What is the difference between Union and Union All in SQL?

Union removes duplicate rows from the combined result set, while Union All keeps every row, including duplicates. Union performs a distinct sort operation, which makes it slower on large datasets, whereas Union All simply concatenates results without extra processing.

Which is better for performance: Union or Union All?

Union All is better for performance because it skips the duplicate-removal step, avoiding an expensive sort or hash operation. Union requires extra CPU and memory to deduplicate rows, so Union All returns results faster, especially on large tables.

Does Union require the same number of columns as Union All?

Yes, both Union and Union All require the same number of columns and compatible data types in each SELECT statement. The column names come from the first query, and the database checks positional compatibility, not column names, for both operators.

Can I use Union All instead of Union to get the same result?

You can use Union All instead of Union only if you are certain the result sets contain no duplicate rows. If duplicates exist, Union All will return them, changing your final output, so verify your data or add a DISTINCT clause to match Union's behavior.

What is the main risk of using Union instead of Union All?

The main risk of using Union is performance degradation on large datasets because it performs an implicit DISTINCT operation. This can cause significant memory and CPU spikes, leading to slower queries or even tempdb overflow in SQL Server environments.

Are Union and Union All compatible with all SQL databases?

Yes, Union and Union All are standard SQL operators supported by all major databases, including MySQL, PostgreSQL, SQL Server, Oracle, and SQLite. However, syntax variations exist for edge cases, such as ORDER BY placement, so check your specific database documentation.

What is a common beginner mistake when using Union or Union All?

A common beginner mistake is forgetting that both operators require identical column counts and compatible data types, causing conversion errors. Another mistake is assuming Union preserves the first query's column names, which it does, but beginners often expect the second query's names to appear.

When should I use Union instead of Union All in a real-world query?

Use Union when you need a clean, deduplicated list from multiple sources, such as combining customer lists from different regions. Use Union All when you need to preserve all records for reporting or when you know duplicates are impossible, like merging transaction logs by date.

Can I switch from Union to Union All without changing my output?

You can switch from Union to Union All without changing output only when your combined datasets are guaranteed to have zero overlapping rows. To verify, run a quick count comparison: if the row count from Union equals the row count from Union All, the switch is safe.

Does Union All affect the ORDER BY clause differently than Union?

No, ORDER BY applies to the final result set for both Union and Union All, and you cannot use ORDER BY inside individual SELECT statements. However, you can use column aliases in the first SELECT to reference in the final ORDER BY, which works identically for both operators.

Difference Between

Difference Between Union and Union All

Written byNex Virox Team

Editorial Team

Reviewed byVarshal Nirbhavane

Senior SEO & Organic Growth Professional · 5+ years

Published: September 9, 202622 min read

Quick answer

The main difference between Union and Union All is that Union removes duplicate rows from the result set, while Union All retains every row, including duplicates. Union is a distinct selector that performs an implicit sort to eliminate repeats, while Union All is a faster concatenation operator that simply appends results. Union is Union a set operation returning unique rows, while Union All returns all rows without deduplication.

Key takeaways

Core distinction: Union removes duplicate rows from the final result set, while Union All preserves every row including duplicates.
How each works: Union performs a distinct sort operation to eliminate duplicates, whereas Union All concatenates results directly without sorting or deduplication.
Performance impact: Union All runs faster and consumes less memory because it skips the duplicate-removal step, making it ideal for large datasets.
Best-fit use case: Choose Union All when merging non-overlapping data sources or when duplicates are acceptable, such as appending logs from multiple servers.
Common mistake: Using Union unnecessarily on unique datasets adds processing overhead; use Union All unless duplicate elimination is explicitly required.

