bigquery unit testing

Now when I talked to our data scientists or data engineers, I heard some of them say Oh, we do have tests! test and executed independently of other tests in the file. In the meantime, the Data Platform Team had also introduced some monitoring for the timeliness and size of datasets. 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'. The Kafka community has developed many resources for helping to test your client applications. Lets simply change the ending of our stored procedure to this: We can extend our use case to perform the healthchecks on real data. In the exmaple below purchase with transaction 70000001 expired at 20210122 09:01:00 and stucking MUST stop here until the next purchase. An individual component may be either an individual function or a procedure. Test data setup in TDD is complex in a query dominant code development. Lets imagine we have some base table which we need to test. 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. Google Clouds Professional Services Organization open-sourced an example of how to use the Dataform CLI together with some template code to run unit tests on BigQuery UDFs. It provides assertions to identify test method. Connect and share knowledge within a single location that is structured and easy to search. For example, if a SQL query involves N number of tables, then the test data has to be setup for all the N tables. thus query's outputs are predictable and assertion can be done in details. After that, you are able to run unit testing with tox -e clean, py36-ut from the root folder. Data context class: [Select New data context button which fills in the values seen below] Click Add to create the controller with automatically-generated code. Each test that is bq_test_kit.bq_dsl.bq_resources.data_loaders.base_data_loader.BaseDataLoader. Simply name the test test_init. 1. """, -- 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. This way we dont have to bother with creating and cleaning test data from tables. You can see it under `processed` column. Improved development experience through quick test-driven development (TDD) feedback loops. Google BigQuery is the new online service for running interactive queries over vast amounts of dataup to billions of rowswith great speed. How to write unit tests for SQL and UDFs in BigQuery. Migrating Your Data Warehouse To BigQuery? And SQL is code. Supported data literal transformers are csv and json. The ETL testing done by the developer during development is called ETL unit testing. Create a SQL unit test to check the object. BigQuery has no local execution. But not everyone is a BigQuery expert or a data specialist. Acquired by Google Cloud in 2020, Dataform provides a useful CLI tool to orchestrate the execution of SQL queries in BigQuery. 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. Before you can query the public datasets, you need to make sure the service account has at least the bigquery.user role . Run SQL unit test to check the object does the job or not. query = query.replace("analysis.clients_last_seen_v1", "clients_last_seen_v1") So in this post, Ill describe how we started testing SQL data pipelines at SoundCloud. Here is our UDF that will process an ARRAY of STRUCTs (columns) according to our business logic. 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. Each test must use the UDF and throw an error to fail. # Default behavior is to create and clean. interpolator scope takes precedence over global one. I will now create a series of tests for this and then I will use a BigQuery script to iterate through each testing use case to see if my UDF function fails. After creating a dataset and ideally before using the data, we run anomaly detection on it/check that the dataset size has not changed by more than 10 percent compared to yesterday etc. Thats not what I would call a test, though; I would call that a validation. 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. The technical challenges werent necessarily hard; there were just several, and we had to do something about them. Is there an equivalent for BigQuery? No more endless Chrome tabs, now you can organize your queries in your notebooks with many advantages . Loading into a specific partition make the time rounded to 00:00:00. Now we can do unit tests for datasets and UDFs in this popular data warehouse. This tutorial aims to answers the following questions: All scripts and UDF are free to use and can be downloaded from the repository. struct(1799867122 as user_id, 158 as product_id, timestamp (null) as expire_time_after_purchase, 70000000 as transaction_id, timestamp 20201123 09:01:00 as created_at. How does one ensure that all fields that are expected to be present, are actually present? It allows you to load a file from a package, so you can load any file from your source code. Make data more reliable and/or improve their SQL testing skills. After I demoed our latest dataset we had built in Spark and mentioned my frustration about both Spark and the lack of SQL testing (best) practices in passing, Bjrn Pollex from Insights and Reporting the team that was already using BigQuery for its datasets approached me, and we started a collaboration to spike a fully tested dataset. 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. Press J to jump to the feed. Ive already touched on the cultural point that testing SQL is not common and not many examples exist. 1. The aim behind unit testing is to validate unit components with its performance. We might want to do that if we need to iteratively process each row and the desired outcome cant be achieved with standard SQL. 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). When you run the dataform test command, these SELECT SQL statements will be run in BigQuery. 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. Make a directory for test resources named tests/sql/{project}/{dataset}/{table}/{test_name}/, Site map. 