I have some data in HDFS /user/Cloudera/Test/*. I am very well able to see the records by running hdfs -dfs -cat Test/*.
Now the same file, I need it to be read as RDD in scala.
I have tried the following in scala shell.
val file = sc.textFile("hdfs://quickstart.cloudera:8020/user/Cloudera/Test")
Then I have written some filter and for loop to read the words. But when I use the Println at last, it says file not found.
Can anyone please help me know what would be the HDFS url in this case.
Note: I am using Cloudera CDH5.0 VM
If you are trying to access your file in spark job then you can simply use URL
val file = sc.textFile("/user/Cloudera/Test")
Spark will automatically detect this file. You do not need to add localhost as prefix because spark job by default read them from HDFS directory.
Hope this solve your query.
Instead of using "quickstart.cloudera" and the port, use just the ip address:
val file = sc.textFile("hdfs://<ip>/user/Cloudera/Test")
Related
I am fairly new to pyspark and am trying to load data from a folder which contains multiple json files.However the load fails. Here is the code that I am using:
spark = SparkSession.builder.master("local[1]") \
.appName('SparkByExamples.com') \
.getOrCreate()
spark.read.json('file_directory/*')
I am getting error as :
Exception in thread "globPath-ForkJoinPool-1-worker-57" java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
I tried setting the path variables for hadoop and spark as well but still no use.
However, if I load a single file from the directory, it loads perfectly.
Can someone please tell me what is going wrong in this case.
I can successfully read all CSV under a directory without adding the asterik.
I think you should try
spark.read.json('file_directory/')
I've read a lot of topic on Internet on how to get working Spark with S3 still there's nothing working properly.
I've downloaded : Spark 2.3.2 with hadoop 2.7 and above.
I've copied only some libraries from Hadoop 2.7.7 (which matches Spark/Hadoop version) to Spark jars folder:
hadoop-aws-2.7.7.jar
hadoop-auth-2.7.7.jar
aws-java-sdk-1.7.4.jar
Still I can't use nor S3N nor S3A to get my file read by spark:
For S3A I have this exception:
sc.hadoopConfiguration.set("fs.s3a.access.key","myaccesskey")
sc.hadoopConfiguration.set("fs.s3a.secret.key","mysecretkey")
val file = sc.textFile("s3a://my.domain:8080/test_bucket/test_file.txt")
com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: AE203E7293ZZA3ED, AWS Error Code: null, AWS Error Message: Forbidden
Using this piece of Python, and some more code, I can list my buckets, list my files, download files, read files from my computer and get file url.
This code gives me the following file url:
https://my.domain:8080/test_bucket/test_file.txt?Signature=%2Fg3jv96Hdmq2450VTrl4M%2Be%2FI%3D&Expires=1539595614&AWSAccessKeyId=myaccesskey
How should I install / set up / download to get spark able to read and write from my S3 server ?
Edit 3:
Using debug tool in comment here's the result.
Seems like the issue is with a signature thing not sure what it means.
First you will need to download aws-hadoop.jar and aws-java-sdk.jar that matches the install of your spark-hadoop release and add them to the jars folder inside spark folder.
Then you will need to precise the server you will use and enable path style if your S3 server do not support dynamic DNS:
sc.hadoopConfiguration.set("fs.s3a.path.style.access","true")
sc.hadoopConfiguration.set("fs.s3a.endpoint","my.domain:8080")
#I had to change signature version because I have an old S3 api implementation:
sc.hadoopConfiguration.set("fs.s3a.signing-algorithm","S3SignerType")
Here's my final code:
sc.hadoopConfiguration.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
val tmp = sc.textFile("s3a://test_bucket/test_file.txt")
sc.hadoopConfiguration.set("fs.s3a.access.key","mykey")
sc.hadoopConfiguration.set("fs.s3a.secret.key","mysecret")
sc.hadoopConfiguration.set("fs.s3a.endpoint","my.domain:8080")
sc.hadoopConfiguration.set("fs.s3a.connection.ssl.enabled","true")
sc.hadoopConfiguration.set("fs.s3a.path.style.access","true")
sc.hadoopConfiguration.set("fs.s3a.signing-algorithm","S3SignerType")
tmp.count()
I would recommand to put most of the settings inside spark-defaults.conf:
spark.hadoop.fs.s3a.impl org.apache.hadoop.fs.s3a.S3AFileSystem
spark.hadoop.fs.s3a.path.style.access true
spark.hadoop.fs.s3a.endpoint mydomain:8080
spark.hadoop.fs.s3a.connection.ssl.enabled true
spark.hadoop.fs.s3a.signing-algorithm S3SignerType
One of the issue I had has been to set spark.hadoop.fs.s3a.connection.timeout to 10 but this value is set in millisecond prior to Hadoop 3 and it gives you a very long timeout; error message would appear 1.5 minute after the attempt to read a file.