Table of Contents18 sections

Difference Between Union and Union All: Comparison Table

Aspect	Union	Union All
Definition	Combines rows from multiple SELECT statements while removing duplicate rows.	Combines rows from multiple SELECT statements while keeping all duplicate rows intact.
Purpose	Produces a distinct result set for reporting or analytics without repetition.	Preserves every record for complete data audits or full export requirements.
Core Mechanism	Performs a sort or hash operation internally to identify and eliminate duplicates.	Appends results sequentially without any duplicate-checking or sorting step.
Duplicate Handling	Removes all duplicate rows across combined result sets automatically.	Retains every duplicate row exactly as returned by each SELECT statement.
Sorting Behavior	May implicitly sort data to detect duplicates, unless ORDER BY specifies otherwise.	Does not sort data; returns rows in the order each query produces them.
Performance	Slower on large datasets due to extra work for duplicate elimination.	Faster on large datasets because it skips sorting and duplicate checks.
Resource Usage	Consumes more CPU and memory for the distinct operation on big inputs.	Uses fewer CPU cycles and less memory, making it more resource-efficient.
Query Execution Time	Typically takes longer to execute compared to UNION ALL on identical data.	Executes quicker, often reducing runtime by 50% or more on big tables.
Result Set Size	Returns fewer rows when duplicates exist across the combined queries.	Returns the exact sum of all rows from every SELECT statement involved.
Use Case Fit	Best for final reports needing clean, unique values like customer lists.	Best for data migration, logging, or merging raw transaction feeds.
Data Integrity	Guarantees no duplicate rows, ensuring consistent unique-key reporting.	Preserves all source rows, which may include intentional or accidental duplicates.
Column Compatibility	Requires same number and compatible data types across all SELECT statements.	Requires same column count and compatible types, identical to UNION.
Order By Placement	Allows ORDER BY only at the end of the entire UNION query.	Allows ORDER BY only at the end; individual query ORDER BY is ignored.
Index Usage	May use indexes poorly due to sorting for distinctness on large scans.	Can leverage indexes more effectively because no sorting is required.
Memory Overhead	Needs temp space to store seen values for duplicate detection.	Streams rows directly, requiring minimal temporary storage.
Disk I/O	May write intermediate sort files to disk for very large result sets.	Avoids disk spills, reducing I/O load on the database server.
Parallelism	Less parallel-friendly due to the global distinct phase after merging.	Highly parallelizable because each query result can be appended independently.
NULL Handling	Treats NULLs as duplicates; only one NULL row appears in output.	Keeps every NULL row from each query, including multiple NULLs.
Compatibility	Works in SQL Server, Oracle, MySQL, PostgreSQL, and standard SQL.	Works in all major SQL databases with identical syntax support.
Syntax Difference	Uses UNION keyword between SELECT statements with no extra clause.	Uses UNION ALL keyword, adding the ALL modifier to the same syntax.
Error Proneness	More prone to performance issues but fewer logical duplicate surprises.	Less prone to performance issues but may deliver unexpected duplicates.
Typical Users	Chosen by data analysts for dashboards and executive summary views.	Preferred by ETL developers for bulk loading and archival processes.
Scalability	Scales poorly beyond millions of rows due to distinct computation cost.	Scales linearly with input size, handling billions of rows efficiently.
Maintenance	Requires careful index tuning to maintain acceptable speed over time.	Requires minimal tuning; simple append logic stays stable.
Debugging Ease	Harder to trace duplicate removal when output differs from source counts.	Easier to verify because row counts match the sum of source queries.
Aggregation Impact	Removes duplicates before aggregation, affecting SUM or COUNT calculations.	Keeps duplicates for aggregation, preserving exact totals from raw data.
Join Behavior	Distinct pass after union can hide unintended duplicate join results.	Reveals all join duplicates, helping identify faulty join conditions.
Subquery Use	Can be used in subqueries but adds cost to outer query processing.	Works efficiently in subqueries, especially for IN or EXISTS checks.
Best-Fit Scenario	Ideal for generating unique product catalogs or deduplicated contact lists.	Ideal for merging daily logs, sensor data, or financial transactions.

What Is Union?

Union is a SQL operator that combines rows from two or more SELECT queries into one result set. It removes duplicate rows by default, returning only distinct values. Union exists to merge related data from separate tables or sources into a single, clean dataset for reporting and analysis.

Definition of Union

Union is a set operation in relational databases that concatenates the result sets of multiple SELECT statements vertically, eliminating duplicate rows. Each SELECT must have the same number of columns with compatible data types. The final result uses column names from the first query only.

