How to create temporary table in Google BigQuery

Mastering Temporary Tables in Google BigQuery

Learn how to effectively create and utilize temporary tables in Google BigQuery for data manipulation, analysis, and optimizing query performance.

Google BigQuery is a powerful, serverless, and highly scalable data warehouse. When working with complex queries, intermediate results, or data transformations, temporary tables become an invaluable tool. Unlike permanent tables, temporary tables are session-scoped or query-scoped, meaning they are automatically deleted after a certain period or when the session ends. This guide will walk you through the various methods of creating and using temporary tables in BigQuery, highlighting their benefits and best practices.

Why Use Temporary Tables?

Temporary tables offer several advantages in BigQuery. They help break down complex queries into smaller, more manageable steps, improving readability and debugging. They can also optimize performance by materializing intermediate results, preventing BigQuery from re-computing the same subquery multiple times. Furthermore, they provide a clean workspace for data manipulation without affecting your permanent datasets, making them ideal for exploratory analysis or one-off tasks.

flowchart TD
    A[Start Complex Query] --> B{Intermediate Calculation 1}
    B --> C[Store as Temp Table 1]
    C --> D{Intermediate Calculation 2}
    D --> E[Store as Temp Table 2]
    E --> F[Final Query using Temp Tables]
    F --> G[End Query]

Workflow illustrating the use of temporary tables in a complex query.

Methods for Creating Temporary Tables

BigQuery provides a few distinct ways to create temporary tables, each suited for different scenarios. The most common methods involve using CREATE TEMPORARY TABLE or WITH clauses (Common Table Expressions - CTEs). Understanding the nuances of each will help you choose the most appropriate approach for your data operations.

1. Using CREATE TEMPORARY TABLE

The CREATE TEMPORARY TABLE statement explicitly creates a temporary table that exists for the duration of your query session. This is particularly useful when you need to perform multiple operations on the same intermediate result set within a single script or session, or when the intermediate result is too large for a CTE to handle efficiently.

CREATE TEMPORARY TABLE my_temp_table AS
SELECT
    column1,
    column2,
    COUNT(*) AS record_count
FROM
    `your_project.your_dataset.your_source_table`
WHERE
    column3 = 'some_value'
GROUP BY
    column1, column2;

-- Now you can query the temporary table
SELECT
    column1,
    record_count
FROM
    my_temp_table
WHERE
    record_count > 100;

Example of creating and querying a temporary table using CREATE TEMPORARY TABLE.

2. Using Common Table Expressions (CTEs) with WITH

CTEs, defined using the WITH clause, allow you to define a temporary, named result set that you can reference within a single SELECT, INSERT, UPDATE, or DELETE statement. They are excellent for improving query readability and breaking down complex logic into logical, reusable blocks. While not physical tables, BigQuery's optimizer often materializes CTEs, providing similar performance benefits to explicit temporary tables for many scenarios.

WITH 
  DailySales AS (
    SELECT
      DATE(sale_timestamp) AS sale_date,
      SUM(amount) AS total_daily_sales
    FROM
      `your_project.your_dataset.sales_data`
    GROUP BY
      sale_date
  ),
  TopSellingDays AS (
    SELECT
      sale_date,
      total_daily_sales
    FROM
      DailySales
    WHERE
      total_daily_sales > 10000
  )
SELECT
  sale_date,
  total_daily_sales
FROM
  TopSellingDays
ORDER BY
  total_daily_sales DESC;

Example of using multiple CTEs to structure a complex query.

Best Practices for Temporary Tables

To maximize the benefits of temporary tables in BigQuery, consider these best practices:

1. Choose the Right Method

For single-query scope and readability, prefer CTEs. For larger, multi-statement intermediate results within a session, CREATE TEMPORARY TABLE is often more suitable.

2. Name Clearly

Use descriptive names for your temporary tables and CTEs to improve code readability and maintainability.

3. Filter Early

Apply filters and aggregations as early as possible in your temporary table or CTE definition to reduce the amount of data processed in subsequent steps.

4. Avoid Overuse

While beneficial, don't create temporary tables or CTEs unnecessarily. Simple queries might not need them, and excessive nesting can sometimes hinder readability.

5. Monitor Performance

Use BigQuery's query plan explanation to understand how your temporary tables and CTEs are being processed and identify potential bottlenecks.