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 manually populated a table as follows:
INSERT INTO country VALUES
-- columns are countryid, name, continent, population, gdp, lifeexpectancy, founded
(1, 'Argentina', 36.3, 348.2, 70.75, 9/7/1816),
(2, 'Brazil', 183.3, 1004, 65.6, 9/7/1822),
(3, 'Canada', 30.1, 658, 77.08, 1/7/1867),
(4, 'England', 60.8, 1256, 75.15, NULL),
(5, 'France', 60, 1000, 75.15, 14/7/1789),
(6, 'Mexico', 107.5, 694.3, 69.36, 16/9/1821),
(7, 'USA', 270, 8003, 75.75, 4/7/1776),
(8, 'Cuba', 11.7, 16.9, 75.95, 24/2/1895),
(9, 'Guatemala', 20, 200, 60, 15/9/1821),
(10, 'Tanzania', 55.57, 47.43, 60.76, 9/12/1961),
(11, 'India', 1324, 2264, 68.35, NULL),
(12, 'South Africa', 55.91, 294.8, 57.44, 31/05/1910),
(13, 'Costa Rica', 4.86, 57.44, 79.59, 15/9/1821),
(14, 'Uganda', 41.49, 25.53, 59.18, NULL);
but when I query
SELECT lifeexpectancy FROM country;
The tables returns a combination of "0" and "[null]" values.
I've tried changing the data_type of lifeexpectancy. Neither text nor numeric data types return values that have been entered.
Any suggestions?
You are not inserting the continent. Presumably you want that, but it is not in your sample data.
You should be using standard date formats. In most databases, YYYY-MM-DD works. And you should list the columns. So:
INSERT INTO country (countryid, name, population, gdp, lifeexpectancy, founded)
VALUES (1, 'Argentina', 36.3, 348.2, 70.75, '1816-07-09'),
(2, 'Brazil', 183.3, 1004, 65.6, '1822-07-09'),
(3, 'Canada', 30.1, 658, 77.08, '1867-07-01'),
(4, 'England', 60.8, 1256, 75.15, NULL),
(5, 'France', 60, 1000, 75.15, '1789-07-14'),
(6, 'Mexico', 107.5, 694.3, 69.36, '1821-09-16'),
(7, 'USA', 270, 8003, 75.75, '1776-07-04'),
(8, 'Cuba', 11.7, 16.9, 75.95, '1895-02-24'),
(9, 'Guatemala', 20, 200, 60, '1821-09-15'),
(10, 'Tanzania', 55.57, 47.43, 60.76, '1961-12-09'),
(11, 'India', 1324, 2264, 68.35, NULL),
(12, 'South Africa', 55.91, 294.8, 57.44, '1910-05-31'),
(13, 'Costa Rica', 4.86, 57.44, 79.59, '1821-09-15'),
(14, 'Uganda', 41.49, 25.53, 59.18, NULL);
I believe you're supplying 6 values instead of 7 (may be because of Auto Increment), if that the case you need to specify the columns here.
Also pass date with single quotes surrounded:
INSERT INTO country (countryid, name, continent, population, gdp, lifeexpectancy, founded)
VALUES
(1, 'Argentina', 36.3, 348.2, 70.75, '9/7/1816'),
(2, 'Brazil', 183.3, 1004, 65.6, '9/7/1822'),
(3, 'Canada', 30.1, 658, 77.08, '1/7/1867'),
(4, 'England', 60.8, 1256, 75.15, NULL),
(5, 'France', 60, 1000, 75.15, '14/7/1789'),
(6, 'Mexico', 107.5, 694.3, 69.36, '16/9/1821'),
(7, 'USA', 270, 8003, 75.75, '4/7/1776'),
(8, 'Cuba', 11.7, 16.9, 75.95, '24/2/1895'),
(9, 'Guatemala', 20, 200, 60, '15/9/1821'),
(10, 'Tanzania', 55.57, 47.43, 60.76, '9/12/1961'),
(11, 'India', 1324, 2264, 68.35, NULL),
(12, 'South Africa', 55.91, 294.8, 57.44, '31/05/1910'),
(13, 'Costa Rica', 4.86, 57.44, 79.59, '15/9/1821'),
(14, 'Uganda', 41.49, 25.53, 59.18, NULL);
While running the following SQL query -
