Alternative Approaches to STRING_AGG in SQL

In structured query language (SQL), data aggregation often focuses on numerical values—calculating sums, averages, or counts. However, situations also arise where textual data from multiple rows needs to be combined into a single, concatenated output. This task, while less common than numeric aggregation, is critical for certain types of reporting, user interface output, and data exports.

To handle this, many modern relational databases support a specialized function known as STRING_AGG. This feature simplifies the task of combining values into one string, using a delimiter of choice. However, not all database environments support STRING_AGG, and some older versions still lack this functionality. As a result, database administrators and developers often need alternative methods to achieve similar results.

This article explores the role of string aggregation in SQL and various alternatives across multiple database systems, offering insights into compatibility and performance along the way.

What Is STRING_AGG and Why Use It?

The STRING_AGG function was introduced to address the need to merge multiple text values from rows into a single string. It accepts two arguments: the column to aggregate and the delimiter that separates each value. This is particularly helpful for transforming a one-to-many relationship into a single row with comma-separated values.

Common uses include combining tags associated with a blog post, listing customer names under each sales representative, or displaying multiple product SKUs linked to a single order. Prior to the introduction of this function, developers had to rely on more verbose workarounds, which often varied depending on the database system in use.

Despite its convenience, STRING_AGG is not universal. Older database systems or lightweight database engines may lack native support. Additionally, organizations working in hybrid or legacy environments may still depend on other strategies.

When STRING_AGG Is Unavailable

In environments where STRING_AGG is not present, alternate solutions must be considered. These alternatives may differ in complexity, efficiency, and compatibility. Some rely on XML-based workarounds, while others utilize functions like GROUP_CONCAT or recursive queries.

The necessity for alternative methods arises from several situations:

• Use of older database versions without native support
  
  

• Porting queries between different database engines
  
  

• Preference for more control over the output format
  
  

• Requirement to ensure backward compatibility

The key to selecting an appropriate alternative lies in understanding the capabilities of the specific database system and the structure of the data being queried.

FOR XML PATH in SQL Server

SQL Server provides a powerful workaround for string aggregation through the use of FOR XML PATH. This method, although initially designed for XML data formatting, can be repurposed to concatenate values from multiple rows.

The technique involves using a subquery that returns values concatenated as XML text, and then transforming the result back into plain text. An additional function, such as STUFF, can be used to remove unwanted characters like leading delimiters.

While not as clean as STRING_AGG, this approach is compatible with older versions of SQL Server and remains widely used.

Advantages include:

• Compatibility with SQL Server versions prior to 2017
  
  

• Flexibility to format the output string
  
  

• Broad community support and documentation

However, it may be harder to read and maintain, especially for teams unfamiliar with XML constructs.

LISTAGG in Oracle Databases

Oracle’s solution to string aggregation is the LISTAGG function. This is Oracle’s direct counterpart to STRING_AGG, offering a simple and efficient way to concatenate values with a separator.

The LISTAGG function accepts two parameters: the column to aggregate and the delimiter. An additional clause allows for ordering the values within the group, providing fine control over the output sequence.

Although LISTAGG is straightforward, earlier versions of Oracle before 11gR2 do not include it, requiring alternative techniques like custom PL/SQL functions or recursive queries. Newer versions have also introduced features to handle overflow and long concatenated strings.

Key benefits include:

• Native support in Oracle 11gR2 and later
  
  

• Option to specify ordering within the aggregation
  
  

• Fast performance for moderate-sized datasets

Its limitation is primarily tied to compatibility and string length limits in some versions.

GROUP_CONCAT in MySQL and MariaDB

MySQL and its close cousin MariaDB provide GROUP_CONCAT as their native string aggregation function. It performs a similar task, allowing for the concatenation of text values across grouped records with a defined separator.

This function includes options to control output order, maximum length, and distinct values. It's one of the more flexible and user-friendly alternatives, and is commonly used in applications that rely on lightweight or open-source database systems.

Use cases include creating CSV outputs, summarizing data, and presenting grouped text data in applications.

