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in Big Data Hadoop & Spark by (11.4k points)

I'm following the great spark tutorial

so i'm trying at 46m:00s to load the but fail to what i'm doing is this:

$ sudo docker run -i -t -h sandbox sequenceiq/spark:1.1.0 /etc/ -bash
bash-4.1# cd /usr/local/spark-1.1.0-bin-hadoop2.4
bash-4.1# ls
bash-4.1# ./bin/spark-shell
scala> val f = sc.textFile("")
14/12/04 12:11:14 INFO storage.MemoryStore: ensureFreeSpace(164073) called with curMem=0, maxMem=278302556
14/12/04 12:11:14 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 160.2 KB, free 265.3 MB)
f: org.apache.spark.rdd.RDD[String] = MappedRDD[1] at textFile at <console>:12
scala> val wc = f.flatMap(l => l.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: hdfs://sandbox:9000/user/root/
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(

how can I load that

1 Answer

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by (32.3k points)
edited by

While Spark supports loading files from the local filesystem, it requires that the files are available on the same path on all nodes in your cluster.

Some network filesystems, like NFS, AFS, and MapR’s NFS layer, are exposed to the user as a regular filesystem.

If your data is already in one of these systems, then you can use it as input by just specifying a file:// path; Spark will handle it as long as the filesystem is mounted at the same path on each node. Every node needs to have the same path

 rdd = sc.textFile("file:///path/to/file")

If your file isn’t already on all nodes in the cluster, you can load it locally on the driver without going through Spark and then call parallelize to distribute the contents to workers.

You can refer to the following video tutorial of spark:

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