APPLY
(SELECT
ItemNumber = ROW_NUMBER() OVER(ORDER BY l.N1),
Item = LTRIM(RTRIM(SUBSTRING(a.text_column, l.N1, l.L1)))
FROM
(SELECT
s.N1,
L1 = ISNULL(NULLIF(CHARINDEX(' ',a.text_column,s.N1),0)-s.N1,4000)
FROM
(SELECT 1
UNION ALL
SELECT t.N+1
FROM
(SELECT TOP (ISNULL(DATALENGTH(a.text_column)/2,0))
ROW_NUMBER() OVER (ORDER BY (SELECT NULL))
FROM E4) t(N)
WHERE SUBSTRING(a.text_column ,t.N,1) = ' '
) s(N1)
) l(N1, L1)
) x
WHERE
x.item <> ''
AND x.Item NOT IN ('0o', '0s', '3a', '3b', '3d', '6b', '6o', 'a', 'a1', 'a2', 'a3', 'a4', 'ab', 'able', 'about', 'above', 'abst', 'ac', 'accordance', 'according', 'accordingly', 'across', 'act', 'actually', 'ad', 'added', 'adj', 'ae', 'af', 'affected', 'affecting', 'affects', 'after', 'afterwards', 'ag', 'again', 'against', 'ah', 'ain', 'ain''t', 'aj', 'al', 'all', 'allow', 'allows', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst', 'amount', 'an', 'and', 'announce', 'another', 'any', 'anybody', 'anyhow', 'anymore', 'anyone', 'anything', 'anyway', 'anyways', 'anywhere', 'ao', 'ap', 'apart', 'apparently', 'appear', 'appreciate', 'appropriate', 'approximately', 'ar', 'are', 'aren', 'arent', 'aren''t', 'arise', 'around', 'as', 'a''s', 'aside', 'ask', 'asking', 'associated', 'at', 'au', 'auth', 'av', 'available', 'aw', 'away', 'awfully', 'ax', 'ay', 'az', 'b', 'b1', 'b2', 'b3', 'ba', 'back', 'bc', 'bd', 'be', 'became', 'because', 'become', 'becomes', 'becoming', 'been', 'before', 'beforehand', 'begin', 'beginning', 'beginnings', 'begins', 'behind', 'being', 'believe', 'below', 'beside', 'besides', 'best', 'better', 'between', 'beyond', 'bi', 'bill', 'biol', 'bj', 'bk', 'bl', 'bn', 'both', 'bottom', 'bp', 'br', 'brief', 'briefly', 'bs', 'bt', 'bu', 'but', 'bx', 'by', 'c', 'c1', 'c2', 'c3', 'ca', 'call', 'came', 'can', 'cannot', 'cant', 'can''t', 'cause', 'causes', 'cc', 'cd', 'ce', 'certain', 'certainly', 'cf', 'cg', 'ch', 'changes', 'ci', 'cit', 'cj', 'cl', 'clearly', 'cm', 'c''mon', 'cn', 'co', 'com', 'come', 'comes', 'con', 'concerning', 'consequently', 'consider', 'considering', 'contain', 'containing', 'contains', 'corresponding', 'could', 'couldn', 'couldnt', 'couldn''t', 'course', 'cp', 'cq', 'cr', 'cry', 'cs', 'c''s', 'ct', 'cu', 'currently', 'cv', 'cx', 'cy', 'cz', 'd', 'd2', 'da', 'date', 'dc', 'dd', 'de', 'definitely', 'describe', 'described', 'despite', 'detail', 'df', 'di', 'did', 'didn', 'didn''t', 'different', 'dj', 'dk', 'dl', 'do', 'does', 'doesn', 'doesn''t', 'doing', 'don', 'done', 'don''t', 'down', 'downwards', 'dp', 'dr', 'ds', 'dt', 'du', 'due', 'during', 'dx', 'dy', 'e', 'e2', 'e3', 'ea', 'each', 'ec', 'ed', 