I am implementing the file source in Spark Structures Streaming and want to process the same file name again if the file has been modified. Basically an update to the file. Currently right now Spark will not process the same file name again once processed. Seems limited compared to Spark Streaming with Dstream. Is there a way to do this? Spark Structured Streaming doesn't document this anywhere it only process new file with different names.
I believe this is somewhat of an anti pattern, but you may be able to dig through the checkpoint data and remove the entry for that original file.
Try looking for the original file name in the /checkpoint/sources// files delete the file or entry. That might cause the stream to pick up the file name again. I haven't tried this myself.
If this is a one time manual update, I would just change the file name to something new and drop it in the source directory. This approach won't be maintainable or automated.
Related
I am regularly uploading data on a parquet file which I use for my data analysis using and I want to ensure that the data in my parquet file are not duplicated. The command I use to do this is:
df.write.parquet('my_directory/', mode='overwrite')
Does this ensure that all my non-duplicated data will not be deleted accidentally at some point.
Cheers
The Overwrite as the name implies it rewrites the whole data into the path that you specify.
Rewrite in the sense, the data that is available in the df will be written to the path by removing the old files available if any in the path specified. So you can consider this as a DELETE and LOAD scenario, where you read all the records from the datasource lets say Oracle and then do your transformations and delete the parquet and write the new content in the dataframe.
The Dataframe.write supports a list of modes to write the content to the target.
mode –
specifies the behavior of the save operation when data already exists.
append: Append contents of this DataFrame to existing data.
overwrite: Overwrite existing data.
ignore: Silently ignore this operation if data already exists.
error or errorifexists (default case): Throw an exception if data already
exists.
If your intention is to add new data to the parquet then you have to do with append but this brings in a new challenge of duplicates if you are dealing with changing data.
Does this ensure that all my non-duplicated data will not be deleted
accidentally at some point.
No. mode='overwrite' only ensures that if data already exists in the target directory, then the existing data would be deleted and new data would be written (analogous to truncate and load in RDBMS tables).
If you want to ensure there is no record level duplicates, the easiest thing to do is this:
df1 = df.dropDuplicates()
df1.write.parquet('my_directory/', mode='overwrite')
I am trying to read the parquet file from hdfs location, do some transformations and overwrite the file in the same location. I had to overwrite the file in the same location because I had to run the same code multiple times.
Here is the code I have written
val df = spark.read.option("header", "true").option("inferSchema", "true").parquet("hdfs://master:8020/persist/local/")
//after applying some transformations lets say the final dataframe is transDF which I want to overwrite at the same location.
transDF.write.mode("overwrite").parquet("hdfs://master:8020/persist/local/")
Now the problem is before reading the parquet file from the given location, spark for some reason I believe it deletes the file at the given location because of overwrite mode. So when executing the code I get the following error.
File does not exist: hdfs://master:8020/persist/local/part-00000-e73c4dfd-d008-4007-8274-d445bdea3fc8-c000.snappy.parquet
Any suggestions on how to solve this problem? Thanks.
The simple answer is that you cannot overwrite what you are reading. The reason behind this is that overwrite would need to delete everything, however, since spark is working in parallel, some portions might still be reading at the time. Furthermore, even if everything was read, spark needs the original file to recalculate tasks which are failed.
Since you need the input for multiple iterations, I would simply make the name of the input and the output into arguments for the function that does one iteration and delete the previous iteration only once the writing is successful.
This is what I have tried and it worked. My requirement was almost same. It was upsert option.
by the way, spark.conf.set('spark.sql.sources.partitionOverwriteMode', 'dynamic') property was set. Even then also the Transform job was failing
Took a backup of S3 folder (final curated layer) before every batch operation
using the dataframe operations, first delete the S3 parquet file location before overwrite
then Append to the particular location
Previously the entire job was running for 1.5Hrs and failing frequently. Now it's taking 10-15mins for the entire operations
I know parquet files store meta data, but is it possible to add custom metadata to a parquet file, using Scala (preferably) using Spark?
The idea is that I store many similar structured parquet files in a Hadoop storage, but each has a uniquely named source (String field, also present as column in the parquet file), however, I'd like to access this information without creating the overhead of actually reading the parquet and possibly even removing this redundant column from the parquet.
I really don't want to put this info in a filename, so my best option is now just to read the first line of each parquet and use the source column as String field.
It works, but I was just wondering if there is a better way.
I have multiple files stored in HDFS, and I need to merge them into one file using spark. However, because this operation is done frequently (every hour). I need to append those multiple files to the source file.
I found that there is the FileUtil that gives the 'copymerge' function. but it doesn't allow to append two files.
Thank you for your help
You can do this with two methods:
sc.textFile("path/source", "path/file1", "path/file2").coalesce(1).saveAsTextFile("path/newSource")
Or as #Pushkr has proposed
new UnionRDD(sc, Seq(sc.textFile("path/source"), sc.textFile("path/file1"),..)).coalesce(1).saveAsTextFile("path/newSource")
If you don't want to create a new source and overwrite the same source every hour, you can use dataframe with save mode overwrite ( How to overwrite the output directory in spark)
What is the best way to import data from few csv files in Spring Batch? I mean one csv file responds to one table in database.
I created one batch configuration class for each table and every table has its own job and step.
Is there any solution to do this in more elegant way?
There's a variety of ways you could tackle the problem, but the simplest job would look something like:
FlatFileItemWriter reader with a DelmitedLineTokenizer and BeanWrapperFieldSetMapper to read the file
Processor if you need to do any additional validation/filtering/transformation
JDBCBatchItemWriter to insert/update the target table
Here's an example that includes more information around specific dependencies, config, etc. The example uses context file config rather than annotation-based, but it should be sufficient to show you the way.
A more complex solution might be a single job with a partitioned step that scans the input folder for files and, leveraging reference table/schema information, creates a reader/writer step for each file that it finds.
You also may want to consider what to do with the files once you're done... Delete them? Compress them?