[scala] How to load local file in sc.textFile, instead of HDFS

I'm following the great spark tutorial

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

$ sudo docker run -i -t -h sandbox sequenceiq/spark:1.1.0 /etc/bootstrap.sh -bash
bash-4.1# cd /usr/local/spark-1.1.0-bin-hadoop2.4
bash-4.1# ls README.md
README.md
bash-4.1# ./bin/spark-shell
scala> val f = sc.textFile("README.md")
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] = README.md 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/README.md
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:285)

how can I load that README.md?

Attention:

Make sure that you run spark in local mode when you load data from local(sc.textFile("file:///path to the file/")) or you will get error like this Caused by: java.io.FileNotFoundException: File file:/data/sparkjob/config2.properties does not exist. Becasuse executors which run on different workers will not find this file in it's local path.

I have a file called NewsArticle.txt on my Desktop.

In Spark, I typed:

val textFile= sc.textFile(“file:///C:/Users/582767/Desktop/NewsArticle.txt”)

I needed to change all the \ to / character for the filepath.

To test if it worked, I typed:

textFile.foreach(println)

I'm running Windows 7 and I don't have Hadoop installed.

This is the solution for this error that i was getting on Spark cluster that is hosted in Azure on a windows cluster:

Load the raw HVAC.csv file, parse it using the function

data = sc.textFile("wasb:///HdiSamples/SensorSampleData/hvac/HVAC.csv")

We use (wasb:///) to allow Hadoop to access azure blog storage file and the three slashes is a relative reference to the running node container folder.

For example: If the path for your file in File Explorer in Spark cluster dashboard is:

sflcc1\sflccspark1\HdiSamples\SensorSampleData\hvac

So to describe the path is as follows: sflcc1: is the name of the storage account. sflccspark: is the cluster node name.

So we refer to the current cluster node name with the relative three slashes.

Hope this helps.

This has happened to me with Spark 2.3 with Hadoop also installed under the common "hadoop" user home directory.Since both Spark and Hadoop was installed under the same common directory, Spark by default considers the scheme as hdfs, and starts looking for the input files under hdfs as specified by fs.defaultFS in Hadoop's core-site.xml. Under such cases, we need to explicitly specify the scheme as file:///<absoloute path to file>.

If your trying to read file form HDFS. trying setting path in SparkConf

 val conf = new SparkConf().setMaster("local[*]").setAppName("HDFSFileReader")
 conf.set("fs.defaultFS", "hdfs://hostname:9000")

I tried the following and it worked from my local file system.. Basically spark can read from local, HDFS and AWS S3 path

listrdd=sc.textFile("file:////home/cloudera/Downloads/master-data/retail_db/products")

While Spark supports loading files from the local filesystem, it requires that the files are available at 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 an 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

Take care to put file:// in front and the use of "/" or "\" according to OS.

You need just to specify the path of the file as "file:///directory/file"

example:

val textFile = sc.textFile("file:///usr/local/spark/README.md")

You do not have to use sc.textFile(...) to convert local files into dataframes. One of options is, to read a local file line by line and then transform it into Spark Dataset. Here is an example for Windows machine in Java:

StructType schemata = DataTypes.createStructType(
            new StructField[]{
                    createStructField("COL1", StringType, false),
                    createStructField("COL2", StringType, false),
                    ...
            }
    );

String separator = ";";
String filePath = "C:\\work\\myProj\\myFile.csv";
SparkContext sparkContext = new SparkContext(new SparkConf().setAppName("MyApp").setMaster("local"));
JavaSparkContext jsc = new JavaSparkContext (sparkContext );
SQLContext sqlContext = SQLContext.getOrCreate(sparkContext );

List<String[]> result = new ArrayList<>();
try (BufferedReader br = new BufferedReader(new FileReader(filePath))) {
    String line;
    while ((line = br.readLine()) != null) {
      String[] vals = line.split(separator);
      result.add(vals);
    }
 } catch (Exception ex) {
       System.out.println(ex.getMessage());
       throw new RuntimeException(ex);
  }
  JavaRDD<String[]> jRdd = jsc.parallelize(result);
  JavaRDD<Row> jRowRdd = jRdd .map(RowFactory::create);
  Dataset<Row> data = sqlContext.createDataFrame(jRowRdd, schemata);

Now you can use dataframe data in your code.

If the file is located in your Spark master node (e.g., in case of using AWS EMR), then launch the spark-shell in local mode first.

$ spark-shell --master=local
scala> val df = spark.read.json("file:///usr/lib/spark/examples/src/main/resources/people.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

scala> df.show()
+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

Alternatively, you can first copy the file to HDFS from the local file system and then launch Spark in its default mode (e.g., YARN in case of using AWS EMR) to read the file directly.

$ hdfs dfs -mkdir -p /hdfs/spark/examples
$ hadoop fs -put /usr/lib/spark/examples/src/main/resources/people.json /hdfs/spark/examples
$ hadoop fs -ls /hdfs/spark/examples
Found 1 items
-rw-r--r--   1 hadoop hadoop         73 2017-05-01 00:49 /hdfs/spark/examples/people.json

$ spark-shell
scala> val df = spark.read.json("/hdfs/spark/examples/people.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

scala> df.show()
+----+-------+
| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

gonbe's answer is excellent. But still I want to mention that file:/// = ~/../../, not $SPARK_HOME. Hope this could save some time for newbs like me.

try

val f = sc.textFile("./README.md")

This has been discussed into spark mailing list, and please refer this mail.

You should use hadoop fs -put <localsrc> ... <dst> copy the file into hdfs:

${HADOOP_COMMON_HOME}/bin/hadoop fs -put /path/to/README.md README.md