Crawler
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articles.csv
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data
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elastic-import.ndjson
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Webcrawler and search for some HPC material
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===========================================
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Using the webcrawler
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--------------------
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There are three scripts in this directory, which are supposed to be run in sequence:
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$ ./fetch-data
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$ ./index-data
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$ ./analysis.py some interesting query
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###Step 1: Fetch the data
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The first script is a web crawler that downloads "interesting" stuff from some HPC related websites.
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###Step 2: Process the data
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The second script walks the file hierarchy created by the first script and turns the HTML and XML markup into pure text.
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From this data, it builds to archive files, one is a two-column CSV file (path and content),
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the other is meant to be used as an input file for an elasticsearch database.
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See the script comment in `index-data` for details.
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###Step 3: Query the data
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The third scrip uses Python's fuzzywuzzy module to match a given query to the contents of the archive file produced by the second script.
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Performance of the search
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-------------------------
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I am not impressed by it.
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Sorry, but I really cannot sell this as a success story.
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The problems that I see:
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* The match percentage depends only on the number of query words that are found in the document.
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As such, the result list does not distinguish between a document that uses the query words once and one that uses them over and over again.
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* The match percentage does not take into account whether the query words appear close together or not.
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* It is completely ignored *where* in the text the query words appear.
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A page that lists for example "Python" in its title is scored equal to a page that contains a link with the tool-tip "a small python script to frobnicate foos".
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Both will be listed as 100 percent matches to the query "python".
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These problems severely limit the usefulness of the query feature, as shown in the examples below.
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###Some example queries
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####Searching for a Python introduction
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$ ./analysis.py python introduction | head
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Match: 100% in URL: data/www.unidata.ucar.edu/software/netcdf/docs/netcdf_introduction.html
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Match: 100% in URL: data/www.unidata.ucar.edu/software/netcdf/docs/faq.html
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Match: 100% in URL: data/www.dkrz.de/up/services/data-management/projects-and-cooperations/ipcc-data/order-ipcc-data-on-dvd/ipcc-ddc-data-format-information.html
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Match: 100% in URL: data/www.dkrz.de/up/services/data-management/projects-and-cooperations/cops/example-files/switchLanguage?set_language=en.html
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Match: 100% in URL: data/www.dkrz.de/up/services/data-management/projects-and-cooperations/cops/example-files.html
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Match: 100% in URL: data/www.dkrz.de/up/services/data-management/esgf-services-1/esgf-preparation/switchLanguage?set_language=en.html
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Match: 100% in URL: data/www.dkrz.de/up/services/data-management/esgf-services-1/esgf-preparation.html
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Match: 100% in URL: data/www.dkrz.de/up/services/data-management/cmip-data-pool/switchLanguage?set_language=en.html
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Match: 100% in URL: data/www.dkrz.de/up/services/data-management/cmip-data-pool.html
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Match: 100% in URL: data/www.dkrz.de/up/services/analysis/visualization/sw/vapor/vapor/switchLanguage?set_language=en.html
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The first document matches because it contains the sentence "C-based 3rd-party netCDF APIs for other languages include Python, Ruby, Perl, Fortran-2003, MATLAB, IDL, and R".
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The document is an introduction, all right, but for NetCDF, not Python.
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Likewise, the FAQ (second document) mentions several times that NetCDF can be used with Python, and it contains two links that lead to "introduction" documents.
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The third link is even more obscure.
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Funnily enough, it mentions the NetCDF Python library again (once), but the word "introduction" does not even appear in the rendered HTML document.
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It is necessary to load the page's source HTML code to find out that there is a link contained within that page that has the tool-tip
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"Short introduction to the OpenStack Swift Storage system",
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and a second link to https://www.dkrz.de/up/my-dkrz/getting-started which is praised in the tool-tip as leading to "a short introduction".
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Googling for "python introduction" yields much better results.
