dewaldabrie

from coroflow import Pipeline, Node
import logging
from .data_collector import agg_data_deserialise, get_symbols, agg_data_update, agg_data_serialize


def main_sync():
    # Create Pipeline with a node for each function passed in.
    
    # Data flows like so:
    # get_symbols -> symbol_data -> agg_data_update

dewaldabrie

"""
Find list of ASX ETF symbols at asxetfs.com
"""
import os
import time
import logging
import pickle
import csv
import pandas as pd

dewaldabrie

"""
Find list of ASX ETF symbols at asxetfs.com
"""
import os
from coroflow import Pipeline, Node
import time
import logging
import pickle
import csv
import pandas as pd

dewaldabrie

import asyncio
import time

tasks = []

async def func(input_q, target_qs, task_id, param):
    print(f"{task_id}: Initialised with param: {param}")

    async def func_inner(input_q, target_qs, inpt):
        print(f"{task_id}: Recieved input: {inpt}")

dewaldabrie

import asyncio
import time

tasks = []

def func(targets, task_id, param):
    print(f"{task_id}: Initialised with param: {param}")

    async def func_inner(targets, inpt):
        await asyncio.sleep(1)  # simulated IO delay

dewaldabrie

import time

def func(targets, task_id, param=None):
    print(f"{task_id}: Initialised with param: {param}")
    while True:
        inpt = (yield)
        print(f"{task_id}: Received input: {inpt}")
        time.sleep(1)  # simulated IO delay
        for target in targets:
            print(f"{task_id}: T1 sending {inpt}")

dewaldabrie

CREATE OR REPLACE PROCEDURE ledger.make_ledger_entries() BEGIN
  
  DECLARE unique_item_names ARRAY<STRING>;
  DECLARE item_idx INT64 DEFAULT 0;
  DECLARE current_table_name STRING;
  
  -- Create some fake staging data for illustration
  -- In reality, this could be ingested from a federated data source
  CREATE OR REPLACE TEMPORARY TABLE new_purchases AS (
    SELECT *

dewaldabrie

WITH basket_1 AS (
  SELECT *
  FROM UNNEST([
    STRUCT("apple" AS item_name, 2 AS quantity),
          ("banana", 0),
          ("pear", 4)
  ])
),
basket_2 AS (
  SELECT *

dewaldabrie

DECLARE start_time TIMESTAMP;
DECLARE stop_time TIMESTAMP;
SET start_time = TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY);
SET stop_time = TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 5 HOUR);

SELECT id
FROM my_dataset.my_table
WHERE created_at BETWEEN start_time and stop_time;

dewaldabrie

-- Example of a persistent function that use libraries stored in Google Cloud Storage
-- Taken from https://cloud.google.com/bigquery/docs/reference/standard-sql/user-defined-functions
CREATE OR REPLACE FUNCTION dataset.myFunc(a FLOAT64, b STRING)
  RETURNS STRING
  LANGUAGE js
  OPTIONS (
    library=["gs://my-bucket/path/to/lib1.js", "gs://my-bucket/path/to/lib2.js"]
  )
  AS
"""