Finding max value from count without using max - sql

I have
Voters(name,vote, score,time)
Where name is the primary key and vote the ID of the person whom the voters voted for.
1 person can vote more than once.
I have to find the name of the person who has voted the most number of times.
{Maximum Count(vote)} without using max command.


If you can use count(), then try ordering your results by it. In MySQL, your query could look like
SELECT name, COUNT(vote) AS total_votes
FROM Voters
GROUP BY name
ORDER BY total_votes DESC
LIMIT 1
This would return the person who has voted the most together with his vote count.


You can use concept from relational algebra. limit 1 does not give good result when more than one user have the same number of votes.
On PostgreSQL:
with t as (select name, count(*) c from voters group by name)
SELECT t.* from t
except
SELECT t.* from t JOIN t t2 ON t2.c > t.c
Example on SQL Fiddle
And sample data to prove this works:
create table voters (name int,vote int);
insert into voters values (1,1);
insert into voters values (1,1);
insert into voters values (1,1);
insert into voters values (1,1);
insert into voters values (1,1);
insert into voters values (1,1);
insert into voters values (1,1);
insert into voters values (1,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (2,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (3,1);
insert into voters values (4,1);
insert into voters values (4,1);
insert into voters values (4,1);
insert into voters values (4,1);
insert into voters values (4,1);
insert into voters values (5,1);
insert into voters values (5,1);
insert into voters values (5,1);
insert into voters values (5,1);
My solution is based on answer to this question: How can I find MAX with relational algebra?
There is also good explanation of this concept in another answer here Aggregate Relational Algebra (Maximum)


Try Following
select count(*) from table where vote=(select max(count(*)) from table) group by vote
Hope this helps you.

Related

SQL Constraint only if column has a specific value

I would like to add a unique constraint to a sql table but only if the column has a specific value. In my case, I have Geographic IDs and Status as fields. The IDs can have multiple status but there should only be one Current (C) status per ID. Is there a way to involve a where clause to have unique values only in the case of C values?
I tried adding a UNIQUE CONSTAINT as well as a filtered index, to no avail.

In SQL Server you can create filtered indexes, e.g.:
create table dbo.Demo (
GeographicID int,
[Status] char(1)
);
create unique index IXUF_Demo_GeographicID_Current
on dbo.Demo (GeographicID)
where [Status]='C';
insert dbo.Demo (GeographicID, [Status]) values (1, 'A');
insert dbo.Demo (GeographicID, [Status]) values (1, 'A');
insert dbo.Demo (GeographicID, [Status]) values (1, 'B');
insert dbo.Demo (GeographicID, [Status]) values (1, 'B');
insert dbo.Demo (GeographicID, [Status]) values (1, 'C'); --Succeeds
insert dbo.Demo (GeographicID, [Status]) values (1, 'C'); --Fails

How to select distinct multi-column values in Oracle SQL?

I am trying to get distinct values with multi column select.
Sample table:
CREATE TABLE DUP_VALUES (ID NUMBER, NAME VARCHAR2(64));
INSERT INTO DUP_VALUES values (1, 'TEST1');
INSERT INTO DUP_VALUES values (2, 'TEST1');
INSERT INTO DUP_VALUES values (3, 'TEST2');
INSERT INTO DUP_VALUES values (4, 'TEST2');
INSERT INTO DUP_VALUES values (5, 'TEST1');
INSERT INTO DUP_VALUES values (6, 'TEST1');
INSERT INTO DUP_VALUES values (7, 'TEST1');
I want to get
ID NAME
1 TEST1
3 TEST2
I tried with SELECT DISTINCT ID, NAME FROM DUP_VALUES
But, I got all values, because ID is unique.

Use aggregation:
select min(id) as id, name
from dup_values
group by name;

