Skip to main content

Vakanties.nl pypi package

Project description

Vaknl-gcp

Package for working with dataclasses and Google cloud instances as Bigquery and Storage. For this to work the dataclasses need to contain only basic python variables as: str, int, dict, list etc.

Prerequisites

These modules dependant on the environment variable GOOGLE_CLOUD_PROJECT a.k.a. gcp project id.

Bigquery

query(query):

Execute query and log errors

returns query_job

stream_to_bigquery(objects: list, table_ref):

Cast python objects to json and stream them to GBQ Note: this is more expensive compared to using buckets but also quicker

returns table ref

write_disposition_bucket(table_ref, blob_name, write_disposition):

Get data from bucket to GBQ using a write_disposition method

Requires: bucket with name storage_to_bigquery-[project-id]

  • WRITE_DISPOSITION_UNSPECIFIED Unknown.
  • WRITE_EMPTY This job should only be writing to empty tables.
  • WRITE_TRUNCATE This job will truncate table data and write from the beginning.
  • WRITE_APPEND This job will append to a table.
returns load_job.result 

Storage

Requires: bucket with name storage_to_bigquery-[project-id]

def storage_to_bigquery(objects: list, table_ref, write_disposition):

Function that stores data into multiple storage blobs. Afterwards these wil be composed into one storage blob The reason for this process is to downsize the sie of the data send to Google Cloud Storage.

Args:
    objects: dataclasses to process
    table_ref: dataset_name.table_name
    write_disposition: how to write to google bigquery
    batch_size: row size blobs will be created in google storage before they are composed and send to bigquery
    objects_name: name of the them objects stored in storage 

batch_storage_to_bigquery(self, objects: list, table_ref, write_disposition, finished: bool = True, batch_size=5000, objects_name=None):

Function that stores data into multiple storage blobs. If finished these wil be composed into one storage blob. The reason for this process is to downsize the sie of the data send to Google Cloud Storage.

Args:
    objects: dataclasses to process
    table_ref: dataset_name.table_name
    write_disposition: how to write to google bigquery
    finished: triggers composing all the blobs and storing them in one procedure
    batch_size: row size blobs will be created in google storage before they are composed and send to bigquery
    objects_name: name of the them objects stored in storage

single_storage_to_bigquery(object, table_ref, write_disposition, batch_size=500, object_name=None):

Function that stores data into a storage blob. Then check if there are more than the batch_size. If so it will compose similar blobs and send them to google bigquery. The reason for this process is to not stream single rows of data into bigquery but wait until there are more and than send them together.

Args:            
    object: dataclass
    table_ref: dataset_name.table_name
    write_disposition: how to write to google bigquery
    batch_size: how many blobs until composing and sending to google bigquery
    object_name: name of the them object stored in storage 

list_blobs_with_prefix(bucket_name, prefix, delimiter=None):):

Lists all the blobs in the bucket that begin with the prefix.

This can be used to list all blobs in a "folder", e.g. "public/".

The delimiter argument can be used to restrict the results to only the
"files" in the given "folder". Without the delimiter, the entire tree under
the prefix is returned. For example, given these blobs:

    a/1.txt
    a/b/2.txt

If you just specify prefix = 'a', you'll get back:

    a/1.txt
    a/b/2.txt

However, if you specify prefix='a' and delimiter='/', you'll get back:

    a/1.txt

Additionally, the same request will return blobs.prefixes populated with:

    a/b/

Args:
    bucket_name: bucket_name
    prefix: string
    delimiter: string

Return:
    list: blobs

Tasks

create_basic_task(url, queue, payload, task_name=None, in_seconds=None):

Creates a task that will be placed in a queue

Arg:
    url: url link like http://example.nl/
    queue: name of queue
    payload: dict
    task_name: str
    in_seconds: int

Returns:
    A :class:`~google.cloud.tasks_v2.types.Task` instance.

Scheduler

get_schedulers():

Gets back all schedulers form a gcp project

returns list[Schedule]

create_schedule(schedule:Schedule):

Creates a new Schedule

Arg: 
    schedule: class: `vaknl-gcp.Scheduler.Schedule`

Returns:
    A :class:`~google.cloud.scheduler_v1.types.Job` instance.

delete_schedule(name:str):

Deletes a schedule

Arg:
    name: str name of the schedule

Returns:
    A :class:`~google.cloud.scheduler_v1.types.Job` instance.

Secrets (beta)

get_default_secret(secret_id):

returns json

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for vaknl-gcp, version 1.3.6
Filename, size File type Python version Upload date Hashes
Filename, size vaknl_gcp-1.3.6-py3-none-any.whl (13.0 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size vaknl-gcp-1.3.6.tar.gz (10.3 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page