select top (5) t3.Model, t3.Manufacturer, t1.Colour, t1.RegistrationNumber, t1.DailyRentalPrice, count(t2.TruckID) as RentedAmount
from [IndividualTruck-PB] t1
inner join [TruckRental-PB] t2 on t1.TruckID = t2.TruckID
inner join [TruckModel-PB] t3 on t1.TruckModelID = t3.ModelID
group by t3.Model, t3.Manufacturer, t1.Colour, t1.RegistrationNumber, t1.DailyRentalPrice
order by RentedAmount desc
Bsically, I'm trying to get the top 5 most rented but don't want the actual count column as output only as a means of ordering the output. Is this possible?
You can try remove the count column and give the formula to order by part:
select top (5) t3.Model, t3.Manufacturer, t1.Colour, t1.RegistrationNumber, t1.DailyRentalPrice
from [IndividualTruck-PB] t1
inner join [TruckRental-PB] t2 on t1.TruckID = t2.TruckID
inner join [TruckModel-PB] t3 on t1.TruckModelID = t3.ModelID
group by t3.Model, t3.Manufacturer, t1.Colour, t1.RegistrationNumber, t1.DailyRentalPrice
order by count(t2.TruckID) desc
My test:
create table A (
col1 varchar(255)
);
insert into A (col1) values ('A');
insert into A (col1) values ('A');
insert into A(col1) values ('A');
insert into A(col1) values ('A');
insert into A(col1) values ('A');
insert into A(col1) values ('A');
insert into A(col1) values ('A');
insert into A(col1) values ('B');
insert into A(col1) values ('B');
insert into A(col1) values ('B');
insert into A(col1) values ('B');
insert into A(col1) values ('B');
insert into A(col1) values ('B');
insert into A(col1) values ('C');
insert into A(col1) values ('C');
insert into A(col1) values ('C');
insert into A(col1) values ('C');
insert into A(col1) values ('C');
insert into A(col1) values ('D');
insert into A(col1) values ('D');
insert into A(col1) values ('D');
insert into A(col1) values ('D');
insert into A(col1) values ('D');
insert into A(col1) values ('E');
insert into A(col1) values ('E');
insert into A(col1) values ('E');
Select for MS SQL Server 2017:
select top(2) col1 from A group by col1 order by count(col1) desc;
Output:
col1
A
B
Related
create table table1(accountno number);
insert into table1 values (1);
insert into table1 values (2);
insert into table1 values (3);
insert into table1 values (4);
insert into table1 values (5);
insert into table1 values (6);
create table table2(accountno number,check_y_n varchar2(20));
insert into table2 (accountno) values (4);
insert into table2 (accountno) values (5);
insert into table2 (accountno) values (6);
insert into table2 (accountno) values (7);
insert into table2 (accountno) values (8);
insert into table2 (accountno) values (9);
I need below two update query in single query using joins. Can anyone help me on this
UPDATE TABLE2 SET check_y_n ='YES' WHERE accountno IN (SELECT accountno FROM TABLE1);
UPDATE TABLE2 SET check_y_n ='NO' WHERE accountno NOT IN (SELECT accountno FROM TABLE1);
Use a CASE expression:
UPDATE TABLE2
SET check_y_n = CASE
WHEN accountno IN (SELECT accountno FROM TABLE1)
THEN 'YES'
ELSE 'NO'
END;
db<>fiddle here
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'
)
I have four main tables:
CREATE TABLE t_users (
user_id varchar PRIMARY KEY,
user_email varchar
);
CREATE TABLE t_items (
item_id varchar PRIMARY KEY,
owner_id varchar not null references t_users(user_id),
title varchar
);
CREATE TABLE t_access_items_users (
access_iu_id varchar PRIMARY KEY,
item_id varchar not null references t_items(item_id),
user_id varchar not null references t_users(user_id)
);
CREATE TABLE t_friends (
friend_id varchar PRIMARY KEY,
from_user_id varchar not null references t_users(user_id),
