I cannot seem to find any documentation, but I want to understand how I can do the following:
We have Spark pipelines that write data to S3 in the standard format where they write several part-... files and the _SUCCESS file to the folder.
We then have further Spark pipelines that read data from those S3 buckets.
We would like to have the pipelines automatically throw an exception (fail) if they try to read from a folder that does not have the _SUCCESS file.
We can create some sort of user-created function to manage this test, but it seems so common that I figured there must be an easy Spark-native way to generate this exception if the file is not found.
Is there such a native Spark way to trigger that exception?
The only way I can think of is using ,
boolean isExists=getFileSystem(spark.sparkContext().hadoopConfiguration())).exists(new Path("location of _SUCCESS file"));
if this returns false throw an exception.
Related
I am using Spark to write and read parquet files on AWS S3. I have parquet files which stored in
's3a://mybucket/file_name.parquet/company_name=company_name/record_day=2019-01-01 00:00:00'
partitioned by 'company_name' and 'record_day'
I want to write basic pipeline to update my parquet files on regularly basis by 'record_day'. To do this, i am gonna use overwrite mode:
df.write.mode('overwrite').parquet(s3a://mybucket/file_name.parquet/company_name='company_name'/record_day='2019-01-01 00:00:00')
But am getting unexpected error 'java.net.URISyntaxException: Relative path in absolute URI: key=2019-01-01 00:00:00'.
I spent several hours searching for the problem but found no solution(. For some tests, I replaced the 'overwrite' parameter with 'append', and everything works fine. I also made a simple dataframe and overwrite mode also works fine on it. I know that i can solve my problem in a different way, by deleting and then writing the particular part, but I would like to understand what the cause of the error is?
Spark 2.4.4 Hadoop 2.8.5
Appreciate any help.
I had the same error and the my solution was to remove the : part in the date.
I am using spark streaming and I want to save each batch of spark streaming on my local in Avro format. I have used saveAsNewAPIHadoopFile to save data in Avro format. This works well. But it overwrites the existing file. Next batch data will overwrite the old data. Is there any way to save Avro file in common directory? I tried by adding some properties of Hadoop job conf for adding a prefix in the file name. But not working any properties.
dstream.foreachRDD {
rdd.saveAsNewAPIHadoopFile(
path,
classOf[AvroKey[T]],
classOf[NullWritable],
classOf[AvroKeyOutputFormat[T]],
job.getConfiguration()
)
}
Try this -
You can make your process split into 2 steps :
Step-01 :- Write Avro file using saveAsNewAPIHadoopFile to <temp-path>
Step-02 :- Move file from <temp-path> to <actual-target-path>
This will definitely solve your problem for now. I will share my thoughts if I get to fulfill this scenario in one step instead of two.
Hope this is helpful.
I have some results from a Spark application saved in the HDFS as files called part-r-0000X (X= 0, 1, etc.). And, because I want to join the whole content in a file, I'm using the following command:
hdfs dfs -getmerge srcDir destLocalFile
The previous command is used in a bash script which makes empty the output directory (where the part-r-... files are saved) and, inside a loop, executes the above getmerge command.
The thing is I need to use the resultant file in another Spark program which need that merged file as input in the HDFS. So I'm saving it as local and then I upload it to the HDFS.
I've thought another option which is write the file from the Spark program in this way:
outputData.coalesce(1, false).saveAsTextFile(outPathHDFS)
But I've read coalesce() doesn't help with the performance.
Any other ideas? suggestions? Thanks!
You wish to merge all the files into a single one so that you can load all the files at once into a Spark rdd, is my guess.
Let the files be in Parts(0,1,....) in HDFS.
Why not load it with wholetextFiles, which actually does what you need.
wholeTextFiles(path, minPartitions=None, use_unicode=True)[source]
Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file.