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####Trying to solve a batch processing problem
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$ ./analysis.py batch job not starting | head
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Match: 100% in URL: data/www.dkrz.de/up/services/code-tuning/debugging/switchLanguage?set_language=en.html
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Match: 100% in URL: data/www.dkrz.de/up/services/code-tuning/debugging.html
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Match: 100% in URL: data/slurm.schedmd.com/srun.html
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Match: 100% in URL: data/slurm.schedmd.com/squeue.html
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Match: 100% in URL: data/slurm.schedmd.com/slurm.conf.html
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Match: 100% in URL: data/slurm.schedmd.com/scontrol.html
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Match: 100% in URL: data/slurm.schedmd.com/sbatch.html
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Match: 100% in URL: data/slurm.schedmd.com/sacct.html
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Match: 100% in URL: data/slurm.schedmd.com/reservations.html
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Match: 100% in URL: data/slurm.schedmd.com/quickstart_admin.html
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The first result describes debugging with ARM DDT, not how to troubleshoot problems with batch jobs.
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The second result is actually the same page as the first.
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The third and seventh results are actually somewhat useful, they are the online version of `man srun` and `man sbatch`.
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The other results are not useful, as they are just further man pages of the other slurm commands,
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and have little information to give on troubleshooting jobs that won't start.
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At least, the slurm.schedmd.com links point the user to the correct software.
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Google, with the same query, does not fare any better, as its results are dominated by Microsoft's Dynamics AX software.
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####Trying to run a program on several nodes
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$ ./analysis.py run program on several nodes in parallel | head
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Match: 100% in URL: data/slurm.schedmd.com/srun.html
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Match: 100% in URL: data/slurm.schedmd.com/quickstart.html
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Match: 100% in URL: data/slurm.schedmd.com/programmer_guide.html
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Match: 100% in URL: data/slurm.schedmd.com/faq.html
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Match: 100% in URL: data/slurm.schedmd.com/download.html
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Match: 100% in URL: data/slurm.schedmd.com/acct_gather_profile_plugins.html
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Match: 100% in URL: data/kb.hlrs.de/platforms/index.php/Open_MPI.html
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Match: 100% in URL: data/kb.hlrs.de/platforms/index.php/NEC_Cluster_cacau_introduction.html
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Match: 100% in URL: data/kb.hlrs.de/platforms/index.php/NEC_Cluster_access_(vulcan).html
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Match: 100% in URL: data/kb.hlrs.de/platforms/index.php/CRAY_XE6_notes_for_the_upgraded_Batch_System.html
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Finally a success, the second result provides the required informations.
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Google provides a wide variety of more or less helpful links, some of which are significantly better geared towards people without a solid education in HPC.
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###Summary
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The more specialized the queries were, the better the results of `./analysis.py` became.
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However, the insensitivity of our algorithm to the locations of the matches and their number, frequently allows entirely unrelated results to float to the top.
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Google does not suffer from this problem.
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Google only defeats itself whenever there is a major non-HPC technology/interpretation/thing that dominates its result list, pushing the useful results out of sight.
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The most important improvement would be to weight in whether a match occurs within a link or its tool-tip.
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The next important improvement would be to weight where the match occurs within the text (title/introductory paragraphs/body/footnotes).
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The third important improvement would be to weight in whether the matches occur in close proximity or not.
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The fourth important improvement would be to consider the amount of matches (passing its relative frequency through a log() function or similar).
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#! /usr/bin/env python3
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# analysis.py [(-l | --library) <library-path>] [<query> ...]
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#
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# Search the library archive (a .csv file produced by index-data) for the words in the query, and print the respective file paths.
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import argparse
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import csv
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from fuzzywuzzy import fuzz
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import sys
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def makeCsvFieldLimitHuge():
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"""The csv module has a fixed limit on field sizes. Fix that."""
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limit = sys.maxsize
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while True:
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try:
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csv.field_size_limit(limit)
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return
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except OverflowError:
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limit = int(limit/2)
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def parseArgs():
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"""Define the options, parse the command line, and return the options object."""
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optionsParser = argparse.ArgumentParser()
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optionsParser.add_argument('-l', '--library', type = str, nargs = 1, default = 'articles.csv', help = "specify the library to search")
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optionsParser.add_argument('query', type = str, nargs='*', default = 'South Kensington, London', help="strings to search in the library")
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return optionsParser.parse_args()
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def readArticles(path: str) -> list:
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"""Read the library file."""
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with open(path, 'r') as csvfile:
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return [ f for f in csv.reader(csvfile) ]
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def query(articles: list, search: str):
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"""Search all the indexed documents for the given words, sort them by how well they match the search, and list all documents that score at least 30%."""
