I have a basic question regarding working with Spark DataFrame.
Consider the following piece of pseudo code:
val df1 = // Lazy Read from csv and create dataframe
val df2 = // Filter df1 on some condition
val df3 = // Group by on df2 on certain columns
val df4 = // Join df3 with some other df
val subdf1 = // All records from df4 where id < 0
val subdf2 = // All records from df4 where id > 0
* Then some more operations on subdf1 and subdf2 which won't trigger spark evaluation yet*
// Write out subdf1
// Write out subdf2
Suppose I start of with main dataframe df1(which I lazy read from the CSV), do some operations on this dataframe (filter, groupby, join) and then comes a point where I split this datframe based on a condition (for eg, id > 0 and id < 0). Then I further proceed to operate on these sub dataframes(let us name these subdf1, subdf2) and ultimately write out both the sub dataframes.
Notice that the write function is the only command that triggers the spark evaluation and rest of the functions(filter, groupby, join) result in lazy evaluations.
Now when I write out subdf1, I am clear that lazy evaluation kicks in and all the statements are evaluated starting from reading of CSV to create df1.
My question comes when we start writing out subdf2. Does spark understand the divergence in code at df4 and store this dataframe when command for writing out subdf1 was encountered? Or will it again start from the first line of creating df1 and re-evaluate all the intermediary dataframes?
If so, is it a good idea to cache the dataframe df4(Assuming I have sufficient memory)?
I'm using scala spark if that matters.
Any help would be appreciated.
No, Spark cannot infer that from your code. It will start all over again. To confirm this, you can do subdf1.explain() and subdf2.explain() and you should see that both dataframes have query plans that start right from the beginning where df1 was read.
So you're right that you should cache df4 to avoid redoing all the computations starting from df1, if you have enough memory. And of course, remember to unpersist by doing df4.unpersist() at the end if you no longer need df4 for any further computations.
Related
I am currently facing some issues in Spark 3.0.2 to efficiently join 2 Spark dataframes when
The 2 Spark DataFrames are partitioned by some key id;
id is part of the join key, but it is not the only one.
My intuition is telling me that the query optimizer is, in this case, not choosing the optimal path. I will illustrate my issue through a minimal example (note that this particular example does not really require a join, it's just for illustrative purposes).
Let's start from the simple case: the 2 dataframes are partitioned by id, and we join by id only:
from pyspark.sql import SparkSession, Row, Window
import pyspark.sql.functions as F
spark = SparkSession.builder.getOrCreate()
# Make up some test dataframe
df = spark.createDataFrame([Row(id=i // 10, order=i % 10, value=i) for i in range(10000)])
# Create the left side of the join (repartitioned by id)
df2 = df.repartition(50, 'id')
# Create the right side of the join (also repartitioned by id)
df3 = df2.select('id', F.col('order').alias('order_alias'), F.lit(0).alias('dummy'))
# Perform the join
joined_df = df2.join(df3, on='id')
joined_df.foreach(lambda x: None)
This results in the following efficient plan:
This plan is efficient: it recognizes that the 2 dataframes are already partitioned by the join key and avoids to re-shuffle them. The 2 dataframes are not only repartitioned, but also colocated.
What happens if there is an additional join key? It results in an inefficient plan:
joined_df = df2.join(df3, on=[df2.id==df3.id, df2.order==df3.order_alias])
joined_df.foreach(lambda x: None)
The plan is inefficient since it is repartitioning the 2 dataframes to do the join. This does not make sense to me. Intuitively, we could use the existing partitions: all keys to be joined will be found in the same partition as before, there is just one additional condition to apply! So I thought: perhaps we could phrase the 2nd condition as a filter?
joined_df.foreach(lambda x: None)
joined_df = df2.join(df3, on='id')
joined_df_filtered = joined_df.filter(df2.order==df3.order_alias)
This however results in the same inefficient plan, since Spark query optimizer will just merge the 2nd filter with the join.
