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import time
import multiprocessing


def delay_print(message):
    time.sleep(1)
    print(message)


for i in range(10):

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You can get some really detailed stats about Logstash pipelines via the HTTP API. See the docs for more!

$ curl -XGET 'localhost:9600/_node/stats/pipelines?pretty'
{
  "host" : "logstash-msk-679b6b8dd9-4pd2h",
  "version" : "7.4.0",
  "http_address" : "0.0.0.0:9600",
  "id" : "ba41dded-1a12-41e9-988f-03bd4eae9d4a",
  "name" : "logstash-msk-679b6b8dd9-4pd2h",

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I had some XML files containing structured data. I wanted to insert this data in to a SQL database. So I needed to figure out how to transform the XML into SQL statements. Turns out, there is something called XLST that can be used to programmatically transform XML files into... well... whatever you want. So here's how I used XSLT to transform XML into SQL statements.

1. Download Saxon-HE from here: https://saxonica.com/download/java.xml
2. Create your xml file:

<!-- /Users/sherwood/Code/cdcatalog.xsl -->
<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="cdcatalog.xsl"?>

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// Here's a quick script you can copy-paste into your browser console to copy a CSV of people search results from LinkedIn
// Example: https://www.linkedin.com/search/results/people/?currentCompany=%5B%223282%22%2C%2236494%22%2C%222142019%22%5D&geoUrn=%5B%22103644278%22%5D&origin=FACETED_SEARCH&profileLanguage=%5B%22en%22%5D&title=sales%20rep

var contentStrings = Array.from(document.querySelectorAll('.entity-result__content')).map(node => node.innerText)

var tuples = contentStrings.map(function(contentString) {
  var split = contentString.split("\n")
  return [split[0], split[split.length-2], split[split.length-1]]
})

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#!/usr/local/bin/python3

import csv
import re
import argparse

CASES_OUT = 'sis-parser-out-cases.csv'
CPTS_OUT = 'sis-parser-out-cpts.csv'

parser = argparse.ArgumentParser(prog="sis-parser.py", description='Parse cases and CPT codes from a SIS Complete CSV export.')

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#!/usr/local/bin/python3

# pip3 install mysql-connector-python
import mysql.connector
import argparse
import csv

DB_USER = 'root'
DB_HOST = 'localhost'
DB_NAME = 'opkit'

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List names of CMS datasets in Socrata:

curl -s 'http://api.us.socrata.com/api/catalog/v1?domains=data.cms.gov&search_context=data.cms.gov&limit=2000' | jq ".results | .[] | .resource.name"

Put CMS dataset names, ids, descriptions, etc. into CSV:

curl -s 'http://api.us.socrata.com/api/catalog/v1?domains=data.cms.gov&search_context=data.cms.gov&limit=2000' | jq ".results | .[] | .resource | [.name, .id, .description, .createdAt, .updatedAt, .data_updated_at] | @csv"

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This is a naive way of comparing two Zod schemas to see if they are equivalent. Basically, it works by serializing the two schemas and comparing the resulting strings. This approach is limited because serialization doesn't capture all of the information about certain Zod types, e.g. functions.

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Usage

1. Adjust code as necessary
2. Convert code to a Bookmarklet using this tool: https://chriszarate.github.io/bookmarkleter/
3. Add to Chrome by following instructions here: https://www.freecodecamp.org/news/what-are-bookmarklets/

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{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "company_name": {
      "type": "string"
    }
  },
  "required": [
    "company_name"