bigquery unit testing bigquery unit testing

How to automate unit testing and data healthchecks. to benefit from the implemented data literal conversion. Now lets imagine that our testData1 dataset which we created and tested above will be passed into a function. Queries are tested by running the query.sql with test-input tables and comparing the result to an expected table. What Is Unit Testing? By `clear` I mean the situation which is easier to understand. When youre migrating to BigQuery, you have a rich library of BigQuery native functions available to empower your analytics workloads. The ETL testing done by the developer during development is called ETL unit testing. Finally, If you are willing to write up some integration tests, you can aways setup a project on Cloud Console, and provide a service account for your to test to use. (Be careful with spreading previous rows (-<<: *base) here) 1. Is your application's business logic around the query and result processing correct. Run SQL unit test to check the object does the job or not. They can test the logic of your application with minimal dependencies on other services. to google-ap@googlegroups.com, de@nozzle.io. main_summary_v4.sql (see, In your unit test cases, mock BigQuery results to return from the previously serialized version of the Query output (see. Our test will be a stored procedure and will test the execution of a big SQL statement which consists of two parts: First part generates a source dataset to work with. How can I check before my flight that the cloud separation requirements in VFR flight rules are met? Find centralized, trusted content and collaborate around the technologies you use most. source, Uploaded The CrUX dataset on BigQuery is free to access and explore up to the limits of the free tier, which is renewed monthly and provided by BigQuery. Ideally, validations are run regularly at the end of an ETL to produce the data, while tests are run as part of a continuous integration pipeline to publish the code that will be used to run the ETL. Right-click the Controllers folder and select Add and New Scaffolded Item. - DATE and DATETIME type columns in the result are coerced to strings Then we need to test the UDF responsible for this logic. "PyPI", "Python Package Index", and the blocks logos are registered trademarks of the Python Software Foundation. TestNG is a testing framework inspired by JUnit and NUnit, but with some added functionalities. We tried our best, using Python for abstraction, speaking names for the tests, and extracting common concerns (e.g. So, this approach can be used for really big queries that involves more than 100 tables. I dont claim whatsoever that the solutions we came up with in this first iteration are perfect or even good but theyre a starting point. The open-sourced example shows how to run several unit tests on the community-contributed UDFs in the bigquery-utils repo. Improved development experience through quick test-driven development (TDD) feedback loops. results as dict with ease of test on byte arrays. Download the file for your platform. Uploaded # create datasets and tables in the order built with the dsl. I'd imagine you have a list of spawn scripts to create the necessary tables with schemas, load in some mock data, then write your SQL scripts to query against them. Import libraries import pandas as pd import pandas_gbq from google.cloud import bigquery %load_ext google.cloud.bigquery # Set your default project here pandas_gbq.context.project = 'bigquery-public-data' pandas_gbq.context.dialect = 'standard'. CREATE TABLE `project.testdataset.tablename` AS SELECT * FROM `project.proddataset.tablename` WHERE RAND () > 0.9 to get 10% of the rows. It is a serverless Cloud-based Data Warehouse that allows users to perform the ETL process on data with the help of some SQL queries. 1. The purpose is to ensure that each unit of software code works as expected. The above shown query can be converted as follows to run without any table created. Given the nature of Google bigquery (a serverless database solution), this gets very challenging. test and executed independently of other tests in the file. You have to test it in the real thing. For example: CREATE TEMP FUNCTION udf_example(option INT64) AS ( CASE WHEN option > 0 then TRUE WHEN option = 0 then FALSE ELSE . Using WITH clause, we can eliminate the Table creation and insertion steps from the picture. The unittest test framework is python's xUnit style framework. Dataforms command line tool solves this need, enabling you to programmatically execute unit tests for all your UDFs. They are narrow in scope. context manager for cascading creation of BQResource. We use this aproach for testing our app behavior with the dev server, and our BigQuery client setup checks for an env var containing the credentials of a service account to use, otherwise it uses the appengine service account. See Mozilla BigQuery API Access instructions to request credentials if you don't already