I have a dataset in parquet in S3 partitioned by date (dt) with oldest date stored in AWS Glacier to save some money. For instance, we have...
s3://my-bucket/my-dataset/dt=2017-07-01/ [in glacier]
...
s3://my-bucket/my-dataset/dt=2017-07-09/ [in glacier]
s3://my-bucket/my-dataset/dt=2017-07-10/ [not in glacier]
...
s3://my-bucket/my-dataset/dt=2017-07-24/ [not in glacier]
I want to read this dataset, but only the a subset of date that are not yet in glacier, eg:
val from = "2017-07-15"
val to = "2017-08-24"
val path = "s3://my-bucket/my-dataset/"
val X = spark.read.parquet(path).where(col("dt").between(from, to))
Unfortunately, I have the exception
java.io.IOException: com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception: The operation is not valid for the object's storage class (Service: Amazon S3; Status Code: 403; Error Code: InvalidObjectState; Request ID: C444D508B6042138)
I seems that spark does not like partitioned dataset when some partitions are in Glacier. I could always read specifically each date, add the column with current date and reduce(_ union _) at the end, but it is ugly like hell and it should not be necessary.
Is there any tip to read available data in the datastore even with old data in glacier?
Error you are getting not related to Apache spark , you are getting exception because of Glacier service in short S3 objects in the Glacier storage class are not accessible in the same way as normal objects, they need to be retrieved from Glacier before they can be read.
Apache Spark cannot handle directly glacier storage TABLE/PARTITION mapped to an S3 .
java.io.IOException:
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception:
The operation is not valid for the object's storage class (Service:
Amazon S3; Status Code: 403; Error Code: InvalidObjectState; Request
ID: C444D508B6042138)
When S3 moves any objects from S3 storage classes
STANDARD,
STANDARD_IA,
REDUCED_REDUNDANCY
to GLACIER storage class, you have object S3 has stored in Glacier which is not visible
to you and S3 will bill only Glacier storage rates.
It is still an S3 object, but has the GLACIER storage class.
When you need to access one of these objects, you initiate a restore,
which temporary copy into S3 .
Move data into S3 bucket read into Apache Spark will resolve your issue.
https://aws.amazon.com/s3/storage-classes/
Note : Apache Spark , AWS athena etc cannot read object directly from glacier if you try will get 403 error.
If you archive objects using the Glacier storage option, you must
inspect the storage class of an object before you attempt to retrieve
it. The customary GET request will work as expected if the object is
stored in S3 Standard or Reduced Redundancy (RRS) storage. It will
fail (with a 403 error) if the object is archived in Glacier. In this
case, you must use the RESTORE operation (described below) to make
your data available in S3.
https://aws.amazon.com/blogs/aws/archive-s3-to-glacier/
403 error is due to the fact you can not read object that is archieve in Glacier, source
Reading Files from Glacier
If you want to read files from Glacier, you need to restore them to s3 before using them in Apache Spark, a copy will be available on s3 for the time mentioned during restore command, for details see here, you can use S3 console, cli or any language to do that too
Discarding some Glacier files that you do not want to restore
Let's say you do not want to restore all the files from Glacier and discard them during processing, from Spark 2.1.1, 2.2.0 you can ignore those files (with IO/Runtime Exception), by setting spark.sql.files.ignoreCorruptFiles to true source
If you define your table through Hive, and use the Hive metastore catalog to query it, it won't try to go onto the non selected partitions.
Take a look at the spark.sql.hive.metastorePartitionPruning setting
try this setting:
ss.sql("set spark.sql.hive.caseSensitiveInferenceMode=NEVER_INFER")
or
add the spark-defaults.conf config:
spark.sql.hive.caseSensitiveInferenceMode NEVER_INFER
The S3 connectors from Amazon (s3://) and the ASF (s3a://) don't work with Glacier. Certainly nobody tests s3a against glacier. and if there were problems, you'd be left to fix them yourself. Just copy the data into s3 or onto local HDFS and then work with it there
Related
I have a Spark DataFrame that I need to write to an S3 Object Lock enabled bucket.
A simple write results in this error
df.write.parquet(output_path)
com.amazonaws.services.s3.model.AmazonS3Exception:
Content-MD5 HTTP header is required for Put Object requests with Object Lock parameters
Any ideas how can I solve this?
There are ways to work this with boto3 type uploads, but how to do it with spark.df.write
s3_client.put_object(
Bucket=<S3_BUCKET_NAME>,
Key=<KEY>,
Body=open(<FILE_NAME>, "rb"),
ContentMD5=<MD5_HASH>
)
not supported in hadoop's s3a connector I'm afraid.
As is usual in OSS projects, your participation will help there: https://issues.apache.org/jira/browse/HADOOP-15224. That JIRA was created in 2018 and hasn't had attention as nobody new it was actually needed. We could maybe revisit that.
Don't know about the EMR s3 connector.
I have pipeline using lambda and EMR, where I read csv from one s3 account A and write parquet to another s3 in account B.
I created EMR in account B and has access to s3 in account B.
I cannot add account A s3 bucket access in EMR_EC2_DefaultRole(as this account is enterprise wide data storage), so i use accessKey, secret key to access account A s3 bucket.This is done through congnito token.
METHOD1
I am using fs.s3 protocol to read csv from s3 from account A and writing to s3 on account B.
I have pyspark code which reads from s3 (A) and write to parquet s3 (B) I submit job 100 of jobs at time.This pyspark code runs in EMR.
