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
To get started, you need to install the Bacalhau client, see more information here
Running CSV to Avro or Parquet Locally
Downloading the CSV file
Let's download the transactions.csv
file:
Copy wget https://cloudflare-ipfs.com/ipfs/QmfKJT13h5k1b23ja3ZCVg5nFL9oKz2bVXc8oXgtwiwhjz/transactions.csv
You can use the CSV files from here
Write the converter.py
Python script, that serves as a CSV converter to Avro or Parquet formats:
Copy # converter.py
import os
import sys
from abc import ABCMeta , abstractmethod
import fastavro
import numpy as np
import pandas as pd
from pyarrow import Table , parquet
class BaseConverter ( metaclass = ABCMeta ):
"""
Base class for converters.
Validate received parameters for future use.
"""
def __init__ (
self ,
csv_file_path : str ,
target_file_path : str ,
) -> None :
self . csv_file_path = csv_file_path
self . target_file_path = target_file_path
@ property
def csv_file_path ( self ):
return self . _csv_file_path
@csv_file_path . setter
def csv_file_path ( self , path ):
if not os . path . isabs (path):
path = os . path . join (os. getcwd (), path)
_ , extension = os . path . splitext (path)
if not os . path . isfile (path) or extension != '.csv' :
raise FileNotFoundError (
f 'No such csv file: {path} '
)
self . _csv_file_path = path
@ property
def target_file_path ( self ):
return self . _target_file_path
@target_file_path . setter
def target_file_path ( self , path ):
if not os . path . isabs (path):
path = os . path . join (os. getcwd (), path)
target_dir = os . path . dirname (path)
if not os . path . isdir (target_dir):
raise FileNotFoundError (
f 'No such directory: {target_dir} \n'
'Choose existing or create directory for result file.'
)
if os . path . isfile (path):
raise FileExistsError (
f 'File {path} has already exists.'
'Usage of existing file may result in data loss.'
)
self . _target_file_path = path
def get_csv_reader ( self ):
"""Return csv reader which read csv file as a stream"""
return pd . read_csv (
self.csv_file_path,
iterator = True ,
chunksize = 100000
)
@abstractmethod
def convert ( self ):
"""Should be implemented in child class"""
pass
class ParquetConverter ( BaseConverter ):
"""
Convert received csv file to parquet file.
Take path to csv file and path to result file.
"""
def convert ( self ):
"""Read csv file as a stream and write data to parquet file."""
csv_reader = self . get_csv_reader ()
writer = None
for chunk in csv_reader :
if not writer :
table = Table . from_pandas (chunk)
writer = parquet . ParquetWriter (
self.target_file_path, table.schema
)
table = Table . from_pandas (chunk)
writer . write_table (table)
writer . close ()
class AvroConverter ( BaseConverter ):
"""
Convert received csv file to avro file.
Take path to csv file and path to result file.
"""
NUMPY_TO_AVRO_TYPES = {
np . dtype ( '?' ): 'boolean' ,
np . dtype ( 'int8' ): 'int' ,
np . dtype ( 'int16' ): 'int' ,
np . dtype ( 'int32' ): 'int' ,
np . dtype ( 'uint8' ): 'int' ,
np . dtype ( 'uint16' ): 'int' ,
np . dtype ( 'uint32' ): 'int' ,
np . dtype ( 'int64' ): 'long' ,
np . dtype ( 'uint64' ): 'long' ,
np . dtype ( 'O' ): [ 'null' , 'string' , 'float' ] ,
np . dtype ( 'unicode_' ): 'string' ,
np . dtype ( 'float32' ): 'float' ,
np . dtype ( 'float64' ): 'double' ,
np . dtype ( 'datetime64' ): {
'type' : 'long' ,
'logicalType' : 'timestamp-micros'
},
}
def get_avro_schema ( self , pandas_df ):
"""Generate avro schema."""
column_dtypes = pandas_df . dtypes
schema_name = os . path . basename (self.target_file_path)
schema = {
'type' : 'record' ,
'name' : schema_name ,
'fields' : [
{
'name' : name ,
'type' : AvroConverter . NUMPY_TO_AVRO_TYPES[dtype]
} for (name , dtype) in column_dtypes . items ()
]
}
return fastavro . parse_schema (schema)
def convert ( self ):
"""Read csv file as a stream and write data to avro file."""
csv_reader = self . get_csv_reader ()
schema = None
with open (self.target_file_path, 'a+b' ) as f :
for chunk in csv_reader :
if not schema :
schema = self . get_avro_schema (chunk)
fastavro . writer (
f,
schema = schema,
records = chunk. to_dict ( 'records' )
)
if __name__ == '__main__' :
converters = {
'parquet' : ParquetConverter ,
'avro' : AvroConverter
}
csv_file , result_path , result_type = sys . argv [ 1 ], sys . argv [ 2 ], sys . argv [ 3 ]
if result_type . lower () not in converters :
raise ValueError (
'Invalid target type. Avalible types: avro, parquet.'
)
converter = converters [ result_type . lower ()](csv_file, result_path)
converter . convert ()
You can find out more information about converter.py
here
Copy pip install fastavro numpy pandas pyarrow
Converting CSV file to Parquet format
Copy python converter.py < path_to_cs v > < path_to_result_fil e > < extensio n >
In our case:
Copy python3 converter.py transactions.csv transactions.parquet parquet
Viewing the parquet file:
Copy import pandas as pd
pd . read_parquet ( 'transactions.parquet' ). head ()
Containerize Script with Docker
To build your own docker container, create a Dockerfile
, which contains instructions to build your image.
Copy 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
See more information on how to containerize your script/app here
We will run the docker build
command to build the container:
Copy docker build -t < hub-use r > / < repo-nam e > : < ta g > .
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:
Copy docker build -t jsacex/csv-to-arrow-or-parquet .
Next, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.
Copy docker push < hub-use r > / < repo-nam e > : < ta g >
In our case:
Copy docker push jsacex/csv-to-arrow-or-parquet
With the command below, we are mounting the CSV file for transactions from IPFS
Copy export 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/transactions.csv
: path to input dataset
../outputs/transactions.parquet parquet
: path to the output
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:
Copy 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:
Copy bacalhau job run convertcsv.yaml
Checking the State of your Jobs
Job status : You can check the status of the job using bacalhau list
.
Copy bacalhau 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 describe
.
Copy 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 (results
) and downloaded our job output to be stored in that directory.
Copy rm -rf results && mkdir -p results # Temporary directory to store the results
bacalhau get ${JOB_ID} --output-dir results # Download the results
To view the file, run the following command:
Copy ls results/outputs
transactions.parquet
Alternatively, you can do this:
Copy import pandas as pd
import os
pd . read_parquet ( 'results/outputs/transactions.parquet' )
If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).
Last updated 3 months ago