BigQuery: Converting key-value pairs in Array to columns

Overview of the Problem

The problem at hand involves converting key-value pairs stored in an array field (event_params) into separate columns. The original table has a repetitive structure, with each row having an arbitrary number of rows inside the event_params field. Each big row can be repeated as it can be generated by the same user. The goal is to transform this data into a format where the key-value pairs are separated into distinct columns.

Background and Context

BigQuery is a fully managed enterprise data warehouse service offered by Google Cloud Platform (GCP). It provides a scalable and flexible platform for analyzing large datasets, leveraging standard SQL syntax for querying.

When working with arrays in BigQuery, it’s essential to understand the nuances of how they are treated. Unlike some other databases, BigQuery doesn’t provide a built-in way to unnest arrays directly. However, there are workarounds that can achieve similar results using subqueries or Common Table Expressions (CTEs).

Understanding the Provided Query

The original query uses the UNNEST function to expand the array into separate rows and then uses conditional statements (IF) to select specific key-value pairs.

SELECT 
  user_id, 
  IF(event_params.key = 'k1', event_params.value.string_value, NULL) AS k1,
  IF(event_params.key = 'k3', event_params.value.string_value, NULL) AS k3,
FROM `my-proj-id.analytics_xxxxx.events_20201030`
  , UNNEST(event_params) AS event_params
WHERE event_name='my-event-name'

However, the query results in NULL values for column k3, which is due to the lack of matching rows in the expanded array.

A Deeper Look at Array Handling in BigQuery

To understand why the original query doesn’t produce the expected results, we need to delve deeper into how arrays are handled in BigQuery.

When using arrays with conditional statements like IF, BigQuery will only return a value if there is a matching row in the array. This means that if an array has multiple rows but none of them contain a specific key-value pair, the corresponding column will be NULL.

A Better Approach: Using Subqueries and CTEs

To achieve the desired outcome, we can use subqueries or CTEs to select specific key-value pairs from the array. This approach allows us to avoid the limitations imposed by the UNNEST function.

# Standard SQL
WITH unnested_event_params AS (
  SELECT 
    user_id,
    KEY AS key,
    VALUE.string_value AS value
  FROM `my-proj-id.analytics_xxxxx.events_20201030`
  WHERE event_name='my-event-name'
  AND KEY IN ('k1', 'k3')
)
SELECT 
  user_id, 
  MAX(CASE WHEN key = 'k1' THEN value END) AS k1,
  MAX(CASE WHEN key = 'k3' THEN value END) AS k3
FROM unnested_event_params
GROUP BY user_id

In this revised query, we first create a CTE (unnested_event_params) that selects the desired rows from the original table. We then use conditional statements within a CASE expression to select specific key-value pairs.

The outer query groups the results by user_id and uses the MAX aggregation function to select the maximum value for each key.

Additional Considerations

There are several additional factors to consider when working with arrays in BigQuery:

• Array length: Be aware that array lengths can be variable, which might impact performance.
• Array nesting: If you need to access nested arrays or objects, use the UNNEST function and work with the resulting table structure.
• Data types: Understand how different data types interact with array operations.

Real-World Example Use Cases

BigQuery’s array handling capabilities are versatile and can be applied in a variety of scenarios:

• E-commerce analysis: Analyzing customer purchase history involves working with arrays containing product IDs, quantities, and prices.
• Log analysis: Examining log data often requires processing arrays that contain timestamps, error messages, and user information.
• Recommendation engines: Building recommendation systems can involve creating complex queries on array fields to suggest products or services based on user preferences.

Conclusion

Converting key-value pairs in an array field into separate columns is a common challenge when working with BigQuery. By understanding the nuances of how arrays are treated and using creative solutions like subqueries and CTEs, you can overcome these limitations and unlock new insights from your data.

Last modified on 2024-06-20