I have a spark sql query, where I have to search for multiple identifiers:
SELECT * FROM my_table WHERE identifier IN ('abc', 'cde', 'efg', 'ghi')
Now I get hundreds of results for each of these matches, where I am only interested in the first match for each identifier, i.e. one row with identifier == 'abc', one where identifier == 'cde' and so on.
What is the best way to reduce my result to only the first row for each match?
The best approach certainly depends a bit on your data and also on what you mean by first. Is that any random row that happens to be returned first? Or first by some particular sort order?
A general flexible approach is using window functions. row_number() allows you to easily filter for the first row by window.
SELECT * FROM (
SELECT *, row_number() OVER (PARTITION BY identifier ORDER BY ???) as row_num
FROM my_table
WHERE identifier IN ('abc', 'cde', 'efg', 'ghi')) tmp
WHERE
row_num = 1
Though, aggregations like first or max_by are often more efficient. But these get quickly inconvenient when dealing with lots of columns.
You can use the first() aggregation function (after grouping by identifier) to only get the first row in each group.
But I don't think you'll be able to select * with this approach. Instead, you can list every individual column you want to get:
SELECT identifier, first(col1), first(col2), first(col3), ...
FROM my_table
WHERE identifier IN ('abc', 'cde', 'efg', 'ghi')
GROUP BY identifier
Another approach would be to fire a query for each identifier value with a limit of 1 and then union all the results.
With the DataFrame API, you can use your original query and then use .dropDuplicates(["identifier"]) on the result to only keep a single row for each identifier value.
Related
So Deduping is one of the basic and imp Datacleaning technique.
There are a number of ways to do that in dataflow.
Like myself doing deduping with help of aggregate transformation where i put key columns(Consider "Firstname" and "LastName" as cols) which are need to be unique in Group by and a column pattern like name != 'Firstname' && name!='LastName'
$$ _____first($$) in aggregate tab.
The problem with this method is ,if we have a total of 200 cols among 300 cols to be considered as Unique cols, Its a very tedious to do include 200 cols in my column Pattern.
Can anyone suggest a better and optimised Deduping process in Dataflow acc to the above situation?
I tried to repro the deduplication process using dataflow. Below is the approach.
List of columns that needs to be grouped by are given in dataflow parameters.
In this repro, three columns are given. This can be extended as per requirements.
Parameter Name: Par1
Type: String
Default value: 'col1,col2,col3'
Source is taken as in below image.
(Group By columns: col1, col2, col3;
Aggregate column: col4)
Then Aggregate transform is taken and in group by,
sha2(256,byNames(split($Par1,','))) is given in columns and it is named as groupbycolumn
In Aggregates, + Add column pattern near column1 and then delete Column1. Then Enter true() in matching condition. Then click on undefined column expression and enter $$ in column name expression and first($$) in value expression.
Output of aggregation function
Data is grouped by col1,col2 and col3 and first value of col4 is taken for every col1,col2 and col3 combination.
Then using select transformation, groupbycolumn from above output can be removed before copying to sink.
Reference: ** MS document** on Mapping data flow script - Azure Data Factory | Microsoft Learn
I'm looking for an inexpensive way to distinguish duplicates and/or uniquely identify rows. I've been looking at the Spark built-ins monotonically_increasing_id() and uuid().
The problem with uuid() is that it does not retain its value and seems to be evaluated on the spot. For example
with uuids as (select uuid() as uuid)
select * from uuids join uuids
produces two different UUIDs.
If I use monotonically_increasing_id(), I get two identical values, but can I trust that to always work? In other words, if I have a CTE, where I have an id column generated by monotonically_increasing_id(), will any later rows derived from a row from that CTE have a consistent value of the id column within the same query?
In pseudo-SQL:
with /* ... */
with_ids as (select monotonically_increasing_id() as id, * from /* ... */),
/* ... */
derived_a as (/* Somehow derived from with_ids */),
derived_b as (/* Somehow derived from with_ids */)
select
(a.id == b.id) as are_same,
(a.id != b.id) as are_different
from derived_a as a
join derived_b as b
Will rows derived from the exact same rows of with_ids have are_same == true? Is it guaranteed that if the original rows were different, then are_different == true? The former is definitely false for uuid().
[Updated] Another example, involving a join and group by:
with
with_ids as (
select
monotonically_increasing_id() as id
,*
from table_a)
joined as (
select struct(a.*) as packed_a, a.id
from with_ids as a
left join table_b as b
on /* whatever */
)
select collect_set(packed_a) as should_be_singular
from joined
group by id
Is the row count in the above equal to the number of rows in table_a and is should_be_singular a single element array?