Highlights of GROUP_CONCAT:

• Built-in and widely used in MySQL and MariaDB
  
  

• Supports ordering and distinct values
  
  

• Simple syntax and easy to use

Drawbacks include length limitations (configurable) and lack of advanced features like error handling in complex concatenations.

Recursive Queries for General Compatibility

In situations where no native aggregation function is available, recursive queries can be used to simulate the behavior of STRING_AGG. These queries rely on common table expressions (CTEs) that refer to themselves to build the string incrementally.

This method is more complex and may be slower for large datasets, but it works in many SQL environments that support recursive CTEs. It is especially useful for developers working with older or minimalist database systems.

The general approach involves:

• Initializing the recursion with a base row
  
  

• Concatenating the current value with the previous result
  
  

• Continuing until all rows have been processed
  
  

This solution provides flexibility and control, though it requires careful handling to avoid performance bottlenecks.

Challenges with recursive queries:

• Increased complexity and maintenance effort
  
  

• Higher memory consumption
  
  

• Risk of recursion depth limits

Despite these challenges, recursive queries remain a valuable tool in scenarios where no other options are available.

PostgreSQL and array_to_string

PostgreSQL, known for its rich set of features, offers multiple ways to achieve string aggregation. One effective approach is to use array_agg in combination with array_to_string. This method first collects values into an array and then converts the array into a delimited string.

This solution provides significant control over data transformation and is often preferred for its clarity and reliability. PostgreSQL also supports the STRING_AGG function natively in recent versions, but the array-based method is useful when additional formatting or filtering is needed.

Advantages of this technique:

• Native support for arrays and string functions
  
  

• High flexibility in formatting and processing
  
  

• Suitable for complex queries with filtering and transformations

It may be slightly more verbose than STRING_AGG, but it offers a powerful and extensible approach to string aggregation.

Performance and Efficiency Considerations

When choosing among alternatives to STRING_AGG, performance should not be overlooked. Aggregating large volumes of data into strings can strain resources, especially if the method involves recursive processing or XML parsing.

Several factors affect performance:

• Size and structure of the dataset
  
  

• Indexing on the relevant columns
  
  

• Memory usage for string concatenation
  
  

• Query plan optimization by the database engine

Native functions like GROUP_CONCAT, LISTAGG, and STRING_AGG generally offer the best performance, followed by XML-based workarounds and recursive queries. Where possible, prefer solutions that leverage built-in functions optimized by the database vendor.

In performance-critical environments, it is also advisable to measure query execution time and resource consumption using query analyzers or explain plans. Breaking down large queries into smaller batches or processing at the application level may also help manage performance issues.

Use Cases Across Different Scenarios

String aggregation is employed in various business scenarios, from data warehousing to user-facing reports. Some practical examples include:

• Creating comma-separated lists of product names per category
  
  

• Generating email lists grouped by department
  
  

• Showing related articles or tags in a content management system
  
  

• Presenting multiple addresses under a single customer profile

Depending on the specific requirements, different aggregation strategies may be more suitable. For instance, if order matters, functions with ordering clauses like LISTAGG or GROUP_CONCAT with ORDER BY are preferable. For environments requiring maximum compatibility, recursive queries or XML-based methods may be the only option.

Best Practices for Implementing Alternatives

When implementing string aggregation using alternate methods, following a few best practices can help ensure maintainability and efficiency:

• Avoid hardcoding logic that is specific to one database system unless the environment is stable
  
  

• Use parameters or configuration settings to control delimiters and output formatting
  
  

• Monitor performance regularly, especially for queries run during peak hours
  
  

• Document the logic thoroughly if using complex methods like recursive queries or XML hacks
  
  

• Validate output string length and data truncation where limits may apply

By incorporating these practices, teams can create robust and adaptable solutions that work across different environments and data volumes.

String aggregation plays a critical role in many SQL-based applications. While STRING_AGG simplifies this task in modern systems, many databases still require alternative techniques due to compatibility or feature availability.

Options such as FOR XML PATH, GROUP_CONCAT, LISTAGG, array functions, and recursive queries offer a wide spectrum of solutions, each with its own benefits and limitations. Choosing the right method involves understanding the capabilities of the database in use, the structure of the data, and performance implications.

