What Is SELECT AS STRUCT in BigQuery?

SELECT AS STRUCT is a SQL construct (commonly used in BigQuery) that returns a query result as one STRUCT (a nested record) instead of a flat set of columns, so you can bundle related fields into a single value and pass it around cleanly.

What is SELECT AS STRUCT in SQL?

In SQL engines that support STRUCT (also called RECORD), SELECT AS STRUCT tells the database: “Take everything I’m selecting and wrap it into one structured value.” That means the output is a single column whose type is a STRUCT, with named fields inside it.

This is especially handy in analytics work where your data is naturally hierarchical: a user has attributes, a session has attributes, an order has line items, a report has grouped metrics plus metadata. Instead of flattening everything (and then re-assembling it later), you can model the result the way you want it to be consumed.

Think of it as building a tidy “object” in SQL: one value that contains multiple named fields you can reference later (for example, my_struct.field_name).

How SELECT AS STRUCT Works

Basic syntax and behavior

At its core, the syntax is simple: you select multiple expressions, but the result is returned as a STRUCT value.

Common patterns look like:

• Wrap a few columns into a single STRUCT for output.
• Create a STRUCT inside a subquery so the outer query can treat it as one unit.
• Build nested results by putting STRUCTs inside ARRAY aggregations.

The STRUCT’s field names come from your selected expressions. If you select a column, its name becomes the field name. If you compute an expression, you’ll typically alias it so the nested field has a stable, readable name.

Difference between SELECT and SELECT AS STRUCT

A regular SELECT returns a row with multiple columns. Each column is a separate top-level value in the result set. That’s perfect for many reporting use cases, but it gets messy when you want to pass around a group of fields together.

SELECT AS STRUCT returns a row that contains one top-level column: the STRUCT. Inside it are the fields you selected. This changes how downstream SQL interacts with the result:

• Flat SELECT: downstream references each column directly.
• SELECT AS STRUCT: downstream references a single column (the STRUCT) and then addresses fields via dot notation.

In practical terms: SELECT AS STRUCT is a packaging tool. It doesn’t magically change your data; it changes the shape of your results so nested logic becomes easier to manage.

Working with arrays of STRUCTs

One of the most powerful uses is producing an ARRAY of STRUCTs. This is how you represent “one-to-many” relationships inside a single row: for example, one customer row with an array of orders, or one campaign row with an array of daily metrics.

Typically, you generate arrays of STRUCTs by combining:

• a subquery that produces rows (each row becomes an element),
• SELECT AS STRUCT to define the element’s fields,
• ARRAY(...) or ARRAY_AGG(...) to collect those elements.

This approach is great when you want a nested schema for storage or for downstream transformations, but it also demands discipline: arrays can grow large, and nested outputs can be harder to visualize if your tools assume flat tables.

Why Analysts Use SELECT AS STRUCT

Building nested results for downstream queries

Analytics questions often start flat and then evolve into “grouped” stories: per user, per session, per campaign, per product category. SELECT AS STRUCT helps you return a result that mirrors that logical grouping.

Instead of outputting 20 columns and hoping everyone remembers which ones belong together, you can output a smaller set of top-level columns where each one is a purposeful bundle (for example, user, traffic_source, metrics).

This also makes joins and merges easier to reason about: if you treat a bundle as a unit, you can pass it through layers of queries without continuously re-aliasing every single field.

Packaging complex values in subqueries and CTEs

Subqueries and CTEs are where SQL logic either becomes elegant… or becomes a spaghetti monster. SELECT AS STRUCT is a clean way to return “one thing” from a subquery even if that “one thing” contains multiple values.

For example, you might compute a set of derived attributes (like channel classification + normalized campaign fields + UTM parsing outputs) and return them as a STRUCT. The outer query can then join, filter, or aggregate while keeping that block intact.

If you’re automating logic with database routines, the same idea applies: keep inputs/outputs well-structured. For more on operational SQL patterns that pair nicely with structured outputs, see stored procedures in SQL.

Preparing data for nested schemas in warehouses

Some warehouses support nested and repeated fields as first-class citizens. In that world, SELECT AS STRUCT is a bridge between raw flat events and curated, query-friendly models.

Typical “warehouse-ready” nested outputs include:

• A table where each row is an entity (user, session, order).
• Repeated fields for related collections (hits, line items, touchpoints).
• Embedded STRUCTs for grouped attributes (device info, geo, attribution metadata).

When you shape results this way, you can reduce join complexity for certain queries and keep related fields consistently grouped across the dataset.

SELECT AS STRUCT Examples

Creating a single STRUCT from multiple columns

Scenario: You want one column called user_profile that carries a few fields together, instead of three separate output columns.

Example (BigQuery-style):

SELECT AS STRUCT
  user_id,
  country,
  device_category
FROM `project.dataset.users`
WHERE user_id = 123;

This returns one STRUCT value with fields user_id, country, and device_category. If you wrap it in an outer query and alias it, you can reference user_profile.country, etc.