How do I include certain conditions in SQL Count

`CREATE TABLE PERSON
(
Person_id NUMBER(3) PRIMARY KEY,
Surname VARCHAR2(20),
First_name VARCHAR2(20),
Sex CHAR(1),
Birth_date DATE,
Street VARCHAR2(40),
Town CHAR(20),
Postcode NUMBER(4),
Next_of_kin NUMBER(3)
);
CREATE TABLE STAFF
(
Person_id NUMBER(3) PRIMARY KEY,
Start_date DATE,
Staff_type VARCHAR2(15),
Charges NUMBER(10,2),
Resign_date DATE,
FOREIGN KEY (Person_id) references PERSON(Person_id)
);
CREATE TABLE WARD (
Ward_code CHAR(3) PRIMARY KEY,
Ward_name VARCHAR2(20),
Bed_count NUMBER(4),
Opened_date DATE,
Last_painted_date DATE,
Daily_charge NUMBER(10,2)
);
CREATE TABLE OPERATION_TYPE (
Op_code CHAR(3) PRIMARY KEY,
Operation_name VARCHAR2(50),
Theatre_fee NUMBER(10,2),
Days_in NUMBER(5)
);
CREATE TABLE ADMISSION (
Admission_id NUMBER(3) PRIMARY KEY,
Patient_id NUMBER(3),
Admission_date DATE NOT NULL,
Expected_op CHAR(3),
Admitted_by NUMBER(3),
Ward_code CHAR(3),
Discharge_date DATE,
FOREIGN KEY (Patient_id) references PERSON(Person_id),
FOREIGN KEY (Expected_op) references OPERATION_TYPE(Op_code),
FOREIGN KEY (Admitted_by) references PERSON(Person_id),
FOREIGN KEY (Ward_code) references WARD(Ward_code)
);
CREATE TABLE OPERATION (
Operation_id NUMBER(3) PRIMARY KEY,
Actual_op CHAR(3),
Admission_id NUMBER(3),
Op_date DATE,
Surgeon NUMBER(3),
Anaesthetist NUMBER(3),
FOREIGN KEY (Surgeon) references PERSON(Person_id),
FOREIGN KEY (Anaesthetist) references PERSON(Person_id),
FOREIGN KEY (Actual_op) references OPERATION_TYPE(Op_code),
FOREIGN KEY (Admission_id) references ADMISSION(Admission_id)
);
CREATE TABLE OBSERVATION(
Admission_id NUMBER(3),
Observ_date DATE,
Observ_time NUMBER(4),
Observ_type CHAR(10),
Observ_value NUMBER(4),
Staff_id NUMBER(3),
PRIMARY KEY (Admission_id,Observ_date,Observ_time,Observ_type),
FOREIGN KEY (Admission_id) references ADMISSION(Admission_id),
FOREIGN KEY (Staff_id) references STAFF(Person_id)
);
REM***********************
REM PERSON TABLE
REM***********************
INSERT INTO PERSON VALUES (101,'Black','Barry','M','31/12/1959','11 High St.','Cooma',2630,102);
INSERT INTO PERSON VALUES (102,'Black','Mary','F','11/04/1965','11 High St.','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (103,'Strathclyde','Albert','M','15/5/1955','3 The Mews','Hawthorne',3171,104);
INSERT INTO PERSON VALUES (104,'Strathclyde','Alice','F','17/7/1955','3 The Mews','Hawthorne',3171,103);
INSERT INTO PERSON VALUES (105,'Green','Gill','F','16/6/1966','124 Main St.','Young',2594,106);
INSERT INTO PERSON VALUES (106,'Green','Graham','M','24/4/1967','124 Main St.','Young',2594,105);
INSERT INTO PERSON VALUES (107,'Gray','Lesley','F','12/9/1972','130 Main St.','Young',2594,109);
INSERT INTO PERSON VALUES (109,'Gray','John','M','14/4/1972','130 Main St.','Young',2594,107);
INSERT INTO PERSON VALUES (110,'Samuelson','Thomas','M','1/1/1964','17 The Mews','Hawthorne',3171,NULL);
INSERT INTO PERSON VALUES (111,'Abrahams','Mary','F','15/5/1967','2177A The Esplanade','Ivanhoe',3878,NULL);
INSERT INTO PERSON VALUES (112,'Aumann','Monica','F','25/5/1955','29 The Esplanade','Ivanhoe',3878,NULL);
INSERT INTO PERSON VALUES (113,'Brown','Melissa','F','8/8/1984','11 East St.','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (114,'Napier','Mary','F','1/1/1971','163 New Rd.','Henty',2658,NULL);
INSERT INTO PERSON VALUES (115,'Nelson','Nigel','M','2/2/1972','165 Young Rd.','Temora',2666,NULL);
INSERT INTO PERSON VALUES (116,'Newman','Olive','F','3/3/1973','21 Olympic Way','Henty',2658,NULL);
INSERT INTO PERSON VALUES (117,'Gray','Lesley','M','31/12/1989','130 Andres St.','Young',2594,105);
INSERT INTO PERSON VALUES (118,'Hon','Tasuku','M','13/3/1953','21 Silcon Height','Henty',2658,NULL);
INSERT INTO PERSON VALUES (119,'Livingstone','Frank','M','3/3/2003','21 Sun Height','Henty',2658,122);
INSERT INTO PERSON VALUES (120,'Giggle','Frank','M','23/3/1975','21 Albrige Close','Cooma',2630,121);
INSERT INTO PERSON VALUES (121,'Giggle','Felicia','F','3/3/1980','21 Albrige Close','Cooma',2630,120);
INSERT INTO PERSON VALUES (122,'Black','Frank Jr','M','13/3/2011','21 Stun Height','Henty',2658,123);
INSERT INTO PERSON VALUES (123,'Black','Frances','F','12/12/2005','21 Stun Height','Henty',2658,122);
INSERT INTO PERSON VALUES (124,'Smith','Buddy','M','11/12/1979','101 High St.','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (125,'Smith','Maxime','F','31/12/1979','101 High St.','Cooma',2630,124);
INSERT INTO PERSON VALUES (126,'Smith','Issac','M','1/12/2007','101 High St.','Cooma',2630,124);
INSERT INTO PERSON VALUES (127,'Smith','Ronny','M','3/12/2009','101 High St.','Cooma',2630,124);
INSERT INTO PERSON VALUES (128,'Giggle','Fanny','F','3/12/2007','121 Close Rose','Hillo',2330,120);
INSERT INTO PERSON VALUES (129,'Murad','Nadia','F','3/3/2000','121 Close Rose', 'Hillo',2330,130);
INSERT INTO PERSON VALUES (130,'Murad','Tange','M','3/3/1999','7711 Albrige Close','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (131,'Rome','Paula','F','23/9/1965','21 Height Close','Cooma',2630,132);
INSERT INTO PERSON VALUES (132,'Rome','Paul','M','13/3/1966','21 Height Close','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (133,'Rome','Fay','M','3/3/2017','21 Height Close','Cooma',2630,132);
