{"id":13877,"date":"2024-03-12T09:39:43","date_gmt":"2024-03-12T09:39:43","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=13877"},"modified":"2025-01-13T09:21:06","modified_gmt":"2025-01-13T09:21:06","slug":"udf-nirvana-a-new-way-to-integrate-udfs-into-the-dbt-ecosystem","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/tech-focus\/udf-nirvana-a-new-way-to-integrate-udfs-into-the-dbt-ecosystem\/","title":{"rendered":"UDF Nirvana? A new way to integrate UDFs into the dbt ecosystem"},"content":{"rendered":"<p>Imagine a world where UDFs (user-defined functions) seamlessly integrate with your dbt workflow, automatically deploying, testing, and documenting themselves alongside your models. That\u2019s been on my wishlist since I started using dbt three years ago.<\/p>\n<p>To proceed with <a href=\"https:\/\/tempered.works\/posts\/2024-01-19-pypi-vulnerabilities-setup\/\" target=\"_blank\" rel=\"noopener\">my recent work on vulnerability management behaviour in the Python ecosystem<\/a>, I needed a solution for matching package versions to vulnerability reports. A reliable pure SQL solution didn\u2019t exist, so I wrote my own, using dbt and UDFs in BigQuery. I wanted a better integrated way of managing this network of 16 or so interrelated UDFs and their tests than my previous solution &#8211; <a href=\"https:\/\/www.equalexperts.com\/blog\/our-thinking\/testing-and-deploying-udfs-with-dbt\/\" target=\"_blank\" rel=\"noopener\">a big lump of SQL in an on-run-start hook<\/a>.<\/p>\n<p>Enter the lightbulb moment: treat UDFs as dbt models with custom materialisation. This dea unlocks a treasure trove of benefits:<\/p>\n<ul>\n<li aria-level=\"1\">Effortless Deployment: No more on-run-start hook hacks. UDFs automatically deploy alongside your models when you run dbt commands like run and build.<\/li>\n<li aria-level=\"1\">Testing Confidence: Sleep soundly knowing your UDF tests are well-integrated into the dbt build and test process.<\/li>\n<li aria-level=\"1\">Seamless Referencing: Forget complex workarounds. UDFs become first-class citizens in your models, referenced like any other dbt object.<\/li>\n<li aria-level=\"1\">Clear Documentation: No more mystery functions. UDFs are included in dbt documentation, making their purpose and usage clear.<\/li>\n<li aria-level=\"1\">Concurrent Efficiency: UDF deployment doesn&#8217;t hold back your workflow. It happens alongside other model operations, optimizing your development time.<\/li>\n<li aria-level=\"1\">Enhanced Metadata: Add descriptions to your UDFs, and they&#8217;ll magically appear in the data warehouse, providing valuable context for users.<\/li>\n<li aria-level=\"1\">Granular Control: Need to exclude a specific UDF for a particular task? No problem! dbt grants you the flexibility to select and exclude UDFs as needed.<\/li>\n<\/ul>\n<p>Creating a custom materialization is pretty straightforward for UDFs, because they are quite simple database objects. My minimal BigQuery implementation is the 25 lines of macro <a href=\"https:\/\/github.com\/brabster\/dbt_materialized_udf\/blob\/f54b2266abeaf788cf7a350f0f0678020d9dedbf\/macros\/materializations\/udf.sql\">here<\/a>. More importantly than that is how you use this new materialization. Here\u2019s a real-life example from my public pypi-vulnerabilities repository:<\/p>\n<p>&nbsp;<\/p>\n<pre>{{ config(\r\n\r\n\u00a0\u00a0\u00a0\u00a0materialized='udf',\r\n\r\n\u00a0\u00a0\u00a0\u00a0parameter_list='specs ARRAY&lt;STRING&gt;, version STRING',\r\n\r\n\u00a0\u00a0\u00a0\u00a0returns='BOOL',\r\n\r\n\u00a0\u00a0\u00a0\u00a0description='True when the version string matches any of an array of specs, where each spec may have an upper and lower bound. Example: 1.1.1 matches &gt;1.1.0,&lt;1.1.2, does not match &gt;1.1.0,&lt;1.1.1\"'\r\n\r\n) }}\r\n\r\nEXISTS (SELECT 1 FROM UNNEST(specs) spec WHERE {{ ref('matches_compound_spec') }}(spec, version))\r\n\r\n<\/pre>\n<p>That macro is called matches_multi_spec.