to_user_id varchar not null references t_users(user_id)
);
With data:
INSERT INTO t_users VALUES ('us123', 'us123#email.com');
INSERT INTO t_users VALUES ('us456', 'us456#email.com');
INSERT INTO t_users VALUES ('us789', 'us789#email.com');
INSERT INTO t_users VALUES ('public', 'public#email.com');
INSERT INTO t_items VALUES ('it123', 'us123', 'title1');
INSERT INTO t_items VALUES ('it456', 'us456', 'title2');
INSERT INTO t_items VALUES ('it678', 'us789', 'title3');
INSERT INTO t_items VALUES ('it323', 'us123', 'title4');
INSERT INTO t_items VALUES ('it764', 'us456', 'title5');
INSERT INTO t_items VALUES ('it826', 'us789', 'title6');
INSERT INTO t_items VALUES ('it568', 'us123', 'title7');
INSERT INTO t_items VALUES ('it038', 'us456', 'title8');
INSERT INTO t_items VALUES ('it728', 'us789', 'title9');
INSERT INTO t_access_items_users VALUES ('aiu123', 'it123', 'us123');
INSERT INTO t_access_items_users VALUES ('aiu456', 'it456', 'us456');
INSERT INTO t_access_items_users VALUES ('aiu678', 'it678', 'us789');
INSERT INTO t_access_items_users VALUES ('aiu323', 'it323', 'us123');
INSERT INTO t_access_items_users VALUES ('aiu764', 'it764', 'us456');
INSERT INTO t_access_items_users VALUES ('aiu826', 'it826', 'us789');
INSERT INTO t_access_items_users VALUES ('aiu568', 'it568', 'us123');
INSERT INTO t_access_items_users VALUES ('aiu038', 'it038', 'us456');
INSERT INTO t_access_items_users VALUES ('aiu728', 'it728', 'us789');
INSERT INTO t_access_items_users VALUES ('aiu728', 'it728', 'us789');
INSERT INTO t_access_items_users VALUES ('apu123', 'it678', 'public');
INSERT INTO t_access_items_users VALUES ('apu222', 'it123', 'public');
INSERT INTO t_access_items_users VALUES ('apu111', 'it456', 'public');
INSERT INTO t_access_items_users VALUES ('aiu333', 'it728', 'public');
INSERT INTO t_access_items_users VALUES ('aiu444', 'it826', 'public');
INSERT INTO t_friends VALUES ('f123', 'us123', 'us456');
Request for a type:
select *
from t_access_items_users
inner join t_items on t_access_items_users.item_id = t_items.item_id
inner join t_users on t_access_items_users.user_id = t_users.user_id
where t_access_items_users.user_id = 'public';
returns 5 rows.
Why the query type:
select
t_items.item_id,
t_items.owner_id,
t_items.title
from t_access_items_users
inner join t_items on t_access_items_users.item_id = t_items.item_id
inner join t_friends on t_items.owner_id = t_friends.from_user_id
where t_friends.to_user_id = 'us456'
or t_access_items_users.user_id = 'public';
returns 4 rows?
How to make the right request to get all need rows?
Ultimately I want to get data where the items have access to the public and with the user's friends' friendships.
How to make a query returning all the elements that:
t_friends.to_user_id = 'us456'
or t_access_items_users.user_id = 'public'
Thank you.
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
Suppose I have a table with a column which looks like:
SELECT Col1
FROM table;
Col1
A
A
B
B
C
C
D
B
E
B
F
I would like to output elements that are between "B"s, which are C, D, E
How can I do that with a query?
declare #t table (ID INT IDENTITY(1,1),col1 VARCHAR(10))
insert into #t (col1) values ('A')
insert into #t (col1) values ('A')
insert into #t (col1) values ('B')
insert into #t (col1) values ('B')
insert into #t (col1) values ('C')
insert into #t (col1) values ('C')
insert into #t (col1) values ('B')
insert into #t (col1) values ('E')
insert into #t (col1) values ('B')
insert into #t (col1) values ('F')
select ID,col1 from #t
where ID between (select MIN(id) from #t WHERE col1 = 'B') and
(select MAX(id) from #t WHERE col1 = 'B')
and col1<>'B'