The documentation for both functions state that they are non-deterministic, but don't really offer any details on when the functions are evaluated or how they should be used.
The issue seems to be mentioned in SPARK-14241 and this question, but it's not clear if and under what conditions monotonically_increasing_id() is consistent within a single SQL statement.
from my past experience when working with row identifiers (uuid, row_number or monotonically_increasing_id) I cache the dataframe.
Then every subsequent calculation using the dataframe will have static row identifiers.
What is the correct behavior of the last and last_value functions in Apache Spark/Databricks SQL. The way I'm reading the documentation (here: https://docs.databricks.com/spark/2.x/spark-sql/language-manual/functions.html) it sounds like it should return the last value of what ever is in the expression.
So if I have a select statement that does something like
select
person,
last(team)
from
(select * from person_team order by date_joined)
group by person
I should get the last team a person joined, yes/no?
The actual query I'm running is shown below. It is returning a different number each time I execute the query.
select count(distinct patient_id) from (
select
patient_id,
org_patient_id,
last_value(data_lot) data_lot
from
(select * from my_table order by data_lot)
where 1=1
and org = 'my_org'
group by 1,2
order by 1,2
)
where data_lot in ('2021-01','2021-02')
;
What is the correct way to get the last value for a given field (for either the team example or my specific example)?
--- EDIT -------------------
I'm thinking collect_set might be useful here, but I get the error shown when I try to run this:
select
patient_id,
last_value(collect_set(data_lot)) data_lot
from
covid.demo
group by patient_id
;
Error in SQL statement: AnalysisException: It is not allowed to use an aggregate function in the argument of another aggregate function. Please use the inner aggregate function in a sub-query.;;
Aggregate [patient_id#89338], [patient_id#89338, last_value(collect_set(data_lot#89342, 0, 0), false) AS data_lot#91848]
+- SubqueryAlias spark_catalog.covid.demo
The posts shown below discusses how to get max values (not the same as last in a list ordered by a different field, I want the last team a player joined, the player may have joined the Reds, the A's, the Zebras, and the Yankees, in that order timewise, I'm looking for the Yankees) and these posts get to the solution procedurally using python/r. I'd like to do this in SQL.
Getting last value of group in Spark
Find maximum row per group in Spark DataFrame
--- SECOND EDIT -------------------
I ended up using something like this based upon the accepted answer.
select
row_number() over (order by provided_date, data_lot) as row_num,
demo.*
from demo
You can assign row numbers based on an ordering on data_lots if you want to get its last value:
select count(distinct patient_id) from (
select * from (
select *,
row_number() over (partition by patient_id, org_patient_id, org order by data_lots desc) as rn
from my_table
where org = 'my_org'
)
where rn = 1
)
where data_lot in ('2021-01','2021-02');
I have this table which has foreign keys from several other keys:
Basically, this table shows which students registered in which module run by which teacher in what term.
I want to query the following:
How many students have registered for more than one module run by a given tutor?
It will look something like this:
For example, Vasiliy Kuznetsov runs two modules: FunPro and NO. If one student registers for both of them, he is counted as one.
My sql oriented mind is telling me this: Count all the rows in which student_id and tutor_id are the same. For example, in one row student_id is 5 and tutor_id is 10, and the same is true for the third row. Then, I count it as one.
How can I do that with DAX formulas?
RowCount:=
COUNTROWS( ModuleRegistration )
StudentsWithTwoOrMoreRegistrations:=
COUNTROWS(
FILTER(
VALUES( ModuleRegistration[Student_ID] )
,[RowCount] >= 2
)
)
I refer to arguments positionally, thus the first argument to a function is (1), the second (2), and so on.
So, [RowCount] is trivial.
[StudentsWithTwoOrMoreRegistrations] is a bit more involved. DAX, being a functional language, is best understood inside-out.
FILTER() takes a table expression in (1) and evaluates a boolean predicate, (2), for each row in (1). It returns all rows from (1) for which (2) evaluates to true.
Our FILTER()'s (1) is VALUES( ModuleRegistration[Student_ID] ). VALUES() returns the unique rows from a field based on current filter context (it respects slicers and filters in the pivot table). Thus, we will return some subset of the unique list of [Student_ID]s.
