pyspark.SparkContext.wholeTextFiles

SparkContext.wholeTextFiles(path: str, minPartitions: Optional[int] = None, use_unicode: bool = True) → pyspark.rdd.RDD[Tuple[str, str]]

Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. The text files must be encoded as UTF-8.

If use_unicode is False, the strings will be kept as str (encoding as utf-8), which is faster and smaller than unicode. (Added in Spark 1.2)

For example, if you have the following files:

hdfs://a-hdfs-path/part-00000
hdfs://a-hdfs-path/part-00001
...
hdfs://a-hdfs-path/part-nnnnn

Do rdd = sparkContext.wholeTextFiles("hdfs://a-hdfs-path"), then rdd contains:

(a-hdfs-path/part-00000, its content)
(a-hdfs-path/part-00001, its content)
...
(a-hdfs-path/part-nnnnn, its content)

Notes

Small files are preferred, as each file will be loaded fully in memory.

Examples

>>> dirPath = os.path.join(tempdir, "files")
>>> os.mkdir(dirPath)
>>> with open(os.path.join(dirPath, "1.txt"), "w") as file1:
...    _ = file1.write("1")
>>> with open(os.path.join(dirPath, "2.txt"), "w") as file2:
...    _ = file2.write("2")
>>> textFiles = sc.wholeTextFiles(dirPath)
>>> sorted(textFiles.collect())
[('.../1.txt', '1'), ('.../2.txt', '2')]

pyspark.SparkContext.version pyspark.RDD.aggregate