Key Characteristics of Union

Characteristic	What It Means in Practice
Duplicate removal	Union filters out identical rows across all combined queries, returning each unique row only once in the final output.
Column count match	Every SELECT statement in a Union must return the same number of columns, or the database throws an error.
Data type compatibility	Corresponding columns in each query must share compatible data types, such as all integers or all character strings.
Ordering requires alias	An ORDER BY clause applies to the entire Union result and must reference the column name from the first SELECT statement.
Performance cost	Union performs a sort or hash operation to identify duplicates, which adds processing time compared to Union All.
Default behavior	Union is equivalent to Union Distinct in most database systems, including PostgreSQL, MySQL, and SQL Server.
Column naming	Output column headers inherit names from the first query; subsequent queries' column names are ignored in the result.
Set semantics	Union follows mathematical set theory, treating results as sets where membership is binary and duplicates vanish.
Cross-table merging	Union can combine data from different tables, schemas, or even different databases within the same server instance.
NULL handling	NULL values are treated as equal for duplicate detection, so two rows with NULL in the same column are considered duplicates.

Common Examples of Union

Sales regional merge - Combine quarterly sales figures from North America and Europe tables into one annual report without double-counting shared customers.
Customer archive consolidation - Merge active customers with archived records from a separate table to create a complete mailing list for a one-time campaign.
Product catalog unification - Combine products from a legacy system and a new ERP table, removing identical SKUs that exist in both sources.
Employee directory build - Merge current staff and former contractor tables to generate a full personnel roster for an audit review.
Log aggregation - Combine error logs from web servers and application servers into a single view, filtering out repeated identical error entries.
Financial statement rollup - Merge monthly transaction tables from different departments into one consolidated profit-and-loss statement.
Inventory reconciliation - Combine warehouse stock counts from two separate facilities, eliminating duplicate part numbers that appear in both locations.
User activity summary - Merge login records from mobile app and website tables to count unique active users across both platforms.
Survey response merge - Combine survey results from email and in-person collection methods, removing identical responses from the same participant.
Shipping manifest creation - Merge orders from two fulfillment centers into one shipping list, ensuring no duplicate order numbers appear.

Advantages and Limitations of Union

Advantages	Limitations
Provides clean, duplicate-free data for accurate reporting and analytics.	Requires more memory and CPU time than Union All due to duplicate elimination sorting.
Simplifies complex queries by combining multiple sources into one logical result set.	Fails immediately if any SELECT statement has mismatched column counts or incompatible data types.
Works across different tables, schemas, and databases, offering broad data integration flexibility.	Cannot preserve duplicate rows that might be meaningful, such as repeated transactions with identical values.
Reduces application-side logic by letting the database handle row deduplication natively.	Performance degrades significantly on large datasets because sorting every row is computationally expensive.
Ensures consistent column naming and ordering from the first query, simplifying downstream processing.	ORDER BY only works on the final result, not on individual subqueries, limiting per-source sorting control.
Follows standard SQL syntax, making queries portable across major database platforms like Oracle and SQLite.	Cannot be used directly with BLOB or TEXT columns in some databases that lack proper comparison operators.
Ideal for building unified views that hide data fragmentation from end users and reporting tools.	Requires explicit casting when data types differ slightly, adding query complexity and maintenance overhead.
Returns deterministic results because duplicate removal follows a consistent set-based logic.	Indexes on source tables cannot speed up the duplicate elimination step, which always requires a full scan.
Supports combining more than two queries, allowing multi-source aggregation in a single statement.	Can produce unexpected NULL handling when comparing columns with different missing-value conventions across sources.
Helps enforce data quality by surfacing exact duplicates that might indicate flawed source data entry.	Not suitable for streaming or real-time processing where latency matters, due to the blocking sort operation.

What Is Union All?

Union All is a SQL set operator that combines rows from two or more queries into a single result set. Unlike Union, Union All retains every duplicate row. It exists to merge datasets completely when duplicate preservation is required for accurate reporting or analysis.

