How to write unit tests for SQL and UDFs in BigQuery. 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. bigquery-test-kit enables Big Query testing by providing you an almost immutable DSL that allows you to : You can, therefore, test your query with data as literals or instantiate BigQuery has no local execution. Currently, the only resource loader available is bq_test_kit.resource_loaders.package_file_loader.PackageFileLoader. to google-ap@googlegroups.com, de@nozzle.io. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. Given that, tests are subject to run frequently while development, reducing the time taken to run the tests is really important. Indeed, if we store our view definitions in a script (or scripts) to be run against the data, we can add our tests for each view to the same script. However, since the shift toward data-producing teams owning datasets which took place about three years ago weve been responsible for providing published datasets with a clearly defined interface to consuming teams like the Insights and Reporting Team, content operations teams, and data scientists. Other teams were fighting the same problems, too, and the Insights and Reporting Team tried moving to Google BigQuery first. If a column is expected to be NULL don't add it to expect.yaml. While rendering template, interpolator scope's dictionary is merged into global scope thus, test and executed independently of other tests in the file. Files This repo contains the following files: Final stored procedure with all tests chain_bq_unit_tests.sql. Just follow these 4 simple steps:1. What I would like to do is to monitor every time it does the transformation and data load. Find centralized, trusted content and collaborate around the technologies you use most. Especially, when we dont have an embedded database server for testing, creating these tables and inserting data into these takes quite some time whenever we run the tests. 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. # table `GOOGLE_CLOUD_PROJECT.my_dataset_basic.my_table` is created. Making statements based on opinion; back them up with references or personal experience. or script.sql respectively; otherwise, the test will run query.sql What Is Unit Testing? In your code, there's two basic things you can be testing: For (1), no unit test is going to provide you actual reassurance that your code works on GCP. - table must match a directory named like {dataset}/{table}, e.g. Create a SQL unit test to check the object. resource definition sharing accross tests made possible with "immutability". query = query.replace("telemetry.main_summary_v4", "main_summary_v4") DSL may change with breaking change until release of 1.0.0. Execute the unit tests by running the following:dataform test. The second one will test the logic behind the user-defined function (UDF) that will be later applied to a source dataset to transform it. 1. 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. To make testing easier, Firebase provides the Firebase Test SDK for Cloud Functions. All Rights Reserved. in tests/assert/ may be used to evaluate outputs. 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. How do I concatenate two lists in Python? that you can assign to your service account you created in the previous step. BigQuery offers sophisticated software as a service (SaaS) technology that can be used for serverless data warehouse operations. As a new bee in python unit testing, I need a better way of mocking all those bigquery functions so that I don't need to use actual bigquery to run a query. For this example I will use a sample with user transactions. For example: CREATE TEMP FUNCTION udf_example(option INT64) AS ( CASE WHEN option > 0 then TRUE WHEN option = 0 then FALSE ELSE . In order to benefit from VSCode features such as debugging, you should type the following commands in the root folder of this project. moz-fx-other-data.new_dataset.table_1.yaml 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. pip3 install -r requirements.txt -r requirements-test.txt -e . 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. Acquired by Google Cloud in 2020, Dataform provides a useful CLI tool to orchestrate the execution of SQL queries in BigQuery. 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. 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). The time to setup test data can be simplified by using CTE (Common table expressions). You can create merge request as well in order to enhance this project. test_single_day Our user-defined function is BigQuery UDF built with Java Script. Before you can query the public datasets, you need to make sure the service account has at least the bigquery.user role . those supported by varsubst, namely envsubst-like (shell variables) or jinja powered. It will iteratively process the table, check IF each stacked product subscription expired or not. Now we could use UNION ALL to run a SELECT query for each test case and by doing so generate the test output. Method: White Box Testing method is used for Unit testing. This is how you mock google.cloud.bigquery with pytest, pytest-mock. For Go, an option to write such wrapper would be to write an interface for your calls, and write an stub implementaton with the help of the. tests/sql/moz-fx-data-shared-prod/telemetry_derived/clients_last_seen_raw_v1/clients_daily_v6.schema.json. But first we will need an `expected` value for each test. e.g. Dataforms command line tool solves this need, enabling you to programmatically execute unit tests for all your UDFs. This makes them shorter, and easier to understand, easier to test. Optionally add query_params.yaml to define query parameters Template queries are rendered via varsubst but you can provide your own e.g. For some of the datasets, we instead filter and only process the data most critical to the business (e.g. 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. Compile and execute your Java code into an executable JAR file Add unit test for your code All of these tasks will be done on the command line, so that you can have a better idea on what's going on under the hood, and how you can run a java application in environments that don't have a full-featured IDE like Eclipse or IntelliJ. Final stored procedure with all tests chain_bq_unit_tests.sql. And it allows you to add extra things between them, and wrap them with other useful ones, just as you do in procedural code. I am having trouble in unit testing the following code block: I am new to mocking and I have tried the following test: Can anybody mock the google stuff and write a unit test please? Here comes WITH clause for rescue. Running your UDF unit tests with the Dataform CLI tool and BigQuery is free thanks to the following: In the following sections, well explain how you can run our example UDF unit tests and then how to start writing your own. Is there an equivalent for BigQuery? Did you have a chance to run. This write up is to help simplify and provide an approach to test SQL on Google bigquery. Add .yaml files for input tables, e.g. Weve been using technology and best practices close to what were used to for live backend services in our dataset, including: However, Spark has its drawbacks. Right-click the Controllers folder and select Add and New Scaffolded Item. 