Convert CSV To Parquet Or Avro

Introduction​

Converting from CSV to parquet or avro reduces the size of the file and allows for faster read and write speeds. With Bacalhau, you can convert your CSV files stored on ipfs or on the web without the need to download files and install dependencies locally.

In this example tutorial we will convert a CSV file from a URL to parquet format and save the converted parquet file to IPFS

Prerequisites​

Running CSV to Avro or Parquet Locally​​

Downloading the CSV file​

Let's download the transactions.csv file:

Writing the Script​

Write the converter.py Python script, that serves as a CSV converter to Avro or Parquet formats:

Installing Dependencies​

Converting CSV file to Parquet format​

In our case:

Viewing the parquet file:​

Containerize Script with Docker​

To build your own docker container, create a Dockerfile, which contains instructions to build your image.

Build the container​

We will run the docker build command to build the container:

Before running the command replace:

repo-name with the name of the container, you can name it anything you want

tag this is not required but you can use the latest tag

In our case:

Push the container​

Next, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.

In our case:

docker push jsacex/csv-to-arrow-or-parquet

Running a Bacalhau Job​

With the command below, we are mounting the CSV file for transactions from IPFS

export JOB_ID=$(bacalhau docker run \
    -i ipfs://QmTAQMGiSv9xocaB4PUCT5nSBHrf9HZrYj21BAZ5nMTY2W  \
    --wait \
    --id-only \
    --output outputs:\outputs \
    --publisher ipfs \
    jsacex/csv-to-arrow-or-parquet \
    -- python3 src/converter.py ../inputs/transactions.csv  /outputs/transactions.parquet parquet)

Structure of the command​

Let's look closely at the command above:

1. bacalhau docker run: call to Bacalhau

2. -i ipfs://QmTAQMGiSv9xocaB4PUCT5nSBHrf9HZrYj21BAZ5nMTY2W: CIDs to use on the job. Mounts them at '/inputs' in the execution.

3. jsacex/csv-to-arrow-or-parque: the name and the tag of the docker image we are using

4. ../inputs/transactions.csv : path to input dataset

5. /outputs/transactions.parquet parquet: path to the output

6. python3 src/converter.py: execute the script

When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.

Declarative job description​

The same job can be presented in the declarative format. In this case, the description will look like this:

name: Convert CSV To Parquet Or Avro
type: batch
count: 1
tasks:
  - name: My main task
    Engine:
      type: docker
      params:
        Image: jsacex/csv-to-arrow-or-parquet
        Entrypoint:
          - /bin/bash
        Parameters:
          - -c
          - python3 src/converter.py ../inputs/transactions.csv  ../outputs/transactions.parquet parquet
    Publisher:
      Type: ipfs
    ResultPaths:
      - Name: outputs
        Path: /outputs
    InputSources:
      - Target: "/inputs"
        Source:
          Type: "ipfs"
          Params:
            CID: "QmTAQMGiSv9xocaB4PUCT5nSBHrf9HZrYj21BAZ5nMTY2W"

The job description should be saved in .yaml format, e.g. convertcsv.yaml, and then run with the command:

bacalhau job run convertcsv.yaml

Checking the State of your Jobs​

Job status: You can check the status of the job using bacalhau job list.

bacalhau job list --id-filter ${JOB_ID} 

When it says Published or Completed, that means the job is done, and we can get the results.

Job information: You can find out more information about your job by using bacalhau job describe.

bacalhau job describe ${JOB_ID}

Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.

rm -rf results && mkdir -p results # Temporary directory to store the results
bacalhau job get ${JOB_ID} --output-dir results # Download the results

Viewing your Job Output​

To view the file, run the following command:

ls results/outputs

transactions.parquet

Alternatively, you can do this:

import pandas as pd
import os
pd.read_parquet('results/outputs/transactions.parquet')

Support​

If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).