FLATTEN for nested data in Snowflake - Deep Dive

Overview - FLATTEN for nested data

What is it?

FLATTEN is a function in Snowflake that helps you work with nested data like arrays or objects inside a table. It takes these nested structures and turns them into simple rows, making it easier to read and analyze. This is useful because many modern data formats store information inside nested lists or maps. FLATTEN helps you break down this complexity into a flat table format.

Why it matters

Without FLATTEN, nested data would be hard to query and understand because it is stored inside layers. Imagine trying to read a list inside a single cell without breaking it apart. FLATTEN solves this by expanding nested data into rows, so you can use regular SQL queries on it. This makes working with complex data from sources like JSON or semi-structured files much easier and faster.

Where it fits

Before learning FLATTEN, you should understand basic SQL queries and how data is stored in tables. Knowing about JSON or semi-structured data formats helps too. After FLATTEN, you can learn about advanced data transformations, joins with nested data, and optimizing queries on semi-structured data.

Mental Model

Core Idea

FLATTEN takes nested lists or objects inside a table and turns each item into its own row so you can work with them like normal table data.

Think of it like...

Imagine you have a box full of smaller boxes, each with toys inside. FLATTEN is like opening the big box and spreading all the toys out on the table so you can see and play with each toy separately.

Table with nested data
┌─────────────┐
│ id │ data  │
├─────────────┤
│ 1  │ [a,b,c]│
│ 2  │ [d,e]  │
└─────────────┘

After FLATTEN
┌────┬─────┐
│ id │ item│
├────┼─────┤
│ 1  │  a  │
│ 1  │  b  │
│ 1  │  c  │
│ 2  │  d  │
│ 2  │  e  │
└────┴─────┘

Build-Up - 7 Steps

1

Foundation

Understanding nested data basics

Concept: Nested data means data stored inside other data, like lists or objects inside a table cell.

In Snowflake, you can store JSON or arrays inside a column. For example, a column might have a list of tags like ["red", "blue", "green"]. This is nested because the list is inside one cell, not spread across rows.

Result

You see that some columns hold complex data structures, not just simple values.

Understanding nested data is key because it changes how you query and analyze data compared to flat tables.

2

Foundation

Basic SQL querying on nested data

Concept: You can select nested data as is, but it stays inside the cell and is hard to analyze directly.

Example query: SELECT id, data FROM my_table; This returns rows with nested arrays or objects still inside the data column.

Result

You get rows with nested data, but can't easily filter or count individual items inside the nested structures.

Knowing that nested data stays nested unless transformed helps you see why FLATTEN is needed.

3

Intermediate

Using FLATTEN to expand arrays

🤔Before reading on: do you think FLATTEN creates new rows for each nested item or just shows the first item? Commit to your answer.

Concept: FLATTEN takes each element inside a nested array and creates a separate row for it.

Example: SELECT id, f.value AS item FROM my_table, LATERAL FLATTEN(input => data) f; This query breaks the array in 'data' into multiple rows, one per item.

Result

You get multiple rows per original row, each with one item from the nested array.

Understanding that FLATTEN expands nested arrays into rows lets you query nested data like normal tables.

4

Intermediate

Working with nested objects using FLATTEN

🤔Before reading on: do you think FLATTEN works only on arrays or also on objects? Commit to your answer.

Concept: FLATTEN can also expand nested objects, turning each key-value pair into a row.

Example: SELECT id, f.key, f.value FROM my_table, LATERAL FLATTEN(input => nested_object_column) f; This breaks the object into rows with keys and values.

Result

You get rows showing each key and its value from the nested object.

Knowing FLATTEN works on both arrays and objects broadens its usefulness for semi-structured data.

5

Intermediate

Using FLATTEN with LATERAL joins

Concept: FLATTEN is used with LATERAL joins to combine the original row with the expanded nested data.

LATERAL allows each row to be joined with the rows produced by FLATTEN for that row's nested data. Example: SELECT t.id, f.value FROM my_table t, LATERAL FLATTEN(input => t.data) f; This keeps the original id with each flattened item.

Result

You get a table where each nested item is paired with its original row's data.

Understanding LATERAL joins is essential to keep context when flattening nested data.

6

Advanced

Handling multi-level nested data with FLATTEN

🤔Before reading on: do you think FLATTEN can handle nested arrays inside arrays directly? Commit to your answer.

Concept: You can apply FLATTEN multiple times to break down deeply nested structures step by step.

Example: SELECT t.id, f1.value AS level1, f2.value AS level2 FROM my_table t, LATERAL FLATTEN(input => t.data) f1, LATERAL FLATTEN(input => f1.value) f2; This flattens two levels of nested arrays.

Result

You get rows showing items from both levels of nested arrays separately.

Knowing how to chain FLATTEN calls helps you work with complex nested data structures.

7

Expert

Performance considerations and best practices

🤔Before reading on: do you think FLATTEN always improves query speed or can it sometimes slow things down? Commit to your answer.

Concept: Using FLATTEN can impact performance; understanding how to optimize queries with it is key for production use.

FLATTEN can increase the number of rows dramatically, which may slow queries. Use filters before flattening to reduce data. Also, avoid flattening large nested data unnecessarily. Use clustering keys and caching to improve performance.

Result

Efficient queries that handle nested data without unnecessary slowdowns.

Knowing FLATTEN's impact on query cost helps you write faster, scalable data pipelines.