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ratio = [ (fuzz.token_set_ratio(f[1], search), f[0]) for f in articles ]
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ratio.sort(reverse=True)
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for x in ratio:
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if x[0] >= 30:
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print("Match: %d%% in URL: %s" %(x))
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def main():
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options = parseArgs()
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makeCsvFieldLimitHuge()
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query(readArticles(options.library), options.query)
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main()
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#! /usr/bin/env bash
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# fetch-data
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#
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# Crawl a number of HPC related sites.
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# The sites are downloaded to a directory called "data", which also contains the respective log files from the downloads.
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# The sites to download are listed at the end of this script.
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dataDir="data"
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wgetFlags="-r -N -k --random-wait --no-parent --adjust-extension --reject=.pdf --follow-tags=a"
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# --follow-tags=a
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# crawlSite <url> <logfile>
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# Download the site at <url> into the directory "data", writing the wget output to <logfile>.
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function crawlSite() {
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local baseUrl="$1"
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local logFile="$2"
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echo "fetching data from $baseUrl..."
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wget $wgetFlags -o "$logFile" "$baseUrl"
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local result=$?
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if ((result)) ; then
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echo "wget exited with error code $result, see $logFile for details"
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fi
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}
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mkdir -p "$dataDir"
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cd "$dataDir"
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#XXX: Add sites to crawl here:
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crawlSite https://www.dkrz.de/up/services/ dkrz.log
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crawlSite https://kb.hlrs.de/platforms/index.php/ hlrs.log
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crawlSite https://slurm.schedmd.com/documentation.html slurm.log
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crawlSite https://hpc.llnl.gov/training/tutorials/ llnl.log
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crawlSite https://www.unidata.ucar.edu/software/netcdf/docs/ netcdf.log
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#!/usr/bin/env python3
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# index-data
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#
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# Walk the sites stored under the "data" directory, and build two archives containing the name and contents of their text containing files (.txt, .html, .xml).
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# This creates two archive files:
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#
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# * A CSV file with two columns.
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# The first column gives the path of each file, while the seconds column gives its respective contents.
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#
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# * A newline-delimited JSON file.
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# This provides the same data in a form, that can hopefully be directly imported into an elastic search database using the _bulk API endpoint.
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# The action lines contain only an empty "index" object, relying on the "_index" to be provided in the request path, the "_id" field is assumed to be assigned randomly.
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# The source lines contain an object of the form
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#
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# {
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# "path" : <path of the text file>
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# "content" : <text content with newlines replaced by spaces>
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# }
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import csv
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import html2text
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import json
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import os
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import re
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kBaseDir = "data"
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kIncludePatterns = [ #regex patterns for the file paths to include in the archive
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r"\.html(?:[?/]|$)",
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r"\.txt(?:[?/]|$)",
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r"\.xml(?:[?/]|$)"
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]
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kCookedIncludeRegex = re.compile("(?:" + ")|(?:".join(kIncludePatterns) + ")")
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#The base directory is expected to contain both downloaded sites contained in directories and download log files.
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#We want to walk all the directories containing the data, and ignore the log files.
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#Get the list of directories.
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directories = next(os.walk(kBaseDir))[1]
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#Walk the directory hierarchy and build a list of files that match one of the include patterns.
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files = []
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for dirName in directories:
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print("scanning " + kBaseDir + "/" + dirName + "...")
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for r, d, f in os.walk(kBaseDir + "/" + dirName):
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for file in f:
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path = os.path.join(r, file)
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if kCookedIncludeRegex.search(path):
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files.append(path)
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#Open the files one by one, convert them into plain text, and concatenate their contents into a CSV file.
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with open("articles.csv", "w") as of:
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o = csv.writer(of)
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with open("elastic-import.ndjson", "w") as jsonFile:
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actionJson = '{ "index" : {} }\n'
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for f in files:
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with open(f) as file:
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print(f)
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data = html2text.html2text(file.read()).replace("\n", " ")
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o.writerow([f, data])
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jsonObject = { 'path': f, 'content': data }
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jsonString = json.dumps(jsonObject, separators = (',', ':')) + "\n"
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jsonFile.write(actionJson)
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jsonFile.write(jsonString)
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