s3-parquetifier 0.0.1

ETL job from CSV to Parquet in AWS S3

S3 Parquetifier

S3 Parquetifier is an ETL tool that can take a file from an S3 bucket convert it to Parquet format and save it to another bucket.

S3 Parquetifier supports the following files

• CSV
• JSON
• TSV

Instructions

How to install

To install the package just run the following

pip install git+git://github.com/siakon89/s3-parquetifier.git

How to use it

S3 parquetifier needs an AWS Account that will have at least read rights for the target bucket and read-write rights for the destination bucket.

You can read the following article on how to set up S3 roles and policies here

Running the Script

from s3_parquetifier import S3Parquetifier

# Call the covertor
S3Parquetifier(
    aws_access_key='<your aws access key>',
    aws_secret_key='<your aws secret key>',
    region='<the region>',
    verbose=True,  # for verbosity or not
    source_bucket='<the bucket's name where the CSVs are>',
    target_bucket='<the bucket's name where you want the parquet file to be saved>',
    type='S3'  # for now only S3
).convert_from_s3(
    source_key='<the key of the object or the folder>',
    target_key='<the key of the object or the folder>',
    file_type='csv'  # for now only CSV,
    chunk_size=100000,  # The number of rows per parquet
    dtype=None,  # A dictionary defining the types of the columns
    skip_rows=None,  # How many rows to skip per chunk
    compression='gzip',  # The compression type
    keep_original_name_locally=True  # In order to keep the original filename or create a random when downloading the file
)

Adding custom pre-processing function

You can add custom pre-processing function on your source file. Because this tool is designed for large files the preprocessing is taking place on every chunk separately. If the full file is needed for the preprocessing then a local preprocessing is needed in the source file.

In the following example, we are going to add custom columns on the chunk with some custom values. We are going to add the columns test1, test2, test3 with the values 1, 2, 3 respectively.

We define our function bellow named pre_process and we also define the arguments for the function kwargs. The chunk DataFrame is not needed in the kwargs, it is taken by default. You have to pass your function as an argument in pre_process_chunk and the arguments in kwargs in the convert_from_s3 method.

from s3_parquetifier import S3Parquetifier


# Add three new columns with custom values
def pre_process(chunk, columns=None, values=None):

    for index, column in enumerate(columns):
        chunk[column] = values[index]

    return chunk

# define the arguments for the pre-processor
kwargs = {
    'columns': ['test1', 'test2', 'test3'],
    'values': [1, 2, 3]
}

# Call the covertor
S3Parquetifier(
    aws_access_key='<your aws access key>',
    aws_secret_key='<your aws secret key>',
    region='<the region>',
    verbose=True,  # for verbosity or not
    source_bucket='<the bucket's name where the CSVs are>',
    target_bucket='<the bucket's name where you want the parquet file to be saved>',
    type='S3'  # for now only S3
).convert_from_s3(
    source_key='<the key of the object or the folder>',
    target_key='<the key of the object or the folder>',
    file_type='csv'  # for now only CSV,
    chunk_size=100000,  # The number of rows per parquet
    dtype=None,  # A dictionary defining the types of the columns
    skip_rows=None,  # How many rows to skip per chunk
    compression='gzip',  # The compression type
    keep_original_name_locally=True,  # In order to keep the original filename or create a random when downloading the file
    pre_process_chunk=pre_process,  # A preprocessing function that will pre-process the each chunk
    kwargs=kwargs  # potential extra arguments for the pre-preocess function
)

ToDo

• Add support for JSON
• Add streaming from url support
• Add support to handle local files too