I have a spark dataframe df as below:
key| date | val | col3
1 1 10 1
1 2 12 1
2 1 5 1
2 2 7 1
3 1 30 2
3 2 20 2
4 1 12 2
4 2 8 2
5 1 0 2
5 2 12 2
I want to:
1) df_pivot = df.groupBy(['date', 'col3']).pivot('key').sum('val')
2) df_pivot.write.parquet('location')
But my data can get really big with millions of unique keys and unique col3.
Is there any way where i do the above operations per partition of col3?
Eg: For partition where col3==1, do the pivot and write the parquet
Note: I do not want to use a for loop!
Related
I have the following abstracted DataFrame (my original DF has 60 billion lines +)
Id Date Val1 Val2
1 2021-02-01 10 2
1 2021-02-05 8 4
2 2021-02-03 2 0
1 2021-02-07 12 5
2 2021-02-05 1 3
My expected ouput is:
Id Date Val1 Val2
1 2021-02-01 10 2
1 2021-02-02 10 2
1 2021-02-03 10 2
1 2021-02-04 10 2
1 2021-02-05 8 4
1 2021-02-06 8 4
1 2021-02-07 12 5
2 2021-02-03 2 0
2 2021-02-04 2 0
2 2021-02-05 1 3
Basically, what I need is: if Val1 or Val2 changes in a period of time, all the values between this two dates must have have the value from previous date. (To be more clearly, look at ID 2).
I know that I can do this in many ways (window function, udf,...) but my doubt is, since my original DF has more than 60 billion lines, what is the best approach to do this processing?
I think the best approach (performance-wise) is performing an inner join (probably with broadcasting). If you worry about the number of records, I suggest you run them by batch (could be the number of records, or by date, or even a random number). The general idea is just to avoid running all at once.
This question already has answers here:
How to pivot Spark DataFrame?
(10 answers)
Closed 4 years ago.
I have the following dataframe
id col1 col2 col3 col4
1 1 10 100 A
1 1 20 101 B
1 1 30 102 C
2 1 10 80 D
2 1 20 90 E
2 1 30 100 F
2 1 40 104 G
So, I want to return a new dataframe, in which I can have in olnly one row the values for the same (col1, col2), and also create a new column with some oeration over both col3 columns, for example
id(1) col1(1) col2(1) col3(1) col4(1) id(2) col1(2) col2(2) col3(3) col4(4) new_column
1 1 10 100 A 2 1 10 80 D (100-80)*100
1 1 20 101 B 2 1 20 90 E (101-90)*100
1 1 30 102 C 2 1 30 100 F (102-100)*100
- - - - - 2 1 40 104 G -
I tried ordering, grouping by (col1, col2) but the grouping returns a RelationalGroupedDataset that I cannot do anything appart of aggregation functions. SO I will appreciate any help. I'm using Scala 2.11 Thanks!
what about joining the df with itself?
something like:
df.as("left")
.join(df.as("right"), Seq("col1", "col2"), "outer")
.where($"left.id" =!= $"right.id")
How can I find mutual followers? I am new to postgres and I am having a hard time constructing this.
followers
u_id f_id
3 1
2 5
4 3
1 2
4 1
1 3
2 8
2 10
1 4
2 4
3 4
4 5
4 8
example of expected output:
u_id f_id
3 1
1 3
4 3
3 4
...
...
If I have an input as below:
sno name time
1 hello 1
1 hello 2
1 hai 3
1 hai 4
1 hai 5
1 how 6
1 how 7
1 are 8
1 are 9
1 how 10
1 how 11
1 are 12
1 are 13
1 are 14
I want to combine the fields having similar values in name as the below output format:
sno name timestart timeend
1 hello 1 2
1 hai 3 5
1 how 6 7
1 are 8 9
1 how 10 11
1 are 12 14
The input will be sorted according to time and only the records which are having the same name for repeated time intervals must be merged.
I am trying to do using spark but I cannot figure out a way to do this using spark functions since I am new to spark. Any suggestions on the approach will be appreciated.
I tried thinking of writing a user-defined function and applying maps to the data frame but I could not come up with the right logic for the function.
PS: I am trying to do this using scala spark.
One way to do so would be to use a plain SQL query.
Let's say df is your input dataframe.
val viewName = s"dataframe"
df.createOrReplaceTempView(viewName)
spark.sql(query(viewName))
def query(viewName: String): String = s"SELECT sno, name, MAX(time) AS timeend, MIN(time) AS timestart FROM $viewName GROUP BY name"
You can of course use df set. This would be something like:
df.groupBy($"name")
.agg($"sno", $"name", max($"time").as("timeend"), min($"time").as("timestart"))
I would like to sum across consecutive rows that share the same label. Any very simple ways to do this?
Example: I start with this table...
qty flag
1 OFF
3 ON
2 ON
2 OFF
9 OFF
4 ON
... and would like to generate...
qty flag
1 OFF
5 ON
11 OFF
4 ON
One method:
q)show t:flip`qty`flag!(1 3 2 2 9 4;`OFF`ON`ON`OFF`OFF`ON)
qty flag
--------
1 OFF
3 ON
2 ON
2 OFF
9 OFF
4 ON
q)show result:select sum qty by d:sums differ flag,flag from t
d flag1| qty
----------| ---
1 OFF | 1
2 ON | 5
3 OFF | 11
4 ON | 4
Then to get it in the format you require:
q)`qty`flag#0!result
qty flag
--------
1 OFF
5 ON
11 OFF
4 ON