Just starting on an AWS Glue to check if certain column CASE_STATUS_ID has value 3 if yes, then change that to 2 and so on. Here is the dynamicFrame code:
datasource0 = glueContext.create_dynamic_frame.from_catalog(database =
"etl_database", table_name = "case_data", transformation_ctx = "datasource0")
If you can point me in a direction to check the current value and update it to the desired value using an if or case structure that would be really appreciated.
Related
I received help with following PySpark to prevent errors when doing a Merge in Databricks, see here
Databricks Error: Cannot perform Merge as multiple source rows matched and attempted to modify the same target row in the Delta table conflicting way
I was wondering if I could get help to modify the code to drop NULLs.
from pyspark.sql.window import Window
from pyspark.sql.functions import row_number
df2 = partdf.withColumn("rn", row_number().over(Window.partitionBy("P_key").orderBy("Id")))
df3 = df2.filter("rn = 1").drop("rn")
Thanks
The code that you are using does not completely delete the rows where P_key is null. It is applying the row number for null values and where row number value is 1 where P_key is null, that row is not getting deleted.
You can instead use the df.na.drop instead to get the required result.
df.na.drop(subset=["P_key"]).show(truncate=False)
To make your approach work, you can use the following approach. Add a row with least possible unique id value. Store this id in a variable, use the same code and add additional condition in filter as shown below.
from pyspark.sql.window import Window
from pyspark.sql.functions import row_number,when,col
df = spark.read.option("header",True).csv("dbfs:/FileStore/sample1.csv")
#adding row with least possible id value.
dup_id = '0'
new_row = spark.createDataFrame([[dup_id,'','x','x']], schema = ['id','P_key','c1','c2'])
#replacing empty string with null for P_Key
new_row = new_row.withColumn('P_key',when(col('P_key')=='',None).otherwise(col('P_key')))
df = df.union(new_row) #row added
#code to remove duplicates
df2 = df.withColumn("rn", row_number().over(Window.partitionBy("P_key").orderBy("id")))
df2.show(truncate=False)
#additional condition to remove added id row.
df3 = df2.filter((df2.rn == 1) & (df2.P_key!=dup_id)).drop("rn")
df3.show()
I Have a Streaming query as below picture, now for every row i need to loop over dataframe do some tranformation and save the result to adls. Can anyone help me how to loop over streaming df. I m struck.
Please check the link for details on foreach and foreachbatch
using-foreach-and-foreachbatch
You can perform operations inside the function process_row() when calling it from pyspark.sql.DataFrame.writeStream interface
def process_row(row):
# Write row to storage (in your case adls)
pass
ehConf['eventhubs.connectionString'] = connectionString
ehConf['eventhubs.connectionString'] = sc.jvm.org.apache.spark.eventhubs.
EventHubsUtils.encrypt(connectionString) df = spark.readStream. \
format("eventhubs").options(**ehConf).load()
df_new = df.writeStream.foreach(process_row).start()
I am trying to compare two tables() by reading as DataFrames. And for each common column in those tables using concatenation of a primary key say order_id with other columns like order_date, order_name, order_event.
The Scala Code I am using
val primary_key=order_id
for (i <- commonColumnsList){
val column_name = i
val tempDataFrameForNew = newDataFrame.selectExpr(s"concat($primaryKey,$i) as concatenated")
val tempDataFrameOld = oldDataFrame.selectExpr(s"concat($primaryKey,$i) as concatenated")
//Get those records which aren common in both old/new tables
matchCountCalculated = tempDataFrameForNew.intersect(tempDataFrameOld)
//Get those records which aren't common in both old/new tables
nonMatchCountCalculated = tempDataFrameOld.unionAll(tempDataFrameForNew).except(matchCountCalculated)
//Total Null/Non-Null Counts in both old and new tables.
nullsCountInNewDataFrame = newDataFrame.select(s"$i").filter(x => x.isNullAt(0)).count().toInt
nullsCountInOldDataFrame = oldDataFrame.select(s"$i").filter(x => x.isNullAt(0)).count().toInt
nonNullsCountInNewDataFrame = newDFCount - nullsCountInNewDataFrame
nonNullsCountInOldDataFrame = oldDFCount - nullsCountInOldDataFrame
//Put the result for a given column in a Seq variable, later convert it to Dataframe.
tempSeq = tempSeq :+ Row(column_name, matchCountCalculated.toString, nonMatchCountCalculated.toString, (nullsCountInNewDataFrame - nullsCountInOldDataFrame).toString,
(nonNullsCountInNewDataFrame - nonNullsCountInOldDataFrame).toString)
}
// Final Step: Create DataFrame using Seq and some Schema.
spark.createDataFrame(spark.sparkContext.parallelize(tempSeq), schema)
The above code is working fine for a medium set of Data, but as the number of Columns and Records increases in my New & Old Table, the execution time is increasing. Any sort of advice is appreciated.
