What Is the ARRAY_TO_STRING Function in SQL?

ARRAY_TO_STRING is a SQL function that turns an array (a list of values) into one text string, inserting a delimiter between elements so the result is readable, exportable, and dashboard-friendly.

What is ARRAY_TO_STRING in SQL?

Basic idea in plain language

Arrays are great for storing “multiple things in one field”: multiple tags, multiple product IDs, multiple event parameters, multiple labels. But arrays are not always great for humans (or BI tools) to read. ARRAY_TO_STRING solves that by joining array elements into a single string like "tag1, tag2, tag3".

Think of it as “make this list look like a sentence.” You pick what goes between items (a comma, a pipe, a line break, whatever), and you get a single value that can flow through reports, exports, and QA checks without complex array handling.

Where you’ll meet it in analytics queries (BigQuery, Postgres, etc.)

You’ll run into arrays constantly in analytics data models:

• Event-level data where parameters are stored as repeated fields or arrays
• Orders with line items stored as arrays of products
• User profiles with multiple labels, segments, or consent flags
• Marketing datasets where a campaign has multiple tags, audiences, or creatives

ARRAY_TO_STRING shows up when you want a single “summary” field for a table, a dashboard dimension, a CSV export, or a quick sanity check. SQL dialect details vary (BigQuery supports ARRAY_TO_STRING; Postgres has similar patterns), but the core idea stays the same: join array values into one string, predictably.

ARRAY_TO_STRING syntax

General syntax and arguments

The general pattern is straightforward: pass the array and the delimiter.

Conceptually: ARRAY_TO_STRING(array_expression, delimiter [, null_replacement])

Where:

• array_expression is the array you want to convert (e.g., an array of strings or IDs)
• delimiter is what you want between values (e.g., ", " or " | ")
• null_replacement (when supported) is the text to use when an element is NULL

One important practical note: many dialects are strict about types. If your array contains integers, you may need to cast elements to strings before joining, depending on your warehouse.

Delimiter and null replacement explained

The delimiter is your formatting lever. Choose it based on where the output is going:

• Dashboards: ", " reads naturally
• Exports/CSV: " | " or "; " is often safer than commas
• Debugging: something loud like " ⟂ " can help spot boundaries

Null replacement matters because arrays often contain NULLs after transformations (filtering, conditional logic, parsing). If you don’t specify how to handle NULLs, your result can become confusing: you might silently drop values, or you might end up with unexpected empty spots depending on the SQL engine.

If your dialect supports a null replacement argument, you can turn NULL into a visible token like "(null)" so you can debug upstream logic faster. Or, in production reporting, you can replace NULL with an empty string to keep the output clean—just be careful not to hide data quality issues.

Common pitfalls with NULLs and empty arrays

Three common “wait, what?” moments:

• Empty array vs NULL array: An empty array means “there are zero items.” A NULL array means “we don’t know the items.” Those are not the same, and they can produce different outputs (empty string vs NULL).
• NULL elements inside the array: Your array might exist, but some elements are NULL. Without a clear strategy, your joined string can mislead you (“did we lose tags, or were there none?”).
• Non-string elements: Arrays of numbers, structs, or mixed types usually require preprocessing (casting or extracting a field) before joining.

Best practice: treat NULL behavior as a deliberate design choice. Don’t let it be “whatever the function decides today.”

Practical examples for analysts

Combining multiple campaign tags into one field

Marketing teams love tags: “brand,” “promo,” “retargeting,” “influencer,” and so on. In raw or intermediate tables, those tags often live as an array per campaign. For reporting, you may want a single column to filter, display, or export.

Example (BigQuery-style):

Imagine a table campaigns with columns: campaign_id, campaign_name, tags (ARRAY<STRING>).

1SELECT  
2campaign_id, 
3campaign_name,  
4ARRAY_TO_STRING(tags, ', ') AS tag_list
5FROM campaigns;

This gives you a readable tag_list for Looker/Power BI/Tableau-style consumption (and for quick spot checks in query results). If you’re iterating fast, tools and prompts can help you draft these patterns—see generate and debug SQL queries with AI for a workflow that keeps you moving while you stay in control of the logic.

Flattening product lists in orders for reporting

Ecommerce order data often stores line items as an array. Analysts frequently need “one row per order” reports (AOV, order counts, refund rates) but also want a human-readable product summary for customer support, QA, or anomaly investigations.

Typical approach: extract the product names (or SKUs) into an array, then ARRAY_TO_STRING them into a single field like "SKU123 | SKU987 | SKU555".

That “product_list” column becomes a powerful debugging companion: when an order’s revenue looks off, you can instantly see what was inside the basket without fully exploding the dataset into line-item granularity.

Turning event parameters into a readable string

Event data is packed with repeated attributes: button text, page categories, experiment variants, error codes, content groups. Often you’ll parse or collect a subset of parameters into an array for a specific analysis, then turn it into a single string for QA and reporting.

Example scenario: You’re investigating form submission drop-offs. For each submission event, you build an array of validation errors (if any), and then convert it to a string for easy triage in a table view.