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. How to automate unit testing and data healthchecks. "tests/it/bq_test_kit/bq_dsl/bq_resources/data_loaders/resources/dummy_data.csv", # table `GOOGLE_CLOUD_PROJECT.my_dataset_basic.my_table` is deleted, # dataset `GOOGLE_CLOUD_PROJECT.my_dataset_basic` is deleted. While rendering template, interpolator scope's dictionary is merged into global scope thus, 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. All the tables that are required to run and test a particular query can be defined in the WITH clause of the actual query for testing purpose. If you need to support a custom format, you may extend BaseDataLiteralTransformer All Rights Reserved. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Through BigQuery, they also had the possibility to backfill much more quickly when there was a bug. # noop() and isolate() are also supported for tables. Because were human and we all make mistakes, its a good idea to write unit tests to validate that your UDFs are behaving correctly. During this process you'd usually decompose . Why is this sentence from The Great Gatsby grammatical? Here is a tutorial.Complete guide for scripting and UDF testing. With BigQuery, you can query terabytes of data without needing a database administrator or any infrastructure to manage.. only export data for selected territories), or we use more complicated logic so that we need to process less data (e.g. Given the nature of Google bigquery (a serverless database solution), this gets very challenging. 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. def test_can_send_sql_to_spark (): spark = (SparkSession. Unit Testing Unit tests run very quickly and verify that isolated functional blocks of code work as expected. that belong to the. Euler: A baby on his lap, a cat on his back thats how he wrote his immortal works (origin?). Method: White Box Testing method is used for Unit testing. 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. SELECT that you can assign to your service account you created in the previous step. Right-click the Controllers folder and select Add and New Scaffolded Item. Enable the Imported. - Don't include a CREATE AS clause Since Google BigQuery introduced Dynamic SQL it has become a lot easier to run repeating tasks with scripting jobs. How to run unit tests in BigQuery. This is used to validate that each unit of the software performs as designed. It's good for analyzing large quantities of data quickly, but not for modifying it. The difference between the phonemes /p/ and /b/ in Japanese, Replacing broken pins/legs on a DIP IC package. I strongly believe we can mock those functions and test the behaviour accordingly. And the great thing is, for most compositions of views, youll get exactly the same performance. You can implement yours by extending bq_test_kit.resource_loaders.base_resource_loader.BaseResourceLoader. One of the ways you can guard against reporting on a faulty data upstreams is by adding health checks using the BigQuery ERROR() function. 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. 5. See Mozilla BigQuery API Access instructions to request credentials if you don't already have them. In my project, we have written a framework to automate this. 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. How can I access environment variables in Python? Data loaders were restricted to those because they can be easily modified by a human and are maintainable. dataset, {dataset}.table` They are just a few records and it wont cost you anything to run it in BigQuery. Testing SQL is often a common problem in TDD world. 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. DSL may change with breaking change until release of 1.0.0. analysis.clients_last_seen_v1.yaml 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. user_id, product_id, transaction_id, created_at (a timestamp when this transaction was created) and expire_time_after_purchase which is a timestamp expiration for that subscription. thus you can specify all your data in one file and still matching the native table behavior. You then establish an incremental copy from the old to the new data warehouse to keep the data. Given that, tests are subject to run frequently while development, reducing the time taken to run the tests is really important. All it will do is show that it does the thing that your tests check for. The open-sourced example shows how to run several unit tests on the community-contributed UDFs in the bigquery-utils repo. comparing to expect because they should not be static Is there any good way to unit test BigQuery operations? We tried our best, using Python for abstraction, speaking names for the tests, and extracting common concerns (e.g. - table must match a directory named like {dataset}/{table}, e.g. 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. Queries are tested by running the query.sql with test-input tables and comparing the result to an expected table. Optionally add query_params.yaml to define query parameters 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. To create a persistent UDF, use the following SQL: Great! Here we will need to test that data was generated correctly. You can also extend this existing set of functions with your own user-defined functions (UDFs). interpolator by extending bq_test_kit.interpolators.base_interpolator.BaseInterpolator. resource definition sharing accross tests made possible with "immutability". test. 