Thank you in Advance.
You can do the following:
1. Outer join the old and new dataframe on priamary key
joined_df = df_old.join(df_new, primary_key, "outer")
2. Cache it if you possibly can. This will save you a lot of time
3. Now you can iterate over columns and compare columns using spark functions (.isNull for not matched, == for matched etc)
for (col <- df_new.columns){
val matchCount = df_joined.filter(df_new[col].isNotNull && df_old[col].isNotNull).count()
val nonMatchCount = ...
}
This should be considerably faster, especially when you can cache your dataframe. If you can't it might be a good idea so save the joined df to disk in order to avoid a shuffle each time
I have duplicate rows of the may contain the same data or having missing values in the PySpark data frame.
The code that I wrote is very slow and does not work as a distributed system.
Does anyone know how to retain single unique values from duplicate rows in a PySpark Dataframe which can run as a distributed system and with fast processing time?
I have written complete Pyspark code and this code works correctly.
But the processing time is really slow and its not possible to use it on a Spark Cluster.
'''
# Columns of duplicate Rows of DF
dup_columns = df.columns
for row_value in df_duplicates.rdd.toLocalIterator():
print(row_value)
# Match duplicates using std name and create RDD
fill_duplicated_rdd = ((df.where((sf.col("stdname") == row_value['stdname'] ))
.where(sf.col("stdaddress")== row_value['stdaddress']))
.rdd.map(fill_duplicates))
# Creating feature names for the same RDD
fill_duplicated_rdd_col_names = (((df.where((sf.col("stdname") == row_value['stdname']) &
(sf.col("stdaddress")== row_value['stdaddress'])))
.rdd.map(fill_duplicated_columns_extract)).first())
# Creating DF using the previous RDD
# This DF stores value of a single set of matching duplicate rows
df_streamline = fill_duplicated_rdd.toDF(fill_duplicated_rdd_col_names)
for column in df_streamline.columns:
try:
col_value = ([str(value[column]) for value in
df_streamline.select(col(column)).distinct().rdd.toLocalIterator() if value[column] != ""])
if len(col_value) >= 1:
# non null or empty value of a column store here
# This value is a no duplicate distinct value
col_value = col_value[0]
#print(col_value)
# The non-duplicate distinct value of the column is stored back to
# replace any rows in the PySpark DF that were empty.
df_dedup = (df_dedup
.withColumn(column,sf.when((sf.col("stdname") == row_value['stdname'])
& (sf.col("stdaddress")== row_value['stdaddress'])
,col_value)
.otherwise(df_dedup[column])))
#print(col_value)
except:
print("None")
'''
There are no error messages but the code is running very slow. I want a solution that fills rows with unique values in PySpark DF that are empty. It can fill the rows with even mode of the value
"""
df_streamline = fill_duplicated_rdd.toDF(fill_duplicated_rdd_col_names)
for column in df_streamline.columns:
try:
# distinct() was replaced by isNOTNULL().limit(1).take(1) to improve the speed of the code and extract values of the row.
col_value = df_streamline.select(column).where(sf.col(column).isNotNull()).limit(1).take(1)[0][column]
df_dedup = (df_dedup
.withColumn(column,sf.when((sf.col("stdname") == row_value['stdname'])
& (sf.col("stdaddress")== row_value['stdaddress'])
,col_value)
.otherwise(df_dedup[column])))
"""
in pyspark what is the best way to do an operation for a id when a groupby does not apply. here is a sample code:
for id in [int(i.id) for i in df.select('id').distinct().collect()]:
temp = df.where("id == {}".format(id))
temp = temp.sort("date")
my_window = Window.partitionBy().orderBy("id")
temp = temp.withColumn("prev_transaction",lag(temp['date']).over(my_window))
temp = temp.withColumn("diff", temp['date']-temp["prev_transaction"))
temp = temp.where('day_diff > 0')
#select a row and so on
whats the best way to optimize this?
I suppose you have transactions data frame you want to add previous transactions column to it.
from pyspark.sql.window import Window
from pyspark.sql import functions as F
windowSpec = Window.partitionBy('id').orderBy('date')
df.withColumn('pre_transactions', F.collect_list('id') \
.over(windowSpec.rangeBetween(Window.unboundedPreceding, 0)))
Code above will add array column with previous transactions untill this transactions .But last transactions will be also added to array. You can remove last transaction by simple custom udf like:
remove_element = lambda l: l[:-1]
remove_udf = udf(remove_element, ArrayType(StringType()))