So, I finally thought that maybe I could force Spark to process the join as I want by adding a dummy cache step, by trying the following:
from pyspark import StorageLevel
joined_df = df2.join(df3, on='id')
# Note that this storage level will not cache anything, it's just to suggest to Spark that I need this intermediate result
joined_df.persist(StorageLevel(False, False, False, False))
# Do the filtering after "persisting" the join
joined_df_filtered = joined_df.filter(df2.order==df3.order_alias)
joined_df_filtered.foreach(lambda x: None)
This results in an efficient plan! It is in fact much faster than the previous ones.
The workaround of "persisting" the first join to force Spark to use a more efficient processing plan is "good enough" for my use case, but I still have a few questions:
Am I missing something in my intuition that Spark should actually be reusing partitions when the partition key is part of the join key, instead of re-shuffling?
Is this expected behavior of the query optimizer? Should a ticket be filed for it?
Is there a better way to force the desired processing plan than adding the "persist" step? It seems more like an indirect workaround than a direct solution.
I have a situation where I have a dataframe df
and let's say I do the following steps:
df1 = df
df2 = df
and then write a query which uses D and E in the joins e.g
df3 = df1.join(df2, df1["column"] = df2["column"])
This is nothing but a self join which is widely needed in ETL.
Why does spark not handle it correctly
I have seen many posts but none of them provide a work around.
UPdate:
If I load the dataframes df1 and df2 from the same s3 location and then perform the join the issue goes away. But when you are doing ETL it may not always be the case where we persist the data and then use it to avoid this scenario.
Any thoughts?
Consider there is spark job has multiple dataframe transitions
val baseDF1 = spark.sql(s"select * from db.table1 where condition1='blah'")
val baseDF2 = spark.sql(s"select * from db.table2 where condition2='blah'")
val df3 = basedDF1.join(baseDF12, basedDF1("col1") <=> basedDF1("col2"))
val df4 = df3.withcolumn("col3").withColumnRename("col4", "newcol4")
val df5 = df4.groupBy("groupbycol").agg(expr("coalesce(first(col5, false))"))
val df6 = df5.withColumn("level1", col("coalesce(first(col5, false))")(0))
.withColumn("level2", col("coalesce(first(col5, false))")(1))
.withColumn("level3", col("coalesce(first(col5, false))")(2))
.withColumn("level4", col("coalesce(first(col5, false))")(3))
.withColumn("level5", col("coalesce(first(col5, false))")(4))
.drop("coalesce(first(col5, false))")
I just wondering how Spark generate the spark SQL logic, is it going to generate the query-like transaction for each data frame, i.e
df1 = select * ....
df2 = select * ....
df3 = df1.join.df2 // spark takes content from df1/df2 instead run each query again for joining
....
df6 = ...
or generate large query by the end of the last dataframe
df6 = select coalesce(first(col5, false)).. from ((select * from table1) join (select * from table2 ) on blah ) group by blah 2...
All I trying to figure out, is how to avoid Spark generate huge query-like logic instead I can let Spark "Commit" somewhere to avoid huge long transaction
the reason behind the inquiry is because current spark job threw following exception
19/12/17 10:57:55 ERROR CodeGenerator: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 567, Column 28: Redefinition of parameter "agg_expr_21"
Spark has two operations - transformation and action.
Transformation happens when a DF is being built using various operations like - select, join, filter etc. It is read to be executed but has not done any work yet, it is being lazy. These transformations can be composed to make new transformation which you do while operating on predefined dataframes, like basedDF1.join(baseDF12, basedDF1("col1") <=> basedDF1("col2")). But again nothing has run.
Action happens when certain operations are called like save, collect, show etc. This is when real work happens. Here each and every 'transformation' that was defined before with be either executed or retrieved from cache. You can save a lot of work for Spark if you can cache some of the complex steps. This can also simplify the plan.
val baseDF1 = spark.sql(s"select * from db.table1 where condition1='blah'")
val baseDF2 = spark.sql(s"select * from db.table2 where condition2='blah'")
baseDF1.cache()
baseDF2.cache()
val df3 = basedDF1.join(baseDF12, basedDF1("col1") <=> basedDF1("col2"))
val df4 = baseDF1.join(baseDF12, basedDF1("col2") === basedDF1("col3"))// different join
When df4 is executed after df3, it won't be selecting from db.table1 and db.table2 but rather reading baseDF1 and baseDF2 from cache. The plan will look simpler too.
if some reason cache is gone then Spark will recompute baseDF1 and baseDF2 as they were defined, so it knows its lineage but didn't execute it.
