{"id":10787,"date":"2022-03-07T09:03:16","date_gmt":"2022-03-07T09:03:16","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=10787"},"modified":"2024-12-10T03:42:42","modified_gmt":"2024-12-10T03:42:42","slug":"testing-and-deploying-udfs-with-dbt","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/our-thinking\/testing-and-deploying-udfs-with-dbt\/","title":{"rendered":"Testing and deploying UDFs with dbt"},"content":{"rendered":"<p>Edit: You might be interested in our updated solution to this problem: <a href=\"https:\/\/www.equalexperts.com\/blog\/tech-focus\/udf-nirvana-a-new-way-to-integrate-udfs-into-the-dbt-ecosystem\/\" target=\"_blank\" rel=\"noopener\">UDF Nirvana? A new way to integrate UDFs into the dbt ecosystem<\/a><\/p>\n<p>Inspired by our previous post on <a href=\"https:\/\/www.equalexperts.com\/blog\/our-thinking\/writing-unit-tests-for-dbt-with-tdd\/\">unit testing dbt<\/a> and <a href=\"https:\/\/discourse.getdbt.com\/t\/using-dbt-to-manage-user-defined-functions\/18\">discussion in the dbt forums<\/a>, I looked into how to get dbt to help me test and deploy UDFs. Although dbt doesn&#8217;t directly support UDFs, it turns out it&#8217;s actually fairly straightforward to define, test and reference them in your models. I&#8217;ll be using BigQuery in this post, but I think the techniques will apply to other warehouses that support UDFs like Snowflake, Redshift and Spark, too.<\/p>\n<h2>What&#8217;s a UDF and what does it have to do with dbt?<\/h2>\n<p>A UDF is a <a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reference\/standard-sql\/user-defined-functions\">User-Defined Function<\/a>. Like any other type of function, they&#8217;re really helpful to capture and name chunks of logic that you can apply in queries, and I talked about them before, starting from Tip 4 in our <a href=\"https:\/\/www.equalexperts.com\/blog\/our-thinking\/data-tips-the-data-warehouse\/\">Data Warehouse Tips post<\/a>. dbt takes care of deploying the tables and views that you might use your UDFs in, so robustly referencing a function from your models is important. When I say &#8216;robustly&#8217;, I mean ensuring that the function exists and that you&#8217;re calling the function from the appropriate location &#8211; for example, avoiding hard-coding your production functions everywhere. There&#8217;s no native support for referencing a UDF like there is with `source` or `ref`, nor any native way to deploy a function in a project. This post explains how you can solve those problems.<\/p>\n<h2>The example<\/h2>\n<p>I have a BigQuery dataset that I use for my personal development work. This is referenced in my own dbt profile. When I run dbt commands on my machine, they take effect here.<\/p>\n<p>There are test and production datasets that my CI system has profiles for. I cannot run ad-hoc dbt commands in these datasets. When I push my changes, the CI system will run any dbt commands necessary to deploy my updates and run tests. I&#8217;ll not be talking about that in this post, but it&#8217;s important that I&#8217;m referencing the UDF that is deployed in the same environment that I&#8217;m working in &#8211; otherwise I can&#8217;t iterate safely, relying on my tests.<\/p>\n<p>The example UDF I&#8217;ll use is a simple predicate function, located in `my_schema`, from string to bool that returns true if and only if the string value can be interpreted as a positive integer:<\/p>\n<pre>CREATE OR REPLACE FUNCTION my_schema.is_positive_int(a_string STRING)\r\n\r\nRETURNS BOOLEAN\r\n\r\nAS (\r\n\r\n\u00a0\u00a0REGEXP_CONTAINS(a_string, r'^[0-9]+$')\r\n\r\n);<\/pre>\n<h2>Defining a UDF in dbt<\/h2>\n<p>We need to write a dbt &#8220;macro&#8221; &#8211; a Jinja template &#8211; that parameterises where the UDF should go. In `macros\/is_positive_int_udf.sql`:<\/p>\n<pre>{% macro is_positive_int_udf() %}\r\n\r\nCREATE OR REPLACE FUNCTION {{ target.schema }}.is_positive_int(a_string STRING)\r\n\r\nRETURNS BOOLEAN\r\n\r\nAS (\r\n\r\n\u00a0\u00a0REGEXP_CONTAINS(a_string, r'^[0-9]+$')\r\n\r\n);\r\n\r\n{% endmacro %}<\/pre>\n<p>Note the macro name matches the file and that `target.schema` is templated out.<\/p>\n<p>To have dbt actually deploy this for us, we add a hook in our `dbt_project.yml`:<\/p>\n<pre>on-run-start:\r\n\r\n- '{{ is_positive_int_udf() }}'<\/pre>\n<p>Now, when we `dbt run`, the UDF will be deployed.