I am working on a project in apache Spark and the requirement is to write the processed output from spark into a specific format like Header -> Data -> Trailer. For writing to HDFS I am using the .saveAsHadoopFile method and writing the data to multiple files using the key as a file name. But the issue is the sequence of the data is not maintained files are written in Data->Header->Trailer or a different combination of three. Is there anything I am missing with RDD transformation?
Ok so after reading from StackOverflow questions, blogs and mail archives from google. I found out how exactly .union() and other transformation works and how partitioning is managed. When we use .union() the partition information is lost by the resulting RDD and also the ordering and that's why My output sequence was not getting maintained.
What I did to overcome the issue is numbering the Records like
Header = 1, Body = 2, and Footer = 3
so using sortBy on RDD which is union of all three I sorted it using this order number with 1 partition. And after that to write to multiple file using key as filename I used HashPartitioner so that same key data should go into separate file.
val header: RDD[(String,(String,Int))] = ... // this is my header RDD`
val data: RDD[(String,(String,Int))] = ... // this is my data RDD
val footer: RDD[(String,(String,Int))] = ... // this is my footer RDD
val finalRDD: [(String,String)] = header.union(data).union(footer).sortBy(x=>x._2._2,true,1).map(x => (x._1,x._2._1))
val output: RDD[(String,String)] = new PairRDDFunctions[String,String](finalRDD).partitionBy(new HashPartitioner(num))
output.saveAsHadoopFile ... // and using MultipleTextOutputFormat save to multiple file using key as filename
This might not be the final or most economical solution but it worked. I am also trying to find other ways to maintain the sequence of output as Header->Body->Footer. I also tried .coalesce(1) on all three RDD's and then do the union but that was just adding three more transformation to RDD's and .sortBy function also take partition information which I thought will be same, but coalesceing the RDDs first also worked. If Anyone has some another approach please let me know, or add more to this will be really helpful as I am new to Spark
References:
Write to multiple outputs by key Spark - one Spark job
Ordered union on spark RDDs
http://apache-spark-user-list.1001560.n3.nabble.com/Union-of-2-RDD-s-only-returns-the-first-one-td766.html -- this one helped a lot
Related
Context
I am trying to use Spark/Scala in order to "edit" multiple parquet files (potentially 50k+) efficiently. The only edit that needs to be done is deletion (i.e. deleting records/rows) based on a given set of row IDs.
The parquet files are stored in s3 as a partitioned DataFrame where an example partition looks like this:
s3://mybucket/transformed/year=2021/month=11/day=02/*.snappy.parquet
Each partition can have upwards of 100 parquet files that each are between 50mb and 500mb in size.
Inputs
We are given a spark Dataset[MyClass] called filesToModify which has 2 columns:
s3path: String = the complete s3 path to a parquet file in s3 that needs to be edited
ids: Set[String] = a set of IDs (rows) that need to be deleted in the parquet file located at s3path
Example input dataset filesToModify:
s3path
ids
s3://mybucket/transformed/year=2021/month=11/day=02/part-1.snappy.parquet
Set("a", "b")
s3://mybucket/transformed/year=2021/month=11/day=02/part-2.snappy.parquet
Set("b")
Expected Behaviour
Given filesToModify I want to take advantage of parallelism in Spark do the following for each row:
Load the parquet file located at row.s3path
Filter so that we exclude any row whose id is in the set row.ids
Count the number of deleted/excluded rows per id in row.ids (optional)
Save the filtered data back to the same row.s3path to overwrite the file
Return the number of deleted rows (optional)
What I have tried
I have tried using filesToModify.map(row => deleteIDs(row.s3path, row.ids)) where deleteIDs is looks like this:
def deleteIDs(s3path: String, ids: Set[String]): Int = {
import spark.implicits._
val data = spark
.read
.parquet(s3path)
.as[DataModel]
val clean = data
.filter(not(col("id").isInCollection(ids)))
// write to a temp directory and then upload to s3 with same
// prefix as original file to overwrite it
writeToSingleFile(clean, s3path)
1 // dummy output for simplicity (otherwise it should correspond to the number of deleted rows)
}
However this leads to NullPointerException when executed within the map operation. If I execute it alone outside of the map block then it works but I can't understand why it doesn't inside it (something to do with lazy evaluation?).
You get a NullPointerException because you try to retrieve your spark session from an executor.
