| """Base class for implementing Lambda handlers as classes. | |
| Used across multiple Lambda functions (included in each zip file). | |
| Add additional features here common to all your Lambdas, like logging.""" | |
| class LambdaBase(object): | |
| @classmethod | |
| def get_handler(cls, *args, **kwargs): | |
| def handler(event, context): | |
| return cls(*args, **kwargs).handle(event, context) | |
| return handler |
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.
The correct way of creating a private frok by duplicating the repo is documented here.
For this assignment the commands are:
git clone --bare [email protected]:usi-systems/easytrace.git
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Data analysis of Linkedin Skills and positions\n", | |
| "Tingxiang Zhu & Mengyao Zhang" | |
| ] | |
| }, |
ALTER TABLE logs.trades CHANGE recentprice price int;
| /* | |
| // AdWords Script: Put Data From AdWords Report In Google Sheets | |
| // -------------------------------------------------------------- | |
| // Copyright 2017 Optmyzr Inc., All Rights Reserved | |
| // | |
| // This script takes a Google spreadsheet as input. Based on the column headers, data filters, and date range specified | |
| // on this sheet, it will generate different reports. | |
| // | |
| // The goal is to let users create custom automatic reports with AdWords data that they can then include in an automated reporting | |
| // tool like the one offered by Optmyzr. |
| service: service-name | |
| provider: | |
| name: aws | |
| runtime: nodejs6.10 | |
| functions: | |
| myfunc: | |
| handler: handler.myfunc |
| jmails.info | |
| sacustomerdelight.co.in | |
| extrobuzzapp.com | |
| ixigo.info | |
| offer4uhub.com | |
| netecart.com | |
| 101coupon.in | |
| freedealcode.in | |
| bankmarket.in | |
| hotoffers.co.in |
I had a really interesting journey today with a thorny little challenge I had while trying to delete all the files in a s3 bucket with tons of nested files.
The bucket path (s3://buffer-data/emr/logs/) contained log files created by ElasticMapReduce jobs that ran every day over a couple of years (from early 2015 to early 2018).
Each EMR job would run hourly every day, firing up a cluster of machines and each machine would output it's logs. That resulted thousands of nested paths (one for each job) that contained thousands of other files. I estimated that the total number of nested files would be between 5-10 million.
I had to estimate this number by looking at samples counts of some of the nested directories, because getting the true count would mean having to recurse through the whole s3 tree which was just too slow. This is also exactly why it was challenging to delete all the files.
Deleting all the files in a s3 object like this is pretty challenging, since s3 doesn't really work like a true f