'edu', 'ee', 'ef', 'effect', 'eg', 'ei', 'eight', 'eighty', 'either', 'ej', 'el', 'eleven', 'else', 'elsewhere', 'em', 'empty', 'en', 'end', 'ending', 'enough', 'entirely', 'eo', 'ep', 'eq', 'er', 'es', 'especially', 'est', 'et', 'et-al', 'etc', 'eu', 'ev', 'even', 'ever', 'every', 'everybody', 'everyone', 'everything', 'everywhere', 'ex', 'exactly', 'example', 'except', 'ey', 'f', 'f2', 'fa', 'far', 'fc', 'few', 'ff', 'fi', 'fifteen', 'fifth', 'fify', 'fill', 'find', 'fire', 'first', 'five', 'fix', 'fj', 'fl', 'fn', 'fo', 'followed', 'following', 'follows', 'for', 'former', 'formerly', 'forth', 'forty', 'found', 'four', 'fr', 'from', 'front', 'fs', 'ft', 'fu', 'full', 'further', 'furthermore', 'fy', 'g', 'ga', 'gave', 'ge', 'get', 'gets', 'getting', 'gi', 'give', 'given', 'gives', 'giving', 'gj', 'gl', 'go', 'goes', 'going', 'gone', 'got', 'gotten', 'gr', 'greetings', 'gs', 'gy', 'h', 'h2', 'h3', 'had', 'hadn', 'hadn''t', 'happens', 'hardly', 'has', 'hasn', 'hasnt', 'hasn''t', 'have', 'haven', 'haven''t', 'having', 'he', 'hed', 'he''d', 'he''ll', 'hello', 'help', 'hence', 'her', 'here', 'hereafter', 'hereby', 'herein', 'heres', 'here''s', 'hereupon', 'hers', 'herself', 'hes', 'he''s', 'hh', 'hi', 'hid', 'him', 'himself', 'his', 'hither', 'hj', 'ho', 'home', 'hopefully', 'how', 'howbeit', 'however', 'how''s', 'hr', 'hs', 'http', 'hu', 'hundred', 'hy', 'i', 'i2', 'i3', 'i4', 'i6', 'i7', 'i8', 'ia', 'ib', 'ibid', 'ic', 'id', 'i''d', 'ie', 'if', 'ig', 'ignored', 'ih', 'ii', 'ij', 'il', 'i''ll', 'im', 'i''m', 'immediate', 'immediately', 'importance', 'important', 'in', 'inasmuch', 'inc', 'indeed', 'index', 'indicate', 'indicated', 'indicates', 'information', 'inner', 'insofar', 'instead', 'interest', 'into', 'invention', 'inward', 'io', 'ip', 'iq', 'ir', 'is', 'isn', 'isn''t', 'it', 'itd', 'it''d', 'it''ll', 'its', 'it''s', 'itself', 'iv', 'i''ve', 'ix', 'iy', 'iz', 'j', 'jj', 'jr', 'js', 'jt', 'ju', 'just', 'k', 'ke', 'keep', 'keeps', 'kept', 'kg', 'kj', 'km', 'know', 'known', 'knows', 'ko', 'l', 'l2', 'la', 'largely', 'last', 'lately', 'later', 'latter', 'latterly', 'lb', 'lc', 'le', 'least', 'les', 'less', 'lest', 'let', 'lets', 'let''s', 'lf', 'like', 'liked', 'likely', 'line', 'little', 'lj', 'll', 'll', 'ln', 'lo', 'look', 'looking', 'looks', 'los', 'lr', 'ls', 'lt', 'ltd', 'm', 'm2', 'ma', 'made', 'mainly', 'make', 'makes', 'many', 'may', 'maybe', 'me', 'mean', 'means', 'meantime', 'meanwhile', 'merely', 'mg', 'might', 'mightn', 'mightn''t', 'mill', 'million', 'mine', 'miss', 'ml', 'mn', 'mo', 'more', 'moreover', 'most', 'mostly', 'move', 'mr', 'mrs', 'ms', 'mt', 'mu', 