\u00a0 `materialized=&#8217;udf&#8217;` is where I tell DBT this is a UDF model, and `{{ ref(&#8216;matches_compound_spec&#8217;) }}` is a standard dbt ref to another UDF &#8211; no more target.schema hacks.<\/p>\n<p>To give you an idea of how well this works &#8211; if I run `dbt build -s +matches_multi_spec`, asking dbt to compile, deploy and test the macro above and everything it depends on, dbt correctly works out the 14 UDFs that have to be built and tested.<\/p>\n<p>&nbsp;<\/p>\n<pre>08:50:05\u00a0 26 of 26 START test test_udf_matches_multi_spec ............... [RUN]\r\n\r\n08:50:05\u00a0 25 of 26 PASS test_udf_matches_compound_spec ............... [PASS in 1.54s]\r\n\r\n08:50:06\u00a0 26 of 26 PASS test_udf_matches_multi_spec ........................ [PASS in 1.60s]\r\n\r\n08:50:06\u00a0\u00a0\r\n\r\n08:50:06\u00a0 Finished running 14 udf models, 12 tests in 0 hours 0 minutes and 12.48 seconds (12.48s).\r\n\r\n08:50:06\u00a0\u00a0\r\n\r\n08:50:06\u00a0 Completed successfully\r\n\r\n08:50:06\u00a0\u00a0\r\n\r\n08:50:06\u00a0 Done. PASS=26 WARN=0 ERROR=0 SKIP=0 TOTAL=26\r\n\r\n<\/pre>\n<p>For a more in-depth explanation of the idea, how it works, and the implications, see the technical walkthrough post on <a href=\"https:\/\/tempered.works\/posts\/2024-02-19-udf-dbt-models\/\" target=\"_blank\" rel=\"noopener\">tempered.works<\/a>. There\u2019s an <a href=\"https:\/\/github.com\/brabster\/dbt_materialized_udf\" target=\"_blank\" rel=\"noopener\">experimental dbt package containing the UDF materialisation<\/a> on GitHub. Finally, the <a href=\"https:\/\/github.com\/brabster\/pypi_vulnerabilities\" target=\"_blank\" rel=\"noopener\">pypi-vulnerabilities GitHub repo<\/a> shows this technique in automated action as part of a real-world data pipeline,<\/p>\n<p>This blog post is more than just a technical solution; it&#8217;s a glimpse into a future where UDFs become integral members of the dbt ecosystem, empowering data professionals to work smarter and more efficiently. So, buckle up and join the ride towards UDF nirvana!<\/p>\n<pre><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Imagine a world where UDFs (user-defined functions) seamlessly integrate with your dbt workflow, automatically deploying, testing, and documenting themselves alongside your models. That\u2019s been on my wishlist since I started using dbt three years ago. To proceed with my recent work on vulnerability management behaviour in the Python ecosystem, I needed a solution for matching [&hellip;]<\/p>\n","protected":false},"author":168,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[711,246,357],"location":[397],"class_list":["post-13877","post","type-post","status-publish","format-standard","hentry","category-tech-focus","tag-nirvana","tag-udf","tag-udfs"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>UDF Nirvana? A new way to integrate UDFs into the dbt ecosystem<\/title>\n<meta name=\"description\" content=\"Enter the lightbulb moment: treat UDFs as dbt models with custom materialisation. 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