We have 2 datasets. Both datasets have a column called Office. Say Dataset1 Office column has values London, Liverpool and Dataset2 the Office column has values Washington, California.
Is there any way in SSRS to do something like UNION ALL (but within SSRS) that is produce a list of all Office values in one table column?
There may be much more elegant ways of doing this but this is all I could come up with..
You need to have a unique numeric id for each record
Assuming you cannot simply union these together in your dataset query, if you can add a numeric ID to each record then you can do it (albeit a bit clunky)
To start with I created two datasets ds1 and ds2 to hold the two lists of office names.
For example the first dataset query was just this...
DECLARE #t TABLE(OfficeID int, Office varchar(20))
INSERT INTO #t VALUES
(1, 'London'),
(2, 'Liverpool')
SELECT * FROM #t
and the second was this.
DECLARE #t TABLE(OfficeID int, Office varchar(20))
INSERT INTO #t VALUES
(10, 'Washington'),
(11, 'California')
SELECT * FROM #t
As you can see, each office now has a unique numeric id
Next I needed another dataset with a list of number that covered the entire range of office ids. In this example the range is just 20 numbers
note: this work on SQL Server for other systems you'll have to come up with another method of getting a list of numbers
So, the query for dsNums is
declare #n table(num int)
insert into #n
select top 20 row_number() over(order by t1.number) as N
from master..spt_values t1
cross join master..spt_values t2
SELECT * FROM #n
Next I added a table to the report, reduced it to two columns and bound it to dsNums. The first column is just the [num] field.
The second is an expression that does two lookups and takes the first non blank result. The expression was
=IIF(
LEN(LOOKUP(Fields!num.Value, Fields!OfficeID.Value, Fields!Office.Value, "ds1"))>0
, LOOKUP(Fields!num.Value, Fields!OfficeID.Value, Fields!Office.Value, "ds1")
, LOOKUP(Fields!num.Value, Fields!OfficeID.Value, Fields!Office.Value, "ds2")
)
If we run the report now we get..
Finally we need to hide the empty rows. To do this I set the Row Visibilty expression to
=LEN(ReportItems!LookupResult.Value) = 0
The result is this (obviously you don't need the num column but it was just for illustration)
Related
I have one table where each row has three columns. The first two columns are a prefix and a value. The third column is what I'm trying to get a distinct count for columns one/two.
Basically I'm trying to get to this.
Account
Totals
prefix & value1
101
prefix & value2
102
prefix & value3
103
I've tried a lot of different versions but I'm basically noodling around this.
select prefix||value as Account, count(distinct thirdcolumn) as Totals from Transactions
It sounds like you want
SELECT
prefix||value Account,
count(distinct thirdcolumn) Totals
FROM Transactions
GROUP BY prefix, value
The count(distinct thirdcolumn) says you want a count of the distinct values in the third column. The GROUP BY prefix, value says you want a row returned for each unique prefix/value combination and that the count applies to that combination.
Note that "thirdcolumn" is a placeholder for the name of your third column, not a magic keyword, since I didn't see the actual name in the post.
If you want the number of rows for each prefix/value pair then you can use:
SELECT prefix || value AS account,
COUNT(*) AS totals
FROM Transactions
GROUP BY prefix, value
You do not want to count the DISTINCT values for prefix/value as if you GROUP BY those values then different values for the pairs will be in different groups so the COUNT of DISTINCT prefix/value pairs would always be one.
I'm trying to speed up the following
create table tab2 parallel 24 nologging compress for query high as
select /*+ parallel(24) index(a ix_1) index(b ix_2)*/
a.usr
,a.dtnum
,a.company
,count(distinct b.usr) as num
,count(distinct case when b.checked_1 = 1 then b.usr end) as num_che_1
,count(distinct case when b.checked_2 = 1 then b.usr end) as num_che_2
from tab a
join tab b on a.company = b.company
and b.dtnum between a.dtnum-1 and a.dtnum-0.0000000001
group by a.usr, a.dtnum, a.company;
by using indexes
create index ix_1 on tab(usr, dtnum, company);
create index ix_2 on tab(usr, company, dtnum, checked_1, checked_2);
but the execution plan tells me that it's going to be an index full scan for both indexes, and the calculations are very long (1 day is not enough).