'much', 'mug', 'must', 'mustn', 'mustn''t', 'my', 'myself', 'n', 'n2', 'na', 'name', 'namely', 'nay', 'nc', 'nd', 'ne', 'near', 'nearly', 'necessarily', 'necessary', 'need', 'needn', 'needn''t', 'needs', 'neither', 'never', 'nevertheless', 'new', 'next', 'ng', 'ni', 'nine', 'ninety', 'nj', 'nl', 'nn', 'no', 'nobody', 'non', 'none', 'nonetheless', 'noone', 'nor', 'normally', 'nos', 'not', 'noted', 'nothing', 'novel', 'now', 'nowhere', 'nr', 'ns', 'nt', 'ny', 'o', 'oa', 'ob', 'obtain', 'obtained', 'obviously', 'oc', 'od', 'of', 'off', 'often', 'og', 'oh', 'oi', 'oj', 'ok', 'okay', 'ol', 'old', 'om', 'omitted', 'on', 'once', 'one', 'ones', 'only', 'onto', 'oo', 'op', 'oq', 'or', 'ord', 'os', 'ot', 'other', 'others', 'otherwise', 'ou', 'ought', 'our', 'ours', 'ourselves', 'out', 'outside', 'over', 'overall', 'ow', 'owing', 'own', 'ox', 'oz', 'p', 'p1', 'p2', 'p3', 'page', 'pagecount', 'pages', 'par', 'part', 'particular', 'particularly', 'pas', 'past', 'pc', 'pd', 'pe', 'per', 'perhaps', 'pf', 'ph', 'pi', 'pj', 'pk', 'pl', 'placed', 'please', 'plus', 'pm', 'pn', 'po', 'poorly', 'possible', 'possibly', 'potentially', 'pp', 'pq', 'pr', 'predominantly', 'present', 'presumably', 'previously', 'primarily', 'probably', 'promptly', 'proud', 'provides', 'ps', 'pt', 'pu', 'put', 'py', 'q', 'qj', 'qu', 'que', 'quickly', 'quite', 'qv', 'r', 'r2', 'ra', 'ran', 'rather', 'rc', 'rd', 're', 'readily', 'really', 'reasonably', 'recent', 'recently', 'ref', 'refs', 'regarding', 'regardless', 'regards', 'related', 'relatively', 'research', 'research-articl', 'respectively', 'resulted', 'resulting', 'results', 'rf', 'rh', 'ri', 'right', 'rj', 'rl', 'rm', 'rn', 'ro', 'rq', 'rr', 'rs', 'rt', 'ru', 'run', 'rv', 'ry', 's', 's2', 'sa', 'said', 'same', 'saw', 'say', 'saying', 'says', 'sc', 'sd', 'se', 'sec', 'second', 'secondly', 'section', 'see', 'seeing', 'seem', 'seemed', 'seeming', 'seems', 'seen', 'self', 'selves', 'sensible', 'sent', 'serious', 'seriously', 'seven', 'several', 'sf', 'shall', 'shan', 'shan''t', 'she', 'shed', 'she''d', 'she''ll', 'shes', 'she''s', 'should', 'shouldn', 'shouldn''t', 'should''ve', 'show', 'showed', 'shown', 'showns', 'shows', 'si', 'side', 'significant', 'significantly', 'similar', 'similarly', 'since', 'sincere', 'six', 'sixty', 'sj', 'sl', 'slightly', 'sm', 'sn', 'so', 'some', 'somebody', 'somehow', 'someone', 'somethan', 'something', 'sometime', 'sometimes', 'somewhat', 'somewhere', 'soon', 'sorry', 'sp', 'specifically', 'specified', 'specify', 'specifying', 'sq', 'sr', 'ss', 'st', 'still', 'stop', 'strongly', 'sub', 'substantially', 'successfully', 'such', 