<\/p>\n<pre>11:11:52\u00a0 Running 1 on-run-start hooks\r\n\r\n11:11:52\u00a0 1 of 1 START hook: my_project.on-run-start.0.................... [RUN]\r\n\r\n11:11:52\u00a0 1 of 1 OK hook: my_project.on-run-start.0....................... [OK in 0.49s]<\/pre>\n<p>Kind of yucky in the console output with just list indices to identify what&#8217;s what &#8211;\u00a0 but not the end of the world.<\/p>\n<h2>Testing a UDF in dbt<\/h2>\n<p>Now our UDF is schema-aware and is being deployed by dbt, we can write tests for it with minimal ceremony. In `tests\/is_positive_int.sql`:<\/p>\n<pre>WITH examples AS (\r\n\r\n\u00a0\u00a0SELECT 'foo' the_string, FALSE expected\r\n\r\n\u00a0\u00a0UNION ALL SELECT '1111', TRUE\r\n\r\n\u00a0\u00a0UNION ALL SELECT '5 not this', FALSE\r\n\r\n\u00a0\u00a0UNION ALL SELECT 'still not this 5', FALSE\r\n\r\n\u00a0\u00a0UNION ALL SELECT NULL, NULL\r\n\r\n)\r\n\r\n\r\n, test AS (\r\n\r\n\u00a0\u00a0SELECT\r\n\r\n\u00a0*,\r\n\r\n\u00a0\u00a0{{ target.schema }}.is_positive_int(the_string) actual\r\n\r\n\u00a0\u00a0FROM examples\r\n\r\n)\r\n\r\n\r\n\r\nSELECT * FROM test WHERE TO_JSON_STRING(actual) != TO_JSON_STRING(expected)<\/pre>\n<p>Running `dbt test` runs the pre-hooks and then the tests, perfect.<\/p>\n<pre>...\r\n\r\n11:25:22\u00a0 Running 1 on-run-start hooks\r\n\r\n11:25:22\u00a0 1 of 1 START hook: my_project.on-run-start.0.................... [RUN]\r\n\r\n11:25:23\u00a0 1 of 1 OK hook: my_project.on-run-start.0....................... [OK in 0.58s]\r\n\r\n...\r\n\r\n11:25:24\u00a0 1 of 1 START test is_positive_int............................... [RUN]\r\n\r\n11:25:25\u00a0 1 of 1 PASS is_positive_int..................................... [PASS in 1.02s]\r\n\r\n\r\n\r\n11:25:26\u00a0 Finished running 1 tests, 1 hooks in 1.46s.<\/pre>\n<h2>Using the UDF in a Model<\/h2>\n<p>We&#8217;ve actually already seen how to do this, in the test.<\/p>\n<p>In `models\/my_model.sql`:<\/p>\n<pre>SELECT {{ target.schema }}.is_positive_int(maybe_positive_int_column)\r\n\r\nFROM {{ ref(...) }}<\/pre>\n<h2>Summary<\/h2>\n<p>There you have it. In summary, to incorporate UDFs neatly into your dbt models:<\/p>\n<ul>\n<li aria-level=\"1\">Write a macro for your UDF, templating the target schema<\/li>\n<li aria-level=\"1\">Add a call to macro to `on-run-start` in your `dbt_project.yaml`<\/li>\n<li aria-level=\"1\">Write some tests, of course<\/li>\n<li aria-level=\"1\">Use the UDF in your models, templating the target schema<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Although dbt doesn\u2019t directly support UDFs, it\u2019s actually fairly straightforward to define, test and reference them in your models.  Data specialist Paul Brabban explains how.<\/p>\n","protected":false},"author":168,"featured_media":10789,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[5],"tags":[185,226,246],"location":[397],"class_list":["post-10787","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-our-thinking","tag-data","tag-dbt","tag-udf"],"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>Testing and deploying UDFs with dbt | Equal Experts<\/title>\n<meta name=\"description\" content=\"Paul Brabban, data specialist at Equal Experts, looks at how to get dbt to help test and deploy User Defined Functions (UDFs).\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.equalexperts.com\/blog\/our-thinking\/testing-and-deploying-udfs-with-dbt\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Testing and deploying UDFs with dbt\" \/>\n<meta property=\"og:description\" content=\"Although dbt doesn\u2019t directly support UDFs, it\u2019s actually fairly straightforward to define, test and reference them in your models. Data specialist Paul Brabban explains how.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.equalexperts.com\/blog\/our-thinking\/testing-and-deploying-udfs-with-dbt\/\" \/>\n<meta property=\"og:site_name\" content=\"Equal Experts\" \/>\n<meta property=\"article:published_time\" content=\"2022-03-07T09:03:16+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-12-10T03:42:42+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2022\/03\/DataTestingCodingUnit_blog_FB.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"630\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Paul Brabban\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Testing and deploying UDFs with dbt\" \/>\n<meta name=\"twitter:description\" content=\"Although dbt doesn\u2019t directly support UDFs, it\u2019s actually fairly straightforward to define, test and reference them in your models. 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