I learned how to use the Levenshtein functions in PostgreSQL
The thing is that there is a limit of 255 characters for these functions in PostgreSQL so you need to truncate the strings before comparing
Also, the trgm functions I’ve used before are case insensitive, but Levenshtein is not, so you need to make sure to lower case both strings first
I learned how to use the Levenshtein functions in PostgreSQL
The thing is that there is a limit of 255 characters for these functions in PostgreSQL so you need to truncate the strings before comparing
Also, the trgm functions I’ve used before are case insensitive, but Levenshtein is not, so you need to make sure to lower case both strings first
<li>I learned how to use the Levenshtein functions in PostgreSQL
<ul>
<li>The thing is that there is a limit of 255 characters for these functions in PostgreSQL so you need to truncate the strings before comparing</li>
<li>Also, the trgm functions I’ve used before are case insensitive, but Levenshtein is not, so you need to make sure to lower case both strings first</li>
</ul>
</li>
</ul>
<ul>
<li>A working query checking for duplicates in the recent AfricaRice items is:</li>
</ul>
<divclass="highlight"><pretabindex="0"style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><codeclass="language-console"data-lang="console"><spanstyle="display:flex;"><span>localhost/dspace= ☘ SELECT text_value FROM metadatavalue WHERE dspace_object_id IN (SELECT uuid FROM item) AND metadata_field_id=64 AND levenshtein_less_equal(LOWER('International Trade and Exotic Pests: The Risks for Biodiversity and African Economies'), LEFT(LOWER(text_value), 255), 3) <= 3;
</span></span></span><spanstyle="display:flex;"><span><spanstyle="color:#960050;background-color:#1e0010"></span>Time: 399.751 ms
</span></span></code></pre></div><ul>
<li>There is a great <ahref="https://www.crunchydata.com/blog/fuzzy-name-matching-in-postgresql">blog post discussing Soundex with Levenshtein</a> and creating indexes to make them faster</li>
<li>I want to do some proper checks of accuracy and speed against my trigram method</li>
<li>This seems low, so it must have been from the request patterns by certain visitors
<ul>
<li>64.39.98.251 is Qualys, and I’m debating blocking <ahref="https://pci.qualys.com/static/help/merchant/getting_started/check_scanner_ip_addresses.htm">all their IPs</a> using a geo block in nginx (need to test)</li>
<li>The top few are known ILRI and other CGIAR scrapers, but 80.248.237.167 is on InternetVikings in Sweden, using a normal user agentand scraping Discover</li>
<li>64.124.8.59 is making requests with a normal user agent and belongs to Castle Global or Zayo</li>
</ul>
</li>
<li>I ran all system updates and rebooted the server (could have just restarted PostgreSQL but I thought I might as well do everything)</li>
<li>I implemented a geo mapping for the user agent mapping AND the nginx <code>limit_req_zone</code> by extracting the networks into an external file and including it in two different geo mapping blocks
<ul>
<li>This is clever and relies on the fact that we can use defaults in both cases</li>
<li>First, we map the user agent of requests from these networks to “bot” so that Tomcat and Solr handle them accordingly</li>
<li>Second, we use this as a key in a <code>limit_req_zone</code>, which relies on a default mapping of ’’ (and nginx doesn’t evaluate empty cache keys)</li>
</ul>
</li>
<li>I noticed that CIP uploaded a number of Georgian presentations with <code>dcterms.language</code> set to English and Other so I changed them to “ka”
<ul>
<li>Perhaps we need to update our list of languages to include all instead of the most common ones</li>
<li>I wrote a script <code>ilri/iso-639-value-pairs.py</code> to extract the names and Alpha 2 codes for all ISO 639-1 languages from pycountry and added them to <code>input-forms.xml</code></li>
