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 nested table structure, like this:
[
{
"startTime": "2017-09-02 09:08:00:000",
"endTime": "2017-09-02 09:09:00:000",
"startTimeMillis": "1504343280000",
"endTimeMillis": "1504343340000",
"uuid": "1748750880",
"country": "CI",
"city": "Punta Arenas",
"x": "-70.906904",
"y": "-53.133514"
},
{
"startTime": "2017-09-02 09:08:00:000",
"endTime": "2017-09-02 09:09:00:000",
"startTimeMillis": "1504343280000",
"endTimeMillis": "1504343340000",
"uuid": "1748750880",
"country": "CI",
"city": "Punta Arenas",
"x": "-70.907353",
"y": "-53.133253"
},
{
"startTime": "2017-09-02 09:08:00:000",
"endTime": "2017-09-02 09:09:00:000",
"startTimeMillis": "1504343280000",
"endTimeMillis": "1504343340000",
"uuid": "1748750880",
"country": "CI",
"city": "Punta Arenas",
"x": "-70.90771",
"y": "-53.133041"
},
{
"startTime": "2017-09-02 09:08:00:000",
"endTime": "2017-09-02 09:09:00:000",
"startTimeMillis": "1504343280000",
"endTimeMillis": "1504343340000",
"uuid": "1748750880",
"country": "CI",
"city": "Punta Arenas",
"x": "-70.908979",
"y": "-53.132287"
}
]
A resulting table is something like this:
Row|startTime|endTime|startTimeMillis|endTimeMillis|uuid|country|city|x|y|
1|2017-09-02 09:08:00:000|2017-09-02 09:09:00:000|1504343280000|1504343340000|1748750880|CI|Punta Arenas|-70.906904|-53.133514|
2|2017-09-02 09:08:00:000|2017-09-02 09:09:00:000|1504343280000|1504343340000|1748750880|CI|Punta Arenas|-70.907353|-53.133253|
3|2017-09-02 09:08:00:000|2017-09-02 09:09:00:000|1504343280000|1504343340000|1748750880|CI|Punta Arenas|-70.90771|-53.133041|
4|2017-09-02 09:08:00:000|2017-09-02 09:09:00:000|1504343280000|1504343340000|1748750880|CI|Punta Arenas|-70.908979|-53.132287|
I'd like to concat the repeated fields x and y to produce a GIS linestring, in a single line, like this:
Row|startTime|endTime|startTimeMillis|endTimeMillis|uuid|country|city|linestring
1|2017-09-02 09:08:00:000|2017-09-02 09:09:00:000|1504343280000|1504343340000|1748750880|CI|Punta Arenas|LINESTRING(-70.906904 -53.133514, -70.907353 -53.133253, -70.90771 -53.133041, -70.908979 -53.132287)
How can I do this? The original x and y values are floats.
Below is for BigQuery Standard SQL
#standardSQL
WITH `yourTable` AS (
SELECT '2017-09-02 09:08:00:000' AS startTime, '2017-09-02 09:09:00:000' AS endTime, 1504343280000 AS startTimeMillis, 1504343340000 AS endTimeMillis, 1748750880 AS uuid, 'CI' AS country, 'Punta Arenas' AS city, -70.906904 AS x, -53.133514 AS y UNION ALL
SELECT '2017-09-02 09:08:00:000', '2017-09-02 09:09:00:000', 1504343280000, 1504343340000, 1748750880, 'CI', 'Punta Arenas', -70.907353, -53.133253 UNION ALL
SELECT '2017-09-02 09:08:00:000', '2017-09-02 09:09:00:000', 1504343280000, 1504343340000, 1748750880, 'CI', 'Punta Arenas', -70.90771, -53.133041 UNION ALL
SELECT '2017-09-02 09:08:00:000', '2017-09-02 09:09:00:000', 1504343280000, 1504343340000, 1748750880, 'CI', 'Punta Arenas', -70.908979, -53.132287
)
SELECT startTime, endTime, startTimeMillis, endTimeMillis, uuid, country, city,
STRING_AGG(CONCAT(CAST(x AS STRING), ' ', CAST(y AS STRING)), ',') AS linestring
FROM `yourTable`
GROUP BY startTime, endTime, startTimeMillis, endTimeMillis, uuid, country, city
You could use the ARRAY_AGG function available in Standard SQL, something like:
#standardSQL
WITH data AS(
SELECT "2017-09-02 09:08:00:000" AS startTime, "2017-09-02 09:09:00:000" endTime, "1504343280000" AS startTimeMillis, "1504343340000" endTimeMillis, "1748750880" AS uuid, "CI" AS country, "Punta Arenas" AS city, "-70.906904" AS x, "-53.133514" AS y UNION ALL
SELECT "2017-09-02 09:08:00:000", "2017-09-02 09:09:00:000", "1504343280000", "1504343340000", "1748750880", "CI", "Punta Arenas", "-70.907353", "-53.133253" UNION ALL
SELECT "2017-09-02 09:08:00:000", "2017-09-02 09:09:00:000", "1504343280000", "1504343340000", "1748750880", "CI", "Punta Arenas", "-70.90771", "-53.133041" UNION ALL
SELECT "2017-09-02 09:08:00:000", "2017-09-02 09:09:00:000", "1504343280000", "1504343340000", "1748750880", "CI", "Punta Arenas", "-70.908979", "-53.132287"
)
SELECT
startTime,
endTime,
startTimeMillis,
endTimeMillis,
uuid,
country,
city,
ARRAY_AGG(STRUCT(x, y)) AS LINESTRING
FROM data
GROUP BY
startTime,
endTime,
startTimeMillis,
endTimeMillis,
uuid,
country,
city
Result:
Even though result is an ARRAY with the elements x and y, notice that they have been structured together as a STRUCT which will allow you to access each field by its respective name.
