I am trying to rewrite this SQL query into Pyspark
SELECT b.BrandId,a.PaymentIdentifier,a.PaymentName
FROM checkpaymentdetailmca a
INNER JOIN dimension b
ON a.Brand = b.Brand
AND a.StoreNumber = b.Storenumber
Here is my attempt at writing this in pyspark:
df = spark.read.parquet('abfss://container_name#xyz.dfs.core.windows.net/folder_name/checkpaymentdetailmca/parquet/**/*')
df1= spark.read.parquet('abfss://container_name#xyz.dfs.core.windows.net/folder_name/dimension//parquet/**/*')
df_final = df.join(df1,df['sbr_brand']==df1['brand'])\
&(df1,df['sbr_number']==df1['store_number'])\
.select(df['payment_number','payment_type_name','payment_identifier'],df1['brand_id'])
display(df_final)
Here is the types of error I get:
'tuple' object has no attribute 'select'
'Column' object is not callable
Related
I have a DF A_DF which has among others two columns say COND_B and COND_C. Then I have 2 different df's B_DF with COND_B column and C_DF with COND_C column.
Now I would like to filter A_DF where the value match in one OR the other. Something like:
df = A_DF.filter((A_DF.COND_B == B_DF.COND_B) | (A_DF.COND_C == C_DF.COND_C))
But I found out it is not possible like this.
EDIT
error: Attribute CON_B#264,COND_C#6 is missing from the schema: [... COND_B#532, COND_C#541 ]. Attribute(s) with the same name appear in the operation: COND_B,COND_C. Please check if the right attribute(s) are used.; looks like I can filter only on same DF because of the #number added on the fly..
So I first tried to do a list from B_DF and C_DF and use filter based on that but it was too expensive to use collect() on 100m of records.
So I tried:
AB_DF = A_DF.join(B_DF, 'COND_B', 'left_semi')
AC_DF = A_DF.join(C_DF, 'COND_C', 'left_semi')
df = AB_DF.unionAll(AC_DF).dropDuplicates()
dropDuplicates() I used to removed duplicate records where both conditions where true. But even with that I got some unexpected results.
Is there some other - smoother solution to do it simply? Something like an EXISTS statement in SQL?
EDIT2
I tried SQL based on #mck response:
e.createOrReplaceTempView('E')
b.createOrReplaceTempView('B')
p.createOrReplaceTempView('P')
df = spark.sql("""select * from E where exists (select 1 from B where E.BUSIPKEY = B.BUSIPKEY) or exists (select 1 from P where E.PCKEY = P.PCKEY)""")
my_output.write_dataframe(df)
with error:
Traceback (most recent call last):
File "/myproject/abc.py", line 45, in my_compute_function
df = spark.sql("""select * from E where exists (select 1 from B where E.BUSIPKEY = B.BUSIPKEY) or exists (select 1 from P where E.PCKEY = P.PCKEY)""")
TypeError: sql() missing 1 required positional argument: 'sqlQuery'
Thanks a lot!
Your idea of using exists should work. You can do:
A_DF.createOrReplaceTempView('A')
B_DF.createOrReplaceTempView('B')
C_DF.createOrReplaceTempView('C')
df = spark.sql("""
select * from A
where exists (select 1 from B where A.COND_B = B.COND_B)
or exists (select 1 from C where A.COND_C = C.COND_C)
""")
I want to replace an column in an dataframe. need to get the scala
syntax code for this
Controlling_Area = CC2
Hierarchy_Name = CC2HIDNE
Need to write as : HIDENE
ie: remove the Controlling_Area present in Hierarchy_Name .
val dfPC = ReadLatest("/Full", "parquet")
.select(
LRTIM( REPLACE(col("Hierarchy_Name"),col("Controlling_Area"),"") ),
Col(ColumnN),
Col(ColumnO)
)
notebook:3: error: not found: value REPLACE
REPLACE(col("Hierarchy_Name"),col("Controlling_Area"),"")
^
Expecting to get the LTRIM and replace code in scala
You can use withColumnRenamed to achieve that:
import org.apache.spark.sql.functions
val dfPC = ReadLatest("/Full", "parquet")
.withColumnRenamed("Hierarchy_Name","Controlling_Area")
.withColumn("Controlling_Area",ltrim(col("Controlling_Area")))
I have a hql file which accepts several arguments and I then in stand alone spark application, I am calling this hql script to create a dataframe.
This is a sample hql code from my script:
select id , name, age, country , created_date
from ${db1}.${table1} a
inner join ${db2}.${table2} b
on a.id = b.id
And in this is how I am calling it in my Spark script:
import scala.io.Source
val queryFile = `path/to/my/file`
val db1 = 'cust_db'
val db2 = 'cust_db2'
val table1 = 'customer'
val table2 = 'products'
val query = Source.fromFile(queryFile).mkString
val df = spark.sql(query)
When I am using this way, I am getting:
org.apache.spark.sql.catylyst.parser.ParserException
Is there a way to pass arguments directly to my hql file and then create a df out of the hive code.