It is not explicit, but to perform spark action, your DeleteIDs function needs to retrieve active spark session. To do so, it calls method getActiveSession from SparkSession object. But when called from an executor, this getActiveSession method returns None as stated in SparkSession's source code:
Returns the default SparkSession that is returned by the builder.
Note: Return None, when calling this function on executors
And thus NullPointerException is thrown when your code starts using this None spark session.
More generally, you can't recreate a dataset and use spark transformations/actions in transformations of another dataset.
So I see two solutions for your problem:
either to rewrite DeleteIDs function's code without using spark, and modify your parquet files by using parquet4s for instance.
or transform filesToModify to a Scala collection and use Scala's map instead of Spark's one.
s3path and ids parameters that are passed to deleteIDs are not actually strings and sets respectively. They are instead columns.
In order to operate over these values you can instead create a UDF that accepts columns instead of intrinsic types, or you can collect your dataset if it is small enough so that you can use the values in the deleteIDs function directly. The former is likely your best bet if you seek to take advantage of Spark's parallelism.
You can read about UDFs here
We have an AWS S3 bucket with millions of documents in a complex hierarchy, and a CSV file with (among other data) links to a subset of those files, I estimate this file will be about 1000 to 10.000 rows. I need to join the data from the CSV file with the contents of the documents for further processing in Spark. In case it matters, we're using Scala and Spark 2.4.4 on an Amazon EMR 6.0.0 cluster.
I can think of two ways to do this. First is to add a transformation on the CSV DataFrame that adds the content as a new column:
val df = spark.read.format("csv").load("<csv file>")
val attempt1 = df.withColumn("raw_content", spark.sparkContext.textFile($"document_url"))
or variations thereof (for example, wrapping it in a udf) don't seem to work, I think because sparkContext.textFile returns an RDD, so I'm not sure it's even supposed to work this way? Even if I get it working, is the best way to keep it performant in Spark?
An alternative I tried to think of is to use spark.sparkContext.wholeTextFiles upfront and then join the two dataframes together:
val df = spark.read.format("csv").load("<csv file>")
val contents = spark.sparkContext.wholeTextFiles("<s3 bucket>").toDF("document_url", "raw_content");
val attempt2 = df.join(contents, df("document_url") === contents("document_url"), "left")
but wholeTextFiles doesn't go into subdirectories and the needed paths are hard to predict, and I'm also unsure of the performance impact of trying to build an RDD of the entire bucket of millions of files if I only need a small fraction of it, since the S3 API probably doesn't make it very fast to list all the objects in the bucket.
Any ideas? Thanks!
I did figure out a solution in the end:
val df = spark.read.format("csv").load("<csv file>")
val allS3Links = df.map(row => row.getAs[String]("document_url")).collect()
val joined = allS3Links.mkString(",")
val contentsDF = spark.sparkContext.wholeTextFiles(joined).toDF("document_url", "raw_content");
The downside to this solution is that it pulls all the urls to the driver, but it's workable in my case (100,000 * ~100 char length strings is not that much) and maybe even unavoidable.
I am new to spark. I have some json data that comes as an HttpResponse. I'll need to store this data in hive tables. Every HttpGet request returns a json which will be a single row in the table. Due to this, I am having to write single rows as files in the hive table directory.
But I feel having too many small files will reduce the speed and efficiency. So is there a way I can recursively add new rows to the Dataframe and write it to the hive table directory all at once. I feel this will also reduce the runtime of my spark code.
Example:
for(i <- 1 to 10){
newDF = hiveContext.read.json("path")
df = df.union(newDF)
}
df.write()
I understand that the dataframes are immutable. Is there a way to achieve this?
Any help would be appreciated. Thank you.
You are mostly on the right track, what you want to do is to obtain multiple single records as a Seq[DataFrame], and then reduce the Seq[DataFrame] to a single DataFrame by unioning them.
Going from the code you provided:
val BatchSize = 100
val HiveTableName = "table"
(0 until BatchSize).
map(_ => hiveContext.read.json("path")).
reduce(_ union _).
write.insertInto(HiveTableName)
Alternatively, if you want to perform the HTTP requests as you go, we can do that too. Let's assume you have a function that does the HTTP request and converts it into a DataFrame:
def obtainRecord(...): DataFrame = ???
