scala - Spark: Reading S3 file exception with Spark 1.5.2 prebuilt with hadoop-2.6 -


i trying read existing file spark based application. here snippet:

sc.hadoopconfiguration.set("fs.s3.awsaccesskeyid", "mykey") sc.hadoopconfiguration.set("fs.s3.awssecretaccesskey", "mysecret")  val = sc.textfile("s3://mybucket/tnrealtime/output/2016/01/27/22/45/00/a.txt").map{line => line.split(",")} val b = a.collect // **error** producing statement

i getting exception:

org.apache.hadoop.mapred.invalidinputexception: input path not exist: s3://snapdeal-personalization-dev-us-west-2/tnrealtime/output/2016/01/27/22/45/00/a.txt     @ org.apache.hadoop.mapred.fileinputformat.liststatus(fileinputformat.java:251)     @ org.apache.hadoop.mapred.fileinputformat.getsplits(fileinputformat.java:270)     @ org.apache.spark.rdd.hadooprdd.getpartitions(hadooprdd.scala:207)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:239)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:237)     @ scala.option.getorelse(option.scala:120)     @ org.apache.spark.rdd.rdd.partitions(rdd.scala:237)     @ org.apache.spark.rdd.mappartitionsrdd.getpartitions(mappartitionsrdd.scala:35)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:239)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:237)     @ scala.option.getorelse(option.scala:120)     @ org.apache.spark.rdd.rdd.partitions(rdd.scala:237)     @ org.apache.spark.rdd.mappartitionsrdd.getpartitions(mappartitionsrdd.scala:35)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:239)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:237)     @ scala.option.getorelse(option.scala:120)     @ org.apache.spark.rdd.rdd.partitions(rdd.scala:237)     @ org.apache.spark.sparkcontext.runjob(sparkcontext.scala:1921)     @ org.apache.spark.rdd.rdd$$anonfun$collect$1.apply(rdd.scala:909)     @ org.apache.spark.rdd.rddoperationscope$.withscope(rddoperationscope.scala:147)     @ org.apache.spark.rdd.rddoperationscope$.withscope(rddoperationscope.scala:108)     @ org.apache.spark.rdd.rdd.withscope(rdd.scala:310)     @ org.apache.spark.rdd.rdd.collect(rdd.scala:908)     @ com.snapdeal.pears.trending.trendingdecay$.load(trendingdecay.scala:68)

strangely, when tried same snippet spark-shell, different error:

java.io.ioexception: no filesystem scheme: s3     @ org.apache.hadoop.fs.filesystem.getfilesystemclass(filesystem.java:2584)     @ org.apache.hadoop.fs.filesystem.createfilesystem(filesystem.java:2591)     @ org.apache.hadoop.fs.filesystem.access$200(filesystem.java:91)     @ org.apache.hadoop.fs.filesystem$cache.getinternal(filesystem.java:2630)     @ org.apache.hadoop.fs.filesystem$cache.get(filesystem.java:2612)     @ org.apache.hadoop.fs.filesystem.get(filesystem.java:370)     @ org.apache.hadoop.fs.path.getfilesystem(path.java:296)     @ org.apache.hadoop.mapred.fileinputformat.singlethreadedliststatus(fileinputformat.java:256)     @ org.apache.hadoop.mapred.fileinputformat.liststatus(fileinputformat.java:228)     @ org.apache.hadoop.mapred.fileinputformat.getsplits(fileinputformat.java:313)     @ org.apache.spark.rdd.hadooprdd.getpartitions(hadooprdd.scala:207)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:239)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:237)     @ scala.option.getorelse(option.scala:120)     @ org.apache.spark.rdd.rdd.partitions(rdd.scala:237)     @ org.apache.spark.rdd.mappartitionsrdd.getpartitions(mappartitionsrdd.scala:35)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:239)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:237)     @ scala.option.getorelse(option.scala:120)     @ org.apache.spark.rdd.rdd.partitions(rdd.scala:237)     @ org.apache.spark.rdd.mappartitionsrdd.getpartitions(mappartitionsrdd.scala:35)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:239)     @ org.apache.spark.rdd.rdd$$anonfun$partitions$2.apply(rdd.scala:237)     @ scala.option.getorelse(option.scala:120)     @ org.apache.spark.rdd.rdd.partitions(rdd.scala:237)     @ org.apache.spark.sparkcontext.runjob(sparkcontext.scala:1921)

can me understand issue.

i'm not sure scenario is, when run spark locally , want access files on s3, specify key , secret in s3-path, this:

sc.textfile("s3://mykey:mysecret@mybucket/tnrealtime/output/2016/01/27/22/45/00/a.txt")

maybe work well.


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