'sufficiently', 'suggest', 'sup', 'sure', 'sy', 'system', 'sz', 't', 't1', 't2', 't3', 'take', 'taken', 'taking', 'tb', 'tc', 'td', 'te', 'tell', 'ten', 'tends', 'tf', 'th', 'than', 'thank', 'thanks', 'thanx', 'that', 'that''ll', 'thats', 'that''s', 'that''ve', 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'thence', 'there', 'thereafter', 'thereby', 'thered', 'therefore', 'therein', 'there''ll', 'thereof', 'therere', 'theres', 'there''s', 'thereto', 'thereupon', 'there''ve', 'these', 'they', 'theyd', 'they''d', 'they''ll', 'theyre', 'they''re', 'they''ve', 'thickv', 'thin', 'think', 'third', 'this', 'thorough', 'thoroughly', 'those', 'thou', 'though', 'thoughh', 'thousand', 'three', 'throug', 'through', 'throughout', 'thru', 'thus', 'ti', 'til', 'tip', 'tj', 'tl', 'tm', 'tn', 'to', 'together', 'too', 'took', 'top', 'toward', 'towards', 'tp', 'tq', 'tr', 'tried', 'tries', 'truly', 'try', 'trying', 'ts', 't''s', 'tt', 'tv', 'twelve', 'twenty', 'twice', 'two', 'tx', 'u', 'u201d', 'ue', 'ui', 'uj', 'uk', 'um', 'un', 'under', 'unfortunately', 'unless', 'unlike', 'unlikely', 'until', 'unto', 'uo', 'up', 'upon', 'ups', 'ur', 'us', 'use', 'used', 'useful', 'usefully', 'usefulness', 'uses', 'using', 'usually', 'ut', 'v', 'va', 'value', 'various', 'vd', 've', 've', 'very', 'via', 'viz', 'vj', 'vo', 'vol', 'vols', 'volumtype', 'vq', 'vs', 'vt', 'vu', 'w', 'wa', 'want', 'wants', 'was', 'wasn', 'wasnt', 'wasn''t', 'way', 'we', 'wed', 'we''d', 'welcome', 'well', 'we''ll', 'well-b', 'went', 'were', 'we''re', 'weren', 'werent', 'weren''t', 'we''ve', 'what', 'whatever', 'what''ll', 'whats', 'what''s', 'when', 'whence', 'whenever', 'when''s', 'where', 'whereafter', 'whereas', 'whereby', 'wherein', 'wheres', 'where''s', 'whereupon', 'wherever', 'whether', 'which', 'while', 'whim', 'whither', 'who', 'whod', 'whoever', 'whole', 'who''ll', 'whom', 'whomever', 'whos', 'who''s', 'whose', 'why', 'why''s', 'wi', 'widely', 'will', 'willing', 'wish', 'with', 'within', 'without', 'wo', 'won', 'wonder', 'wont', 'won''t', 'words', 'world', 'would', 'wouldn', 'wouldnt', 'wouldn''t', 'www', 'x', 'x1', 'x2', 'x3', 'xf', 'xi', 'xj', 'xk', 'xl', 'xn', 'xo', 'xs', 'xt', 'xv', 'xx', 'y', 'y2', 'yes', 'yet', 'yj', 'yl', 'you', 'youd', 'you''d', 'you''ll', 'your', 'youre', 'you''re', 'yours', 'yourself', 'yourselves', 'you''ve', 'yr', 'ys', 'yt', 'z', 'zero', 'zi', 'zz')
GROUP BY x.Item
ORDER BY COUNT(*) DESC
Here's the result of the above code, as you can see it's not counting correctly:
Word Counts
server 2
sql 2
data 1
database 1
popular 1
powerful 1
Can anyone help on this? Would be really appreciated!