If use_unicode is False, the strings will be kept as str (encoding as utf-8), which is faster and smaller than unicode. (Added in Spark 1.2)
For example, if you have the following files:
hdfs://a-hdfs-path/part-00000 hdfs://a-hdfs-path/part-00001 ... hdfs://a-hdfs-path/part-nnnnn
Do rdd = sparkContext.wholeTextFiles(“hdfs://a-hdfs-path”), then rdd contains:
(a-hdfs-path/part-00000, its content) (a-hdfs-path/part-00001, its content) ... (a-hdfs-path/part-nnnnn, its content)
Try SPARK BucketBy.
This is a nice feature via df.write.saveAsTable(), but this format can only be read by SPARK. Data shows up in Hive metastore but cannot be read by Hive, IMPALA.
The best solution that I've found so far was:
outputData.saveAsTextFile(outPath, classOf[org.apache.hadoop.io.compress.GzipCodec])
Which saves the outputData in compressed part-0000X.gz files under the outPath directory.
And, from the other Spark app, it reads those files using this:
val inputData = sc.textFile(inDir + "part-00*", numPartition)
Where inDir corresponds to the outPath.
Scala 2.12 and Spark 2.2.1 here. I used the following code to write the contents of a DataFrame to S3:
myDF.write.mode(SaveMode.Overwrite)
.parquet("s3n://com.example.mybucket/mydata.parquet")
When I go to com.example.mybucket on S3 I actually see a directory called "mydata.parquet", as well as file called "mydata.parquet_$folder$"!!! If I go into the mydata.parquet directory I see two files under it:
_SUCCESS; and
part-<big-UUID>.snappy.parquet
Whereas I was just expecting to see a single file called mydata.parquet living in the root of the bucket.
Is something wrong here (if so, what?!?) or is this expected with the Parquet file format? If its expected, which is the actual Parquet file that I should read from:
mydata.parquet directory?; or
mydata.parquet_$folder$ file?; or
mydata.parquet/part-<big-UUID>.snappy.parquet?
Thanks!
The mydata.parquet/part-<big-UUID>.snappy.parquet is the actual parquet data file. However, often tools like Spark break data sets into multiple part files, and expect to be pointed to a directory that contains multiple files. The _SUCCESS file is a simple flag indicating that the write operation has completed.
According to the api to save the parqueat file it saves inside the folder you provide. Sucess is incidation that the process is completed scuesffuly.
S3 create those $folder if you write directly commit to s3. What happens is it writes to temporory folders and copies to the final destination inside the s3. The reason is there no concept of rename.
Look at the s3-distcp and also DirectCommiter for performance issue.
The $folder$ marker is used by s3n/amazon's emrfs to indicate "empty directory". ignore.
The _SUCCESS file is, as the others note, a 0-byte file. ignore
all other .parquet files in the directory are the output; the number you end up with depends on the number of tasks executed on the input
When spark uses a directory (tree) as a source of data, all files beginning with _ or . are ignored; s3n will strip out those $folder$ things too. So if you use the path for a new query, it will only pick up that parquet file.
I have a directory in an azure data lake that has the following path:
'adl://home/../psgdata/clusters/iptiqadata-prod-cluster-eus2-01/psgdata/mib'
Within this directory there are a number of other directories (50) that have the format 20190404.
The directory 'adl://home/../psgdata/clusters/iptiqadata-prod-cluster-eus2-01/psgdata/mib/20180404' contains 100 or so xml files which I am working with.
I can create an rdd for each of the sub-folders which works fine, but ideally I want to pass only the top path, and have spark recursively find the files. I have read other SO posts and tried using a wildcard thus:
pathWild = 'adl://home/../psgdata/clusters/iptiqadata-prod-cluster-eus2-01/psgdata/mib/*'
rdd = sc.wholeTextFiles(pathWild)
rdd.count()
But it just freezes and does nothing at all, seems to completely destroy the kernel. I am working in Jupyter on Spark 2.x. New to spark. Thanks!
Try this:
pathWild = 'adl://home/../psgdata/clusters/iptiqadata-prod-cluster-eus2-01/psgdata/mib/*/*'