Please, can you help me with this question below? The image with the error is available in the question.
I use Azure databricks for data engineering. Running the same code in databricks community runs without error, but in Azure returns the error that path was not found. Has anyone been through this situation?
I'm using sparkfiles.
cnae = 'https://servicodados.ibge.gov.br/api/v2/cnae/subclasses'
from pyspark import SparkFiles
spark.sparkContext.addFile(cnae)
cnaeDF = spark.read.option("multiLine", True).option("mode", "PERMISSIVE").json("file://"+SparkFiles.get("subclasses"))
pixel raster: rendered error message & stuff
It seems like a bug on runtime 10 as spark.sparkContext.addFile(cnae) add it to local storage:
/local_disk0/spark-f1411c54-0a2e-4138-a0ed-c2e6bbfe5ca4/userFiles-7616de8f-3e03-493c-89e6-50fa1f7324ca/subclasses
but SparkFiles.get("subclasses") want to read it from dbfs storage (I tried to add it all possible ways)...
but when magic command is run:
%sh
cp -r /local_disk0/spark-f1411c54-0a2e-4138-a0ed-c2e6bbfe5ca4 /dbfs/local_disk0/
then it is possible to read it without problem
Related
Looking for databricks python/pyspark code to copy azure blob from one container to another container older than 30 days
The copy code is simple as follows.
dbutils.fs.cp("/mnt/xxx/file_A", "/mnt/yyy/file_A", True)
The difficult part is checking blob modification time. According to the doc, the modification time will only get returned by using dbutils.fs.ls command on Databricks Runtime 10.2 or above. You may check the Runtime version using the command below.
spark.conf.get("spark.databricks.clusterUsageTags.sparkVersion")
The returned value will be Databricks Runtime followed by Scala versions.
If you get lucky with the version, you can can do something like:
import time
ts_now = time.time()
for file in dbutils.fs.ls('/mnt/xxx'):
if ts_now - file.modificationTime > 30 * 86400:
dbutils.fs.cp(f'/mnt/xxx/{file.name}', f'/mnt/yyy/{file.name}', True)
I am trying to create a CDK Code Construct for my python scripts, in stack i have added s3 and lambda.
When I am trying to execute cdk deploy, it is exiting after 0% progress or it is giving following error.
When i tried for s3 only it is working fine but when i added the lambda it is giving me error.
file_feed_lambda = _lambda.Function(
self, id='MyLambdaHandler001',
runtime=_lambda.Runtime.PYTHON_3_7,
code=_lambda.Code.asset('lambda'),
handler='lambda_function.lambda_handler',
)
bucket = s3.Bucket(self,
"FeedBucket-01")
Note : cdk diff and cdk synth are working properly
Apparently error which is showing is wrong i have updated the version of node and cdk to latest.
After update I have received the meaningful error which was socket time out.
After setting the proxy it worked for me.
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'm trying to encrypt a file on GCS with my own key using gsutil rewrite command (following https://cloud.google.com/storage/docs/using-encryption-keys)
As instructed I'm using a boto file including
[GSUtil]
encryption_key = p9syBNA0ycKxGotK3XinNZC6aCpdn3ZQ7WWOhKNgBaY=
It is working without a problem on small files but fails constantly on big ones.
I'm running the command:
gsutil rewrite -k -O gs://ywz-tmp/bigfile.txt
Is that a know issue?
Any workaround?
Feel free to use the file and key (both were generated for this post)
The fix for this issue should be in production now.
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.