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Use nested and repeated fields

BigQuery can be used with many different data modelling methods, and generally provides high performance across many data model methodologies. To further tune a data model for performance, one method you might consider is data denormalization, which means adding columns of data to a single table to reduce or remove table joins.

Best practice: Use nested and repeated fields to denormalize data storage and increase query performance.

Denormalization is a common strategy for increasing read performance for relational datasets that were previously normalized. The recommended way to denormalize data in BigQuery is to use nested and repeated fields. It's best to use this strategy when the relationships are hierarchical and frequently queried together, such as in parent-child relationships.

The storage savings from using normalized data has less of an effect in modern systems. Increases in storage costs are worth the performance gains of using denormalized data. Joins require data coordination (communication bandwidth). Denormalization localizes the data to individual slots, so that execution can be done in parallel.

To maintain relationships while denormalizing your data, you can use nested and repeated fields instead of completely flattening your data. When relational data is completely flattened, network communication (shuffling) can negatively impact query performance.

For example, denormalizing an orders schema without using nested and repeated fields might require you to group the data by a field like order_id (when there is a one-to-many relationship). Because of the shuffling involved, grouping the data is less effective than denormalizing the data by using nested and repeated fields.

In some circumstances, denormalizing your data and using nested and repeated fields doesn't result in increased performance. Avoid denormalization in these use cases:

  • You have a star schema with frequently changing dimensions.
  • BigQuery complements an Online Transaction Processing (OLTP) system with row-level mutation but can't replace it.

Using nested and repeated fields

BigQuery doesn't require a completely flat denormalization. You can use nested and repeated fields to maintain relationships.

  • Nesting data (STRUCT)

    • Nesting data lets you represent foreign entities inline.
    • Querying nested data uses "dot" syntax to reference leaf fields, which is similar to the syntax using a join.
    • Nested data is represented as a STRUCT type in GoogleSQL.
  • Repeated data (ARRAY)

    • Creating a field of type RECORD with the mode set to REPEATED lets you preserve a one-to-many relationship inline (so long as the relationship isn't high cardinality).
    • With repeated data, shuffling is not necessary.
    • Repeated data is represented as an ARRAY. You can use an ARRAY function in GoogleSQL when you query the repeated data.
  • Nested and repeated data (ARRAY of STRUCTs)

    • Nesting and repetition complement each other.
    • For example, in a table of transaction records, you could include an array of line item STRUCTs.

For more information, see Specify nested and repeated columns in table schemas.

For more information about denormalizing data, see Denormalization.


Consider an Orders table with a row for each line item sold:

Order_Id Item_Name
001 A1
001 B1
002 A1
002 C1

If you wanted to analyze data from this table, you would need to use a GROUP BY clause, similar to the following:

FROM Orders
GROUP BY Order_Id;

The GROUP BY clause involves additional computation overhead, but this can be avoided by nesting repeated data. You can avoid using a GROUP BY clause by creating a table with one order per row, where the order line items are in a nested field:

Order_Id Item_Name
001 A1

002 A1


In BigQuery, you typically specify a nested schema as an ARRAY of STRUCT objects. You use the UNNEST operator to flatten the nested data, as shown in the following query:

    STRUCT('001' AS Order_Id, ['A1', 'B1'] AS Item_Name),
    STRUCT('002' AS Order_Id, ['A1', 'C1'] AS Item_Name)

This query yields results similar to the following:

Query output with unnested data

If this data wasn't nested, you could potentially have several rows for each order, one for each item sold in that order, which would result in a large table and an expensive GROUP BY operation.


You can see the performance difference in queries that use nested fields as compared to those that don't by following the steps in this section.

  1. Create a table based on the bigquery-public-data.stackoverflow.comments public dataset:

    AS (
  2. Using the stackoverflow table, run the following query to see the earliest comment for each user:

      ARRAY_AGG(STRUCT(post_id, creation_date AS earliest_comment) ORDER BY creation_date ASC LIMIT 1)[OFFSET(0)].*
    GROUP BY user_id
    ORDER BY user_id ASC;

    This query takes about 25 seconds to run and processes 1.88 GB of data.

  3. Create a second table with identical data that creates a comments field using a STRUCT type to store the post_id and creation_date data, instead of two individual fields:

    AS (
      ARRAY_AGG(STRUCT(post_id, creation_date) ORDER BY creation_date ASC) AS comments
    GROUP BY user_id;
  4. Using the stackoverflow_nested table, run the following query to see the earliest comment for each user:

      (SELECT AS STRUCT post_id, creation_date as earliest_comment FROM UNNEST(comments) ORDER BY creation_date ASC LIMIT 1).*
    ORDER BY user_id ASC;

    This query takes about 10 seconds to run and processes 1.28 GB of data.

  5. Delete the stackoverflow and stackoverflow_nested tables when you are finished with them.