INSERT INTO PERSON VALUES (134,'Murad','Michelle','F','3/3/2001','1 Height Close','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (135,'Trump','Donald','M','13/3/1956','222 White House Avenue','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (136,'Trump','Melania','F','3/3/1992','222 White House Avenue','Cooma',2630,135);
INSERT INTO PERSON VALUES (137,'Trump','Baron','M','3/6/2005','222 White House Avenue','Cooma',2630,135);
INSERT INTO PERSON VALUES (138,'Johnson','Boris','M','23/9/1965','10 Downing Street','Wagga Wagga',2999,NULL);
INSERT INTO PERSON VALUES (139,'Cordeiro','Wayne','M','3/3/1965','777 Hawaii Close Rose', 'Hillo',7770, NULL);
INSERT INTO PERSON VALUES (140,'Cordeiro','Anne','F','23/4/1968','777 Hawaii Close Rose', 'Hillo',7770, 139);
INSERT INTO PERSON VALUES (141,'Thatcher','Margaret','F','21/1/1955','120 Main Sq.','Wagga Wagga',2999,142);
INSERT INTO PERSON VALUES (142,'Thatcher','Denis','M','23/9/1955','120 Main Sq.','Wagga Wagga',2999,NULL);
INSERT INTO PERSON VALUES (143,'Thatcher','Carols','F','1/9/1985','120 Main Sq.','Wagga Wagga',2999,142);
INSERT INTO PERSON VALUES (144,'Nelson','Nigel','M','22/2/1992','15 Young Rd.','Temora',2666,NULL);
INSERT INTO PERSON VALUES (145,'Neon','Gela','F','2/2/1972','1465 Main Rd.','Temora',2666,NULL);
INSERT INTO PERSON VALUES (146,'Twain','Shane','F','21/1/1995','A-129 Main Rose Sq.','Wagga Wagga',2650,147);
INSERT INTO PERSON VALUES (147,'Twain','Dens','M','23/9/1985','A-129 Main Rose Sq.','Wagga Wagga',2650,NULL);
INSERT INTO PERSON VALUES (148,'Trump','Ivanka','F','5/3/1985','222 White House Avenue','Cooma',2630,135);
INSERT INTO PERSON VALUES (149,'Trump','Eric','M','3/12/1975','222 White House Avenue','Cooma',2630,135);
INSERT INTO PERSON VALUES (150,'Gates','Bill','M','5/3/1975','2 Rosey Lane','Cooma',2630,NULL);
INSERT INTO PERSON VALUES (151,'Bucket','Eric','M','3/1/1985','11 Oserey Avenue','Cooma',2630,NULL);
REM*******************************************************
REM STAFF TABLE
REM nursing services will not be charged to the patients
REM*******************************************************
INSERT INTO STAFF VALUES (103,'1/1/2009','Surgeon',4525,NULL);
INSERT INTO STAFF VALUES (110,'5/5/2009','Surgeon',5600,NULL);
INSERT INTO STAFF VALUES (118,'1/5/2016','Surgeon',7890,NULL);
INSERT INTO STAFF VALUES (111,'1/1/2009','Anaesthetist',5900,NULL);
INSERT INTO STAFF VALUES (112,'3/3/2009','Anaesthetist',4788,NULL);
INSERT INTO STAFF VALUES (114,'1/1/2009','Senior Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (115,'2/2/2016','Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (116,'3/3/2014','Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (125,'23/3/2014','Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (135,'1/1/2019','Senior Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (136,'2/2/2016','Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (138,'23/3/2019','Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (139,'23/3/2020','Senior Nurse',NULL,NULL);
INSERT INTO STAFF VALUES (150,'1/11/2019','Anaesthetist',6900,NULL);
INSERT INTO STAFF VALUES (151,'31/1/2019','Anaesthetist',5900,NULL);
REM***********************
REM WARD TABLE
REM***********************
INSERT INTO WARD VALUES ('C','Covid Bay',90,'31/12/2019',NULL,150.00);
INSERT INTO WARD VALUES ('A','Abraham',80,'1/12/2015','12/05/2011',350.00);
INSERT INTO WARD VALUES ('N','Nightingale',50,'1/12/2017','12/05/2012',450.00);
INSERT INTO WARD VALUES ('F','Flemming',75,'15/11/2009',NULL,230.00);
INSERT INTO WARD VALUES ('L','Lister',80,'1/1/2009','20/12/2013',200.00);
INSERT INTO WARD VALUES ('P','Pasteur',60,'1/12/2009','12/12/2011',250.00);
REM***********************
REM OPERATION_TYPE TABLE
REM***********************
INSERT INTO OPERATION_TYPE VALUES ('LO','Lobotomy',700.00,10);
INSERT INTO OPERATION_TYPE VALUES ('CS','Caesarean',5700.00,3);
INSERT INTO OPERATION_TYPE VALUES ('CT','Cataract',670.00,1);
INSERT INTO OPERATION_TYPE VALUES ('AP','Appendicectomy',500.00,5);
INSERT INTO OPERATION_TYPE VALUES ('HB','Heart Bypass',2000.00,14);
INSERT INTO OPERATION_TYPE VALUES ('HT','Heart Transplant',5000.00,30);
INSERT INTO OPERATION_TYPE VALUES ('HY','Hysterectomy',800.00,7);
INSERT INTO OPERATION_TYPE VALUES ('LA','Leg Amputation',1500.00,10);
INSERT INTO OPERATION_TYPE VALUES ('TS','Tonsillectomy',700.00,7);
INSERT INTO OPERATION_TYPE VALUES ('LP','Laparoscopy',500.00,1);
INSERT INTO OPERATION_TYPE VALUES ('AR','Arthroscopy ',700.00,17);
REM***********************
REM ADMISSION TABLE
REM***********************
INSERT INTO ADMISSION VALUES (205,101,'2/2/2011','HB',114,'P','21/2/2011');
INSERT INTO ADMISSION VALUES (275,101,'1/9/2010','HY',115,'L','1/11/2010');
INSERT INTO ADMISSION VALUES (286,101,'3/5/2016','AR',116,'A','3/7/2016');
INSERT INTO ADMISSION VALUES (303,101,'3/4/2018','LA',125,'F', '13/5/2018');
INSERT INTO ADMISSION VALUES (298,103,'23/1/2016','TS',114,'L','24/04/2016');
INSERT INTO ADMISSION VALUES (299,103,'23/3/2018','AP',114,'L','23/4/2018');
INSERT INTO ADMISSION VALUES (305,103,'23/4/2018','HT',125,'F','29/5/2018');
INSERT INTO ADMISSION VALUES (321,103,'13/8/2018','AR',125,'F', '23/10/2018');
INSERT INTO ADMISSION VALUES (283,105,'3/12/2015','AR',116,'A','5/12/2015');
INSERT INTO ADMISSION VALUES (278,105,'1/1/2011','HB',115,'P','30/1/2011');
INSERT INTO ADMISSION VALUES (307,105,'3/4/2018','TS',125,'F', '13/5/2018');
INSERT INTO ADMISSION VALUES (276,106,'24/8/2010','LA',114,'P','15/9/2010');
