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A handful of utilities.

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A handful of utilities predominantly made to develop basic Cloud Run services that connect to the same databases. Any configuration variable should be passed as an environment variable from Cloud Run.


Training a ML model usually require multiple databases access. Although it is doable to instantiate a connection to Postgresql or Elasticsearch everytime one needs to, it can actually become quickly cumbersome to do it for a certain number or models. To make everything a tiny bit more DRY, pynidus wraps very small chunks of code to deal with config files and clients instantiation.

from pynidus.base import MultiClient

config = {
    "pg_dev": {
        "host": "host",
        "user": "user",
        "password": "password",
        "database": "database",
        "port": 5432
    "pg_logs": {
        "host": "host",
        "user": "user",
        "password": "password"
        "database": "database"
    "es_dev": {
        "host": "host",
        "user": "user",
        "password": "password"

mc = MultiClient(config)
mc.pg_client["logs"].query("SELECT * FROM some_table")
mc.pg_client["dev"].query("SELECT * FROM another_table")

Or if you want to use it inside a custom Class:

class CustomClass(MultiClient):

    def __init__(self, **kwargs):

cc.pg_client["logs"].query("SELECT * FROM some_table")
cc.pg_client["dev"].query("SELECT * FROM another_table")


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