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. A unit test is a type of software test that focuses on components of a software product. Lets say we have a purchase that expired inbetween. If you provide just the UDF name, the function will use the defaultDatabase and defaultSchema values from your dataform.json file. pip install bigquery-test-kit Immutability allows you to share datasets and tables definitions as a fixture and use it accros all tests, Please try enabling it if you encounter problems. - query_params must be a list. You do not have permission to delete messages in this group, Either email addresses are anonymous for this group or you need the view member email addresses permission to view the original message. Reddit and its partners use cookies and similar technologies to provide you with a better experience. Chaining SQL statements and missing data always was a problem for me. His motivation was to add tests to his teams untested ETLs, while mine was to possibly move our datasets without losing the tests. You will have to set GOOGLE_CLOUD_PROJECT env var as well in order to run tox. Now it is stored in your project and we dont need to create it each time again. If you are running simple queries (no DML), you can use data literal to make test running faster. How to automate unit testing and data healthchecks. Tests of init.sql statements are supported, similarly to other generated tests. We will provide a few examples below: Junit: Junit is a free to use testing tool used for Java programming language. after the UDF in the SQL file where it is defined. For example, if a SQL query involves N number of tables, then the test data has to be setup for all the N tables. 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). Run SQL unit test to check the object does the job or not. Why are physically impossible and logically impossible concepts considered separate in terms of probability? Unit tests generated by PDK test only whether the manifest compiles on the module's supported operating systems, and you can write tests that test whether your code correctly performs the functions you expect it to. Test data is provided as static values in the SQL queries that the Dataform CLI executes; no table data is scanned and no bytes are processed per query. See Mozilla BigQuery API Access instructions to request credentials if you don't already have them. They are just a few records and it wont cost you anything to run it in BigQuery. 1. Include a comment like -- Tests followed by one or more query statements The aim behind unit testing is to validate unit components with its performance. our base table is sorted in the way we need it. telemetry.main_summary_v4.sql 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. I'm a big fan of testing in general, but especially unit testing. 1. After that, you are able to run unit testing with tox -e clean, py36-ut from the root folder. 1. https://cloud.google.com/bigquery/docs/reference/standard-sql/scripting, https://cloud.google.com/bigquery/docs/information-schema-tables. BigQuery doesn't provide any locally runnabled server, How do you ensure that a red herring doesn't violate Chekhov's gun? What is Unit Testing? For example, For every (transaction_id) there is one and only one (created_at): Now lets test its consecutive, e.g. The dashboard gathering all the results is available here: Performance Testing Dashboard The open-sourced example shows how to run several unit tests on the community-contributed UDFs in the bigquery-utils repo. EXECUTE IMMEDIATE SELECT CONCAT([, STRING_AGG(TO_JSON_STRING(t), ,), ]) data FROM test_results t;; SELECT COUNT(*) as row_count FROM yourDataset.yourTable. Consider that we have to run the following query on the above listed tables. Using WITH clause, we can eliminate the Table creation and insertion steps from the picture. Who knows, maybe youd like to run your test script programmatically and get a result as a response in ONE JSON row. https://cloud.google.com/bigquery/docs/information-schema-tables. Now when I talked to our data scientists or data engineers, I heard some of them say Oh, we do have tests! So, this approach can be used for really big queries that involves more than 100 tables. CleanBeforeAndKeepAfter : clean before each creation and don't clean resource after each usage. The Kafka community has developed many resources for helping to test your client applications. Unit tests are a good fit for (2), however your function as it currently stands doesn't really do anything. bq_test_kit.resource_loaders.package_file_loader, # project() uses default one specified by GOOGLE_CLOUD_PROJECT environment variable, # dataset `GOOGLE_CLOUD_PROJECT.my_dataset_basic` is created. While it might be possible to improve the mocks here, it isn't going to provide much value to you as a test. Fortunately, the owners appreciated the initiative and helped us. When you run the dataform test command, these SELECT SQL statements will be run in BigQuery. Make data more reliable and/or improve their SQL testing skills. BigQuery has no local execution. that belong to the. It's faster to run query with data as literals but using materialized tables is mandatory for some use cases. (Recommended). e.g. In the example provided, there is a file called test_cases.js that contains unit test inputs and expected outputs for the UDFs tested. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. query = query.replace("analysis.clients_last_seen_v1", "clients_last_seen_v1") Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The diagram above illustrates how the Dataform CLI uses the inputs and expected outputs in test_cases.js to construct and execute BigQuery SQL queries. CleanAfter : create without cleaning first and delete after each usage. You can define yours by extending bq_test_kit.interpolators.BaseInterpolator. If it has project and dataset listed there, the schema file also needs project and dataset. e.g. I have run into a problem where we keep having complex SQL queries go out with errors. interpolator scope takes precedence over global one. query parameters and should not reference any tables. Unit Testing Unit tests run very quickly and verify that isolated functional blocks of code work as expected. In automation testing, the developer writes code to test code. How to automate unit testing and data healthchecks. This procedure costs some $$, so if you don't have a budget allocated for Q.A. The purpose is to ensure that each unit of software code works as expected. Refer to the json_typeof UDF in the test_cases.js for an example of this implementation. But with Spark, they also left tests and monitoring behind. Also, I have seen docker with postgres DB container being leveraged for testing against AWS Redshift, Spark (or was it PySpark), etc. only export data for selected territories), or we use more complicated logic so that we need to process less data (e.g.
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