By mastering these alternatives, developers and database professionals can ensure that their systems remain flexible, future-proof, and capable of generating complex reports or outputs without relying solely on one specific SQL function.

Expanding on the Need for Alternatives

In SQL programming, it is common to encounter situations where built-in features are unavailable or limited due to system constraints or legacy versions. While the STRING_AGG function is a modern and effective way to concatenate text values across rows, it is not universally available in all environments. This makes it crucial to explore multiple approaches that provide similar outcomes without depending on a specific function.

String aggregation is essential for transforming relational data into more user-friendly formats. Whether for reporting, exports, user interfaces, or dashboards, being able to turn multiple values into a single line of text with a delimiter is a routine requirement in many SQL-based applications. This section takes a deeper look at alternative approaches, supported functions, and how to apply them effectively in various database systems.

Revisiting SQL Server: Going Beyond STRING_AGG

In modern SQL Server editions, STRING_AGG offers native support for text aggregation. However, for older editions where this function is not supported, the most commonly used technique is leveraging the FOR XML PATH approach combined with the STUFF function.

The idea behind FOR XML PATH is to create a virtual XML representation of your result set, then extract the plain text from it. This method is widely used and relatively easy to implement, although it may be less intuitive for those unfamiliar with XML.

The STUFF function is particularly useful here. It helps remove the initial separator (like a comma or space) from the final output. Without STUFF, your result may contain an unwanted prefix. While this approach is more verbose than STRING_AGG, it has become a standard workaround for SQL Server users who need compatibility across versions.

Other techniques, such as using cursors or loops, are technically viable but generally discouraged due to performance inefficiencies and increased complexity.

Oracle: Making the Most of LISTAGG

In Oracle databases, LISTAGG serves as the native method for string aggregation. Introduced in Oracle 11g Release 2, it quickly became the go-to function for turning multiple rows into a single string value. What makes LISTAGG particularly useful is its inclusion of ordering capabilities, allowing the developer to control the sequence of concatenated values within a group.

Despite its usefulness, LISTAGG does have limitations. In earlier versions, the function would throw errors if the resulting string exceeded a certain length. Later Oracle versions improved this by introducing error-handling options to deal with overflow conditions.

In cases where LISTAGG is unavailable, Oracle developers have resorted to user-defined aggregate functions written in PL/SQL. While effective, these custom functions introduce additional complexity and may not be portable to other systems.

Understanding when to use built-in features and when to resort to custom solutions is key in Oracle-based environments, especially when working in regulated industries where database upgrades are tightly controlled.

MySQL and MariaDB: Versatile Use of GROUP_CONCAT

In MySQL and its fork, MariaDB, GROUP_CONCAT is the default tool for string aggregation. It is user-friendly and supports several options, including distinct value aggregation, ordering, and maximum length adjustments through system variables.

The syntax is straightforward and easy to grasp. By default, GROUP_CONCAT uses a comma as the delimiter, but this can be customized as needed. It also integrates well with clauses like ORDER BY to ensure predictable output.

A key consideration when using GROUP_CONCAT is the maximum output length, which is controlled by a system variable. If the aggregated string exceeds this length, it will be truncated. Developers can increase this limit by adjusting the variable for their session or globally, but it requires careful monitoring to avoid memory-related performance issues.

GROUP_CONCAT remains one of the more accessible and efficient tools for string aggregation in lightweight or open-source environments, making it popular among small to mid-sized applications and web-based systems.

PostgreSQL: Leveraging Arrays and String Functions

PostgreSQL is known for its extensibility and offers multiple methods for performing string aggregation. While recent versions support STRING_AGG directly, one alternative that provides extra flexibility is the use of arrays in combination with the array_agg and array_to_string functions.

This approach collects values into an array, then transforms the array into a single string with a specified delimiter. This method is especially useful when advanced filtering or transformations are needed before the final string output is generated.

Another benefit of this method is its robustness. It gracefully handles null values and provides a solid fallback in environments where STRING_AGG is not preferred or available. This method also aligns well with PostgreSQL’s support for complex data types and advanced aggregation functions.