INSERT INTO Countries ('sno', 'Name' ) VALUES
(1, 'Afghanistan'),
(2, 'Albania'),
(3, 'Algeria'),
(4, 'American Samoa'),
(5, 'Andorra'),
(6, 'Angola'),
(7, 'Anguilla'),
(8, 'Antarctica'),
(9, 'Antigua and Barbuda'),
(10, 'Argentina'),
(11, 'Armenia'),
(12, 'Armenia'),
(13, 'Aruba'),
(14, 'Australia'),
(15, 'Austria'),
(16, 'Azerbaijan'),
(17, 'Azerbaijan'),
(18, 'Bahamas'),
(19, 'Bahrain'),
(20, 'Bangladesh'),
(21, 'Barbados'),
(22, 'Belarus'),
(23, 'Belgium'),
(24, 'Belize'),
(25, 'Benin'),
(26, 'Bermuda'),
(27, 'Bhutan'),
(28, 'Bolivia'),
(29, 'Bosnia and Herzegovina'),
(30, 'Botswana'),
(31, 'Bouvet Island'),
(32, 'Brazil'),
(33, 'British Indian Ocean Territory'),
(34, 'Brunei Darussalam'),
(35, 'Bulgaria'),
(36, 'Burkina Faso'),
(37, 'Burundi'),
(38, 'Cambodia'),
(39, 'Cameroon'),
(40, 'Canada'),
(41, 'Cape Verde'),
(42, 'Cayman Islands'),
(43, 'Central African Republic'),
(44, 'Chad'),
(45, 'Chile'),
(46, 'China'),
(47, 'Christmas Island'),
(48, 'Cocos (Keeling) Islands'),
(49, 'Colombia'),
(50, 'Comoros'),
(51, 'Congo'),
(52, 'Congo, The Democratic Republic of The'),
(53, 'Cook Islands'),
(54, 'Costa Rica'),
(55, 'Cote Divoire'),
(56, 'Croatia'),
(57, 'Cuba'),
(58, 'Cyprus'),
(59, 'Cyprus'),
(60, 'Czech Republic'),
(61, 'Denmark'),
(62, 'Djibouti'),
(63, 'Dominica'),
(64, 'Dominican Republic'),
(65, 'Easter Island'),
(66, 'Ecuador'),
(67, 'Egypt'),
(68, 'El Salvador'),
(69, 'Equatorial Guinea'),
(70, 'Eritrea'),
(71, 'Estonia'),
(72, 'Ethiopia'),
(73, 'Falkland Islands (Malvinas)'),
(74, 'Faroe Islands'),
(75, 'Fiji'),
(76, 'Finland'),
(77, 'France'),
(78, 'French Guiana'),
(79, 'French Polynesia'),
(80, 'French Southern Territories'),
(81, 'Gabon'),
(82, 'Gambia'),
(83, 'Georgia'),
(84, 'Georgia'),
(85, 'Germany'),
(86, 'Ghana'),
(87, 'Gibraltar'),
(88, 'Greece'),
(89, 'Greenland'),
(90, 'Greenland'),
(91, 'Grenada'),
(92, 'Guadeloupe'),
(93, 'Guam'),
(94, 'Guatemala'),
(95, 'Guinea'),
(96, 'Guinea-bissau'),
(97, 'Guyana'),
(98, 'Haiti'),
(99, 'Heard Island and Mcdonald Islands'),
(100, 'Honduras'),
(101, 'Hong Kong'),
(102, 'Hungary'),
(103, 'Iceland'),
(104, 'India'),
(105, 'Indonesia'),
(106, 'Indonesia'),
(107, 'Iran'),
(108, 'Iraq'),
(109, 'Ireland'),
(110, 'Israel'),
(111, 'Italy'),
(112, 'Jamaica'),
(113, 'Japan'),
(114, 'Jordan'),
(115, 'Kazakhstan'),
(116, 'Kazakhstan'),
(117, 'Kenya'),
(118, 'Kiribati'),
(119, 'Korea, North'),
(120, 'Korea, South'),
(121, 'Kosovo'),
(122, 'Kuwait'),
(123, 'Kyrgyzstan'),
(124, 'Laos'),
(125, 'Latvia'),
(126, 'Lebanon'),
(127, 'Lesotho'),
(128, 'Liberia'),
(129, 'Libyan Arab Jamahiriya'),
(130, 'Liechtenstein'),
(131, 'Lithuania'),
(132, 'Luxembourg'),
(133, 'Macau'),
(134, 'Macedonia'),
(135, 'Madagascar'),
(136, 'Malawi'),
(137, 'Malaysia'),
(138, 'Maldives'),
(139, 'Mali'),
(140, 'Malta'),
(141, 'Marshall Islands'),
(142, 'Martinique'),
(143, 'Mauritania'),
(144, 'Mauritius'),
(145, 'Mayotte'),
(146, 'Mexico'),
(147, 'Micronesia, Federated States of'),
(148, 'Moldova, Republic of'),
(149, 'Monaco'),
(150, 'Mongolia'),
(151, 'Montenegro'),
