python - How to read gz compressed file by pyspark

Python - How to read gz compressed file by pyspark

To read a Gzip compressed file in PySpark, you can use the textFile method along with the wholeTextFiles method in the SparkContext to read compressed files. Here's an example:

from pyspark import SparkContext, SparkConf # Initialize Spark conf = SparkConf().setAppName("GzipFileExample") sc = SparkContext(conf=conf) # Replace 'your_file.gz' with the actual path to your Gzip compressed file file_path = 'your_file.gz' # Read the compressed file using textFile compressed_rdd = sc.textFile(file_path) # If you want to read the content along with the file name (for whole directories) # you can use wholeTextFiles method # compressed_rdd = sc.wholeTextFiles(file_path) # Perform operations on the RDD as needed # For example, you can collect and print the content for line in compressed_rdd.collect(): print(line) # Stop the SparkContext sc.stop()

Replace 'your_file.gz' with the actual path to your Gzip compressed file. The textFile method can handle compressed files, and you can perform various Spark operations on the resulting RDD.

Note: When reading a Gzip compressed file, each line in the compressed file is treated as a separate record in the RDD.

Make sure you have PySpark installed (pip install pyspark) and that you have a running Spark cluster or use Spark in local mode for testing.

Examples

  1. Read Gzipped Text File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.gz" df = spark.read.text(file_path)

    Description: Use PySpark to read a gzipped text file. PySpark automatically handles gzip compression.

  2. Read Gzipped CSV File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.csv.gz" df = spark.read.option("header", "true").csv(file_path)

    Description: Read a gzipped CSV file in PySpark. The option("header", "true") is used to treat the first row as headers.

  3. Read Gzipped Parquet File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.parquet.gz" df = spark.read.parquet(file_path)

    Description: Use PySpark to read a gzipped Parquet file. PySpark automatically supports reading compressed Parquet files.

  4. Read Gzipped JSON File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.json.gz" df = spark.read.json(file_path)

    Description: Read a gzipped JSON file in PySpark. PySpark can handle compressed JSON files out of the box.

  5. Read Gzipped Avro File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.avro.gz" df = spark.read.format("avro").load(file_path)

    Description: Use PySpark to read a gzipped Avro file. Specify the format as "avro" when using the read.format method.

  6. Read Gzipped ORC File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.orc.gz" df = spark.read.format("orc").load(file_path)

    Description: Read a gzipped ORC file in PySpark. Specify the format as "orc" when using the read.format method.

  7. Read Gzipped Sequence File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.seq.gz" df = spark.read.format("sequence").load(file_path)

    Description: Read a gzipped Sequence file in PySpark. Specify the format as "sequence" when using the read.format method.

  8. Read Gzipped Delta Table in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() table_path = "your_delta_table_path" df = spark.read.format("delta").load(table_path)

    Description: Read a gzipped Delta table in PySpark. Specify the format as "delta" when using the read.format method.

  9. Read Gzipped XML File in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.xml.gz" df = spark.read.format("xml").option("rowTag", "root").load(file_path)

    Description: Read a gzipped XML file in PySpark. Specify the format as "xml" and provide the row tag using the option method.

  10. Read Gzipped Custom Format in PySpark:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("example").getOrCreate() file_path = "your_file.custom.gz" df = spark.read.format("com.example.custom").load(file_path)

    Description: Read a gzipped file in a custom format in PySpark. Specify the custom format using the read.format method. Adjust "com.example.custom" with your actual custom format.

More Tags

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