Using arrays for aggregation may seem slightly more complex at first, but it unlocks a level of flexibility that can be valuable in sophisticated data models.

Using Recursive Queries for Maximum Compatibility

When native aggregation functions are not available, a widely accepted approach is the use of recursive common table expressions (CTEs). These recursive queries simulate string aggregation by iteratively building a concatenated string, row by row.

This method is especially useful in database systems that support CTEs but lack aggregation functions. Recursive queries work by starting with a base row, appending the next value using a UNION ALL, and continuing the process until all rows are covered.

Despite being slower and more complex than other methods, recursive CTEs offer universal compatibility. They are supported in several SQL dialects including SQL Server, PostgreSQL, and some configurations of SQLite. However, they should be used with caution, especially when working with large datasets, due to their recursive nature and potential impact on performance.

Recursive aggregation should be considered a fallback method rather than a primary strategy. It is best used in situations where compatibility trumps efficiency or where minimal tooling is available.

Handling Null Values in String Aggregation

One of the challenges in text aggregation is managing null values. In some database systems, the presence of a null value can cause the entire aggregated result to become null. In others, nulls may simply be ignored during concatenation.

Understanding the behavior of your chosen method is crucial. For instance:

• In SQL Server’s FOR XML PATH, nulls are ignored by default.
  
  

• In MySQL’s GROUP_CONCAT, nulls are excluded from the result set.
  
  

• In Oracle’s LISTAGG, nulls are also ignored unless explicitly handled.
  
  

• In PostgreSQL’s array_agg, nulls are included in the array, but can be filtered out before conversion to string.
  
  

When accuracy of data representation matters, such as in audits or user reports, it is important to explicitly handle null values—either by excluding them, converting them to a placeholder (like an empty string), or treating them with conditional logic.

Applying ISNULL, COALESCE, or equivalent functions can help sanitize data before aggregation, ensuring consistent output.

Sorting Within Aggregated Results

Another important aspect of string aggregation is control over the order in which values appear in the final string. Some native functions support built-in ordering clauses, while others require additional query logic to achieve the desired sequence.

For example:

• LISTAGG in Oracle allows WITHIN GROUP (ORDER BY column_name) to specify order.
  
  

• GROUP_CONCAT supports an ORDER BY clause inside the function.
  
  

• array_agg in PostgreSQL can be paired with a SORT operation before conversion to string.

Ordering becomes especially important when the output string is consumed by downstream applications or systems that rely on predictable data formatting. Without controlled ordering, the results may appear inconsistent or misleading.

In workarounds such as XML or recursive queries, sorting must be done at the subquery or CTE level. Although more complex, it ensures that aggregation does not sacrifice readability or meaning.

Use Cases for String Aggregation in Reporting and Applications

The ability to join values into a single string is not just a technical curiosity—it serves meaningful roles in real-world applications. Some examples include:

• Displaying a list of products in an invoice under a single order entry
  
  

• Summarizing tags or labels in content management systems
  
  

• Creating comma-separated lists for data exports like CSV files
  
  

• Presenting grouped customer feedback in a single report row

These use cases span industries and domains, from retail to healthcare to finance. As such, string aggregation techniques must be reliable, flexible, and compatible with various data sources and systems.

Developers working in enterprise environments may also use string aggregation for internal audit logs, workflow traces, and other back-end systems that require grouped outputs in readable formats.

Considerations for Internationalization and Encoding

When aggregating string values, encoding and character set handling become critical—especially in multilingual environments. Delimiters such as commas, semicolons, or pipes may have different meanings or formatting implications in different regions.

Some best practices include:

• Using Unicode-aware functions or data types (NVARCHAR, UTF8, etc.)
  
  

• Escaping delimiters when values themselves may contain similar characters
  
  

• Choosing delimiters based on regional formatting conventions
  
  

• Testing output on systems with different default encodings

Neglecting these considerations can result in broken data, misinterpretations, or even application crashes when data is exported or parsed incorrectly.

Planning for Future Compatibility

As database systems evolve, previously unavailable features may be introduced in newer versions. For example, STRING_AGG was not available in SQL Server before 2017, but is now widely supported.