Under the Hood

FLATTEN works by taking a nested array or object stored in a single cell and producing a virtual table of rows, one per element or key-value pair. Internally, Snowflake reads the nested data structure and generates a set of rows on the fly during query execution. This is done without physically changing the stored data, using a lateral join to combine the original row with the expanded elements.

Why designed this way?

Nested data is common in modern data sources like JSON, but relational databases expect flat tables. FLATTEN was designed to bridge this gap by allowing users to query nested data using familiar SQL without needing to transform or duplicate data beforehand. The lateral join approach keeps the original row context while expanding nested elements, balancing flexibility and performance.

Original Table
┌────┬───────────────┐
│ id │ nested_data   │
├────┼───────────────┤
│ 1  │ [a,b,c]       │
└────┴───────────────┘

Query Execution
┌───────────────┐
│ FLATTEN(input)│
└──────┬────────┘
       │
       ▼
Expanded Rows
┌────┬─────┐
│ id │ val │
├────┼─────┤
│ 1  │  a  │
│ 1  │  b  │
│ 1  │  c  │
└────┴─────┘

Myth Busters - 4 Common Misconceptions

Quick: Does FLATTEN modify the original table data permanently? Commit to yes or no.

Common Belief: FLATTEN changes the original nested data in the table by breaking it into rows permanently.

Reality: FLATTEN only creates a temporary view of the nested data as rows during query time; it does not alter stored data.

Why it matters: Thinking FLATTEN changes data can lead to confusion about data integrity and unnecessary data duplication.

Quick: Can FLATTEN be used without a LATERAL join? Commit to yes or no.

Common Belief: You can use FLATTEN alone without joining it to the original table.

Reality: FLATTEN must be used with LATERAL or a similar join to relate expanded rows back to the original data row.

Why it matters: Misusing FLATTEN without LATERAL causes errors or meaningless results, blocking correct data analysis.

Quick: Does FLATTEN work only on arrays? Commit to yes or no.

Common Belief: FLATTEN only works on arrays, not on objects or maps.

Reality: FLATTEN works on both arrays and objects, expanding arrays into rows and objects into key-value pairs.

Why it matters: Limiting FLATTEN to arrays reduces its usefulness and causes missed opportunities to analyze nested objects.

Quick: Does flattening always improve query speed? Commit to yes or no.

Common Belief: Using FLATTEN always makes queries faster because it simplifies nested data.

Reality: FLATTEN can increase the number of rows dramatically, sometimes slowing queries if not used carefully.

Why it matters: Assuming FLATTEN always speeds queries can lead to inefficient designs and slow performance.

Expert Zone

1

FLATTEN returns metadata columns like index and path that help track the position of each nested element, useful for complex queries.

2

The order of elements after FLATTEN is preserved by default, but can be controlled using the 'outer' and 'recursive' options for special cases.

3

Using FLATTEN with large nested arrays can cause data explosion; experts use filters and limits before flattening to control query size.

When NOT to use

Avoid FLATTEN when you only need to access a single nested element or when the nested data is very large and flattening would cause performance issues. Instead, use direct JSON path expressions or lateral views with filters to minimize data expansion.

Production Patterns

In production, FLATTEN is often combined with filtering and aggregation to analyze nested logs or event data. It is used in ETL pipelines to normalize semi-structured data before loading into flat tables. Experts also use it with Snowflake streams and tasks for incremental processing of nested data.

Connections

JSON Path Expressions

Builds-on

Understanding FLATTEN helps you grasp how JSON path expressions extract nested data, as FLATTEN expands the data while JSON paths select specific parts.

Relational Joins

Same pattern

FLATTEN uses lateral joins to combine expanded nested data with original rows, showing how relational join concepts extend to semi-structured data.

Data Normalization in Databases

Builds-on

FLATTEN automates a form of normalization by turning nested data into flat rows, similar to how database normalization organizes data into tables.

Common Pitfalls

#1 Flattening without preserving original row context

Wrong approach:

SELECT f.value FROM my_table, FLATTEN(input => data) f;

Correct approach:

SELECT t.id, f.value FROM my_table t, LATERAL FLATTEN(input => t.data) f;

Root cause:

Not using LATERAL join causes loss of original row information, making it impossible to relate flattened data back to its source.

#2 Flattening large nested arrays without filtering

Wrong approach:

SELECT t.id, f.value FROM my_table t, LATERAL FLATTEN(input => t.data) f;

Correct approach:

SELECT t.id, f.value FROM my_table t, LATERAL FLATTEN(input => t.data) f WHERE f.value IS NOT NULL;

Root cause:

Not filtering before flattening can cause huge result sets, slowing queries and increasing costs.

#3 Assuming FLATTEN modifies stored data

Wrong approach:

UPDATE my_table SET data = FLATTEN(data);

Correct approach:

Use FLATTEN only in SELECT queries to expand nested data temporarily.

Root cause:

Misunderstanding FLATTEN as a data transformation function rather than a query-time expansion.

Key Takeaways

FLATTEN is a Snowflake function that turns nested arrays or objects into rows for easier querying.

It works with LATERAL joins to keep the original row context while expanding nested data.

FLATTEN does not change stored data; it only creates a temporary expanded view during queries.

Using FLATTEN on large nested data can impact performance, so filtering and careful query design are important.

Understanding FLATTEN bridges the gap between semi-structured nested data and traditional relational SQL querying.