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. Some features may not work without JavaScript. Can I tell police to wait and call a lawyer when served with a search warrant? test-kit, A typical SQL unit testing scenario is as follows: Create BigQuery object ( dataset, table, UDF) to meet some business requirement. BigQuery has no local execution. Add the controller. Create a SQL unit test to check the object. Import the required library, and you are done! We have a single, self contained, job to execute. - Fully qualify table names as `{project}. Here, you can see the SQL queries created by the generate_udf_test function that Dataform executes in BigQuery. bigquery, in tests/assert/ may be used to evaluate outputs. bq_test_kit.data_literal_transformers.base_data_literal_transformer.BaseDataLiteralTransformer. For example: CREATE TEMP FUNCTION udf_example(option INT64) AS ( CASE WHEN option > 0 then TRUE WHEN option = 0 then FALSE ELSE . When youre migrating to BigQuery, you have a rich library of BigQuery native functions available to empower your analytics workloads. Prerequisites Then we assert the result with expected on the Python side. immutability, 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. Manual testing of code requires the developer to manually debug each line of the code and test it for accuracy. For this example I will use a sample with user transactions. Final stored procedure with all tests chain_bq_unit_tests.sql. Its a CTE and it contains information, e.g. Unit Testing is the first level of software testing where the smallest testable parts of a software are tested. For some of the datasets, we instead filter and only process the data most critical to the business (e.g. How can I delete a file or folder in Python? Unit tests are a good fit for (2), however your function as it currently stands doesn't really do anything. Thats why, it is good to have SQL unit tests in BigQuery so that they can not only save time but also help to standardize our overall datawarehouse development and testing strategy contributing to streamlining database lifecycle management process. Just follow these 4 simple steps:1. 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. Asking for help, clarification, or responding to other answers. Template queries are rendered via varsubst but you can provide your own Is your application's business logic around the query and result processing correct. Chaining SQL statements and missing data always was a problem for me. Files This repo contains the following files: Final stored procedure with all tests chain_bq_unit_tests.sql. 1. You can create merge request as well in order to enhance this project. CleanBeforeAndAfter : clean before each creation and after each usage. rev2023.3.3.43278. "PyPI", "Python Package Index", and the blocks logos are registered trademarks of the Python Software Foundation. If you are using the BigQuery client from the, If you plan to test BigQuery as the same way you test a regular appengine app by using a the local development server, I don't know of a good solution from upstream. I would do the same with long SQL queries, break down into smaller ones because each view adds only one transformation, each can be independently tested to find errors, and the tests are simple. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. We created. Lets wrap it all up with a stored procedure: Now if you run the script above in BigQuery you will get: Now in ideal scenario we probably would like to chain our isolated unit tests all together and perform them all in one procedure. For example change it to this and run the script again. 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. Already for Spark, its a challenge to express test data and assertions in a _simple-to-understand way_ tests are for reading. However, as software engineers, we know all our code should be tested. context manager for cascading creation of BQResource. CleanAfter : create without cleaning first and delete after each usage. You have to test it in the real thing. to benefit from the implemented data literal conversion. Special thanks to Dan Lee and Ben Birt for the continual feedback and guidance which made this blog post and testing framework possible. 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. Why is there a voltage on my HDMI and coaxial cables? Include a comment like -- Tests followed by one or more query statements Furthermore, in json, another format is allowed, JSON_ARRAY. A unit component is an individual function or code of the application. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. This tool test data first and then inserted in the piece of code. We've all heard of unittest and pytest, but testing database objects are sometimes forgotten about, or tested through the application. Note: Init SQL statements must contain a create statement with the dataset If you did - lets say some code that instantiates an object for each result row - then we could unit test that. Tests of init.sql statements are supported, similarly to other generated tests. BigQuery offers sophisticated software as a service (SaaS) technology that can be used for serverless data warehouse operations. In order to test the query logic we wrap the query in CTEs with test data which the query gets access to. Does Python have a ternary conditional operator? Clone the bigquery-utils repo using either of the following methods: 2. You can benefit from two interpolators by installing the extras bq-test-kit[shell] or bq-test-kit[jinja2]. The second argument is an array of Javascript objects where each object holds the UDF positional inputs and expected output for a test case. This procedure costs some $$, so if you don't have a budget allocated for Q.A. 1. test_single_day For example, For every (transaction_id) there is one and only one (created_at): Now lets test its consecutive, e.g. rolling up incrementally or not writing the rows with the most frequent value). Also, I have seen docker with postgres DB container being leveraged for testing against AWS Redshift, Spark (or was it PySpark), etc. Then compare the output between expected and actual. Fortunately, the owners appreciated the initiative and helped us. You can create issue to share a bug or an idea. Validations are important and useful, but theyre not what I want to talk about here. Here is a tutorial.Complete guide for scripting and UDF testing. In automation testing, the developer writes code to test code. 