Convert CSV To Parquet Or Avro¶
Introduction¶
Converting from csv to parquet or avro reduces the size of 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
TD;LR¶
Converting CSV stored in public storage with Bacalhau
Running CSV to Arvo or Parquet Locally¶
Installing dependencies
%cd csv_to_avro_or_parquet
Install the following dependencies¶
Run the following commands:
%%bash
pip install fastavro
%%bash
pip install numpy
%%bash
pip install pandas
%%bash
pip install pyarrow
%%bash
python3 src/converter.py ./movies.csv ./movies.parquet parquet
# python converter.py path_to_csv path_to_result_file extension
Viewing the parquet file
import pandas as pd
pd.read_parquet('./movies.parquet').head()
Containerize Script with Docker¶
:::info You can skip this section entirely and directly go to running on bacalhau :::
To build your own docker container, create a Dockerfile, which contains instructions to build your image.
FROM python:3.8
RUN apt update && apt install git
RUN git clone https://github.com/bacalhau-project/Sparkov_Data_Generation
WORKDIR /Sparkov_Data_Generation/
RUN pip3 install -r requirements.txt
:::info See more information on how to containerize your script/apphere :::
Build the container¶
We will run docker build command to build the container;
docker build -t <hub-user>/<repo-name>:<tag> .
Before running the command replace;
hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create docker account, and use the username of the account you created
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:
docker build -t jsacex/csv-to-arrow-or-parquet
Push the container¶
Next, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.
docker push <hub-user>/<repo-name>:<tag>
In our case:
docker push jsacex/csv-to-arrow-or-parquet
Running a Bacalhau Job¶
To submit a job, we are going to either mount the script from a IPFS or from an URL.
Mounting the CSV File from IPFS¶
With the command below, we are gmounting the CSV file for transactions from IPFS
!command -v bacalhau >/dev/null 2>&1 || (export BACALHAU_INSTALL_DIR=.; curl -sL https://get.bacalhau.org/install.sh | bash)
path=!echo $PATH
%env PATH=./:{path[0]}
%%bash --out job_id
bacalhau docker run \
-i ipfs://QmTAQMGiSv9xocaB4PUCT5nSBHrf9HZrYj21BAZ5nMTY2W \
--wait \
--id-only \
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:
bacalhau docker run: call to bacalhau-i ipfs://QmTAQMGiSv9xocaB4PUCT5nSBHrf9HZrYj21BAZ5nMTY2W: CIDs to use on the job. Mounts them at '/inputs' in the execution.jsacex/csv-to-arrow-or-parque: the name and the tag of the docker image we are using../inputs/movies.csv: path to input dataset../outputs/movies.parquet parquet: path to outputpython3 src/converter.py: execute the script
Mounting the CSV File from a URL¶
To mount the CSV file from a URL
%%bash --out job_id
bacalhau docker run \
-i https://raw.githubusercontent.com/bacalhau-project/csv_to_avro_or_parquet/master/movies.csv \
jsacex/csv-to-arrow-or-parquet \
-- python3 src/converter.py ../inputs/movies.csv ../outputs/movies.parquet parquet
Structure of the command¶
Let's look closely at the command above:
bacalhau docker run: call to bacalhau-i https://raw.githubusercontent.com/bacalhau-project/csv_to_avro_or_parquet/master/movies.csv: URL:path of the input data volumes downloaded from a URL sourcejsacex/csv-to-arrow-or-parque: the name and the tag of the docker image we are using../inputs/movies.csv: path to input dataset../outputs/movies.parquet parquet: path to outputpython3 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.
%env JOB_ID={job_id}
env: JOB_ID=bacalhau describe 71ecde0e-dac3-4c8d-bf2e-7a92cc54425e
Checking the State of your Jobs¶
- Job status: You can check the status of the job using
bacalhau list.
:::note Replace the {JOB_ID} with your generated ID. :::
%%bash
bacalhau list --id-filter={JOB_ID}
CREATED ID JOB STATE VERIFIED PUBLISHED 16:53:30 71ecde0e Docker jsacex/csv-to... Completed ipfs://QmP5PbbJZ1fdq...
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 describe.
%%bash
bacalhau describe {JOB_ID}
- Job download: You can download your job results directly by using
bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.
%%bash
rm -rf results && mkdir -p results
bacalhau get ${JOB_ID} --output-dir results
Viewing your Job Output¶
To view the file, run the following command:
%%bash
ls results/outputs
Alternatively, you can do this.
import pandas as pd
import os
pd.read_parquet('results/outputs/transactions.parquet')