The HDFS hdfs dfs command is quite unwieldily. For example, every time you want to list files, you need to type hdfs dfs -ls. Is there a straightforward way, other than command-line aliases, to get a more usable command? One for which you don't need to type a dash before every command?
Right now the only idea I have is:
alias dls="hdfs dfs -ls"
Related
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 have multiple parquet files in different directories and would like to read them in sequence by parameterization in Scala.
The problem is the schema information is not standard and column names vary drastically.
For example: what might be called load_date in 1 directory can be called load_dt in a parquet file from another directory.
So i'm being forced to use different read.parquet().select statements for each directory. (there are more than 30)
Is there a way by which i can use the same statement and switch schema information based on a parameter of some sort? Maybe like a client name or ID?
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)
I used saveAsTextFile("outputPath") to save file using scala in spark.
I want to read the saved file one by one like getline command in c or java from HDFS.
How can I use this?
Is it possible to read the file one by one?
I am trying to save an RDD to a text file. My instance of Spark is running on Linux and connected to Azure Blob
val rdd = sc.textFile("wasb:///HdiSamples/HdiSamples/SensorSampleData/hvac/HVAC.csv")
//find the rows which have only one digit in the 7th column in the CSV
val rdd1 = rdd.filter(s => s.split(",")(6).length() == 1)
rdd1.saveAsTextFile("wasb:///HVACOut")
When I look at the output, it is not as a single text file but as a series of application/octet-stream files in a folder called HVACOut.
How can I output it as a single text file instead?
Well I am not sure you can get just one file without a directory. If you do
rdd1 .coalesce(1).saveAsTextFile("wasb:///HVACOut")
you will get one file inside a directory called "HVACOut" the file should like something like part-00001. This is because your rdd is a disturbed on in your cluster with what they call partitions. When you do a call to save (all save functions) it is going to make a file per partition. So by call coalesce(1) your telling you want 1 partition.
Hope this helps.
After finished provisioning a Apache Spark cluster on Azure HDInsight, you can go to the built-in Jupyter notebook for your cluster at: https://YOURCLUSTERNAME.azurehdinsight.net/jupyter.
There you will find sample notebook with example on how to do this.
Specifically, for scala, you can go to the notebook named "02 - Read and write data from Azure Storage Blobs (WASB) (Scala)".
Copying some of the code and comments here:
Note:
Because CSV is not natively supported by Spark, so there is no built-in way to write an RDD to a CSV file. However, you can work around this if you want to save your data as CSV.
Code:
csvFile.map((line) => line.mkString(",")).saveAsTextFile("wasb:///example/data/HVAC2sc.csv")
Hope this helps!