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https://issues.apache.org/jira/browse/SPARK-21374?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16093417#comment-16093417
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Andrey Taptunov edited comment on SPARK-21374 at 7/19/17 5:19 PM:
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[~ste...@apache.org]
Indeed, while working on PR and debugging the code I see that code works only
accidentally because caching is turned on by default.
1. Thanks for the advice. I doubt that it's related to type of the filesystem -
I've only mentioned filesystem explicitly to show why "awsAcceesKeyId" not
"access.key" is used with "s3" scheme in the example. Sorry for the confusion.
2. Unfortunately, it's not that easy - this example is only simplified version
of what happens in my project. We don't have information about which buckets
user will try to access in interactive mode so I can not enumerate them all in
configuration.
was (Author: andrey.t):
[~ste...@apache.org]
Indeed, while working on PR and debugging the code I see that current solution
works because caching is turned on by default.
1. Thanks for the advice. I doubt that it's related to type of the filesystem -
I've only mentioned filesystem explicitly to show why "awsAcceesKeyId" not
"access.key" is used with "s3" scheme in the example. Sorry for the confusion.
2. Unfortunately, it's not that easy - this example is only simplified version
of what happens in my project. We don't have information about which buckets
user will try to access in interactive mode so I can not enumerate them all in
configuration.
> Reading globbed paths from S3 into DF doesn't work if filesystem caching is
> disabled
> ------------------------------------------------------------------------------------
>
> Key: SPARK-21374
> URL: https://issues.apache.org/jira/browse/SPARK-21374
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.0.2, 2.1.1
> Reporter: Andrey Taptunov
>
> *Motivation:*
> In my case I want to disable filesystem cache to be able to change S3's
> access key and secret key on the fly to read from buckets with different
> permissions. This works perfectly fine for RDDs but doesn't work for DFs.
> *Example (works for RDD but fails for DataFrame):*
> {code:java}
> import org.apache.spark.SparkContext
> import org.apache.spark.SparkConf
> import org.apache.spark.sql.SparkSession
> object SimpleApp {
> def main(args: Array[String]) {
> val awsAccessKeyId = "something"
> val awsSecretKey = "something else"
> val conf = new SparkConf().setAppName("Simple
> Application").setMaster("local[*]")
> val sc = new SparkContext(conf)
> sc.hadoopConfiguration.set("fs.s3.awsAccessKeyId", awsAccessKeyId)
> sc.hadoopConfiguration.set("fs.s3.awsSecretAccessKey", awsSecretKey)
> sc.hadoopConfiguration.setBoolean("fs.s3.impl.disable.cache",true)
>
> sc.hadoopConfiguration.set("fs.s3.impl","org.apache.hadoop.fs.s3native.NativeS3FileSystem")
> sc.hadoopConfiguration.set("fs.s3.buffer.dir","/tmp")
> val spark = SparkSession.builder().config(conf).getOrCreate()
> val rddFile = sc.textFile("s3://bucket/file.csv").count // ok
> val rddGlob = sc.textFile("s3://bucket/*").count // ok
> val dfFile = spark.read.format("csv").load("s3://bucket/file.csv").count
> // ok
>
> val dfGlob = spark.read.format("csv").load("s3://bucket/*").count
> // IllegalArgumentExcepton. AWS Access Key ID and Secret Access Key must
> be specified as the username or password (respectively)
> // of a s3 URL, or by setting the fs.s3.awsAccessKeyId or
> fs.s3.awsSecretAccessKey properties (respectively).
>
> sc.stop()
> }
> }
> {code}
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