You can also use checkpoint to break up the lineage of overall execution, hence simplify it. I think this can help your case.
I have also saved intermediate dataframe to a temporary file and read It back as a dataframe and use it down the line. This breaks up the complexity at the cost of extra io. I won’t recommend it unless other methods didn’t work.
I am not sure about the error you are getting.
I have two dataframes that need to be cross joined on a 20-node cluster. However because of their size, a simple crossjoin is failing. I am looking to partition the data and perform the crossjoin and am looking for an efficient way to do it.
Simple Algorithm
Manually split file f1 into three and read into dataframes: df1A, df1B, df1C. Manually split file f2 into four and ready into dataframes: df2A, df2B, df2C, df2D. Cross join df1A X df2A, df1A X df2B,..,df1A X df2D,...,df1C X df2D. Save each cross join in a file and manually put together all files. This way Spark can perform each cross join parallely and things should complete fairly quickly.
Question
Is there is more efficient way of accomplishing this by reading both files into two dataframes, then partitioning each dataframe into 3 and 4 "pieces" and for each partition of one dataframe cross join with every partition of the other dataframe?
Data frame can be partitioned ether range or hash .
val df1 = spark.read.csv("file1.txt")
val df2 = spark.read.csv("file2.txt")
val partitionedByRange1 = df1.repartitionByRange(3, $"k")
val partitionedByRange2 = df2.repartitionByRange(4, $"k")
val result =partitionedByRange1.crossJoin(partitionedByRange2);
NOTE : set property spark.sql.crossJoin.enabled=true
You can convert this in to a rdd and then use cartesian operation on that RDD. You should then be able to save that RDD to a file. Hope that helps
I have the following code that is simply doing some joins and then outputting the data;
from pyspark.sql.functions import udf, struct
from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark import SparkConf
from pyspark.sql.functions import broadcast
conf = SparkConf()
conf.set('spark.logConf', 'true')
spark = SparkSession \
.builder \
.config(conf=conf) \
.appName("Generate Parameters") \
.getOrCreate()
spark.sparkContext.setLogLevel("OFF")
df1 = spark.read.parquet("/location/mydata")
df1 = df1.select([c for c in df1.columns if c in ['sender','receiver','ccc,'cc','pr']])
df2 = spark.read.csv("/location/mydata2")
cond1 = [(df1.sender == df2._c1) | (df1.receiver == df2._c1)]
df3 = df1.join(broadcast(df2), cond1)
df3 = df3.select([c for c in df3.columns if c in['sender','receiver','ccc','cc','pr']])
df1 is 1,862,412,799 rows and df2 is 8679 rows
when I then call;
df3.count()
It just seems to sit there with the following
[Stage 33:> (0 + 200) / 200]
Assumptions for this answer:
df1 is the dataframe containing 1,862,412,799 rows.
df2 is the dataframe containing 8679 rows.
df1.count() returns a value quickly (as per your comment)
There may be three areas where the slowdown is occurring:
The imbalance of data sizes (1,862,412,799 vs 8679):
Although spark is amazing at handling large quantities of data, it doesn't deal well with very small sets. If not specifically set, Spark attempts to partition your data into multiple parts and on small files this can be excessively high in comparison to the actual amount of data each part has. I recommend trying to use the following and see if it improves speed.
df2 = spark.read.csv("/location/mydata2")
df2 = df2.repartition(2)
Note: The number 2 here is just an estimated number, based on how many partitions would suit the amount of rows that are in that set.
Broadcast Cost:
The delay in the count may be due to the actual broadcast step. Your data is being saved and copied to every node within your cluster before the join, this all happening together once count() is called. Depending on your infrastructure, this could take some time. If the above repartition doesn't work, try removing the broadcast call. If that ends up being the delay, it may be good to confirm that there are no bottlenecks within your cluster or if it's necessary.
Unexpected Merge Explosion
I do not imply that this is an issue, but it is always good to check that the merge condition you have set is not creating unexpected duplicates. It is a possibility that this may be happening and creating the slow down you are experiencing when actioning the processing of df3.