When designing systems today, it is a good idea to build with future compatibility in mind. This may involve:

• Using database abstraction layers
  
  

• Documenting workarounds clearly
  
  

• Testing on the target environment's expected upgrade path
  
  

• Avoiding deeply embedded hacks unless absolutely necessary

Future-proofing ensures that upgrades are smooth and technical debt does not accumulate due to outdated query logic.

The absence of a built-in STRING_AGG function in a database system does not prevent developers from achieving the same result. Whether through XML tricks, recursive queries, array transformations, or native functions like GROUP_CONCAT and LISTAGG, there are many paths to the same destination.

Each approach has its strengths and limitations. The best choice depends on the database engine, version, data structure, and specific use case. As string aggregation becomes a more common need across business applications, understanding these techniques becomes essential for any SQL practitioner.

Applying and Comparing STRING_AGG Alternatives in Real-World SQL Environments

String aggregation is more than just a technical workaround—it plays a central role in how information is summarized and displayed in many real-world applications. Whether it's for exporting lists, generating reports, or presenting user-facing data in grouped formats, the ability to transform multiple rows into a single, concatenated string is a core SQL task.

While some databases provide STRING_AGG natively, others require alternative techniques. These options vary in complexity, compatibility, and performance. Understanding how to apply them practically, what their limits are, and how they compare in live environments is key to building efficient and maintainable SQL-based systems.

This article explores practical use cases for each approach, performance trade-offs, optimization strategies, and guidance on when to use which method.

Real-World Scenarios for String Aggregation

String aggregation is used in a wide range of industries and systems. Below are some examples where this functionality is often implemented:

• Creating a comma-separated list of skills associated with a candidate profile in a job portal
  
  

• Showing all products in a specific order as a single row in an e-commerce report
  
  

• Listing multiple error messages tied to a transaction for audit purposes
  
  

• Summarizing all courses completed by a student in a learning management system
  
  

• Collecting and presenting user tags in social media or content-sharing platforms
  
  

These use cases demand not just correct results but also maintainable logic and predictable performance, especially when queries are embedded in scheduled tasks or exposed through APIs.

Practical Use of FOR XML PATH in SQL Server

The method based on FOR XML PATH continues to be heavily used in older SQL Server environments. In practice, this technique can be incorporated into stored procedures, views, or inline queries.

Developers often combine it with STUFF to remove the leading separator and wrap it in a subquery to achieve grouping. Despite being more verbose, this method is extremely reliable and well-documented.

When deployed in production, care should be taken to handle potential XML escape issues. Since the method relies on an XML intermediary, characters like &, <, and > can cause issues if not properly encoded. Adding TYPE to the XML clause and then casting the result helps prevent malformed outputs.

This method remains a strong choice in production systems still running SQL Server 2016 or earlier.

LISTAGG in Oracle with Advanced Features

Oracle’s LISTAGG function is effective for grouping values in a readable format. In addition to standard string aggregation, recent versions of Oracle offer features such as handling overflow errors with the ON OVERFLOW TRUNCATE clause. This prevents the function from failing when the result exceeds the length limit.

In enterprise applications, this is particularly helpful when aggregating fields that vary in length or may grow unpredictably. For example, aggregating customer feedback comments or open ticket titles under a support agent might generate very large strings.

Oracle developers should also be aware that the order of values in LISTAGG must be specified explicitly to ensure consistent outputs. Omitting the order may lead to unexpected or random sequences depending on how Oracle processes the query internally.

Where performance is critical, testing should be conducted with large datasets to evaluate whether custom PL/SQL solutions might offer better control or memory handling.

GROUP_CONCAT in MySQL for Web-Based Applications

Web applications powered by MySQL often use GROUP_CONCAT for displaying lists of items—whether it's tags, filenames, categories, or names. This function is highly useful in content-heavy applications such as blogging platforms or forums.

One practical concern in MySQL is the maximum result length of the aggregation, controlled by the group_concat_max_len system variable. For applications generating long strings, such as those exporting data to CSV format, the default limit may be insufficient.