INSERT INTO ADMISSION VALUES (287,106,'3/5/2016','TS',114,'A','3/6/2016');
INSERT INTO ADMISSION VALUES (274,109,'1/9/2019','AP',114,'P','9/9/2019');
INSERT INTO ADMISSION VALUES (288,109,'23/5/2016','LO',114,'F','3/07/2016');
INSERT INTO ADMISSION VALUES (301,112,'13/4/2018','AP',125,'F','16/4/2018');
INSERT INTO ADMISSION VALUES (304,112,'23/4/2019','LO',114,'L','23/5/2019');
INSERT INTO ADMISSION VALUES (279,113,'3/9/2010','TS',115,'F','10/9/2010');
INSERT INTO ADMISSION VALUES (284,113,'3/12/2015','HY',116,'A','03/01/2016');
INSERT INTO ADMISSION VALUES (285,113,'3/5/2016','HT',116,'A','3/6/2016');
INSERT INTO ADMISSION VALUES (300,113,'23/4/2018','AR',114,'L','25/6/2018');
INSERT INTO ADMISSION VALUES (306,113,'13/8/2018','AP',125,'L', '13/9/2018');
INSERT INTO ADMISSION VALUES (277,114,'20/9/2010','AP',115,'P','30/9/2010');
INSERT INTO ADMISSION VALUES (289,115,'11/4/2016','LO',114,'L','3/6/2016');
INSERT INTO ADMISSION VALUES (290,115,'5/7/2016','TS',114,'L','3/09/2016');
INSERT INTO ADMISSION VALUES (308,115,'23/3/2018','AR',114,'L','25/3/2018');
INSERT INTO ADMISSION VALUES (280,117,'13/9/2010','AP',115,'F','25/9/2010');
INSERT INTO ADMISSION VALUES (281,117,'3/9/2014','HB',116,'A','21/9/2014');
INSERT INTO ADMISSION VALUES (282,117,'3/12/2015','LA',116,'A','14/12/2015');
INSERT INTO ADMISSION VALUES (309,126,'23/3/2018','TS',125,'L', '13/5/2018');
INSERT INTO ADMISSION VALUES (310,127,'13/5/2018','AP',125,'L', '28/5/2018');
INSERT INTO ADMISSION VALUES (311,124,'3/5/2018','LO',125,'A', '23/5/2018');
INSERT INTO ADMISSION VALUES (312,127,'21/6/2019','LO',125,'L','22/8/2019');
INSERT INTO ADMISSION VALUES (313,124,'22/6/2019','AP',125,'A','22/7/2019');
INSERT INTO ADMISSION VALUES (314,109,'21/6/2019','LO',125,'L','22/7/2019');
INSERT INTO ADMISSION VALUES (315,126,'12/6/2019','AP',125,'A','22/9/2019');
INSERT INTO ADMISSION VALUES (316,114,'22/7/2019','HB',125,'A','12/12/2019');
INSERT INTO ADMISSION VALUES (318,128,'3/5/2019','LA',116,'A','4/6/2019');
INSERT INTO ADMISSION VALUES (319,129,'23/3/2019','TS',125,'L', '13/4/2019');
INSERT INTO ADMISSION VALUES (320,130,'3/5/2019','LA',116,'F','4/6/2019');
INSERT INTO ADMISSION VALUES (328,119,'3/3/2019','TS',115,'N', '3/4/2019');
INSERT INTO ADMISSION VALUES (322,132,'3/5/2019','LA',116,'A','4/6/2019');
INSERT INTO ADMISSION VALUES (323,133,'23/3/2019','TS',125,'L', '13/4/2019');
INSERT INTO ADMISSION VALUES (324,131,'3/5/2018','LA',116,'F','4/5/2018');
INSERT INTO ADMISSION VALUES (325,118,'3/6/2019','TS',115,'P', '3/7/2019');
INSERT INTO ADMISSION VALUES (326,102,'3/5/2018','LA',116,'F','4/5/2018');
INSERT INTO ADMISSION VALUES (327,104,'3/6/2019','TS',115,'P', '3/7/2019');
INSERT INTO ADMISSION VALUES (339,107,'3/6/2019','TS',115,'P', '3/7/2019');
INSERT INTO ADMISSION VALUES (329,110,'3/6/2019','TS',115,'P', '3/7/2019');
INSERT INTO ADMISSION VALUES (330,111,'3/6/2019','TS',115,'P', '3/7/2019');
INSERT INTO ADMISSION VALUES (331,116,'3/6/2019','TS',115,'P', '3/7/2019');
INSERT INTO ADMISSION VALUES (332,121,'21/6/2019','TS',125,'L','22/7/2019');
INSERT INTO ADMISSION VALUES (333,123,'22/7/2019','AP',125,'A','22/9/2019');
INSERT INTO ADMISSION VALUES (334,134,'21/8/2019','AP',115,'L','22/12/2019');
INSERT INTO ADMISSION VALUES (335,128,'12/8/2020','AP',115,'A',NULL);
INSERT INTO ADMISSION VALUES (336,125,'22/7/2020','HB',115,'A',NULL);
INSERT INTO ADMISSION VALUES (337,120,'21/8/2020','AP',116,'L',NULL);
INSERT INTO ADMISSION VALUES (338,130,'22/7/2020','AP',125,'N',NULL);
INSERT INTO ADMISSION VALUES (340,131,'22/8/2020','AP',125,'N',NULL);
INSERT INTO ADMISSION VALUES (341,122,'22/8/2020','AP',125,'N',NULL);
INSERT INTO ADMISSION VALUES (490,139,'11/8/2020','AP',125,'F',NULL);
INSERT INTO ADMISSION VALUES (491,140,'7/9/2020','AP',138,'F',NULL);
REM***********************
REM OPERATION TABLE
REM***********************
INSERT INTO OPERATION VALUES (317,'HB',205,'4/2/2011',110,111);
INSERT INTO OPERATION VALUES (355,'LA',276,'25/8/2010',103,111);
INSERT INTO OPERATION VALUES (363,'AP',276,'3/9/2010',103,111);
INSERT INTO OPERATION VALUES (360,'AP',277,'22/9/2010',103,111);
INSERT INTO OPERATION VALUES (361,'AP',274,'2/9/2010',103,111);
INSERT INTO OPERATION VALUES (362,'HT',275,'3/9/2010',110,112);
INSERT INTO OPERATION VALUES (364,'AP',279,'3/9/2010',103,111);
INSERT INTO OPERATION VALUES (365,'AP',280,'6/9/2010',103,111);
INSERT INTO OPERATION VALUES (366,'HB',281,'6/9/2014',103,111);
INSERT INTO OPERATION VALUES (367,'HY',284,'6/12/2015',103,111);
INSERT INTO OPERATION VALUES (368,'LO',285,'6/5/2016',118,112);
INSERT INTO OPERATION VALUES (369,'AP',286,'6/5/2016',118,112);
INSERT INTO OPERATION VALUES (370,'TS',287,'7/5/2016',118,112);
INSERT INTO OPERATION VALUES (371,'LA',288,'26/5/2016',110,111);
INSERT INTO OPERATION VALUES (372,'LO',289,'16/4/2016',110,111);
INSERT INTO OPERATION VALUES (373,'TS',290,'6/5/2016',110,111);
INSERT INTO OPERATION VALUES (381,'TS',298,'26/2/2016',110,111);
INSERT INTO OPERATION VALUES (382,'AP',299,'26/3/2018',110,111);
INSERT INTO OPERATION VALUES (383,'TS',300,'26/5/2018',110,111);
INSERT INTO OPERATION VALUES (384,'TS',309,'16/4/2018',110,111);
INSERT INTO OPERATION VALUES (385,'LO',309,'26/3/2018',110,111);
INSERT INTO OPERATION VALUES (386,'AP',310,'18/5/2018',110,111);
INSERT INTO OPERATION VALUES (387,'AP',311,'4/5/2018',110,111);
INSERT INTO OPERATION VALUES (388,'LO',311,'11/5/2018',110,111);
