Query Engine Basics
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In the video and course examples, you will see the JSON_EXTRACT function. While this function is fully supported and works correctly, it is considered legacy syntax in BigQuery.
For your own future projects, we recommend using the modern standard functions:
JSON_VALUE: extracts scalar values (like strings or numbers) and removes extra quotes automatically;JSON_QUERY: extracts complex JSON objects or arrays.
Get acquainted with the table used throughout the lesson and discover how BigQuery executes queries internally. Focus on uploading CSV data and analyzing query behavior using execution graphs to better understand performance and cost.
You start by uploading a CSV file through the BigQuery web interface. The process is straightforward: select Local File, enable Autodetect, and complete the upload. BigQuery automatically infers the schema and prepares the data for querying.
Every time a query runs, BigQuery generates an execution graph. This graph visually represents how data moves through different processing stages, such as filtering, joins, and aggregations.
Execution graphs make it easy to identify where query time and cost are concentrated. Complex queries are broken into multiple nodes and paths, showing how BigQuery executes operations in parallel to improve efficiency. This visibility makes execution graphs a powerful tool for query optimization.
Discover the JSON_EXTRACT function to retrieve specific values from JSON objects using path notation, such as $.product.id. This is especially useful when working with nested data structures commonly found in event data or ecommerce datasets.
Combining execution graph analysis with functions like JSON_EXTRACT helps you write more efficient, scalable queries while maintaining control over performance and cost.
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