Returning an array of STRUCTs from a subquery

Scenario: One row per campaign, with an array that lists the top 3 ads (id + name + spend). This is a classic “nested reporting” shape.

SELECT
  c.campaign_id,
  c.campaign_name,
  ARRAY(
    SELECT AS STRUCT
      a.ad_id,
      a.ad_name,
      a.spend
    FROM `project.dataset.ads` a
    WHERE a.campaign_id = c.campaign_id
    ORDER BY a.spend DESC
    LIMIT 3
  ) AS top_ads
FROM `project.dataset.campaigns` c;

Now top_ads is an array of STRUCTs. Each element has consistent fields, and you can UNNEST it later if you need a flat view.

If you’re building queries like this frequently and want help generating variations safely (different limits, filters, or nested blocks), you may find ideas in generating complex SQL queries with AI.

Combining SELECT AS STRUCT with GROUP BY for reporting

Scenario: You’re building a daily report per channel, but you also want a nested “metrics” package (sessions, orders, revenue) so downstream queries can treat the metric block as one unit.‍

SELECT  
	event_date,  
	channel,  
    (SELECT AS STRUCT    
    	COUNT(DISTINCT session_id) AS sessions,    
        COUNTIF(event_name = 'purchase') AS orders,    
        SUM(IFNULL(revenue, 0)) AS revenue  
     ) AS metrics
FROM `project.dataset.events`
GROUP BY event_date, channel;

The output stays compact: two grouping dimensions plus one nested STRUCT of metrics. Later, you can select metrics.sessions without retyping the aggregations.

Common Pitfalls and Best Practices

When NOT to use SELECT AS STRUCT

SELECT AS STRUCT is awesome when you truly want nested outputs. But it can be overkill when the consumer expects a flat table.

Avoid it when

• Your BI tool struggles with nested fields or requires custom handling to display them
• You’re building a “final” reporting table meant for broad self-serve use and the team isn’t comfortable with STRUCTs/UNNEST patterns.
• You’re using nesting to hide modeling issues (like inconsistent keys or poorly defined grain).

Also remember: nesting doesn’t replace good relational design. If your logic depends on stable relationships, define them clearly and enforce them where possible (conceptually and in ETL). For background on structural integrity, revisit key constraints in SQL.

Dealing with NULLs and missing fields

NULL handling becomes more “interesting” in nested outputs because you can have

• a NULL STRUCT (the whole thing is missing),
• or a STRUCT where some fields are NULL.

Best practices:

• Alias fields explicitly in computed expressions so downstream users know what they’re looking at.
• Use IFNULL/COALESCE thoughtfully for metrics (for example, revenue) where NULL could break sums or comparisons.
• Be consistent across rows: a “metrics” STRUCT should always have the same set of fields, even if values are NULL.

If your nested output includes sensitive attributes, be careful not to “accidentally package” fields that shouldn’t travel together. Data protection rules often apply at the field level, not just at the table level. When in doubt, apply governance patterns such as data masking techniques before you construct widely shared STRUCTs.

Keeping nested results readable for BI tools

Even when your warehouse loves nested data, humans and BI tools might not. The goal is to keep nesting intentional, not confusing.

Practical tips:

• Name STRUCT columns like containers (metrics, user, attribution), not like single fields.
• Keep nesting shallow unless you have a strong reason; deeply nested structures are harder to debug.
• Provide a flattened view (or a helper query) when business users need simple tables. You can always UNNEST arrays or project STRUCT fields into columns.
• Document the grain: is one row per user, per session, per campaign-day? Nesting can hide grain issues until someone aggregates incorrectly.

SELECT AS STRUCT in Real Analytics Workflows

Where it fits in data marts and reporting

SELECT AS STRUCT shines in the “middle layer” of analytics: the transformations that turn raw events into clean, queryable models.

In a data mart pipeline, it often appears when you:

• Create curated entity tables with embedded attribute groups (like device/geo/traffic source blocks).
• Build reporting-friendly datasets that bundle metrics while preserving dimensional fields.
• Generate nested outputs for APIs or downstream applications that prefer structured payloads.

But don’t let the hype distract you from fundamentals: when your model depends on relationships between entities, keep your keys explicit and consistent. Nested structures complement relational modeling; they don’t replace it. If you need a refresher on the relational backbone, see primary and foreign keys.

In practice, SELECT AS STRUCT is one of those “power moves” you use when assembling clean, reusable datasets: you keep related fields together, make transformations modular, and reduce repeated logic across reports.

If you’re designing a data mart that has to serve both deep analysis and repeatable reporting, structured outputs can help you keep complex logic organized while still leaving the option to flatten results for BI consumption.

Want to keep your reporting datasets structured, consistent, and ready for analysis? OWOX Data Marts helps you organize marketing and analytics data into a modeling layer you can trust—so your SQL stays sharp and your outputs stay usable.