You can make use of String_split here, such as
select value Word, Count(*) Counts
from words
cross apply String_Split(text_column, ' ')
where value not in(exclude list)
group by value
order by counts desc;
You should should the string_split function -- like this
SELECT id, value as aword
FROM words
CROSS APPLY STRING_SPLIT(text_column, ',');
This will create a table with all the words by id -- to get the count do this:
SELECT aword, count(*) as counts
FROM (
SELECT id, value as aword
FROM words
CROSS APPLY STRING_SPLIT(text_column, ',');
) x
GROUP BY aword
You may need to lower case the LOWER(text_column) if you want it to not matter
If you don't have access to STRING_SPLIT function, you can use weird xml trick to convert space to a word node and then shred it with nodes function:
select word, COUNT(*)
from (
select n.value('.', 'nvarchar(50)') AS word
from (
VALUES
(1, 'SQL Server is a popular database management system'),
(2, 'It is widely used for data storage and retrieval'),
(3, 'SQL Server is a powerful tool for data analysis')
) AS t (id, txt)
CROSS APPLY (
SELECT CAST('<x>' + REPLACE(txt, ' ', '</x><x>') + '</x>' AS XML) x
) x
CROSS APPLY x.nodes('x') z(n)
) w
GROUP BY word
Of course, this will fail on "bad" words and invalid xml-characters but it can be worked on. Text processing has never been SQL Server's strong-point though, so probably better to use some NLP library to do this kind of stuff
i have a reporting query which have 2 long sub-query
SELECT r1.code_centre, r1.libelle_centre, r1.id_equipe, r1.equipe, r1.id_file_attente,
r1.libelle_file_attente,r1.id_date, r1.tranche, r1.id_granularite_de_periode,r1.granularite,
r1.ContactsTraites, r1.ContactsenParcage, r1.ContactsenComm, r1.DureeTraitementContacts,
r1.DureeComm, r1.DureeParcage, r2.AgentsConnectes, r2.DureeConnexion, r2.DureeTraitementAgents,
r2.DureePostTraitement
FROM
( SELECT cc.id_centre_contact, cc.code_centre, cc.libelle_centre, a.id_equipe, a.equipe,
a.id_file_attente, f.libelle_file_attente, a.id_date, g.tranche, g.id_granularite_de_periode,
g.granularite, sum(Nb_Contacts_Traites) as ContactsTraites,
sum(Nb_Contacts_en_Parcage) as ContactsenParcage,
sum(Nb_Contacts_en_Communication) as ContactsenComm,
sum(Duree_Traitement/1000) as DureeTraitementContacts,
sum(Duree_Communication / 1000 + Duree_Conference / 1000 + Duree_Com_Interagent / 1000) as DureeComm,
sum(Duree_Parcage/1000) as DureeParcage
FROM agr_synthese_activite_media_fa_agent a, centre_contact cc,
direction_contact dc, granularite_de_periode g, media m, file_attente f
WHERE m.id_media = a.id_media
AND cc.id_centre_contact = a.id_centre_contact
AND a.id_direction_contact = dc.id_direction_contact
AND dc.direction_contact ='INCOMING'
AND a.id_file_attente = f.id_file_attente
AND m.media = 'PHONE'
AND ( ( g.valeur_min = date_format(a.id_date,'%d/%m') and g.granularite = 'Jour')
or ( g.granularite = 'Heure' and a.id_th_heure = g.id_granularite_de_periode) )
GROUP by cc.id_centre_contact, a.id_equipe, a.id_file_attente, a.id_date, g.tranche,
g.id_granularite_de_periode) r1,
(
(SELECT cc.id_centre_contact,cc.code_centre, cc.libelle_centre, a.id_equipe, a.equipe,
a.id_date, g.tranche, g.id_granularite_de_periode,g.granularite,
count(distinct a.id_agent) as AgentsConnectes,
sum(Duree_Connexion / 1000) as DureeConnexion,
sum(Duree_en_Traitement / 1000) as DureeTraitementAgents,
sum(Duree_en_PostTraitement / 1000) as DureePostTraitement
FROM activite_agent a, centre_contact cc, granularite_de_periode g
WHERE ( g.valeur_min = date_format(a.id_date,'%d/%m') and g.granularite = 'Jour')
AND cc.id_centre_contact = a.id_centre_contact
GROUP BY cc.id_centre_contact, a.id_equipe, a.id_date, g.tranche, g.id_granularite_de_periode )
UNION
(SELECT cc.id_centre_contact,cc.code_centre, cc.libelle_centre, a.id_equipe, a.equipe,
