BigQuery's Querying Features
BigQuery is a powerful data warehousing solution from Google Cloud that allows users to run super-fast SQL queries against large datasets. This article introduces the core querying functionalities of BigQuery.
Saving and Sharing Queries
Once you've written a query in BigQuery, there are several options available to you regarding how you save and share that query:
-
Saving Queries for Personal Use
If you've formulated a particularly useful query, or one that you intend to use again, BigQuery offers functionality to save these for future use. -
Sharing with Limited Access URL
By generating a limited access URL, you can grant others the ability to view your query results, without giving them direct access to modify or run new queries on your data. -
Storing and Sharing within a Google Cloud Project
For team members and collaborators who also use Google Cloud, you can store your queries within the project. This feature ensures seamless collaboration as everyone with access to the project can view, modify, or rerun the saved query, depending on the permissions granted.
Scheduling Queries
Sometimes, we don’t just want to run a query once. There are instances when we want to run a specific query periodically, say, at the end of every day or week.
With BigQuery's scheduling feature, you can set up regular intervals at which certain queries are run. This is highly useful for regularly updating tables, generating reports, or any other periodic task that needs the freshest data.
User-Defined Functions（UDF）
User-Defined Functions (UDFs) enable users to expand the capabilities of BigQuery SQL by defining their custom functions. These can be written using either SQL or JavaScript.
Authenticated UDF
One of the unique features is the Authenticated UDF. Even if certain users don’t have direct access to datasets, through these UDFs, they can still access the tables. This ensures tighter data control, where you only allow access to data via the UDFs.
Procedural Language
Procedural language in BigQuery allows for more complex sequences of operations than just plain SQL. It's more than just querying; it's about controlling the flow of operations.
- Instead of executing one SQL command at a time, BigQuery’s procedural language supports the execution of multiple SQL statements in a single request.
- Just like traditional programming, BigQuery supports the use of variables within its procedural language. This allows for more dynamic and customizable operations.
- If-Else, Loops, and more. Control statements can be used to dictate the flow of the procedure based on conditions or repetitive requirements.
Stored Procedures
Stored Procedures encapsulate a series of operations into a single callable routine.
With stored procedures, you can bunch multiple statements together. This provides an organized way to perform repetitive tasks without writing the same sequences repeatedly.
While UDFs and stored procedures might seem similar, they serve different purposes. A UDF is generally a single function that returns a value, while a stored procedure can contain multiple SQL statements and doesn’t necessarily have to return a value.
Once defined, these procedures can be invoked from other queries, providing a modular approach to querying.
Debugging Statements and Debugging Functions
The ASSERT statement allows users to set conditions on their queries, and if these conditions aren't met, an error is returned. This is especially useful in scenarios where certain criteria must be satisfied for the query to make sense.
With the ERROR statement, users can generate intentional errors to understand the behavior of their queries better.
INFORMATION_SCHEMA
BigQuery's INFORMATION_SCHEMA serves as a roadmap to your database's metadata, granting you insights into its inner architecture.
Ryusei Kakujo
Focusing on data science for mobility
Bench Press 100kg!