In such cases, developers can modify the session-level setting before running the query to prevent truncation. Additionally, GROUP_CONCAT works well in combination with the DISTINCT keyword to eliminate duplicates and ORDER BY for sorting results.

It is also common to filter nulls and empty values using WHERE clauses or conditional expressions to avoid unnecessary separators in the output.

For lightweight or embedded applications, GROUP_CONCAT remains a flexible and easy-to-use choice.

PostgreSQL and Use of Array Aggregation

In PostgreSQL, combining array_agg with array_to_string allows for highly customizable aggregation. This combination is especially effective in use cases where filtering, deduplication, or formatting is required.

For example, to aggregate a list of completed modules by a user in a course application, developers can first apply filtering logic inside the array_agg and then convert the resulting array into a delimited string.

PostgreSQL also supports advanced window functions, enabling developers to add rankings, filters, or cumulative logic into the aggregation process. This makes PostgreSQL one of the most flexible environments for complex string aggregation.

Another approach available in PostgreSQL is the use of custom aggregate functions. Developers can define new types of aggregation that handle special scenarios, such as prefixing each value, applying custom logic on nulls, or even introducing conditional delimiters.

This system-level customization makes PostgreSQL ideal for projects that require high levels of control and precision in how aggregation is performed.

Recursive CTE Aggregation in Legacy Systems

Recursive common table expressions (CTEs) allow developers to create a looping mechanism within SQL, building up a string result over successive iterations. This technique is useful in environments where none of the standard aggregation functions are available.

While not the most efficient method, recursive CTEs can achieve the desired outcome when portability and compatibility are top priorities. They are particularly relevant in systems like older SQLite builds or minimal SQL engines used in embedded applications.

One real-world use is in migration scripts or temporary solutions during system upgrades, where developers need to replicate aggregation logic without relying on unsupported functions.

Recursive CTEs should be optimized by limiting the number of records involved, avoiding unnecessary joins, and testing on subsets of data. Since recursion can consume more memory and take longer to execute, profiling and tuning are critical.

As a fallback method, recursive CTEs provide wide compatibility at the expense of complexity and performance.

Performance Testing and Optimization Strategies

When multiple methods are available, choosing the right one often depends on performance. Aggregating text data is not a lightweight operation, especially when dealing with large volumes of data, long strings, or frequent queries.

Some techniques to improve performance include:

• Indexing the columns used in filtering and grouping
  
  

• Reducing the dataset using pre-aggregation or subqueries
  
  

• Limiting string length where possible to prevent overflows
  
  

• Avoiding unnecessary sorting within aggregation unless required
  
  

• Using temporary tables or materialized views for intermediate steps
  
  

Additionally, developers should consider breaking complex string aggregation into stages. For instance, first filtering or grouping data, then performing aggregation on the smaller result set.

Using built-in database profiling tools can help identify bottlenecks. Features such as execution plans, runtime statistics, and memory usage snapshots provide insights into where optimizations are needed.

Designing Portable SQL Queries

Portability is often a concern in cross-platform applications. If your application needs to support multiple database systems or migrate data between them, using standardized logic is important.

Strategies to improve query portability include:

• Wrapping queries in views or stored procedures
  
  

• Avoiding system-specific keywords or syntax
  
  

• Using conditional logic in code to determine which query to run
  
  

• Testing queries across environments regularly
  
  

• Documenting why a specific aggregation method was chosen

If possible, use abstraction layers in application code to switch between queries based on the target database. This reduces vendor lock-in and allows smoother transitions during upgrades or migrations.

Summary

String aggregation is an indispensable feature in SQL-based systems, enabling users to convert multiple rows into a single readable string. While STRING_AGG is a simple and efficient solution in systems that support it, there are robust alternatives available for nearly every SQL platform.

Whether using FOR XML PATH, GROUP_CONCAT, LISTAGG, array functions, or recursive queries, each method offers specific strengths tailored to different needs. Real-world usage shows that understanding these options allows for better decisions in terms of performance, compatibility, and maintainability.

By combining the right technique with thoughtful query design and performance optimization, developers can implement string aggregation reliably across diverse environments.