(152, 'Montserrat'),
(153, 'Morocco'),
(154, 'Mozambique'),
(155, 'Myanmar'),
(156, 'Namibia'),
(157, 'Nauru'),
(158, 'Nepal'),
(159, 'Netherlands'),
(160, 'Netherlands Antilles'),
(161, 'New Caledonia'),
(162, 'New Zealand'),
(163, 'Nicaragua'),
(164, 'Niger'),
(165, 'Nigeria'),
(166, 'Niue'),
(167, 'Norfolk Island'),
(168, 'Northern Mariana Islands'),
(169, 'Norway'),
(170, 'Oman'),
(171, 'Pakistan'),
(172, 'Palau'),
(173, 'Palestinian Territory'),
(174, 'Panama'),
(175, 'Papua New Guinea'),
(176, 'Paraguay'),
(177, 'Peru'),
(178, 'Philippines'),
(179, 'Pitcairn'),
(180, 'Poland'),
(181, 'Portugal'),
(182, 'Puerto Rico'),
(183, 'Qatar'),
(184, 'Reunion'),
(185, 'Romania'),
(186, 'Russia'),
(187, 'Russia'),
(188, 'Rwanda'),
(189, 'Saint Helena'),
(190, 'Saint Kitts and Nevis'),
(191, 'Saint Lucia'),
(192, 'Saint Pierre and Miquelon'),
(193, 'Saint Vincent and The Grenadines'),
(194, 'Samoa'),
(195, 'San Marino'),
(196, 'Sao Tome and Principe'),
(197, 'Saudi Arabia'),
(198, 'Senegal'),
(199, 'Serbia and Montenegro'),
(200, 'Seychelles'),
(201, 'Sierra Leone'),
(202, 'Singapore'),
(203, 'Slovakia'),
(204, 'Slovenia'),
(205, 'Solomon Islands'),
(206, 'Somalia'),
(207, 'South Africa'),
(208, 'South Georgia and The South Sandwich Islands'),
(209, 'Spain'),
(210, 'Sri Lanka'),
(211, 'Sudan'),
(212, 'Suriname'),
(213, 'Svalbard and Jan Mayen'),
(214, 'Swaziland'),
(215, 'Sweden'),
(216, 'Switzerland'),
(217, 'Syria'),
(218, 'Taiwan'),
(219, 'Tajikistan'),
(220, 'Tanzania, United Republic of'),
(221, 'Thailand'),
(222, 'Timor-leste'),
(223, 'Togo'),
(224, 'Tokelau'),
(225, 'Tonga'),
(226, 'Trinidad and Tobago'),
(227, 'Tunisia'),
(228, 'Turkey'),
(229, 'Turkey'),
(230, 'Turkmenistan'),
(231, 'Turks and Caicos Islands'),
(232, 'Tuvalu'),
(233, 'Uganda'),
(234, 'Ukraine'),
(235, 'United Arab Emirates'),
(236, 'United Kingdom'),
(237, 'United States'),
(238, 'United States Minor Outlying Islands'),
(239, 'Uruguay'),
(240, 'Uzbekistan'),
(241, 'Vanuatu'),
(242, 'Vatican City'),
(243, 'Venezuela'),
(244, 'Vietnam'),
(245, 'Virgin Islands, British'),
(246, 'Virgin Islands, U.S.'),
(247, 'Wallis and Futuna'),
(248, 'Western Sahara'),
(249, 'Yemen'),
(250, 'Yemen'),
(251, 'Zambia'),
(252, 'Zimbabwe');
following error code is seen on microsoft sql server-
Msg 215, Level 16, State 1, Line 1
Parameters supplied for object 'Countries' which is not a function. If the parameters are intended as a table hint, a WITH keyword is required.
Please help by sharing the reason ?
Don't use single quotes for column name.
INSERT INTO Countries ([sno], [Name])...
You can use brackets normally.
Or you can use double quote " if SET QUOTED_IDENTIFIER is ON (it should be by default)
Currently you have string literals which is causing SQL Server to interpret the code way different to what you expect
You could leave all the bracketed section out all together: SQL Fiddle
INSERT INTO Countries VALUES
This is therefore none explicit and not best practice (see comment by #AaronBertrand) - but still an alternative.