mehd-io

import logging
from datetime import datetime
import functools

# Setting up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def measure_time(func):
    @functools.wraps(func)

djouallah

import requests
import time
import json
import base64
 
def get_notebook_content(notebook_id_or_name):
    nb = notebookutils.notebook.get(notebook_id_or_name)
    workspaceId = nb['workspaceId']
    notebookId = nb['id']
    format = 'ipynb'

marklit

COPY (
    WITH a AS (
        SELECT h3_cell_to_parent(h3_string_to_h3(SUBSTR(id, 0, 17)), 2) h3_2,
               COUNT(*) num_recs
        FROM read_parquet('s3://overturemaps-us-west-2/release/2024-05-16-beta.0/theme=places/type=place/*.parquet',
                          filename=true, 
                          hive_partitioning=1)
        GROUP BY 1
    )
    SELECT h3_cell_to_boundary_wkt(h3_2),

Bilbottom

/*
    Mermaid + DuckDB for generating customer hierarchy diagrams

    DuckDB version: 0.10.2

    Bill Wallis, 2024-05-09
*/


select version();

Bilbottom

mitchellh

I get asked pretty regularly what my opinion is on merge commits vs rebasing vs squashing. I've typed up this response so many times that I've decided to just put it in a gist so I can reference it whenever it comes up again.

I use merge, squash, rebase all situationally. I believe they all have their merits but their usage depends on the context. I think anyone who says any particular strategy is the right answer 100% of the time is wrong, but I think there is considerable acceptable leeway in when you use each. What follows is my personal and professional opinion:

JHibbard

from pathlib import Path


def make_table_uris(name: str, basepath: str='.'):
  """Example function for generating normalized table URIs
  
  Args:
    name: name of table to generate table URIs for
    basepath: directory to nest table URIs under
    

bluet

Database Naming Convention and Data Warehouse Design Principles

• hackmd version: https://hackmd.io/1eeNAS1oQuSvMA0q6y_QuA?view
• gist version: https://gist.github.com/bluet/23e7697b86144561c4a3d804903d059d

[TOC]

ETL: From Data Lake to Data Warehouse

1. Extract 部份：取出要的資料、去雜訊、資料標準化、parsing...
2. Transform：aggregation、mapping 、combined、Change Data Types

dgkeyes

dk_summarize_with_totals <- function(.data, group_by_var, mean_var){

     groups_summary <- .data %>%
          dplyr::group_by({{ group_by_var }}) %>%
          dplyr::summarize(mean = mean({{ mean_var }})) %>%
          dplyr::rename("group" = {{ group_by_var }} )

     overall_summary <-.data %>%
          dplyr::summarize(mean = mean({{ mean_var }})) %>%
          dplyr::mutate(group = "Total")

rgreenjr

-- show running queries (pre 9.2)
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%'