I have a DataFrame with two columns, id1, id2 and what I'd like to get is to count the number of distinct values of these two columns. Essentially this is count(set(id1+id2)).
How can I do that with PySpark?
Thanks!
Please note that this isn't a duplicate as I'd like for PySpark to calculate the count(). Of course it's possible to get the two lists id1_distinct and id2_distinct and put them in a set() but it doesn't seem to me the proper solution when dealing with big data and it's not really in the PySpark spirit
You can combine the two columns into one using union, and get the countDistinct:
import pyspark.sql.functions as F
cnt = df.select('id1').union(df.select('id2')).select(F.countDistinct('id1')).head()[0]
Related
I have a df1 based on disticnt values containing two columns, date and value. There is df2 that has multiple column but contains the date and value column. For each distinct value from df1, i want to filter the df2 such that the records before the date from df1 are dropped. It would be rather easy for a single disticnt value, i can use something like filter by vlaue and then gt(lit(date), however i have over 500 such distinct pairs in df1. For single operation, it takes around 20 minute so if i use a loop then it is computationally not feasible. Perhaps some body can advice me on a better methodology here.
have tried multiple methodlogies but nothing has worked yet.
I have a use-case where I need to select certain columns from a dataframe containing atleast 30 columns and millions of rows.
I'm loading this data from a cassandra table using scala and apache-spark.
I selected the required columns using: df.select("col1","col2","col3","col4")
Now I have to perform a basic groupBy operation to group the data according to src_ip,src_port,dst_ip,dst_port and I also want to have the latest value from a received_time column of the original dataframe.
I want a dataframe with distinct src_ip values with their count and latest received_time in a new column as last_seen.
I know how to use .withColumn and also, I think that .map() can be used here.
Since I'm relatively new in this field, I really don't know how to proceed further. I could really use your help to get done with this task.
Assuming you have a dataframe df with src_ip,src_port,dst_ip,dst_port and received_time, you can try:
val mydf = df.groupBy(col("src_ip"),col("src_port"),col("dst_ip"),col("dst_port")).agg(count("received_time").as("row_count"),max(col("received_time")).as("max_received_time"))
The above line calculates the count of timestamp received against the group by columns as well as the max timestamp for that group by columns.
I have a situation where i have list of rows with multiple columns and have to group by with a lookup key which is a unique column in all rows ..
I have to sum up values of a given column (say x) for all the rows from the lookup key (say lookup_1) in scala only using group by aggregate or any other method available ..
I can't convert to dataframe and use aggregate ..
I am sorry if I can't put code because policy doesn't allow so ..
Your quick help would be appreciated or guidance !!!
I have a scenario where my dataframe has 3 columns a,b and c. I need to validate if the length of all the columns is equal to 100. Based on validation I am creating status column like a_status,b_status,c_status with values 5 (Success) and 10 (Failure). In Failure scenarios I need to update count and create new columns a_sample,b_sample,c_sample with some 5 failure sample values separated by ",". For creating samples column I tried like this
df= df.select(df.columns.toList.map(col(_)) :::
df.columns.toList.map( x => (lit(getSample(df.select(x, x + "_status").filter(x + "_status=10" ).select(x).take(5))).alias(x + "_sample")) ).toList: _* )
getSample method will just get array of rows and concatenate as a string. This works fine for limited columns and data size. However if the number of columns > 200 and data is > 1 million rows it creates huge performance impact. Is there any alternate approach for same.
While the details of your problem statement are unclear, you can break up the task into two parts:
Transform data into a format where you identify several different types of rows you need to sample.
Collect sample by row type.
The industry jargon for "row type" is stratum/strata and the way to do (2), without collecting data to the driver, which you don't want to do when the data is large, is via stratified sampling, which Spark implements via df.stat.sampleBy(). As a statistical function, it doesn't work with exact row numbers but fractions. If you absolutely must get a sample with an exact number of rows there are two strategies:
Oversample by fraction and then filter unneeded rows, e.g., using the row_number() window function followed by a filter 'row_num < n.
Build a custom user-defined aggregate function (UDAF), firstN(col, n). This will be much faster but a lot more work. See https://docs.databricks.com/spark/latest/spark-sql/udaf-scala.html
An additional challenge for your use case is that you want this done per column. This is not a good fit with Spark's transformations such as grouping or sampleBy, which operate on rows. The simple approach is to make several passes through the data, one column at a time. If you absolutely must do this in a single pass through the data, you'll need to build a much more custom UDAF or Aggregator, e.g., the equivalent of takeFirstNFromAWhereBHasValueC(n, colA, colB, c).
Assume the following two Dataframes in pyspark with equal number of rows:
df1:
|_ Column1a
|_ Column1b
df2:
|_ Column2a
|_ Column2b
I wish to create a a new DataFrame "df" which has Column1a and Column 2a only. What could be the best possible solution for it?
Denny Lee's answer is the way.
It involves creating another column on both the DataFrames which is the Unique_Row_ID for every row. We then perform a join on Unique_Row_ID. And then drop Unique_Row_ID if required.