About the data. Table tab has over 3 mln records. None of the single columns are unique. The unique values here are pairs of (usr, dtnum), where dtnum is a date with time written as a number in the format yyyy,mmddhh24miss. Columns checked_1, checked_2 have values from set (null, 0, 1, 2). Company holds an id for a company.
Each pair can only have one value checked_1, checked_2 and company as it is unique. Each user can be in multple pairs with different dtnum.
Edit
#Roberto Hernandez: I've attached the picture with the execution plan. As for parallel 24, in our company we are told to create tables with options 'parallel [num] nologging compress for query high'. I'm using 24 but I'm no expert in this field.
#Sayan Malakshinov: http://sqlfiddle.com/#!4/40b6b/2 Here I've simplified by giving data with checked_1 = checked_2, but in real life this may not be true.
#scaisEdge:
For
create index my_id1 on tab (company, dtnum);
create index my_id2 on tab (company, dtnum, usr);
I get
For table tab Your join condition is based on columns
company, datun
so you index should be primarly based on these columns
create index my_id1 on tab (company, datum);
The indexes you are using are useless because don't contain in left most position columsn use ij join /where condition
Eventually you can add user right most potition for avoid the needs of table access and let the db engine retrive alla the inf inside the index values
create index my_id1 on tab (company, datum, user, checked_1, checked_2);
Indexes (bitmap or otherwise) are not that useful for this execution. If you look at the execution plan, the optimizer thinks the group-by is going to reduce the output to 1 row. This results in serialization (PX SELECTOR) So I would question the quality of your statistics. What you may need is to create a column group on the three group-by columns, to improve the cardinality estimate of the group by.
I have a table with 3 columns:
table1: ID, CODE, RESULT, RESULT2, RESULT3
I have this SAS code:
data table1
set table1;
BY ID, CODE;
IF FIRST.CODE and RESULT='A' THEN OUTPUT;
ELSE IF LAST.CODE and RESULT NE 'A' THEN OUTPUT;
RUN;
So we are grouping the data by ID and CODE, and then writing to the dataset if certain conditions are met. I want to write a hive query to replicate this. This is what I have:
proc sql;
create table temp as
select *, row_number() over (partition by ID, CODE) as rowNum
from table1;
create table temp2 as
select a.ID, a.CODE, a.RESULT, a.RESULT2, a.RESULT3
from temp a
inner join (select ID, CODE, max(rowNum) as maxRowNum
from temp
group by ID, CODE) b
on a.ID=b.ID and a.CODE=b.CODE
where (a.rowNum=1 and a.RESULT='A') or (a.rowNum=b.maxRowNum and a.RESULT NE 'A');
quit;
There are two issues I see with this.
1) The row that is first or last in each BY group is entirely dependant on the order of rows in table1 in SAS, we aren't ordering by anything. I don't think row order is preserved when translating to a hive query.
2) The SAS code is taking the first row in each BY GROUP or the last, not both. I think that my HIVE query is taking both, resulting in more rows than I want.
Any suggestions or insight on how to improve my query is appreciated. Is it even possible to replicate this SAS code in HIVE?
The SAS code has a by statement (BY ID CODE;), which tells SAS that the set dataset is sorted at those levels. So, not a random selection for first. and last..
That said, we can replicate this in HIVE by using the first_value and last_value window functions.