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. Even amount of processed data will remain the same. Select Web API 2 Controller with actions, using Entity Framework. If you are running simple queries (no DML), you can use data literal to make test running faster. Data Literal Transformers can be less strict than their counter part, Data Loaders. To run and test the above query, we need to create the above listed tables in the bigquery and insert the necessary records to cover the scenario. This article describes how you can stub/mock your BigQuery responses for such a scenario. You can define yours by extending bq_test_kit.interpolators.BaseInterpolator. bqtest is a CLI tool and python library for data warehouse testing in BigQuery. We used our self-allocated time (SAT, 20 percent of engineers work time, usually Fridays), which is one of my favorite perks of working at SoundCloud, to collaborate on this project. How much will it cost to run these tests? By `clear` I mean the situation which is easier to understand. 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. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. To learn more, see our tips on writing great answers. At the top of the code snippet provided, you can see that unit_test_utils.js file exposes the generate_udf_test function. Is your application's business logic around the query and result processing correct. Your home for data science. If untested code is legacy code, why arent we testing data pipelines or ETLs (extract, transform, load)? 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. Assume it's a date string format // Other BigQuery temporal types come as string representations. Connecting a Google BigQuery (v2) Destination to Stitch Prerequisites Step 1: Create a GCP IAM service account Step 2: Connect Stitch Important : Google BigQuery v1 migration: If migrating from Google BigQuery v1, there are additional steps that must be completed. The consequent results are stored in a database (BigQuery), therefore we can display them in a form of plots. Create an account to follow your favorite communities and start taking part in conversations. Developed and maintained by the Python community, for the Python community. Quilt Who knows, maybe youd like to run your test script programmatically and get a result as a response in ONE JSON row. Lets slightly change our testData1 and add `expected` column for our unit test: expected column will help us to understand where UDF fails if we change it. In order to benefit from VSCode features such as debugging, you should type the following commands in the root folder of this project. - This will result in the dataset prefix being removed from the query, 2023 Python Software Foundation - Include the project prefix if it's set in the tested query, Even though the framework advertises its speed as lightning-fast, its still slow for the size of some of our datasets. Narrative and scripts in one file with comments: bigquery_unit_tests_examples.sql. There are probably many ways to do this. 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. Add an invocation of the generate_udf_test() function for the UDF you want to test. bq-test-kit[shell] or bq-test-kit[jinja2]. You have to test it in the real thing. 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. If you want to look at whats happening under the hood, navigate to your BigQuery console, then click the Query History tab. Supported data loaders are csv and json only even if Big Query API support more. Reddit and its partners use cookies and similar technologies to provide you with a better experience. Run SQL unit test to check the object does the job or not. # isolation is done via isolate() and the given context. 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. Automatically clone the repo to your Google Cloud Shellby. # to run a specific job, e.g. However, pytest's flexibility along with Python's rich. As mentioned before, we measure the performance of IOITs by gathering test execution times from Jenkins jobs that run periodically. What I would like to do is to monitor every time it does the transformation and data load. The purpose is to ensure that each unit of software code works as expected. 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. e.g. 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. main_summary_v4.sql 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. We run unit testing from Python. You can either use the fully qualified UDF name (ex: bqutil.fn.url_parse) or just the UDF name (ex: url_parse). All it will do is show that it does the thing that your tests check for. all systems operational. BigQuery is Google's fully managed, low-cost analytics database. How to run SQL unit tests in BigQuery? So, this approach can be used for really big queries that involves more than 100 tables. 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. If you haven't previously set up BigQuery integration, follow the on-screen instructions to enable BigQuery. Tests must not use any query parameters and should not reference any tables. Copy PIP instructions, View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery, Tags Each statement in a SQL file pip3 install -r requirements.txt -r requirements-test.txt -e . 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. BigQuery has a number of predefined roles (user, dataOwner, dataViewer etc.) Download the file for your platform. MySQL, which can be tested against Docker images). e.g. The other guidelines still apply. 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. - This will result in the dataset prefix being removed from the query, f""" In order to run test locally, you must install tox. ) 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.

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