Our FILTER()'s (2) is [RowCount] >= 2. For each [Student_ID] in (1), we'll evaluate [RowCount], checking how many times that student appears in ModuleRegistration. [RowCount] is evaluated in the combination of filter context from the pivot table (the [Faculty Name] field in your sample pivot provides filter context) and row context from FILTER()'s (1). Thus it counts how many times the student appears in ModuleRegistration for the [Faculty Name] on the pivot table row.
We check that [RowCount] is >= 2.
You've not indicated if your measure needs to handle grand totals, or how you might want to see that. If you need more help for the grand total to get it to behave the way you like, let me know.
Edit for grand total
There are a few ways you might want to handle grand totals. I'm gong to assume that you want a unique count of students.
StudentsWithTwoOrMoreRegistrations:=
COUNTROWS(
SUMMARIZE(
FILTER(
SUMMARIZE(
ModuleRegistration
,ModuleRegistration[Tutor_ID]
,ModuleRegistration[Student_ID]
)
,[RowCount] >= 2
)
,ModuleRegistration[Student_ID]
)
)
WTF happened to our measure?
Let's examine:
Starting with the innermost SUMMARIZE(). SUMMARIZE() navigates relationships outward from the table in (1) and groups by the columns listed in (2)-(N) (these don't have to be from the table in (1), but must be reachable by navigating relationships).
This is equivalent to the following in SQL:
SELECT
mr.Tutor_ID
,mr.Student_ID
FROM ModuleRegistration mr
We use FILTER() on this table like earlier. [RowCount] is evaluated in the combination of filter context from the pivot table and the row in the table, defined by our SUMMARIZE() above.
Now our row context is instead of just a student, a student-tutor pair. This pair will have a [RowCount] >= 2 when the student has taken more than one module from a tutor.
Our FILTER() returns the pairs which have a [RowCount] >= 2. This output table has two fields, [Tutor_ID] and [Student_ID], but we want to count distinct [Student_ID]s out of this.
Thus, we use the table from FILTER() as our (1) in the outer SUMMARIZE(). We group only by the values of [Student_ID]. We then count the rows of this table.
When only one [Faculty_Name] is in context, e.g. on a pivot table row, then our inner SUMMARIZE() is grouping by a single value of [Tutor_ID] and whatever [Student_ID]s are associated with it. This is identical to our earlier measure.
When we have many [Tutor_ID]s in context, like in the grand total, then we'll see the appropriate behavior of only counting each [Student_ID] once.
Since SSRS doesn't allow filters on aggregates, I found some code which helped me come up with the below query. However, when I run it I get:
Each GROUP BY expression must contain at least one column that is not an outer reference
I have searched everywhere but can't find how to fix this. I've even removed the two extra tables from the query so there were no joins at all. I need to not return any order where the total of the lines on the order is less than $500 and greater than 0.
SELECT
tdsls041_sales_order_lines.company,
tdsls041_sales_order_lines.order_number,
tdsls041_sales_order_lines.amount,
tdsls041_sales_order_lines.item,
tdsls041_sales_order_lines.container
FROM
tdsls041_sales_order_lines AS tdsls041_sales_order_lines
WHERE
(tdsls041_sales_order_lines.company = 610) AND
(tdsls041_sales_order_lines.order_number IN
(SELECT
tdsls041_sales_order_lines.order_number
FROM
tdsls041_sales_order_lines AS tdsls041_sales_order_lines_1
GROUP BY
tdsls041_sales_order_lines.order_number
HAVING
(SUM(tdsls041_sales_order_lines.amount) <= 500) OR
SUM(tdsls041_sales_order_lines.amount) > 0))
The issue that SQL Server is complaining about is that the Grouping wants an aggregate function in the SELECT statement. Unfortunately, you want to use IN which you need a list of Order Numbers.
You just need to add an aggregate function to your subquery and then add another layer to select just the Order Numbers from that.
SELECT T1.company, T1.order_number, T1.amount, T1.item, T1.container
FROM tdsls041_sales_order_lines AS T1
WHERE (T1.company = 610) AND (T1.order_number IN
(SELECT order_number FROM
(SELECT TSOL.order_number, SUM(TSOL.amount) AS TTL
FROM tdsls041_sales_order_lines AS TSOL
GROUP BY TSOL.order_number
HAVING (SUM(TSOL.amount) <= 500) OR
SUM(TSOL.amount) > 0) AS T2) )
You can filter on aggreagates in Chart and Tables. You have to put the aggregate filter on your GROUP instead of on the table itself (Group Properties->Filters tab).