PS:
Special thanks to Steve Loughran.
Thank you a lot for the precious help.
I am using Scala in Apache Spark. I am very new to the platform. I cannot save a collection to file using the following code:
val x = sc.parallelize(Array(2,4,1))
x.saveAsTextFile("/temp/demo")
This is most likely a problem about permissions.
Try to write to a directory you have write permissions, e.g. your home.
I'm trying to monitor a repository in HDFS to read and process data in files copied to it (to copy files from local system to HDFS I use hdfs dfs -put ), sometimes it generates the problem : Spark Streaming: java.io.FileNotFoundException: File does not exist: .COPYING so I read the problems in forums and the question here Spark Streaming: java.io.FileNotFoundException: File does not exist: <input_filename>._COPYING_
According to what I read the problem is linked to Spark streaming reading the file before it finishes being copied in HDFS and on Github :
https://github.com/maji2014/spark/blob/b5af1bdc3e35c53564926dcbc5c06217884598bb/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala , they say that they corrected the problem but only for FileInputDStream as I could see but I'm using textFileStream
When I tried to use FileInputDStream the IDE throws an error the Symbol is not accessible from this place.
Does anyone know how to filter out the files that are still COPYING because I tried :
var lines = ssc.textFileStream(arg(0)).filter(!_.contains("_COPYING_")
but that didn't work and it's expected because the filter should be applied on the name of the file process I guess which I can't access
As you can see I did plenty of research before asking the question but didn't get lucky ,
Any help please ?
So I had a look: -put is the wrong method. Look at the final comment: you have to use -rename in your shell script to have an atomical transaction on the HDFS.
From my machine, I've configured the hadoop core-site.xml to recognize the gs:// scheme and added gcs-connector-1.2.8.jar as a Hadoop lib. I can run hadoop fs -ls gs://mybucket/ and get the expected results. However, if I try to do the analogue from java using:
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
FileStatus[] status = fs.listStatus(new Path("gs://mybucket/"));
I get the files under root in my local HDFS instead of in gs://mybucket/, but with those files prepended with gs://mybucket. If I modify the conf with conf.set("fs.default.name", "gs://mybucket"); before obtaining the fs, then I can see the files on GCS.
My question is:
1. Is this expected behavior?
2. Is there a disadvantage to using this hadoop FileSystem api as opposed to the google cloud storage client api?
As to your first question, "expected" is questionable, but I think I can at least explain. When FileSystem.get() is used the default FileSystem is returned and by default that is HDFS. My guess is that the HDFS client (DistributedFileSystem) has code to prepend scheme + authority automatically to all files in the filesystem.
Instead of using FileSystem.get(conf), try
FileSystem gcsFs = new Path("gs://mybucket/").getFS(conf)
On disadvantages, I could probably argue that if you end up needing to access the object-store directly then you'll end up writing code to interact with the storage APIs directly anyways (and there are things that do not translate very well to the Hadoop FS API, e.g., object composition, complex object write preconditions other than simple object overwrite protection, etc).
I am admittedly biased (working on the team), but if you're intending to use GCS from Hadoop Map/Reduce, from Spark, etc, the GCS connector for Hadoop should be a fairly safe bet.