Thank you all!
I'm using Mikhail Berlyant solution!
SELECT
w.startTime, w.endTime, w.startTimeMillis, w.endTimeMillis,
jams_u.uuid, jams_u.country, jams_u.city, jams_u.street,
jams_u.roadType, jams_u.turnType,
jams_u.type, jams_u.length, jams_u.speed, jams_u.level, jams_u.delay,
jams_u.startNode, jams_u.endNode, jams_u.pubMillis,
TIMESTAMP_MILLIS(jams_u.pubMillis) as pubdatetime_utc,
STRING_AGG(CONCAT(CAST(line_u.x AS STRING),' ',CAST(line_u.y AS STRING))) linestring_4326
FROM
a_import.table w,
UNNEST(jams) jams_u,
UNNEST(line) line_u
GROUP BY
w.startTime, w.endTime, w.startTimeMillis, w.endTimeMillis,
jams_u.uuid, jams_u.country, jams_u.city, jams_u.street,
jams_u.roadType, jams_u.turnType,
jams_u.type, jams_u.length, jams_u.speed, jams_u.level, jams_u.delay,
jams_u.startNode, jams_u.endNode, jams_u.pubMillis,
pubdatetime_utc
One concern with the proposed solutions that use GROUP BY only - without an ORDER BY operator within group, the order of the elements in the GROUP BY group is undefined. So you can get arbitrary order of points in the linestring, which is probably not what you want. Unfortunately, with small inline datasets you get stable results, but this might break once you have real data.
To solve this you need to define which attributes define group, and which define order. E.g. if uuid defines a linestring, and start timestamp defines order (they would need to be different, unlike in your sample), your query might group by uuid, and sort by timestamp.
I also prefer to use new Geospatial functions to construct WKT linestring, rather than string concatenation, which gives:
#standardSQL
WITH `yourTable` AS (
SELECT * FROM UNNEST([
STRUCT('2017-09-02 09:08:00:000' AS startTime, '2017-09-02 09:09:00:000' AS endTime, 1504343280002 AS startTimeMillis, 1504343340000 AS endTimeMillis,
1748750880 AS uuid, 'CI' AS country, 'Punta Arenas' AS city, -70.906904 AS x, -53.133514 AS y),
STRUCT('2017-09-02 09:08:00:000', '2017-09-02 09:09:00:000', 1504343280001, 1504343340000, 1748750880, 'CI', 'Punta Arenas', -70.907353, -53.133253),
STRUCT('2017-09-02 09:08:00:000', '2017-09-02 09:09:00:000', 1504343280004, 1504343340000, 1748750880, 'CI', 'Punta Arenas', -70.90771, -53.133041),
STRUCT('2017-09-02 09:08:00:000', '2017-09-02 09:09:00:000', 1504343280003, 1504343340000, 1748750880, 'CI', 'Punta Arenas', -70.908979, -53.132287)])
)
SELECT uuid, MIN(startTime) startTime, MAX(endTime) endTime,
ANY_VALUE(country), ANY_VALUE(city),
ST_MakeLine(ARRAY_AGG(ST_GeogPoint(x, y)
ORDER BY startTime, startTimeMillis)) line
FROM `yourTable`
GROUP BY uuid