Hi Sean and Gourav
Thanks for the suggestions. I thought that both the sql and dataframe apis are
wrappers around the same frame work? Ie. catalysts.
I tend to mix and match my code. Sometimes I find it easier to write using sql
some times dataframes. What is considered best practices?
Here is an example
Case 1
for i in range( 1, len( self.sampleNamesList ) ): # iterate 16000 times!
sampleName = self.sampleNamesList[i]
sampleDF= quantSparkDFList[i]
sampleSDF.createOrReplaceTempView( "sample" )
# the sample name must be quoted else column names with a '-'
# like GTEX-1117F-0426-SM-5EGHI will generate an error
# spark thinks the '-' is an expression. '_' is also
# a special char for the sql like operator
# https://stackoverflow.com/a/63899306/4586180
sqlStmt = '\t\t\t\t\t\tselect rc.*, `{}` \n\
from \n\
rawCounts as rc, \n\
sample \n\
where \n\
rc.Name == sample.Name \n'.format( sampleName )
rawCountsSDF = self.spark.sql( sqlStmt )
rawCountsSDF.createOrReplaceTempView( "rawCounts"
case 2
for i in range( 1, len(dfList) ):
df2 = dfList[i]
retDF = retDF.join( df2.selectExpr("*"), on=["Name"] )
I think my out of memory exception maybe because the query plan is huge. I have
not figure out how to figure out if that is my bug or not. My untested work
around is organize the data so that each massive join is run on 1/5 of the
total data set, then union them all together. Each “part” will still need to
iterate 16000 times
In general I assume we want to avoid for loops. I assume Spark is unable to
optimize them. It would be nice if spark provide some sort of join all function
even if it used a for loop to hide this from me
Happy holidays
Andy
From: Sean Owen <sro...@gmail.com>
Date: Friday, December 24, 2021 at 8:30 AM
To: Gourav Sengupta <gourav.sengu...@gmail.com>
Cc: Andrew Davidson <aedav...@ucsc.edu.invalid>, Nicholas Gustafson
<njgustaf...@gmail.com>, User <user@spark.apache.org>
Subject: Re: AnalysisException: Trouble using select() to append multiple
columns
(that's not the situation below we are commenting on)
On Fri, Dec 24, 2021, 9:28 AM Gourav Sengupta
<gourav.sengu...@gmail.com<mailto:gourav.sengu...@gmail.com>> wrote:
Hi,
try to write several withColumns in a dataframe with functions and then see the
UI for time differences. This should be done with large data sets of course, in
order of a around 200GB +
With scenarios involving nested queries and joins the time differences shown in
the UI becomes a bit more visible.
Regards,
Gourav Sengupta
On Fri, Dec 24, 2021 at 2:48 PM Sean Owen
<sro...@gmail.com<mailto:sro...@gmail.com>> wrote:
Nah, it's going to translate to the same plan as the equivalent SQL.
On Fri, Dec 24, 2021, 5:09 AM Gourav Sengupta
<gourav.sengu...@gmail.com<mailto:gourav.sengu...@gmail.com>> wrote:
Hi,
please note that using SQL is much more performant, and easier to manage these
kind of issues. You might want to look at the SPARK UI to see the advantage of
using SQL over dataframes API.
Regards,
Gourav Sengupta
On Sat, Dec 18, 2021 at 5:40 PM Andrew Davidson <aedav...@ucsc.edu.invalid>
wrote:
Thanks Nicholas
Andy
From: Nicholas Gustafson <njgustaf...@gmail.com<mailto:njgustaf...@gmail.com>>
Date: Friday, December 17, 2021 at 6:12 PM
To: Andrew Davidson <aedav...@ucsc.edu.invalid>
Cc: "user@spark.apache.org<mailto:user@spark.apache.org>"
<user@spark.apache.org<mailto:user@spark.apache.org>>
Subject: Re: AnalysisException: Trouble using select() to append multiple
columns
Since df1 and df2 are different DataFrames, you will need to use a join. For
example: df1.join(df2.selectExpr(“Name”, “NumReads as ctrl_2”), on=[“Name”])
On Dec 17, 2021, at 16:25, Andrew Davidson <aedav...@ucsc.edu.invalid> wrote:
Hi I am a newbie
I have 16,000 data files, all files have the same number of rows and columns.