Reading using following setting
hadoop_config = sc._jsc.hadoopConfiguration()
hadoop_config.set("fs.s3.awsAccessKeyId", dl_access_key)
hadoop_config.set("fs.s3.awsSecretAccessKey", dl_secret_key)
hadoop_config.set("fs.s3.awsSessionToken", dl_session_key)
spark_df_csv = spark_session.read.option("Header", "True").csv("s3://somepath")
Writing:
I am using s3a protocol s3a://some_bucket/
It works but sometimes i see
_temporary folder present in s3 bucket and not all csv converted to parquet
When i enable EMR concurrency to 256 (EMR-5.28) and submit 100 jobs it this i get _temporary rename error.
Issues:
This method creates temporary folder and sometimes it doesn't deletes it.I can see _temporary folder in s3 bucket.
when i enable EMR concurrency (EMR latest versin5.28) it allows to run steps in parallel, i get rename _temporary error for some of the files.
METHOD2:
I feel s3a is not good for parallel job.
So i want to read and write using fs.s3 as it has better file commiters.
So i did this initially i set hadoop configuration as above to account A and then unset the configuration, so that it can access default account B eventually s3 bucket.
In this way
hadoop_config = sc._jsc.hadoopConfiguration()
hadoop_config.unset("fs.s3.awsAccessKeyId")
hadoop_config.unset("fs.s3.awsSecretAccessKey")
hadoop_config.unset("fs.s3.awsSessionToken")
spark_df_csv.repartition(1).write.partitionBy(['org_id', 'institution_id']). \
mode('append').parquet(write_path)
Issues:
This works but the issue is let say if i trigger lambda which in turn submit job for 100 files (in loop) some 10 odd files result in access denied while writing file to s3 bucket.
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\n ... 1 more\nCaused by: com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception: Access Denied (Service:
This could be because of either this unset is not working sometimes or
because of parallel run Spark context/session set unset happening in paralleling? I mean spark context for one job is unsettling the hadoop configuration and other is setting it, which may cause this issue, though not sure how spark context works in parallel.
Isn't each job has separate Spark context and session.
Please suggest alternatives for my situation.
I have requirement to load data from one Redshift cluster to another, each in different region.
For same first creating table in target schema and then on source running UNLOAD command, which will put files in an S3 bucket.
To load data from S3 to target Redshift schema, I will use python code in AWS Lambda. The AWS Lambda function will trigger on the S3 path where all files will come when the unload command is executed.
After ingestion I need to run a transformation query that will put data in fact and dimension tables. How can I know the status for target table that a particular table loaded successfully?
The Lambda python code will triggered for each file, and will copy data to the target table. If I know the status I will issue the transformation query.
Is there any feature in AWS Lambda that will help me to know the status of the COPY command?
I have an EC2 instance running pyspark and I'm able to connect to it (ssh) and run interactive code within a Jupyter Notebook.
I have a S3 bucket with a csv file that I want to read, when I attempt to read it with:
spark = SparkSession.builder.appName('Basics').getOrCreate()
df = spark.read.csv('https://s3.us-east-2.amazonaws.com/bucketname/filename.csv')
Which throws a long Python error message and then something related to:
Py4JJavaError: An error occurred while calling o131.csv.
Specify S3 path along with access key and secret key as following:
's3n://<AWS_ACCESS_KEY_ID>:<AWS_SECRET_ACCESS_KEY>#my.bucket/folder/input_data.csv'
Access key-related information can be introduced in the typical username + password manner for URLs. As a rule, the access protocol should be s3a, the successor to s3n (see Technically what is the difference between s3n, s3a and s3?). Putting this together, you get
spark.read.csv("s3a://<AWS_ACCESS_KEY_ID>:<AWS_SECRET_ACCESS_KEY>#bucketname/filename.csv")
As an aside, some Spark execution environments, e.g., Databricks, allow S3 buckets to be mounted as part of the file system. You can do the same when you build a cluster using something like s3fs.
I am trying to run my spark job on Amazon EKS cluster. My spark job required some static data (reference data) at each data nodes/worker/executor and this reference data is available at S3.
Can somebody kindly help me to find out a clean and performant solution to mount S3 bucket on pods ?
S3 API is an option and I am using it for my input records and output results. But "Reference data" is static data so I dont want to download it in each run/execution of my spark job. In first run job will download the data and upcoming jobs will check if data is already available locally and there is no need to download it again.
We recently opensourced a project that looks to automate this steps for you: https://github.com/IBM/dataset-lifecycle-framework
Basically you can create a dataset:
apiVersion: com.ie.ibm.hpsys/v1alpha1
kind: Dataset
metadata:
name: example-dataset
spec:
local:
type: "COS"
accessKeyID: "iQkv3FABR0eywcEeyJAQ"
secretAccessKey: "MIK3FPER+YQgb2ug26osxP/c8htr/05TVNJYuwmy"
endpoint: "http://192.168.39.245:31772"
bucket: "my-bucket-d4078283-dc35-4f12-a1a3-6f32571b0d62"
region: "" #it can be empty
And then you will get a pvc you can mount in your pods
in general, you just don't do that. You should instead interact directly with S3 API to retrieve/store what you need (probably via some tools like aws cli).
As you run in AWS, you can have IAM configured in a way that your nodes can access particular data authorized on "infrastructure" level, or you can provide S3 access tokens via secrets/confogmaps/env etc.
S3 is not a filesystem, so don't expect it to behave like one (even if there are FUSE clients that emulate FS for your needs, this is rarely the right solution)