have them. e.g. - Fully qualify table names as `{project}. Are there tables of wastage rates for different fruit and veg? datasets and tables in projects and load data into them. We'll write everything as PyTest unit tests, starting with a short test that will send SELECT 1, convert the result to a Pandas DataFrame, and check the results: import pandas as pd. Because were human and we all make mistakes, its a good idea to write unit tests to validate that your UDFs are behaving correctly. How do I concatenate two lists in Python? python -m pip install -r requirements.txt -r requirements-test.txt -e . Manually clone the repo and change into the correct directory by running the following: The first argument is a string representing the name of the UDF you will test. This tutorial provides unit testing template which could be used to: https://cloud.google.com/blog/products/data-analytics/command-and-control-now-easier-in-bigquery-with-scripting-and-stored-procedures. Quilt Make Sure To Unit Test Your BigQuery UDFs With Dataform, Apache Cassandra On Anthos: Scaling Applications For A Global Market, Artifact Registry For Language Packages Now Generally Available, Best JanSport Backpack Bags For Every Engineer, Getting Started With Terraform And Datastream: Replicating Postgres Data To BigQuery, To Grow The Brake Masters Network, IT Team Chooses ChromeOS, Building Streaming Data Pipelines On Google Cloud, Whats New And Whats Next With Google Cloud Databases, How Google Is Preparing For A Post-Quantum World, Achieving Cloud-Native Network Automation At A Global Scale With Nephio. A typical SQL unit testing scenario is as follows: During this process youd usually decompose those long functions into smaller functions, each with a single clearly defined responsibility and test them in isolation. The following excerpt demonstrates these generated SELECT queries and how the input(s) provided in test_cases.js are passed as arguments to the UDF being tested. If none of the above is relevant, then how does one perform unit testing on BigQuery? Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2. BigQuery scripting enables you to send multiple statements to BigQuery in one request, to use variables, and to use control flow statements such as IF and WHILE. All it will do is show that it does the thing that your tests check for. Unit Testing is defined as a type of software testing where individual components of a software are tested. after the UDF in the SQL file where it is defined. Execute the unit tests by running the following:dataform test. Special thanks to Dan Lee and Ben Birt for the continual feedback and guidance which made this blog post and testing framework possible. SELECT How do I align things in the following tabular environment? Now it is stored in your project and we dont need to create it each time again. DSL may change with breaking change until release of 1.0.0. Data loaders were restricted to those because they can be easily modified by a human and are maintainable. BigQuery has no local execution. Supported templates are Some combination of DBT, Great Expectations and a CI/CD pipeline should be able to do all of this. ( Follow Up: struct sockaddr storage initialization by network format-string, Linear regulator thermal information missing in datasheet. Other teams were fighting the same problems, too, and the Insights and Reporting Team tried moving to Google BigQuery first. Dataset and table resource management can be changed with one of the following : The DSL on dataset and table scope provides the following methods in order to change resource strategy : Contributions are welcome. All the datasets are included. While testing activity is expected from QA team, some basic testing tasks are executed by the . One of the ways you can guard against reporting on a faulty data upstreams is by adding health checks using the BigQuery ERROR() function. - If test_name is test_init or test_script, then the query will run init.sql Its a nested field by the way. Generate the Dataform credentials file .df-credentials.json by running the following:dataform init-creds bigquery. clients_daily_v6.yaml A substantial part of this is boilerplate that could be extracted to a library. | linktr.ee/mshakhomirov | @MShakhomirov. Narrative and scripts in one file with comments: bigquery_unit_tests_examples.sql. Lets chain first two checks from the very beginning with our UDF checks: Now lets do one more thing (optional) convert our test results to a JSON string. Google BigQuery is a highly Scalable Data Warehouse solution to store and query the data in a matter of seconds. Copy PIP instructions, View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery, Tags The framework takes the actual query and the list of tables needed to run the query as input. You can see it under `processed` column. You will see straight away where it fails: Now lets imagine that we need a clear test for a particular case when the data has changed. However, pytest's