FIRST.CODE should replicate to
first_value(code) over (partition by Id order by code)fcode
Similarly, LAST.CODE would be
last_value(code) over (partition by Id order by code)lcode
Once you have the fcode and lcode columns, use case when statements for the result column criteria. Like,
case when (code=fcode and result='A') or (code=lcode and result<>'A')
then 1 else 0 end as op_flag
Then the fetch the table with where op_flag = 1
SAMPLE
select id, code, result from (
select *,
first_value(code) over (partition by id order by code)fcode,
last_value(code) over (partition by id order by code)lcode
from footab) f
where (code=fcode and result='A') or (code=lcode and result<>'A')
Regarding point 1) the BY group processing requires the input data to be sorted or indexed on BY variables, so though the code contains no ordering, the source data is processed in order. If the input data was not indexed/sorted, SAS will throw error.
Regarding this, possible differences are on rows with same values of BY variables, especially if the RESULT is different.
In SAS, I would pre-sort data by ID, CODE, RESULT, then use BY ID CODE in order to not be influenced by order of rows.
Regarding 2) FIRST and LAST can be both true in SAS. Since your condition for first and last on RESULT is different, I guess this is not a source of differences.
I guess you could add another field as
row_number() over (partition by ID, CODE desc) as rowNumDesc
to detect last row with rowNumDesc = 1 (so that you skip the join).
EDIT:
I think the two programs above both include random selection of rows for groups with same values of ID and CODE variables, especially with same values of RESULT. But you should get same number of rows from both. If not, just debug it.
However the random aspect in SAS code/storage is based on physical order of rows, while the ROW_NUMBERs randomness within a group will be influenced by the implementation of the function in the engine.
I have two hive tables (t1 and t2) that I would like to compare. The second table has 5 additional columns that are not in the first table. Other than the five disjoint fields, the two tables should be identical. I am trying to write a query to check this. Here is what I have so far:
SELECT * FROM t1
UNION ALL
select * from t2
GROUP BY some_value
HAVING count(*) == 2
If the tables are identical, this should return 0 records. However, since the second table contains 5 extra fields, I need to change the second select statement to reflect this. There are almost 60 column names so I would really hate to write it like this:
SELECT * FROM t1
UNION ALL
select field1, field2, field3,...,fieldn from t2
GROUP BY some_value
HAVING count(*) == 2
I have looked around and I know there is no select * EXCEPT syntax, but is there a way to do this query without having to explicity name each column that I want included in the final result?
You should have used UNION DISTINCT for the logic you are applying.
However, the number and names of columns returned by each select_statement have to be the same otherwise a schema error is thrown.
You could have a look at this Python program that handles such comparisons of Hive tables (comparing all the rows and all the columns), and would show you in a webpage the differences that might appear: https://github.com/bolcom/hive_compared_bq
To skip the 5 extra fields, you could use the "--ignore-columns" option.
I've got a table big_table, with 4 million record, they are clustered in 40 groups through a column called "process_type_cod". The list of values that this column may assume is in a second table. Let's call it small_table.
So, we have big_table with a NOT NULL FK called process_type_cod that points to small_table (assume the colum name is the same on both tables).
I want N record (i.e. 10) from big_table, for each record of the small_table.
I.e.
10 record from big_table related to the first record of small_table
UNION
10 different record from big_table related to the second record of small table, and so on.
Is it possible to obtain with a single SQL function?
I recommend an analytical function such as rank() or row_number(). You could do this with hard-coded unions, but the analytical function does all the hard work for you.
select *
from
(
select
bt.col_a,
bt.col_b,
bt.process_type_cod,
row_number() over ( partition by process_type_cod order by col_a nulls last ) rank
from small_table st
inner join big_table bt
on st.process_type_cod = bt.process_type_cod
)
where rank < 11
;
You may not even need that join since big_table has all of the types you care about. In that case, just change the 'from clause' to use big_table and drop the join.
What this does is performs the query and then sorts the records using the 'order by' operator in the partition statement. For a given group (here we grouped by col_a), a numerical row number (i.e. 1, 2, 3, 4, 5, n+1...) is applied to each record consecutively. In the outer where clause, just filter by the records with a number lower than N.