bigquery unit testing bigquery unit testing

python -m pip install -r requirements.txt -r requirements-test.txt -e . The scenario for which this solution will work: The code available here: https://github.com/hicod3r/BigQueryUnitTesting and uses Mockito https://site.mockito.org/, https://github.com/hicod3r/BigQueryUnitTesting, You need to unit test a function which calls on BigQuery (SQL,DDL,DML), You dont actually want to run the Query/DDL/DML command, but just work off the results, You want to run several such commands, and want the output to match BigQuery output format, Store BigQuery results as Serialized Strings in a property file, where the query (md5 hashed) is the key. Start Bigtable Emulator during a test: Starting a Bigtable Emulator container public BigtableEmulatorContainer emulator = new BigtableEmulatorContainer( DockerImageName.parse("gcr.io/google.com/cloudsdktool/google-cloud-cli:380..-emulators") ); Create a test Bigtable table in the Emulator: Create a test table NUnit : NUnit is widely used unit-testing framework use for all .net languages. 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. Here is our UDF that will process an ARRAY of STRUCTs (columns) according to our business logic. CleanAfter : create without cleaning first and delete after each usage. 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. The dashboard gathering all the results is available here: Performance Testing Dashboard apps it may not be an option. In such a situation, temporary tables may come to the rescue as they don't rely on data loading but on data literals. 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. Automatically clone the repo to your Google Cloud Shellby. Validations are important and useful, but theyre not what I want to talk about here. Now we can do unit tests for datasets and UDFs in this popular data warehouse. bq_test_kit.data_literal_transformers.json_data_literal_transformer, bq_test_kit.interpolators.shell_interpolator, f.foo, b.bar, e.baz, f._partitiontime as pt, '{"foobar": "1", "foo": 1, "_PARTITIONTIME": "2020-11-26 17:09:03.967259 UTC"}', bq_test_kit.interpolators.jinja_interpolator, create and delete table, partitioned or not, transform json or csv data into a data literal or a temp table. 5. pip install bigquery-test-kit Many people may be more comfortable using spreadsheets to perform ad hoc data analysis. Already for Spark, its a challenge to express test data and assertions in a _simple-to-understand way_ tests are for reading. comparing to expect because they should not be static 1. In the meantime, the Data Platform Team had also introduced some monitoring for the timeliness and size of datasets. Assume it's a date string format // Other BigQuery temporal types come as string representations. bigquery, e.g. Acquired by Google Cloud in 2020, Dataform provides a useful CLI tool to orchestrate the execution of SQL queries in BigQuery. A unit test is a type of software test that focuses on components of a software product. At the top of the code snippet provided, you can see that unit_test_utils.js file exposes the generate_udf_test function. Post Graduate Program In Cloud Computing: https://www.simplilearn.com/pgp-cloud-computing-certification-training-course?utm_campaign=Skillup-CloudComputing. In the exmaple below purchase with transaction 70000001 expired at 20210122 09:01:00 and stucking MUST stop here until the next purchase. Data loaders were restricted to those because they can be easily modified by a human and are maintainable. Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? """, -- 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. How does one ensure that all fields that are expected to be present, are actually present? 1. All Rights Reserved. The tests had to be run in BigQuery, for which there is no containerized environment available (unlike e.g. Prerequisites There are probably many ways to do this. 1. Add an invocation of the generate_udf_test() function for the UDF you want to test. tests/sql/moz-fx-data-shared-prod/telemetry_derived/clients_last_seen_raw_v1/clients_daily_v6.schema.json. 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. How do I concatenate two lists in Python? context manager for cascading creation of BQResource. 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. 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. Even though BigQuery works with sets and doesnt use internal sorting we can ensure that our table is sorted, e.g. 2. How much will it cost to run these tests? Import the required library, and you are done! - table must match a directory named like {dataset}/{table}, e.g. All it will do is show that it does the thing that your tests check for. Each statement in a SQL file Then compare the output between expected and actual. And the great thing is, for most compositions of views, youll get exactly the same performance. How can I access environment variables in Python? Add .yaml files for input tables, e.g. 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. BigQuery doesn't provide any locally runnabled server, ', ' AS content_policy Now it is stored in your project and we dont need to create it each time again. Data Literal Transformers allows you to specify _partitiontime or _partitiondate as well, Decoded as base64 string. How do I align things in the following tabular environment? You have to test it in the real thing. Some features may not work without JavaScript. Donate today! Of course, we educated ourselves, optimized our code and configuration, and threw resources at the problem, but this cost time and money. 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. Note: Init SQL statements must contain a create statement with the dataset Tests must not use any query parameters and should not reference any tables. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. You then establish an incremental copy from the old to the new data warehouse to keep the data. 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. 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 this example with UDF (just add this code in the end of the previous SQL where we declared UDF) to see how the source table from testData1 will be processed: What we need to test now is how this function calculates newexpire_time_after_purchase time. Add the controller. thus you can specify all your data in one file and still matching the native table behavior. We can now schedule this query to run hourly for example and receive notification if error was raised: In this case BigQuery will send an email notification and other downstream processes will be stopped. This is the default behavior. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Improved development experience through quick test-driven development (TDD) feedback loops. e.g. You will have to set GOOGLE_CLOUD_PROJECT env var as well in order to run tox. BigQuery has no local execution. Since Google BigQuery introduced Dynamic SQL it has become a lot easier to run repeating tasks with scripting jobs. telemetry_derived/clients_last_seen_v1 Then, a tuples of all tables are returned. query = query.replace("telemetry.main_summary_v4", "main_summary_v4") This page describes best practices and tools for writing unit tests for your functions, such as tests that would be a part of a Continuous Integration (CI) system. While rendering template, interpolator scope's dictionary is merged into global scope thus, To learn more, see our tips on writing great answers. If the test is passed then move on to the next SQL unit test. 