Parameters can be injected with such code:
val parametersMap = Map("db1" -> db1, "db2" -> db2, "table1" -> table1, "table2" -> table2)
val injectedQuery = parametersMap.foldLeft(query)((acc, cur) => acc.replace("${" + cur._1 + "}", cur._2))
def main(inputs, output):
sdf = spark.read.csv(inputs, schema=observation_schema)
sdf.registerTempTable('filtertable')
result = spark.sql("""
SELECT * FROM filtertable WHERE qflag IS NULL
""").show()
temp_max = spark.sql(""" SELECT date, station, value FROM filtertable WHERE (observation = 'TMAX')""").show()
temp_min = spark.sql(""" SELECT date, station, value FROM filtertable WHERE (observation = 'TMIN')""").show()
result = temp_max.join(temp_min, condition1).select(temp_max('date'), temp_max('station'), ((temp_max('TMAX')-temp_min('TMIN'))/10)).alias('Range'))
Error:
Traceback (most recent call last):
File "/Users/syedikram/Documents/temp_range_sql.py", line 96, in <module>
main(inputs, output)
File "/Users/syedikram/Documents/temp_range_sql.py", line 52, in main
result = temp_max.join(temp_min, condition1).select(temp_max('date'), temp_max('station'), ((temp_max('TMAX')-temp_min('TMIN')/10)).alias('Range'))
AttributeError: 'NoneType' object has no attribute 'join'
Performing on join operation gives me Nonetype object error. Looking online didn't help as there is little documentation online for pyspark sql.
What am I doing wrong here?
Remove the .show() from temp_max and temp_min because show only prints a string and does not return anything (hence you get AttributeError: 'NoneType' object has no attribute 'join').
I'm not sure why this is happening. In PySpark, I read in two dataframes and print out their column names and they are as expected, but then when do a SQL join I get an error that cannot resolve column name given the inputs. I have simplified the merge just to get it to work, but I will need to add in more join conditions which is why I'm using SQL (will be adding in: "and b.mnvr_bgn < a.idx_trip_id and b.mnvr_end > a.idx_trip_data"). It appears that the column 'device_id' is being renamed to '_col7' in the df mnvr_temp_idx_prev_temp
mnvr_temp_idx_prev = mnvr_3.select('device_id', 'mnvr_bgn', 'mnvr_end')
print mnvr_temp_idx_prev.columns
['device_id', 'mnvr_bgn', 'mnvr_end']
raw_data_filtered = raw_data.select('device_id', 'trip_id', 'idx').groupby('device_id', 'trip_id').agg(F.max('idx').alias('idx_trip_end'))
print raw_data_filtered.columns
['device_id', 'trip_id', 'idx_trip_end']
raw_data_filtered.registerTempTable('raw_data_filtered_temp')
mnvr_temp_idx_prev.registerTempTable('mnvr_temp_idx_prev_temp')
test = sqlContext.sql('SELECT a.device_id, a.idx_trip_end, b.mnvr_bgn, b.mnvr_end \
FROM raw_data_filtered_temp as a \
INNER JOIN mnvr_temp_idx_prev_temp as b \
ON a.device_id = b.device_id')
Traceback (most recent call last): AnalysisException: u"cannot resolve 'b.device_id' given input columns: [_col7, trip_id, device_id, mnvr_end, mnvr_bgn, idx_trip_end]; line 1 pos 237"
Any help is appreciated!
I would recommend renaming the name of the field 'device_id' in at least one of the data frame. I modified your query just a bit and tested it(in scala). Below query works
test = sqlContext.sql("select * FROM raw_data_filtered_temp a INNER JOIN mnvr_temp_idx_prev_temp b ON a.device_id = b.device_id")
[device_id: string, mnvr_bgn: string, mnvr_end: string, device_id: string, trip_id: string, idx_trip_end: string]
Now if you are doing a 'select * ' in above statement, it will work. But if you try to select 'device_id', you will get an error "Reference 'device_id' is ambiguous" . As you can see in the above 'test' data frame definition, it has two fields with the same name(device_id). So to avoid this, I recommend changing field name in one of the dataframes.
mnvr_temp_idx_prev = mnvr_3.select('device_id', 'mnvr_bgn', 'mnvr_end')
.withColumnRenamned("device_id","device")
raw_data_filtered = raw_data.select('device_id', 'trip_id', 'idx').groupby('device_id', 'trip_id').agg(F.max('idx').alias('idx_trip_end'))
Now use dataframes or sqlContext
//using dataframes with multiple conditions
val test = mnvr_temp_idx_prev.join(raw_data_filtered,$"device" === $"device_id"
&& $"mnvr_bgn" < $"idx_trip_id","inner")
//in SQL Context
test = sqlContext.sql("select * FROM raw_data_filtered_temp a INNER JOIN mnvr_temp_idx_prev_temp b ON a.device_id = b.device and a. idx_trip_id < b.mnvr_bgn")
Above queries will work for your problem. And if your data set is too large, I would recommend to not use '>' or '<' operators in Join condition as it causes cross join which is a costly operation if data set is large. Instead use them in WHERE condition.