a.id_date, g.tranche, g.id_granularite_de_periode,g.granularite,
count(distinct a.id_agent) as AgentsConnectes,
sum(Duree_Connexion / 1000) as DureeConnexion,
sum(Duree_en_Traitement / 1000) as DureeTraitementAgents,
sum(Duree_en_PostTraitement / 1000) as DureePostTraitement
FROM activite_agent a, centre_contact cc, granularite_de_periode g
WHERE ( g.granularite = 'Heure'
AND a.id_th_heure = g.id_granularite_de_periode)
AND cc.id_centre_contact = a.id_centre_contact
GROUP BY cc.id_centre_contact,a.id_equipe, a.id_date, g.tranche, g.id_granularite_de_periode)
) r2
WHERE r1.id_centre_contact = r2.id_centre_contact
AND r1.id_equipe = r2.id_equipe AND r1.id_date = r2.id_date
AND r1.tranche = r2.tranche AND r1.id_granularite_de_periode = r2.id_granularite_de_periode
GROUP BY r1.id_centre_contact , r1.id_equipe, r1.id_file_attente,
r1.id_date, r1.tranche, r1.id_granularite_de_periode
ORDER BY r1.code_centre, r1.libelle_centre, r1.equipe,
r1.libelle_file_attente, r1.id_date, r1.id_granularite_de_periode,r1.tranche
the EXPLAIN shows
| id | select_type | table | type| possible_keys | key | key_len | ref| rows | Extra |
'1', 'PRIMARY', '<derived3>', 'ALL', NULL, NULL, NULL, NULL, '2520', 'Using temporary; Using filesort'
'1', 'PRIMARY', '<derived2>', 'ALL', NULL, NULL, NULL, NULL, '4378', 'Using where; Using join buffer'
'3', 'DERIVED', 'a', 'ALL', 'fk_Activite_Agent_centre_contact', NULL, NULL, NULL, '83433', 'Using temporary; Using filesort'
'3', 'DERIVED', 'g', 'ref', 'Index_granularite,Index_Valeur_min', 'Index_Valeur_min', '23', 'func', '1', 'Using where'
'3', 'DERIVED', 'cc', 'ALL', 'PRIMARY', NULL, NULL, NULL, '6', 'Using where; Using join buffer'
'4', 'UNION', 'g', 'ref', 'PRIMARY,Index_granularite', 'Index_granularite', '23', '', '24', 'Using where; Using temporary; Using filesort'
'4', 'UNION', 'a', 'ref', 'fk_Activite_Agent_centre_contact,fk_activite_agent_TH_heure', 'fk_activite_agent_TH_heure', '5', 'reporting_acd.g.Id_Granularite_de_periode', '2979', 'Using where'
'4', 'UNION', 'cc', 'ALL', 'PRIMARY', NULL, NULL, NULL, '6', 'Using where; Using join buffer'
NULL, 'UNION RESULT', '<union3,4>', 'ALL', NULL, NULL, NULL, NULL, NULL, ''
'2', 'DERIVED', 'g', 'range', 'PRIMARY,Index_granularite,Index_Valeur_min', 'Index_granularite', '23', NULL, '389', 'Using where; Using temporary; Using filesort'
'2', 'DERIVED', 'a', 'ALL', 'fk_agr_synthese_activite_media_fa_agent_centre_contact,fk_agr_synthese_activite_media_fa_agent_direction_contact,fk_agr_synthese_activite_media_fa_agent_file_attente,fk_agr_synthese_activite_media_fa_agent_media,fk_agr_synthese_activite_media_fa_agent_th_heure', NULL, NULL, NULL, '20903', 'Using where; Using join buffer'
'2', 'DERIVED', 'cc', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_Centre_Contact', '1', ''
'2', 'DERIVED', 'f', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_File_Attente', '1', ''
'2', 'DERIVED', 'dc', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_Direction_Contact', '1', 'Using where'
'2', 'DERIVED', 'm', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_Media', '1', 'Using where'
don't know it very clear, but i think is the problem of seems it take full scaning
than i change all the sub-query to views(create view as select sub-query), and the result is the same
thanks for any advice
Subquery and view will most of the time give you same result speed-wise.
If your subquery is not variabe, consider creating table that has same structure as your view, and occasionally do:
truncate table my_table;
insert into my_table select * from my_view;
...to cache your subquery data. If properly indexed, it will marginalize time lost on storing results of subquery in table, if data is not that frequently changed, or at least if you don't need up-to-date information on second-to-second basis.