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 How to link multiple queries and test execution. 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. Now lets imagine that our testData1 dataset which we created and tested above will be passed into a function. You can implement yours by extending bq_test_kit.resource_loaders.base_resource_loader.BaseResourceLoader. This is used to validate that each unit of the software performs as designed. connecting to BigQuery and rendering templates) into pytest fixtures. CREATE TABLE `project.testdataset.tablename` AS SELECT * FROM `project.proddataset.tablename` WHERE RAND () > 0.9 to get 10% of the rows. SQL unit tests in BigQuery Aims The aim of this project is to: How to write unit tests for SQL and UDFs in BigQuery. Not all of the challenges were technical. By: Michaella Schaszberger (Strategic Cloud Engineer) and Daniel De Leo (Strategic Cloud Engineer)Source: Google Cloud Blog, If theres one thing the past 18 months have taught us, its that the ability to adapt to, The National Institute of Standards and Technology (NIST) on Tuesday announced the completion of the third round of, In 2007, in order to meet ever increasing traffic demands of YouTube, Google started building what is now, Today, millions of users turn to Looker Studio for self-serve business intelligence (BI) to explore data, answer business. Now we could use UNION ALL to run a SELECT query for each test case and by doing so generate the test output. Go to the BigQuery integration page in the Firebase console. BigQuery helps users manage and analyze large datasets with high-speed compute power. bq_test_kit.data_literal_transformers.base_data_literal_transformer.BaseDataLiteralTransformer. and table name, like so: # install pip-tools for managing dependencies, # install python dependencies with pip-sync (provided by pip-tools), # run pytest with all linters and 8 workers in parallel, # use -k to selectively run a set of tests that matches the expression `udf`, # narrow down testpaths for quicker turnaround when selecting a single test, # run integration tests with 4 workers in parallel. If you're not sure which to choose, learn more about installing packages. that defines a UDF that does not define a temporary function is collected as a How to link multiple queries and test execution. all systems operational. # clean and keep will keep clean dataset if it exists before its creation. Even amount of processed data will remain the same. It's also supported by a variety of tools and plugins, such as Eclipse, IDEA, and Maven. Indeed, BigQuery works with sets so decomposing your data into the views wont change anything. .builder. Supported templates are EXECUTE IMMEDIATE SELECT CONCAT([, STRING_AGG(TO_JSON_STRING(t), ,), ]) data FROM test_results t;; SELECT COUNT(*) as row_count FROM yourDataset.yourTable. Google BigQuery is a serverless and scalable enterprise data warehouse that helps businesses to store and query data. Currently, the only resource loader available is bq_test_kit.resource_loaders.package_file_loader.PackageFileLoader. Each test that is expected to fail must be preceded by a comment like #xfail, similar to a SQL dialect prefix in the BigQuery Cloud Console. Interpolators enable variable substitution within a template. Validations are what increase confidence in data, and tests are what increase confidence in code used to produce the data. (Recommended). https://cloud.google.com/bigquery/docs/reference/standard-sql/scripting, https://cloud.google.com/bigquery/docs/information-schema-tables. {dataset}.table` Run your unit tests to see if your UDF behaves as expected:dataform test. The Kafka community has developed many resources for helping to test your client applications. "PyPI", "Python Package Index", and the blocks logos are registered trademarks of the Python Software Foundation. Is your application's business logic around the query and result processing correct. Does Python have a ternary conditional operator? 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. What is Unit Testing? isolation, Final stored procedure with all tests chain_bq_unit_tests.sql. Is there any good way to unit test BigQuery operations? Instead it would be much better to user BigQuery scripting to iterate through each test cases data, generate test results for each case and insert all results into one table in order to produce one single output. expected to fail must be preceded by a comment like #xfail, similar to a SQL Select Web API 2 Controller with actions, using Entity Framework. The above shown query can be converted as follows to run without any table created. Not the answer you're looking for? They can test the logic of your application with minimal dependencies on other services. All it will do is show that it does the thing that your tests check for. You can create merge request as well in order to enhance this project. table, query = query.replace("analysis.clients_last_seen_v1", "clients_last_seen_v1") The unittest test framework is python's xUnit style framework. And it allows you to add extra things between them, and wrap them with other useful ones, just as you do in procedural code. No more endless Chrome tabs, now you can organize your queries in your notebooks with many advantages . When youre migrating to BigQuery, you have a rich library of BigQuery native functions available to empower your analytics workloads. If you did - lets say some code that instantiates an object for each result row - then we could unit test that. Tests must not use any You can define yours by extending bq_test_kit.interpolators.BaseInterpolator. 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. 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. You can see it under `processed` column. Unit Testing Unit tests run very quickly and verify that isolated functional blocks of code work as expected. This procedure costs some $$, so if you don't have a budget allocated for Q.A. How do you ensure that a red herring doesn't violate Chekhov's gun? Even though the framework advertises its speed as lightning-fast, its still slow for the size of some of our datasets. # create datasets and tables in the order built with the dsl. Quilt 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). Optionally add query_params.yaml to define query parameters Run it more than once and you'll get different rows of course, since RAND () is random. It struck me as a cultural problem: Testing didnt seem to be a standard for production-ready data pipelines, and SQL didnt seem to be considered code. In fact, data literal may add complexity to your request and therefore be rejected by BigQuery. bq_test_kit.bq_dsl.bq_resources.data_loaders.base_data_loader.BaseDataLoader. I strongly believe we can mock those functions and test the behaviour accordingly. Queries are tested by running the query.sql with test-input tables and comparing the result to an expected table. It's good for analyzing large quantities of data quickly, but not for modifying it. This makes them shorter, and easier to understand, easier to test.