The row ids are identical and are in the same order. I want to create a new
data frame that contains the 3rd column from each data file
I wrote a test program that uses a for loop and Join. It works with my small
test set. I get an OOM when I try to run using the all the data files. I
realize that join ( map reduce) is probably not a great solution for my problem
Recently I found several articles that take about the challenge with using
withColumn() and talk about how to use select() to append columns
https://mungingdata.com/pyspark/select-add-columns-withcolumn/
https://stackoverflow.com/questions/64627112/adding-multiple-columns-in-pyspark-dataframe-using-a-loop
I am using pyspark spark-3.1.2-bin-hadoop3.2
I wrote a little test program. It am able to append columns created using
pyspark.sql.function.lit(). I am not able to append columns from other data
frames
df1
DataFrame[Name: string, ctrl_1: double]
+-------+------+
| Name|ctrl_1|
+-------+------+
| txId_1| 0.0|
| txId_2| 11.0|
| txId_3| 12.0|
| txId_4| 13.0|
| txId_5| 14.0|
| txId_6| 15.0|
| txId_7| 16.0|
| txId_8| 17.0|
| txId_9| 18.0|
|txId_10| 19.0|
+-------+------+
# use select to append multiple literals
allDF3 = df1.select( ["*", pyf.lit("abc").alias("x"),
pyf.lit("mn0").alias("y")] )
allDF3
DataFrame[Name: string, ctrl_1: double, x: string, y: string]
+-------+------+---+---+
| Name|ctrl_1| x| y|
+-------+------+---+---+
| txId_1| 0.0|abc|mn0|
| txId_2| 11.0|abc|mn0|
| txId_3| 12.0|abc|mn0|
| txId_4| 13.0|abc|mn0|
| txId_5| 14.0|abc|mn0|
| txId_6| 15.0|abc|mn0|
| txId_7| 16.0|abc|mn0|
| txId_8| 17.0|abc|mn0|
| txId_9| 18.0|abc|mn0|
|txId_10| 19.0|abc|mn0|
+-------+------+---+---+
df2
DataFrame[Name: string, Length: int, EffectiveLength: double, TPM: double,
NumReads: double]
+-------+------+---------------+----+--------+
| Name|Length|EffectiveLength| TPM|NumReads|
+-------+------+---------------+----+--------+
| txId_1| 1500| 1234.5|12.1| 0.1|
| txId_2| 1510| 1244.5|13.1| 11.1|
| txId_3| 1520| 1254.5|14.1| 12.1|
| txId_4| 1530| 1264.5|15.1| 13.1|
| txId_5| 1540| 1274.5|16.1| 14.1|
| txId_6| 1550| 1284.5|17.1| 15.1|
| txId_7| 1560| 1294.5|18.1| 16.1|
| txId_8| 1570| 1304.5|19.1| 17.1|
| txId_9| 1580| 1314.5|20.1| 18.1|
|txId_10| 1590| 1324.5|21.1| 19.1|
+-------+------+---------------+----+--------+
s2Col = df2["NumReads"].alias( 'ctrl_2' )
print("type(s2Col) = {}".format(type(s2Col)) )
type(s2Col) = <class 'pyspark.sql.column.Column'>
allDF4 = df1.select( ["*", s2Col] )
~/extraCellularRNA/sparkBin/spark-3.1.2-bin-hadoop3.2/python/pyspark/sql/dataframe.py
in select(self, *cols)
1667 [Row(name='Alice', age=12), Row(name='Bob', age=15)]
1668 """
-> 1669 jdf = self._jdf.select(self._jcols(*cols))
1670 return DataFrame(jdf, self.sql_ctx)
1671
../../sparkBin/spark-3.1.2-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py
in __call__(self, *args)
1303 answer = self.gateway_client.send_command(command)
1304 return_value = get_return_value(
-> 1305 answer, self.gateway_client, self.target_id,
self.name<http://self.name>)
1306
1307 for temp_arg in temp_args:
~/extraCellularRNA/sparkBin/spark-3.1.2-bin-hadoop3.2/python/pyspark/sql/utils.py
in deco(*a, **kw)
115 # Hide where the exception came from that shows a
non-Pythonic
116 # JVM exception message.
--> 117 raise converted from None
118 else:
119 raise
AnalysisException: Resolved attribute(s) NumReads#14 missing from
Name#0,ctrl_1#2447 in operator !Project [Name#0, ctrl_1#2447, NumReads#14 AS
ctrl_2#2550].;
!Project [Name#0, ctrl_1#2447, NumReads#14 AS ctrl_2#2550]
+- Project [Name#0, NumReads#4 AS ctrl_1#2447]
+- Project [Name#0, NumReads#4]
+- Relation[Name#0,Length#1,EffectiveLength#2,TPM#3,NumReads#4] csv
Any idea what my bug is?
Kind regards
Andy