INSERT INTO OPERATION VALUES (389,'AP',315,'13/6/2019',118,111);
INSERT INTO OPERATION VALUES (390,'HB',316,'23/7/2019',118,111);
INSERT INTO OPERATION VALUES (391,'LO',312,'22/6/2019',118,111);
INSERT INTO OPERATION VALUES (392,'HT',341,'28/8/2019',118,112);
INSERT INTO OPERATION VALUES (393,'HT',340,'27/8/2019',118,112);
INSERT INTO OPERATION VALUES (394,'LO',491,'26/4/2020',103,112);
REM***********************
REM OBSERVATION TABLE
REM***********************
INSERT INTO OBSERVATION VALUES (205,'2/2/2011',1500,'Temp',38,114);
INSERT INTO OBSERVATION VALUES (274,'4/9/2010',0601,'Temp',39,116);
INSERT INTO OBSERVATION VALUES (275,'1/9/2010',1400,'Pulse',64,115);
INSERT INTO OBSERVATION VALUES (275,'1/9/2010',1400,'Temp',38,115);
INSERT INTO OBSERVATION VALUES (275,'3/9/2010',1800,'Temp',40,116);
INSERT INTO OBSERVATION VALUES (275,'3/9/2010',2200,'Temp',40,116);
INSERT INTO OBSERVATION VALUES (275,'4/9/2010',0610,'Pulse',82,116);
INSERT INTO OBSERVATION VALUES (275,'4/9/2010',0610,'Temp',38,116);
INSERT INTO OBSERVATION VALUES (277,'24/9/2010',0600,'Temp',39,115);
INSERT INTO OBSERVATION VALUES (284,'5/12/2015',0600,'Temp',39,115);
INSERT INTO OBSERVATION VALUES (284,'5/12/2015',0600,'Pulse',89,115);
INSERT INTO OBSERVATION VALUES (284,'6/12/2015',0600,'Temp',37,115);
INSERT INTO OBSERVATION VALUES (284,'6/12/2015',0600,'Pulse',87,115);
INSERT INTO OBSERVATION VALUES (285,'7/5/2016',0600,'Temp',38,115);
INSERT INTO OBSERVATION VALUES (285,'8/5/2016',0600,'Temp',38,115);
INSERT INTO OBSERVATION VALUES (285,'8/5/2016',0600,'Pulse',82,115);
INSERT INTO OBSERVATION VALUES (285,'9/5/2016',0600,'Temp',37,115);
INSERT INTO OBSERVATION VALUES (286,'7/5/2016',0600,'Temp',39,125);
INSERT INTO OBSERVATION VALUES (286,'8/5/2016',0600,'Temp',33,125);
INSERT INTO OBSERVATION VALUES (286,'8/5/2016',0600,'Pulse',86,125);
INSERT INTO OBSERVATION VALUES (286,'9/5/2016',0600,'Temp',36,125);
INSERT INTO OBSERVATION VALUES (287,'7/5/2016',0600,'Temp',35,115);
INSERT INTO OBSERVATION VALUES (287,'8/5/2016',0600,'Temp',37,115);
INSERT INTO OBSERVATION VALUES (287,'8/5/2016',0600,'Pulse',82,125);
INSERT INTO OBSERVATION VALUES (287,'9/5/2016',0600,'Temp',37,125);
INSERT INTO OBSERVATION VALUES (289,'19/5/2016',0600,'Temp',39,115);
INSERT INTO OBSERVATION VALUES (289,'20/5/2016',1400,'Temp',38,115);
INSERT INTO OBSERVATION VALUES (289,'22/5/2016',0800,'Temp',37,115);
INSERT INTO OBSERVATION VALUES (289,'26/5/2016',0700,'Temp',37,115);
INSERT INTO OBSERVATION VALUES (289,'29/5/2016',0700,'Temp',38,115);
INSERT INTO OBSERVATION VALUES (300,'23/4/2018',1400,'Pulse',74,115);
INSERT INTO OBSERVATION VALUES (300,'23/4/2018',1400,'Temp',38,115);
INSERT INTO OBSERVATION VALUES (300,'23/4/2018',1800,'Temp',30,116);
INSERT INTO OBSERVATION VALUES (300,'23/4/2018',2200,'Temp',40,116);
INSERT INTO OBSERVATION VALUES (300,'24/4/2018',0610,'Pulse',82,116);
INSERT INTO OBSERVATION VALUES (300,'24/4/2018',0610,'Temp',38,116);
INSERT INTO OBSERVATION VALUES (303,'13/4/2018',1400,'Pulse',64,125);
INSERT INTO OBSERVATION VALUES (303,'13/4/2018',1400,'Temp',38,125);
INSERT INTO OBSERVATION VALUES (303,'13/4/2018',1800,'Temp',30,125);
INSERT INTO OBSERVATION VALUES (303,'23/4/2018',2200,'Temp',40,116);
INSERT INTO OBSERVATION VALUES (303,'23/4/2018',0610,'Pulse',82,116);
INSERT INTO OBSERVATION VALUES (303,'23/4/2018',0610,'Temp',38,116);
INSERT INTO OBSERVATION VALUES (311,'3/5/2018',1400,'Pulse',54,115);
INSERT INTO OBSERVATION VALUES (311,'4/5/2018',1400,'Temp',38,115);
INSERT INTO OBSERVATION VALUES (311,'3/5/2018',1800,'Temp',40,116);
INSERT INTO OBSERVATION VALUES (311,'4/5/2018',2200,'Temp',41,116);
INSERT INTO OBSERVATION VALUES (311,'4/5/2018',1800,'Temp',39,116);
INSERT INTO OBSERVATION VALUES (311,'5/5/2018',0610,'Pulse',72,116);
INSERT INTO OBSERVATION VALUES (311,'5/5/2018',0610,'Temp',38,116);
INSERT INTO OBSERVATION VALUES (310,'13/5/2018',1400,'Pulse',84,125);
INSERT INTO OBSERVATION VALUES (310,'13/5/2018',1400,'Temp',28,125);
INSERT INTO OBSERVATION VALUES (310,'14/5/2018',1800,'Temp',60,125);
INSERT INTO OBSERVATION VALUES (310,'15/5/2018',2200,'Temp',40,116);
INSERT INTO OBSERVATION VALUES (310,'16/5/2018',0610,'Pulse',92,116);
INSERT INTO OBSERVATION VALUES (310,'16/5/2018',0610,'Temp',35,116);
INSERT INTO OBSERVATION VALUES (341,'22/8/2019',0610,'Temp',39,116);
INSERT INTO OBSERVATION VALUES (340,'25/8/2019',0610,'Temp',39,116);
INSERT INTO OBSERVATION VALUES (340,'25/8/2019',0610,'Pulse',75,116);
INSERT INTO OBSERVATION VALUES (340,'26/8/2019',0610,'Temp',37,116);
INSERT INTO OBSERVATION VALUES (340,'26/8/2019',0610,'Pulse',70,116);
INSERT INTO OBSERVATION VALUES (340,'27/8/2019',0610,'Pulse',68,116);
INSERT INTO OBSERVATION VALUES (340,'27/8/2019',0610,'Temp',37,116);
INSERT INTO OBSERVATION VALUES (340,'29/8/2019',0610,'Pulse',73,116);
INSERT INTO OBSERVATION VALUES (490,'17/4/2020',0600,'Temp',38,125);
INSERT INTO OBSERVATION VALUES (490,'18/4/2020',0600,'Temp',37,125);
INSERT INTO OBSERVATION VALUES (490,'19/4/2020',0600,'Temp',38,125);
select town, c.count(2021- to_char(birth_date,'YYYY'))100/count() as "Population"
from person, person c
group by town
Using Apex Oracle here, I need to set a condition for c.count where it only takes into account >60, but whenever I put the condition in, errors pop up, would greatly appreciate any help possible.