The output might look like:

• "email_missing | phone_invalid"
• "" (no errors)
• "(null)" (errors array is unknown due to tracking gap)

This is the kind of high-signal column that helps analysts and engineers align quickly: you’re not just counting failures—you’re seeing what failed.

ARRAY_TO_STRING vs other string/array functions

ARRAY_TO_STRING vs STRING_AGG (conceptual difference)

ARRAY_TO_STRING joins elements that are already in an array. STRING_AGG aggregates multiple rows into one string.

That difference is huge in analytics design:

• ARRAY_TO_STRING: “I already have a list in this row; make it readable.”
• STRING_AGG: “I have many rows; combine them into one string per group.”

In practice, you might use both in the same pipeline. For example, you could use STRING_AGG to build a list of campaign names per account (row aggregation), and use ARRAY_TO_STRING to format a repeated field that already exists (array formatting).

Working with arrays before and after ARRAY_TO_STRING

ARRAY_TO_STRING is usually not the first step—it’s a finishing move. Common “before” operations include:

• Filtering: remove empty values or keep only specific tags
• Sorting: make output stable (so the same set produces the same string)
• De-duplication: avoid repeated labels like “promo, promo, promo”
• Casting: convert IDs to text to avoid type errors

After you’ve converted to a string, treat it like what it is: presentation output. It’s great for reading, but not ideal for advanced logic. If you need to analyze individual elements again (e.g., “contains tag X”), it’s usually better to keep the array too, or derive a boolean flag, rather than splitting the string later.

How ARRAY_TO_STRING fits into reporting workflows

Cleaning data for dashboards and exports

Dashboards want clean dimensions. Exports want predictable formatting. ARRAY_TO_STRING helps you take complex structures and deliver a tidy, single-column result.

This becomes especially important when your reporting tables are built with solid relational design. Knowing how entities relate (and how joins affect duplication) is part of the game—if you’re brushing up on modeling foundations, see primary and foreign keys to keep your “one row per X” logic tight.

In operational terms, ARRAY_TO_STRING is often used in:

• Semantic layers where you want a “labels” column for filtering
• QA queries that surface arrays as readable diagnostics
• Automated CSV extracts where arrays would otherwise break formatting

OWOX Data Marts context: using arrays and strings in mart layers

In a mart workflow, arrays often appear in staging or intermediate layers (raw event parameters, nested ecommerce items, multi-valued attributes). When you build reporting-ready tables, you decide what should remain as structured data and what should be “flattened” for consumption.

ARRAY_TO_STRING is a practical tool at that boundary: you can keep the detailed array for deep dives while also creating a readable string column for everyday reporting. If you orchestrate transformations, this kind of logic is frequently wrapped into repeatable routines—using stored procedures in SQL can help you standardize those patterns so every dataset doesn’t reinvent the same formatting rules.

Common mistakes and best practices

Handling delimiters safely (no accidental splits)

A delimiter is not just decoration—it’s a parsing contract. If your values can contain the delimiter, your output becomes ambiguous. For example, joining values with a comma is risky if values themselves can include commas (campaign names, product titles, location strings).

Best practices:

• Pick a delimiter that is unlikely to appear in real values (pipe | is a common choice).
• If the string will be parsed later, consider escaping or replacing delimiter characters inside elements before joining.
• Document the delimiter choice in the query or transformation so downstream users don’t guess.

Dealing with NULLs and unexpected array content

Arrays can surprise you: NULL elements, empty strings, duplicate entries, mixed casing, or even unexpected types after upstream schema changes. Don’t just join and hope.

Practical guardrails:

• Normalize elements (trim whitespace, standardize case) before joining if the output is used for comparisons.
• Make NULL handling explicit (either replace NULLs with a token for debugging, or filter them out intentionally).
• Be careful when arrays contain identifiers that must obey relational rules; if you rely on uniqueness, understand how constraints are supposed to work—key constraints in SQL is a useful reference point.

If the resulting string could expose sensitive data (emails, phone numbers, internal IDs), treat it as a data governance moment. Mask before you materialize or export—data masking techniques in SQL can help you keep reporting useful without leaking what shouldn’t leave the warehouse.

Keeping queries readable when using ARRAY_TO_STRING

ARRAY_TO_STRING can turn into a “nested function sandwich” if you’re not careful. The readability win of a nice output string isn’t worth it if the SQL becomes impossible to maintain.

Best practices that keep you fast:

• Build the array in a separate expression (CTE or subquery), then join it in the final SELECT.
• Use clear aliases like tag_list, product_list, error_list.
• Comment tricky NULL handling decisions so future-you doesn’t have to reverse-engineer intent.

The goal is simple: make multi-valued fields readable without making your SQL unreadable.

Want to turn messy, nested analytics data into clean, report-ready tables? Build your next mart layer with OWOX Data Marts and keep arrays, strings, and transformations organized from raw to dashboard. Sign up and start shipping datasets your BI tools actually enjoy.