| # -------------------------------------------------------------------- | |
| # Recursively find pdfs from the directory given as the first argument, | |
| # otherwise search the current directory. | |
| # Use exiftool and qpdf (both must be installed and locatable on $PATH) | |
| # to strip all top-level metadata from PDFs. | |
| # | |
| # Note - This only removes file-level metadata, not any metadata | |
| # in embedded images, etc. | |
| # | |
| # Code is provided as-is, I take no responsibility for its use, |
| ffmpeg -ss $INPUT_START_TIME -t $LENGTH -i $INPUT_FILENAME \ | |
| -vf "fps=$OUTPUT_FPS,scale=$OUTPUT_WIDTH:-1:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse" \ | |
| -loop $NUMBER_OF_LOOPS $OUTPUT_FILENAME | |
| # Change these placeholders: | |
| # * $INPUT_START_TIME - number of seconds in the input video to start from. | |
| # * $LENGTH - number of seconds to convert from the input video. | |
| # * $INPUT_FILENAME - path to the input video. | |
| # * $OUTPUT_FPS - ouput frames per second. Start with `10`. | |
| # * $OUTPUT_WIDTH - output width in pixels. Aspect ratio is maintained. |
| #!/usr/bin/env bash | |
| START_TIME=$SECONDS | |
| set -e | |
| echo "-----START GENERATING HLS STREAM-----" | |
| # Usage create-vod-hls-gpu.sh SOURCE_FILE [OUTPUT_NAME] | |
| [[ ! "${1}" ]] && echo "Usage: create-vod-hls.sh SOURCE_FILE [OUTPUT_NAME]" && exit 1 | |
| # comment/add lines here to control which renditions would be created | |
| renditions=( |
| wrk -c 500 -d 30s -t 12 -R 20k -L http://localhost:8888 | |
| Running 30s test @ http://localhost:8888 | |
| 12 threads and 500 connections | |
| Thread calibration: mean lat.: 456.139ms, rate sampling interval: 3485ms | |
| Thread calibration: mean lat.: 458.787ms, rate sampling interval: 3125ms | |
| Thread calibration: mean lat.: 294.161ms, rate sampling interval: 1654ms | |
| Thread calibration: mean lat.: 391.126ms, rate sampling interval: 3123ms | |
| Thread calibration: mean lat.: 325.404ms, rate sampling interval: 2699ms | |
| Thread calibration: mean lat.: 359.069ms, rate sampling interval: 2533ms | |
| Thread calibration: mean lat.: 72.867ms, rate sampling interval: 222ms |
| #!/usr/bin/env bash | |
| START_TIME=$SECONDS | |
| set -e | |
| echo "-----START GENERATING HLS STREAM-----" | |
| # Usage create-vod-hls.sh SOURCE_FILE [OUTPUT_NAME] | |
| [[ ! "${1}" ]] && echo "Usage: create-vod-hls.sh SOURCE_FILE [OUTPUT_NAME]" && exit 1 | |
| # comment/add lines here to control which renditions would be created | |
| renditions=( |
This document contains excerpts from my web server logs collected over a period of 7 years that shows various kinds of recon and attack vectors.
There were a total of 37.2 million lines of logs out of which 1.1 million unique HTTP requests (Method + URI) were found.
$ sed 's/^.* - - \[.*\] "\(.*\) HTTP\/.*" .*/\1/' access.log > requests.txt
As a developer, it bothers me when someone sends me a large pdf file compared to the number of pages. Recently, I recieved a 12MB scanned document for just one letter-sized page... so I got to googlin, like I usually do, and found ghostscript!
to learn more abot ghostscript (gs): https://www.ghostscript.com/
What we are interested in, is the gs command line tool, which provides many options for manipulating PDF, but we are interested in compressign those large PDF's into small yet legible documents.
credit goes to this answer on askubuntu forum: https://askubuntu.com/questions/3382/reduce-filesize-of-a-scanned-pdf/3387#3387?newreg=bceddef8bc334e5b88bbfd17a6e7c4f9
running:
bash create-vod-hls.sh beach.mkv
will produce:
beach/
|- playlist.m3u8
|- 360p.m3u8
The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.
In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.
This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.