I'm assuming you don't really need a cross join of 'person' with 'person'. So I'm dropping that logic from the sources.
I don't have and Oracle instance to work with, but I believe the code below, which uses a case statement to achieve your conditional need, should work for you:
select town,
c.count(
case when ap.age > 60 then ap.age end
) 100 / count() as "Population"
from person
cross apply (select age = 2021 - to_char(birth_date,'YYYY')) ap
group by town

BigQuery SQL: Execute query over rolling time window

Using BigQuery and Standard SQL I am trying to calculate the retention rate for users seen in one period, compared to users seen in period after. I want to calculate this daily, using the same period offset which will slide as time passes by.
The data used is Google Analytics data, which include these fields:
https://support.google.com/analytics/answer/3437719?hl=en
I have the query to calculate retention, and I know how to set this up to run daily, but I would like to create some history to start with, and then I need to simulate that this query has been running daily for some time.
So my question is how can I create retention rate numbers, for each day, given that the first period is e.g. 60-31 days ago and the following period is 30-1 day(s) ago, all relative to each day.
The query I have:
WITH
users_seen_on_start AS (
SELECT DISTINCT
fullVisitorId AS users
FROM `project.view.ga_sessions_*`, UNNEST(hits) as hits
WHERE _TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d',DATE_SUB(CURRENT_DATE(), INTERVAL 60 DAY))
AND FORMAT_DATE('%Y%m%d',DATE_SUB(CURRENT_DATE(), INTERVAL 31 DAY))
),
num_users AS (
SELECT
count(*) AS num_users_in_cohort
FROM users_seen_on_start
),
engaged_user_by_day AS (
SELECT
COUNT (DISTINCT fullVisitorId) as num_engaged_users
FROM `project.view.ga_sessions_*`, UNNEST(hits) as hits INNER JOIN users_seen_on_start ON users = fullVisitorId
WHERE _TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d',DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d',DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
)
SELECT
num_engaged_users,
num_users_in_cohort ,
ROUND((num_engaged_users / num_users_in_cohort), 3) as retention_rate
FROM engaged_user_by_day CROSS JOIN num_users
Output from query:
num_engaged_users num_users_in_cohort retention_rate
100871 130632 0.772
Desired output:
date num_engaged_users num_users_in_cohort retention_rate
20190101 100871 130632 0.772
20190102 102356 128044 0.799
Sample data:
users_seen_on_start:
CREATE TABLE users_seen_on_start(
users INTEGER NOT NULL PRIMARY KEY
);
INSERT INTO users_seen_on_start(users) VALUES (6854940999573646134);
INSERT INTO users_seen_on_start(users) VALUES (9215697890064860396);
INSERT INTO users_seen_on_start(users) VALUES (5595285367064974856);
INSERT INTO users_seen_on_start(users) VALUES (2054889847396937366);
INSERT INTO users_seen_on_start(users) VALUES (2159837518531156200);
INSERT INTO users_seen_on_start(users) VALUES (2297077047785095499);
INSERT INTO users_seen_on_start(users) VALUES (15934479773952228986);
INSERT INTO users_seen_on_start(users) VALUES (18388188973174323198);
INSERT INTO users_seen_on_start(users) VALUES (13527051077114159514);
INSERT INTO users_seen_on_start(users) VALUES (10527965347657532651);
INSERT INTO users_seen_on_start(users) VALUES (10056509199904199853);
INSERT INTO users_seen_on_start(users) VALUES (721447367663373337);
INSERT INTO users_seen_on_start(users) VALUES (7418392997259835212);
INSERT INTO users_seen_on_start(users) VALUES (1739158781654194388);
INSERT INTO users_seen_on_start(users) VALUES (13485010633919602577);
INSERT INTO users_seen_on_start(users) VALUES (11647513515368913077);
INSERT INTO users_seen_on_start(users) VALUES (14723171573825482124);
INSERT INTO users_seen_on_start(users) VALUES (316809625899342248);
INSERT INTO users_seen_on_start(users) VALUES (736697877724685769);
INSERT INTO users_seen_on_start(users) VALUES (1069762618672583190);
INSERT INTO users_seen_on_start(users) VALUES (6216571959193109764);
INSERT INTO users_seen_on_start(users) VALUES (8276320148745358024);
INSERT INTO users_seen_on_start(users) VALUES (4390033140354437765);
INSERT INTO users_seen_on_start(users) VALUES (4691956767605638049);
INSERT INTO users_seen_on_start(users) VALUES (8853050929187030210);
INSERT INTO users_seen_on_start(users) VALUES (4866380534293592106);
INSERT INTO users_seen_on_start(users) VALUES (9336123194114580988);
INSERT INTO users_seen_on_start(users) VALUES (9102157575556710064);
INSERT INTO users_seen_on_start(users) VALUES (5656668438554927436);
INSERT INTO users_seen_on_start(users) VALUES (1488391481235428518);
INSERT INTO users_seen_on_start(users) VALUES (2840931994944989396);
INSERT INTO users_seen_on_start(users) VALUES (2881922818148829205);
INSERT INTO users_seen_on_start(users) VALUES (15266979732129081227);
INSERT INTO users_seen_on_start(users) VALUES (17452034639473427980);
INSERT INTO users_seen_on_start(users) VALUES (16885946609916150102);
INSERT INTO users_seen_on_start(users) VALUES (11414196691107747488);
INSERT INTO users_seen_on_start(users) VALUES (11428367061145620067);
INSERT INTO users_seen_on_start(users) VALUES (11589939716097939663);
INSERT INTO users_seen_on_start(users) VALUES (9471966568512958356);
INSERT INTO users_seen_on_start(users) VALUES (10302973548806993195);
INSERT INTO users_seen_on_start(users) VALUES (11655856328192191298);
INSERT INTO users_seen_on_start(users) VALUES (13935768668138194306);
INSERT INTO users_seen_on_start(users) VALUES (12094331062677811830);
INSERT INTO users_seen_on_start(users) VALUES (10077917656361210181);
INSERT INTO users_seen_on_start(users) VALUES (12524832796889539656);
INSERT INTO users_seen_on_start(users) VALUES (12545063140779927439);
INSERT INTO users_seen_on_start(users) VALUES (12842029924433102779);
INSERT INTO users_seen_on_start(users) VALUES (642899976804427792);
INSERT INTO users_seen_on_start(users) VALUES (6445850403127955479);
INSERT INTO users_seen_on_start(users) VALUES (6564816699382875533);
INSERT INTO users_seen_on_start(users) VALUES (4991902095596735494);
INSERT INTO users_seen_on_start(users) VALUES (9240481697386039624);
INSERT INTO users_seen_on_start(users) VALUES (7462479064488338261);
INSERT INTO users_seen_on_start(users) VALUES (7954751513116206324);
INSERT INTO users_seen_on_start(users) VALUES (7916442053878140133);
INSERT INTO users_seen_on_start(users) VALUES (5943783673017806941);
INSERT INTO users_seen_on_start(users) VALUES (8019839094524452470);
INSERT INTO users_seen_on_start(users) VALUES (1325025305488677572);
INSERT INTO users_seen_on_start(users) VALUES (2757934917480873578);
