How to use S3 Select with tab separated csv files

Question

I’m using this script to query data from a CSV file that’s saved on an AWS S3 Bucket. It works well with CSV files that were originally saved in Comma Separated format but I have a lot of data saved with tab delimiter (Sep=’t’) which makes the code fail.

The original data is very massive which makes it difficult to rewrite it. Is there a way to query data where we specify the delimiter/separator for the CSV file?

I used it from this post: https://towardsdatascience.com/how-i-improved-performance-retrieving-big-data-with-s3-select-2bd2850bc428 … I’d like to thank the writer for the tutorial which helped me save a lot of time.

Here’s the code:

import boto3
import os
import pandas as pd

S3_KEY = r'source/df.csv'
S3_BUCKET = 'my_bucket'
TARGET_FILE = 'dataset.csv'

aws_access_key_id= 'my_key'
aws_secret_access_key= 'my_secret'

s3_client = boto3.client(service_name='s3',
                         region_name='us-east-1',
                         aws_access_key_id=aws_access_key_id,
        aws_secret_access_key=aws_secret_access_key)

query = """SELECT column1
        FROM S3Object
        WHERE column1 = '4223740573'"""

result = s3_client.select_object_content(Bucket=S3_BUCKET,
                                         Key=S3_KEY,
                                         ExpressionType='SQL',
                                         Expression=query,
                                         InputSerialization={'CSV': {'FileHeaderInfo': 'Use'}},
                                         OutputSerialization={'CSV': {}})

# remove the file if exists, since we append filtered rows line by line
if os.path.exists(TARGET_FILE):
    os.remove(TARGET_FILE)

with open(TARGET_FILE, 'a+') as filtered_file:
    # write header as a first line, then append each row from S3 select
    filtered_file.write('Column1n')
    for record in result['Payload']:
        if 'Records' in record:
            res = record['Records']['Payload'].decode('utf-8')
            filtered_file.write(res)
result = pd.read_csv(TARGET_FILE)

Asked By: Ahmed Sabry

||

Source

Answer 1

The InputSerialization option also allows you to specify:

RecordDelimiter – A single character used to separate individual records in the input. Instead of the default value, you can specify an arbitrary delimiter.

So you could try:

result = s3_client.select_object_content(
    Bucket=S3_BUCKET,
    Key=S3_KEY,
    ExpressionType='SQL',
    Expression=query,
    InputSerialization={'CSV': {'FileHeaderInfo': 'Use', 'RecordDelimiter': 't'}},
    OutputSerialization={'CSV': {}})

Answered By: Marcin

Answer 2

Actually, I had a TSV file, and I used this InputSerialization:

InputSerialization={'CSV': {'FileHeaderInfo': 'None', 'RecordDelimiter': 'n', 'FieldDelimiter': 't'}}

It works for files and have Enters between records, and not tabs, but tabs between fields.

Answered By: Elvira Gandelman

How to use S3 Select with tab separated csv files

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