flexibility along with Python's rich. Im looking forward to getting rid of the limitations in size and development speed that Spark imposed on us, and Im excited to see how people inside and outside of our company are going to evolve testing of SQL, especially in BigQuery. Create a linked service to Google BigQuery using UI Use the following steps to create a linked service to Google BigQuery in the Azure portal UI. testing, Although this approach requires some fiddling e.g. Files This repo contains the following files: Final stored procedure with all tests chain_bq_unit_tests.sql. As the dataset, we chose one: the last transformation job of our track authorization dataset (called the projector), and its validation step, which was also written in Spark. # Default behavior is to create and clean. No more endless Chrome tabs, now you can organize your queries in your notebooks with many advantages . integration: authentication credentials for the Google Cloud API, If the destination table is also an input table then, Setting the description of a top level field to, Scalar query params should be defined as a dict with keys, Integration tests will only successfully run with service account keys Test data setup in TDD is complex in a query dominant code development. - Include the dataset prefix if it's set in the tested query, e.g. Did you have a chance to run. Its a CTE and it contains information, e.g. How to run SQL unit tests in BigQuery? Ive already touched on the cultural point that testing SQL is not common and not many examples exist. We've all heard of unittest and pytest, but testing database objects are sometimes forgotten about, or tested through the application. Now we could use UNION ALL to run a SELECT query for each test case and by doing so generate the test output. If the test is passed then move on to the next SQL unit test. - query_params must be a list. This affects not only performance in production which we could often but not always live with but also the feedback cycle in development and the speed of backfills if business logic has to be changed retrospectively for months or even years of data. Run this SQL below for testData1 to see this table example. For example, if your query transforms some input data and then aggregates it, you may not be able to detect bugs in the transformation purely by looking at the aggregated query result. For example, For every (transaction_id) there is one and only one (created_at): Now lets test its consecutive, e.g. This allows to have a better maintainability of the test resources. Press J to jump to the feed. Our user-defined function is BigQuery UDF built with Java Script. dsl, moz-fx-other-data.new_dataset.table_1.yaml We at least mitigated security concerns by not giving the test account access to any tables. We shared our proof of concept project at an internal Tech Open House and hope to contribute a tiny bit to a cultural shift through this blog post. consequtive numbers of transactions are in order with created_at timestmaps: Now lets wrap these two tests together with UNION ALL: Decompose your queries, just like you decompose your functions. Using BigQuery requires a GCP project and basic knowledge of SQL. It is distributed on npm as firebase-functions-test, and is a companion test SDK to firebase . Here we will need to test that data was generated correctly. In fact, they allow to use cast technique to transform string to bytes or cast a date like to its target type. Don't get me wrong, I don't particularly enjoy writing tests, but having a proper testing suite is one of the fundamental building blocks that differentiate hacking from software engineering. connecting to BigQuery and rendering templates) into pytest fixtures. The ideal unit test is one where you stub/mock the bigquery response and test your usage of specific responses, as well as validate well formed requests. This article describes how you can stub/mock your BigQuery responses for such a scenario. Before you can query the public datasets, you need to make sure the service account has at least the bigquery.user role . You can easily write your own UDF unit tests by creating your own Dataform project directory structure and adding a test_cases.js file with your own test cases. bq-test-kit[shell] or bq-test-kit[jinja2]. Install the Dataform CLI tool:npm i -g @dataform/cli && dataform install, 3. A Medium publication sharing concepts, ideas and codes. Indeed, BigQuery works with sets so decomposing your data into the views wont change anything. bq_test_kit.bq_dsl.bq_resources.data_loaders.base_data_loader.BaseDataLoader. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. Inspired by their initial successes, they gradually left Spark behind and moved all of their batch jobs to SQL queries in BigQuery. bqtk, The information schema tables for example have table metadata. If you reverse engineer a stored procedure it is typically a set of SQL scripts that are frequently used to serve the purpose. rename project as python-bigquery-test-kit, fix empty array generation for data literals, add ability to rely on temp tables or data literals with query template DSL, fix generate empty data literal when json array is empty, add data literal transformer package exports, Make jinja's local dictionary optional (closes #7), Wrap query result into BQQueryResult (closes #9), Fix time partitioning type in TimeField (closes #3), Fix table reference in Dataset (closes #2), BigQuery resource DSL to create dataset and table (partitioned or not). After that, you are able to run unit testing with tox -e clean, py36-ut from the root folder. How Intuit democratizes AI development across teams through reusability. I searched some corners of the internet I knew of for examples of what other people and companies were doing, but I didnt find a lot (I am sure there must be some out there; if youve encountered or written good examples, Im interested in learning about them). from pyspark.sql import SparkSession. But still, SoundCloud didnt have a single (fully) tested batch job written in SQL against BigQuery, and it also lacked best practices on how to test SQL queries. Specifically, it supports: Unit testing of BigQuery views and queries Data testing of BigQuery tables Usage bqtest datatest cloversense-dashboard.data_tests.basic_wagers_data_tests secrets/key.json Development Install package: pip install . There are probably many ways to do this. Furthermore, in json, another format is allowed, JSON_ARRAY. - Columns named generated_time are removed from the result before Even though BigQuery works with sets and doesnt use internal sorting we can ensure that our table is sorted, e.g. dataset, Then compare the output between expected and actual. That way, we both get regression tests when we re-create views and UDFs, and, when the view or UDF test runs against production, the view will will also be tested in production. The expected output you provide is then compiled into the following SELECT SQL statement which is used by Dataform to compare with the udf_output from the previous SQL statement: When you run the dataform test command, dataform calls BigQuery to execute these SELECT SQL statements and checks for equality between the actual and expected output of these SQL queries. The aim behind unit testing is to validate unit components with its performance. Import the required library, and you are done! When everything is done, you'd tear down the container and start anew. This procedure costs some $$, so if you don't have a budget allocated for Q.A. Add an invocation of the generate_udf_test() function for the UDF you want to test. You can export all of your raw events from Google Analytics 4 properties to BigQuery, and. To me, legacy code is simply code without tests. Michael Feathers. rev2023.3.3.43278. isolation, It provides assertions to identify test method. It allows you to load a file from a package, so you can load any file from your source code. Add the controller. The time to setup test data can be simplified by using CTE (Common table expressions). This way we don't have to bother with creating and cleaning test data from tables. Template queries are rendered via varsubst but you can provide your own Are you passing in correct credentials etc to use BigQuery correctly. e.g. In particular, data pipelines built in SQL are rarely tested. hence tests need to be run in Big Query itself. In their case, they had good automated validations, business people verifying their results, and an advanced development environment to increase the confidence in their datasets. Are you sure you want to create this branch? I strongly believe we can mock those functions and test the behaviour accordingly. Then, Dataform will validate the output with your expectations by checking for parity between the results of the SELECT SQL statements. To learn more, see our tips on writing great answers. (Recommended). CleanAfter : create without cleaning first and delete after each usage. I have run into a problem where we keep having complex SQL queries go out with errors. Your home for data science. They lay on dictionaries which can be in a global scope or interpolator scope. In my project, we have written a framework to automate this. All tables would have a role in the query and is subjected to filtering and aggregation. Those extra allows you to render you query templates with envsubst-like variable or jinja. At the top of the code snippet provided, you can see that unit_test_utils.js file exposes the generate_udf_test function. If you did - lets say some code that instantiates an object for each result row - then we could unit test that. This is the default behavior. Of course, we educated ourselves, optimized our code and configuration, and threw resources at the problem, but this cost time and money. A unit test is a type of software test that focuses on components of a software product. query = query.replace("analysis.clients_last_seen_v1", "clients_last_seen_v1") thus you can specify all your data in one file and still matching the native table behavior. Hash a timestamp to get repeatable results. If untested code is legacy code, why arent we testing data pipelines or ETLs (extract, transform, load)? Testing SQL is often a common problem in TDD world. Prerequisites Chaining SQL statements and missing data always was a problem for me. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. BigQuery offers sophisticated software as a service (SaaS) technology that can be used for serverless data warehouse operations. test_single_day How to link multiple queries and test execution. In this example we are going to stack up expire_time_after_purchase based on previous value and the fact that the previous purchase expired or not. Is there any good way to unit test BigQuery operations? What I would like to do is to monitor every time it does the transformation and data load. With BigQuery, you can query terabytes of data without needing a database administrator or any infrastructure to manage.. These tables will be available for every test in the suite. that defines a UDF that does not define a temporary function is collected as a Creating all the tables and inserting data into them takes significant time. You could also just run queries or interact with metadata via the API and then check the results outside of BigQuery in whatever way you want. I'm a big fan of testing in general, but especially unit testing. We have a single, self contained, job to execute. Data Literal Transformers can be less strict than their counter part, Data Loaders. Clone the bigquery-utils repo using either of the following methods: Automatically clone the repo to your Google Cloud Shell by clicking here. While rendering template, interpolator scope's dictionary is merged into global scope thus, You can create issue to share a bug or an idea. We already had test cases for example-based testing for this job in Spark; its location of consumption was BigQuery anyway; the track authorization dataset is one of the datasets for which we dont expose all data for performance reasons, so we have a reason to move it; and by migrating an existing dataset, we made sure wed be able to compare the results. e.g. in tests/assert/ may be used to evaluate outputs. Lets imagine we have some base table which we need to test. If you need to support a custom format, you may extend BaseDataLiteralTransformer The schema.json file need to match the table name in the query.sql file. Automated Testing. Given that, tests are subject to run frequently while development, reducing the time taken to run the tests is really important. Manually raising (throwing) an exception in Python, How to upgrade all Python packages with pip. query = query.replace("telemetry.main_summary_v4", "main_summary_v4") interpolator scope takes precedence over global one. those supported by varsubst, namely envsubst-like (shell variables) or jinja powered. - table must match a directory named like {dataset}/{table}, e.g. Its a nice and easy way to work with table data because you can pass into a function as a whole and implement any business logic you need. This is used to validate that each unit of the software performs as designed. In automation testing, the developer writes code to test code. If you're not sure which to choose, learn more about installing packages. How to automate unit testing and data healthchecks. """, -- replace monetizing policies in non-monetizing territories and split intervals, -- now deduplicate / merge consecutive intervals with same values, Leveraging a Manager Weekly Newsletter for Team Communication. The difference between the phonemes /p/ and /b/ in Japanese, Replacing broken pins/legs on a DIP IC package. You can benefit from two interpolators by installing the extras bq-test-kit[shell] or bq-test-kit[jinja2]. Is there an equivalent for BigQuery? Below is an excerpt from test_cases.js for the url_parse UDF which receives as inputs a URL and the part of the URL you want to extract, like the host or the path, and returns that specified part from the URL path. Or 0.01 to get 1%. Some of the advantages of having tests and not only validations are: My team, the Content Rights Team, used to be an almost pure backend team. Simply name the test test_init. You can also extend this existing set of functions with your own user-defined functions (UDFs). pip install bigquery-test-kit While youre still in the dataform_udf_unit_test directory, set the two environment variables below with your own values then create your Dataform project directory structure with the following commands: 2. thus query's outputs are predictable and assertion can be done in details. If you need to support more, you can still load data by instantiating Also, it was small enough to tackle in our SAT, but complex enough to need tests. The purpose of unit testing is to test the correctness of isolated code. You have to test it in the real thing. Some features may not work without JavaScript.