INSERT INTO users_seen_on_start(users) VALUES (2953784252203011629);
INSERT INTO users_seen_on_start(users) VALUES (15663806630564163334);
INSERT INTO users_seen_on_start(users) VALUES (15822234287772625947);
INSERT INTO users_seen_on_start(users) VALUES (16086946171320332009);
INSERT INTO users_seen_on_start(users) VALUES (18326627563023885086);
INSERT INTO users_seen_on_start(users) VALUES (10177146105583960910);
INSERT INTO users_seen_on_start(users) VALUES (11536897925313534298);
INSERT INTO users_seen_on_start(users) VALUES (9521017502744452252);
INSERT INTO users_seen_on_start(users) VALUES (13846940074652198876);
INSERT INTO users_seen_on_start(users) VALUES (12072437921316824606);
INSERT INTO users_seen_on_start(users) VALUES (12130911952369094625);
INSERT INTO users_seen_on_start(users) VALUES (9944467373343765193);
INSERT INTO users_seen_on_start(users) VALUES (10130381296105130198);
INSERT INTO users_seen_on_start(users) VALUES (14503197259750222085);
INSERT INTO users_seen_on_start(users) VALUES (14514330935424697592);
INSERT INTO users_seen_on_start(users) VALUES (14700594671656063689);
INSERT INTO users_seen_on_start(users) VALUES (14735848995356133105);
INSERT INTO users_seen_on_start(users) VALUES (14880164794899465972);
INSERT INTO users_seen_on_start(users) VALUES (12844072088150040888);
INSERT INTO users_seen_on_start(users) VALUES (13244940156815171079);
INSERT INTO users_seen_on_start(users) VALUES (260647987138229776);
INSERT INTO users_seen_on_start(users) VALUES (4223941834652936903);
INSERT INTO users_seen_on_start(users) VALUES (7094513577121476923);
INSERT INTO users_seen_on_start(users) VALUES (9277783379267650134);
INSERT INTO users_seen_on_start(users) VALUES (5996874826262341487);
INSERT INTO users_seen_on_start(users) VALUES (6070125918500272373);
INSERT INTO users_seen_on_start(users) VALUES (1530161613058767114);
INSERT INTO users_seen_on_start(users) VALUES (3564084216977083409);
INSERT INTO users_seen_on_start(users) VALUES (2096791516261012274);
INSERT INTO users_seen_on_start(users) VALUES (17168509252900308876);
INSERT INTO users_seen_on_start(users) VALUES (17616873481220648376);
INSERT INTO users_seen_on_start(users) VALUES (17998058763193684336);
INSERT INTO users_seen_on_start(users) VALUES (16355698068697852664);
INSERT INTO users_seen_on_start(users) VALUES (18429432702234588790);
INSERT INTO users_seen_on_start(users) VALUES (11376613349708048591);
INSERT INTO users_seen_on_start(users) VALUES (11409024415220391296);
INSERT INTO users_seen_on_start(users) VALUES (11497048558563286896);
INSERT INTO users_seen_on_start(users) VALUES (11461240236069178124);
INSERT INTO users_seen_on_start(users) VALUES (10315048118076394592);
INSERT INTO users_seen_on_start(users) VALUES (10534194857330671443);
INSERT INTO users_seen_on_start(users) VALUES (13087206783728054302);
Sample data engaged_user_by_day:
CREATE TABLE engaged_user_by_day(
users INTEGER NOT NULL PRIMARY KEY
);
INSERT INTO engaged_user_by_day(users) VALUES (6854940999573646134);
INSERT INTO engaged_user_by_day(users) VALUES (9215697890064860396);
INSERT INTO engaged_user_by_day(users) VALUES (5595285367064974856);
INSERT INTO engaged_user_by_day(users) VALUES (2054889847396937366);
INSERT INTO engaged_user_by_day(users) VALUES (2159837518531156200);
INSERT INTO engaged_user_by_day(users) VALUES (2297077047785095499);
INSERT INTO engaged_user_by_day(users) VALUES (15934479773952228986);
INSERT INTO engaged_user_by_day(users) VALUES (18388188973174323198);
INSERT INTO engaged_user_by_day(users) VALUES (13527051077114159514);
INSERT INTO engaged_user_by_day(users) VALUES (10527965347657532651);
INSERT INTO engaged_user_by_day(users) VALUES (10056509199904199853);
INSERT INTO engaged_user_by_day(users) VALUES (721447367663373337);
INSERT INTO engaged_user_by_day(users) VALUES (7418392997259835212);
INSERT INTO engaged_user_by_day(users) VALUES (1739158781654194388);
INSERT INTO engaged_user_by_day(users) VALUES (13485010633919602577);
INSERT INTO engaged_user_by_day(users) VALUES (11647513515368913077);
INSERT INTO engaged_user_by_day(users) VALUES (14723171573825482124);
INSERT INTO engaged_user_by_day(users) VALUES (316809625899342248);
INSERT INTO engaged_user_by_day(users) VALUES (736697877724685769);
INSERT INTO engaged_user_by_day(users) VALUES (1069762618672583190);
INSERT INTO engaged_user_by_day(users) VALUES (6216571959193109764);
INSERT INTO engaged_user_by_day(users) VALUES (8276320148745358024);
INSERT INTO engaged_user_by_day(users) VALUES (4390033140354437765);
INSERT INTO engaged_user_by_day(users) VALUES (4691956767605638049);
INSERT INTO engaged_user_by_day(users) VALUES (8853050929187030210);
INSERT INTO engaged_user_by_day(users) VALUES (4866380534293592106);
INSERT INTO engaged_user_by_day(users) VALUES (9336123194114580988);
INSERT INTO engaged_user_by_day(users) VALUES (9102157575556710064);
INSERT INTO engaged_user_by_day(users) VALUES (5656668438554927436);
INSERT INTO engaged_user_by_day(users) VALUES (1488391481235428518);
INSERT INTO engaged_user_by_day(users) VALUES (2840931994944989396);
INSERT INTO engaged_user_by_day(users) VALUES (2881922818148829205);
INSERT INTO engaged_user_by_day(users) VALUES (15266979732129081227);
INSERT INTO engaged_user_by_day(users) VALUES (17452034639473427980);
INSERT INTO engaged_user_by_day(users) VALUES (16885946609916150102);
INSERT INTO engaged_user_by_day(users) VALUES (11414196691107747488);
INSERT INTO engaged_user_by_day(users) VALUES (11428367061145620067);
INSERT INTO engaged_user_by_day(users) VALUES (11589939716097939663);
INSERT INTO engaged_user_by_day(users) VALUES (9471966568512958356);
INSERT INTO engaged_user_by_day(users) VALUES (10302973548806993195);
INSERT INTO engaged_user_by_day(users) VALUES (11655856328192191298);
INSERT INTO engaged_user_by_day(users) VALUES (13935768668138194306);
INSERT INTO engaged_user_by_day(users) VALUES (12094331062677811830);
INSERT INTO engaged_user_by_day(users) VALUES (10077917656361210181);
INSERT INTO engaged_user_by_day(users) VALUES (12524832796889539656);
INSERT INTO engaged_user_by_day(users) VALUES (12545063140779927439);
INSERT INTO engaged_user_by_day(users) VALUES (12842029924433102779);
INSERT INTO engaged_user_by_day(users) VALUES (642899976804427792);
INSERT INTO engaged_user_by_day(users) VALUES (6445850403127955479);
INSERT INTO engaged_user_by_day(users) VALUES (6564816699382875533);
INSERT INTO engaged_user_by_day(users) VALUES (4991902095596735494);
INSERT INTO engaged_user_by_day(users) VALUES (9240481697386039624);
INSERT INTO engaged_user_by_day(users) VALUES (7462479064488338261);

