I have a dataframe with similar schema, I need to append the data into the AVRO file. I don't like to add the avro file into folder as a part. For your information, my AVRO file is not into the folder as a part. Can you please help me to solve the task.
You can write the data by using mode overwrite while writing the dataframe.
But the part file is created as spark is distributed processing and each executor spits out a files based on the amount of data
Related
In a pyspark session when I do this:
df = spark.read.parquet(file)
df.write.csv('output')
it creates a directory called output with a bunch of files, one of which is a target csv file with unpredictable name, example: part-00006-80ba8022-33cb-4478-aab3-29f08efc160a-c000.csv
Is there a way to know what the output file name is after the .csv() call?
When you read a parquet file in the dataframe it will have some partitions as we are using distributed storage here. Similarly when you save that dataframe as an csv file it would get saved in an distributed manner based on the number of partitions that dataframe had.
The path that you provide at the time of writing the csv file would create a folder with that name is what happens and then you would have multiple partitions files inside that folder. Each file would have some portion of data and when you combine all that partitions file you get the entire content of the csv file.
Also if you read that folder path then you would be able to see the entire content of the csv file. This is the default behaviour of how spark and distributed computing works.
We are currently using Spark Job on Databricks which do processing on our data lake which in S3.
Once the processing is done we export our result to S3 bucket using normal
df.write()
The issue is when we write dataframe to S3 the name of file is controlled by Spark, but as per our agreement we need to rename this files to a meaningful name.
Since S3 doesn't have renaming feature we are right now using boto3 to copy and paste file with expected name.
This process is very complex and not scalable with more client getting onboard.
Do we have any better solution to rename exported files from spark to S3 ?
It's not possible to do it directly in Spark's save
Spark uses Hadoop File Format, which requires data to be partitioned - that's why you have part- files. If the file is small enough to fit into memory, one work around is to convert to a pandas dataframe and save as csv from there.
df_pd = df.toPandas()
df_pd.to_csv("path")
I have an external ORC table with a large number of the small files, which are coming from the source on daily basis. I need to merge these files into larger files.
I tried to load ORC files to the spark and save with overwrite method
val fileName = "/user/db/table_data/" //This table contains multiple partition on date column with small data files.
val df = hiveContext.read.format("orc").load(fileName)
df.repartition(1).write.mode(SaveMode.Overwrite).partitionBy("date").orc("/user/db/table_data/)
But mode(SaveMode.Overwrite) is deleting all the data from the HDFS. When I tried without mode(SaveMode.Overwrite) method, it was throwing error file already exists.
Can anyone help me to proceed?
As suggested by #Avseiytsev, I have stored by merged orc files in different folder as source in HDFS and moved the data to the table path after the completion of the job.
I am getting daily files in a folder in linux server, How should I add these to my spark structured streaming dataframe? (Delta Update)
Have you read the document?
File source - Reads files written in a directory as a stream of data. Supported file formats are text, csv, json, parquet. See the docs of the DataStreamReader interface for a more up-to-date list, and supported options for each file format. Note that the files must be atomically placed in the given directory, which in most file systems, can be achieved by file move operations.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#input-sources
I have a spark-submit job I wrote that reads an in directory of json docs, does some processing on them using data frames, and then writes to an out directory. For some reason, though, it creates individual avro, parquet or json files when I use df.save or df.write methods.
In fact, I even used the saveAsTable method and it did the same thing with parquet.gz files in the hive warehouse.
It seems to me that this is inefficient and negates the use of a container file format. Is this right? Or is this normal behavior and what I'm seeing just an abstraction in HDFS?
If I am right that this is bad, how do I write the data frame from many files into a single file?
As #zero323 told its normal behavior due to many workers(to support fault tolerance).
I would suggest you to write all the records in parquet or avro file which has avro generic record using something like this
dataframe.write().mode(SaveMode.Append).
format(FILE_FORMAT).partitionBy("parameter1", "parameter2").save(path);
but it wont write in to single file but it will group similar kind of Avro Generic records to one file(may be less number of medium sized) files