how to shorten the runtime of a bigquery query - sql

Question

I have two tables:
1. PEOPLE (PK, Name, Address, Zip, <<some random other columns>>)
2. EMAIL (PK, Name, Address, Zip, Email)
This is a one-to-many table where they are linked by Name, Address, and Zip.
What I need is:
PEOPLE (PK, Name, Address, Zip, <<some random other columns>>, FK_Email1, Email1, FK_Email2, Email2, FK_Email3, Email3)
What I have so far is this:
#standardSQL
SELECT a.PK, a.FK, Source, FirstName, LastName, MiddleName, SuffixName, Gender, Age, DOB, Address, Address2, City, State, Zip, Zip4, Cleaned_HouseNumber, Cleaned_Street, Cleaned_City, Cleaned_County, Cleaned_State, Cleaned_Zip, TimeZone, Income, HomeValue, Networth, MaritalStatus, IsRenter, HasChildren, CreditRating, Investor, LinesOfCredit, InvestorRealEstate, Traveler, Pets, MailResponder, Charitable, PolicalDonations, PoliticalParty, ATTOM_ID, GEOID, SCORE, Latitude, Longitude, SpouseFirstName, SpouseLastName, HomeAvailableHomeEquity, HomeTotalLoans, HomeLoan1Amount, HomeLoan2Amount, HomeValueRangeCode, HomeValueRangeText, HomeMarketValue, HomeAssessedValue, HomeLoanToValue, HomeSQFT, HomeLotSQFT, HomeYearBuilt, HomePurchaseDate, HomeLoan1Date, HomeLoan2Date, HomeParcelNumber, HomePropertyType, DNC, HomeCompanyOwned, HomeTrustOwned, HomeOwnerOccupied, HomeType, HomePool, HomeGarage, HomeHeating, HomeCooling, HomeBedrooms, HomeBathrooms, HomeNumberOfUnits, MailingAddress, MailingCity, MailingState, MailingZip, MailingZip4, Married, Divorce, Education, Occupation, Ethnicity, LANGUAGE, RELIGION,
FK_Email[SAFE_ORDINAL(1)] FK_Email1, Emails[SAFE_ORDINAL(1)] Email1, FK_Email[SAFE_ORDINAL(2)] as FK_Email2, Emails[SAFE_ORDINAL(2)] Email2, FK_Email[SAFE_ORDINAL(3)] as FK_Email3, Emails[SAFE_ORDINAL(3)] Email3
FROM (
SELECT
P.PK, P.FK, P.Source, P.FirstName, P.LastName, MiddleName, SuffixName, Gender, Age, DOB, P.Address, Address2, P.City, P.State, P.Zip, Zip4, Cleaned_HouseNumber, Cleaned_Street, Cleaned_City, Cleaned_County, Cleaned_State, Cleaned_Zip, TimeZone, Income, HomeValue, Networth, MaritalStatus, IsRenter, HasChildren, CreditRating, Investor, LinesOfCredit, InvestorRealEstate, Traveler, Pets, MailResponder, Charitable, PolicalDonations, PoliticalParty, ATTOM_ID, GEOID, SCORE, Latitude, Longitude, SpouseFirstName, SpouseLastName, HomeAvailableHomeEquity, HomeTotalLoans, HomeLoan1Amount, HomeLoan2Amount, HomeValueRangeCode, HomeValueRangeText, HomeMarketValue, HomeAssessedValue, HomeLoanToValue, HomeSQFT, HomeLotSQFT, HomeYearBuilt, HomePurchaseDate, HomeLoan1Date, HomeLoan2Date, HomeParcelNumber, HomePropertyType, DNC, HomeCompanyOwned, HomeTrustOwned, HomeOwnerOccupied, HomeType, HomePool, HomeGarage, HomeHeating, HomeCooling, HomeBedrooms, HomeBathrooms, HomeNumberOfUnits, MailingAddress, MailingCity, MailingState, MailingZip, MailingZip4, Married, Divorce, Education, Occupation, Ethnicity, LANGUAGE, RELIGION
, ARRAY_AGG(E.Email) Emails, ARRAY_AGG(E.PK) FK_Email
FROM `db.ds.table1` P
left JOIN `db.ds.table2` E
ON P.FirstName = E.FirstName
AND P.LastName = E.LastName
AND P.Address = E.Address
AND P.Zip = E.Zip
Group by 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87
) a
my problem is this already goes past the six out time limit.
Is there anyway to make this run faster?
Thanks!

Answer 1

I feel below does the same but in more optimized way
#standardSQL
SELECT
PK, FK, Source, P.FirstName, P.LastName, MiddleName, SuffixName, Gender, Age, DOB, P.Address, Address2, City, State, P.Zip, Zip4, Cleaned_HouseNumber, Cleaned_Street, Cleaned_City, Cleaned_County, Cleaned_State, Cleaned_Zip, TimeZone, Income, HomeValue, Networth, MaritalStatus, IsRenter, HasChildren, CreditRating, Investor, LinesOfCredit, InvestorRealEstate, Traveler, Pets, MailResponder, Charitable, PolicalDonations, PoliticalParty, ATTOM_ID, GEOID, SCORE, Latitude, Longitude, SpouseFirstName, SpouseLastName, HomeAvailableHomeEquity, HomeTotalLoans, HomeLoan1Amount, HomeLoan2Amount, HomeValueRangeCode, HomeValueRangeText, HomeMarketValue, HomeAssessedValue, HomeLoanToValue, HomeSQFT, HomeLotSQFT, HomeYearBuilt, HomePurchaseDate, HomeLoan1Date, HomeLoan2Date, HomeParcelNumber, HomePropertyType, DNC, HomeCompanyOwned, HomeTrustOwned, HomeOwnerOccupied, HomeType, HomePool, HomeGarage, HomeHeating, HomeCooling, HomeBedrooms, HomeBathrooms, HomeNumberOfUnits, MailingAddress, MailingCity, MailingState, MailingZip, MailingZip4, Married, Divorce, Education, Occupation, Ethnicity, LANGUAGE, RELIGION,
FK_Email[SAFE_ORDINAL(1)] FK_Email1, Emails[SAFE_ORDINAL(1)] Email1, FK_Email[SAFE_ORDINAL(2)] AS FK_Email2, Emails[SAFE_ORDINAL(2)] Email2, FK_Email[SAFE_ORDINAL(3)] AS FK_Email3, Emails[SAFE_ORDINAL(3)] Email3
FROM `db.ds.table1` P
LEFT JOIN (
SELECT FirstName, LastName, Address, Zip,
ARRAY_AGG(Email LIMIT 3) Emails, ARRAY_AGG(PK LIMIT 3) FK_Email
FROM `db.ds.table2`
GROUP BY FirstName, LastName, Address, Zip
) E
ON P.FirstName = E.FirstName
AND P.LastName = E.LastName
AND P.Address = E.Address
AND P.Zip = E.Zip