Rolling data in SQL can be implemented using a self-join.
Please see an example of this below that finds data from the past 30 days relative to each day:
from atable a1
left join atable a2 on a1.custid=a2.custid
and datediff(day,a1.dt,a2.dt)<=30 and datediff(day,a1.dt,a2.dt)>0

There are some links that address a time window:
The BigQuery LAG function.
Query: Daily retention
Retention, Using BigQuery and a
Simple Data Model
However, you use case sounds more like Custom Retention Cohorts Using BigQuery and Google Analytics. Even when the link describe a use case for Firebase, the queries explained in there address your main inquiry. For example, the following query address the retention across every three days:
WITH analytics_data AS (
SELECT user_pseudo_id, event_timestamp, event_name, device.mobile_model_name,
UNIX_MICROS(TIMESTAMP("2018-08-01 00:00:00", "-7:00")) AS start_day,
3600*1000*1000*24*3 AS one_week_micros -- Note the *3 at the end here
FROM `firebase-public-project.analytics_153293282.events_*`
WHERE _table_suffix BETWEEN '20180731' AND '20180829'
)

SQL Server: Find the group of records existing in another group of records

I'm new to SQL Server and I searched for a solution to find, if a group is included in another group.
The query result should be grp_id 2 because 'A'+'B' is included in grp 3 and 5.
The result should be the grp_id of the the groups, that are included in other groups. With this result i´ll make an update of another table, joined with the grp_id.
The result should be:
+----+
| id |
+----+
| 2 |
+----+
I stuck in SQL because I do not find a solution to compare the groups. The idea was using bitwise comparison. But for that I had to add the value of each item in a field. I think there could be an easier way.
Thank you and best regards!
Eric
create table tmp_grpid (grp_id int);
create table tmp_grp (grp_id int, item_val nvarchar(10));
insert into tmp_grpid(grp_id) values (1);
insert into tmp_grpid(grp_id) values (2);
insert into tmp_grpid(grp_id) values (3);
insert into tmp_grpid(grp_id) values (4);
insert into tmp_grpid(grp_id) values (5);
--
insert into tmp_grp(grp_id, item_val) values (1, 'A');
insert into tmp_grp(grp_id, item_val) values (2, 'A');
insert into tmp_grp(grp_id, item_val) values (2, 'B');
insert into tmp_grp(grp_id, item_val) values (3, 'A');
insert into tmp_grp(grp_id, item_val) values (3, 'B');
insert into tmp_grp(grp_id, item_val) values (3, 'C');
insert into tmp_grp(grp_id, item_val) values (4, 'A');
insert into tmp_grp(grp_id, item_val) values (4, 'C');
insert into tmp_grp(grp_id, item_val) values (4, 'D');
insert into tmp_grp(grp_id, item_val) values (5, 'A');
insert into tmp_grp(grp_id, item_val) values (5, 'B');
insert into tmp_grp(grp_id, item_val) values (5, 'E');

Geez!
Technically speaking, group one is found in all other groups right? So, first a cross join to itself would be best with the condition that the values are the same AND that the groups are different, but before we do that we need to know how many items belong to each group so that's why we have the first select as a group that includes the count of elements per group, then join that with the cross join...Hope this helps.
select distinct dist_grpid
from
(select grp_id, count(*) cc from tmp_grp group by grp_id) g
inner join
(
select dist.grp_id dist_grpid, tmp_grp.grp_id, count(*) cc
from
tmp_grp dist
cross join tmp_grp
where
dist.item_val = tmp_grp.item_val and
dist.grp_id != tmp_grp.grp_id
group by
dist.grp_id,
tmp_grp.grp_id
) cj on g.grp_id = cj.dist_grpid and g.cc = cj.cc