You can do something along the lines of:
val HiveTableName = "table"
val OtherHiveTableName = "other_table"
val jsonArray = ???
val batched: DataFrame =
jsonArray.
map { parameter =>
obtainRecord(parameter)
}.
reduce(_ union _)
batched.write.insertInto(HiveTableName)
batched.select($"...").write.insertInto(OtherHiveTableName)
You are clearly misusing Spark. Apache Spark is analytical system, not a database API. There is no benefit of using Spark to modify Hive database like this. It will only bring a severe performance penalty without benefiting from any of the Spark features, including distributed processing.
Instead you should use Hive client directly to perform transactional operations.
If you can batch-download all of the data (for example with a script using curl or some other program) and store it in a file first (or many files, spark can load an entire directory at once) you can then load that file(or files) all at once into spark to do your processing. I would also check to see it the webapi as any endpoints to fetch all the data you need instead of just one record at a time.
Usually I load csv files and then I run different kind of aggregations like for example "group by" with Spark. I was wondering if it is possible to start this sort of operations during the file loading (typically a few millions of rows) instead of sequentialize them and if it can be worthy (as time saving).
Example:
val csv = sc.textFile("file.csv")
val data = csv.map(line => line.split(",").map(elem => elem.trim))
val header = data.take(1)
val rows = data.filter(line => header(0) != "id")
val trows = rows.map(row => (row(0), row))
trows.groupBy(//row(0) etc.)
For my understanding of how Spark works, the groupBy (or aggregate) will be "postponed" to the loading in memory of the whole file csv. If this is correct, can the loading and the grouping run at the "same" time instead of sequencing the two steps?
the groupBy (or aggregate) will be "postponed" to the loading in memory of the whole file csv.
It is not the case. At the local (single partition) level Spark operates on lazy sequences so operations belonging to a single task (this includes map side aggregation) can squashed together.
In other words when you have chain of methods operations are performed line-by-line not transformation-by-transformation. In other words the first line will be mapped, filtered, mapped once again and passed to aggregator before the next one is accessed.
To start a group by on load operation You could proceed with 2 options:
Write your own loader and make your own group by inside that + aggregationByKey. The cons of that is writting more code & more maintanance.
Use Parquet format files as input + DataFrames, due it's columnar it will read only desired columns used in your groupBy. so it should be faster. - DataFrameReader
df = spark.read.parquet('file_path')
df = df.groupBy('column_a', 'column_b', '...').count()
df.show()
Due Spark is Lazy it won't load your file until you call action methods like show/collect/write. So Spark will know which columns read and which ignore on the load process.
I use this method to write csv file. But it will generate a file with multiple part files. That is not what I want; I need it in one file. And I also found another post using scala to force everything to be calculated on one partition, then get one file.
First question: how to achieve this in Python?
In the second post, it is also said a Hadoop function could merge multiple files into one.
Second question: is it possible merge two file in Spark?
You can use,
df.coalesce(1).write.csv('result.csv')
Note:
when you use coalesce function you will lose your parallelism.
You can do this by using the cat command line function as below. This will concatenate all of the part files into 1 csv. There is no need to repartition down to 1 partition.
import os
test.write.csv('output/test')
os.system("cat output/test/p* > output/test.csv")
Requirement is to save an RDD in a single CSV file by bringing the RDD to an executor. This means RDD partitions present across executors would be shuffled to one executor. We can use coalesce(1) or repartition(1) for this purpose. In addition to it, one can add a column header to the resulted csv file.
First we can keep a utility function for make data csv compatible.
def toCSVLine(data):
return ','.join(str(d) for d in data)
Let’s suppose MyRDD has five columns and it needs 'ID', 'DT_KEY', 'Grade', 'Score', 'TRF_Age' as column Headers. So I create a header RDD and union MyRDD as below which most of times keeps the header on top of the csv file.
unionHeaderRDD = sc.parallelize( [( 'ID','DT_KEY','Grade','Score','TRF_Age' )])\
.union( MyRDD )
unionHeaderRDD.coalesce( 1 ).map( toCSVLine ).saveAsTextFile("MyFileLocation" )
saveAsPickleFile spark context API method can be used to serialize data that is saved in order save space. Use pickFile to read the pickled file.
I needed my csv output in a single file with headers saved to an s3 bucket with the filename I provided. The current accepted answer, when I run it (spark 3.3.1 on a databricks cluster) gives me a folder with the desired filename and inside it there is one csv file (due to coalesce(1)) with a random name and no headers.
I found that sending it to pandas as an intermediate step provided just a single file with headers, exactly as expected.
my_spark_df.toPandas().to_csv('s3_csv_path.csv',index=False)