Principle:DataExpert io Data engineer handbook Aggregation Transformation

Overview

Aggregation Transformation is a pattern for summarizing array-structured metrics in analytical queries. It combines array element extraction, null handling, and grouping operations to produce condensed, time-partitioned summaries from raw array-based data.

Theory

Array-based aggregation involves several coordinated operations:

Array Element Extraction - The get() function (or index-based access) extracts individual elements from array columns by position, allowing each element to be treated as a separate scalar value for aggregation.
Null Handling with COALESCE - The COALESCE() function provides default values when array elements are null, ensuring that missing data does not propagate through the aggregation pipeline.
SUM Aggregation - The SUM() function aggregates extracted values across rows within each group, producing totals for each array position.
GROUP BY Partitioning - The GROUP BY clause partitions data by meaningful dimensions (e.g., month start date), enabling aggregation at the desired granularity.
Date Partition Filtering - A WHERE clause filters on a date partition column to support incremental processing, ensuring only the relevant slice of data is processed in each run.

When to Apply

This pattern is appropriate when:

Summarizing metrics stored in array columns by time period
Extracting positional elements from arrays for independent aggregation
Implementing incremental processing with date-based partition filtering
Handling sparse or nullable array data that requires default values

Theoretical Basis

The pattern rests on the following SQL constructs:

get() / array indexing - positional access into array columns
COALESCE() - null-safe value substitution
SUM() - additive aggregation across grouped rows
GROUP BY - row partitioning for aggregation
WHERE on date partition - predicate pushdown for incremental data processing

-- Conceptual pattern
SELECT
    month_start,
    SUM(COALESCE(hit_array[0], 0)) AS num_hits_first_day,
    SUM(COALESCE(hit_array[1], 0)) AS num_hits_second_day,
    SUM(COALESCE(hit_array[2], 0)) AS num_hits_third_day
FROM user_site_hits
WHERE date_partition = '2024-01-01'
GROUP BY month_start

Related Pages

Implementation:DataExpert_io_Data_engineer_handbook_Do_monthly_user_site_hits_transformation

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