Definition of Union All

Union All concatenates the result sets of multiple SELECT statements vertically, returning all rows from each query without removing duplicates or performing sorting. Each SELECT statement must have the same number of columns with compatible data types. Union All does not deduplicate or reorder the combined output.

Key Characteristics of Union All

Characteristic	What It Means in Practice
Duplicate Retention	Union All keeps every row from all queries, including identical rows, which is essential for summing totals across overlapping datasets.
No Sorting	Union All returns rows in the order they are retrieved from each query, without performing an implicit sort for duplicate elimination.
Faster Execution	Union All skips the deduplication step, making it significantly faster than Union on large tables with many repeated values.
Column Count Match	Each SELECT statement must contain the same number of columns; mismatched counts cause a syntax error in all SQL databases.
Data Type Compatibility	Corresponding columns must have compatible data types; implicit conversion occurs where allowed, but incompatible types raise an error.
No Implicit Distinct	Union All never applies a DISTINCT operation, so the result size equals the sum of all input row counts exactly.
Column Names from First Query	The result set uses column names from the first SELECT statement; subsequent query column names are ignored in the output.
Works with ORDER BY	An ORDER BY clause can be applied to the entire Union All result, but it must reference column positions or first-query aliases.
Memory Efficiency	Union All streams rows directly to output without building a temporary deduplication table, reducing memory and disk usage.
Useful for Partitioning	Union All is ideal for combining data from sharded tables or monthly partitions where duplicates cannot exist across segments.

Common Examples of Union All

Sales Data Aggregation - Combining daily sales tables from multiple regions into one total report without losing any transaction records.
Log File Consolidation - Merging error logs from different servers into a single audit trail for security analysis and troubleshooting.
Historical Data Merge - Appending current year data to archived previous years for trend analysis while preserving all historical entries.
Customer Record Unification - Stacking customer lists from different CRM systems to create a complete mailing list with possible duplicates.
Inventory Reconciliation - Combining warehouse stock counts from separate facilities to calculate total on-hand units across all locations.
Financial Statement Compilation - Merging quarterly revenue figures from subsidiary companies into a consolidated corporate income statement.
User Activity Tracking - Joining clickstream data from web and mobile platforms into one analytics dataset for complete user journey mapping.
Backup Verification - Comparing row counts between production and backup tables by using Union All with a COUNT query per table.
Configuration Management - Stacking server configuration entries from development, staging, and production environments for compliance audits.
Survey Response Compilation - Combining survey results from different collection channels (online, phone, paper) into one analysis table.

Advantages and Limitations of Union All

Advantages	Limitations
Union All executes faster than Union because it skips the duplicate-removal sorting step entirely, improving query performance.	Union All can return massive result sets with redundant rows, increasing network transfer time and client-side processing load.
Union All preserves exact row counts, making it essential for financial totals, inventory counts, and other precise numeric aggregations.	Union All requires all queries to have identical column counts, which complicates combining tables with different schemas.
Union All uses less memory than Union since it does not build a hash table or sort operation for deduplication.	Union All does not eliminate accidental duplicates, potentially skewing analytical results if source data contains unexpected overlaps.
Union All works efficiently with large tables, streaming results without the overhead of a temporary distinct operation.	Union All cannot be used with a global ORDER BY that references column names from later queries; only first-query aliases work.
Union All is ideal for partitioning large datasets across multiple tables, enabling parallel query execution and simpler maintenance.	Union All requires compatible data types across corresponding columns, forcing explicit CAST operations for mismatched types.
Union All preserves the original row order from each query, which is useful when chronological order matters in the output.	Union All does not support using column aliases from subsequent SELECT statements in an outer ORDER BY clause.
Union All simplifies ETL processes by allowing direct concatenation of staging tables without intermediate deduplication steps.	Union All can hide data quality issues, such as duplicate primary keys, which remain unnoticed until downstream validation fails.
Union All works with any number of SELECT statements, making it scalable for combining dozens of source tables in one query.	Union All with many subqueries can become difficult to read and maintain, especially when each query has complex WHERE clauses.
Union All avoids the performance penalty of sorting, which is particularly beneficial when combining millions of rows from multiple sources.	Union All does not guarantee any specific output order unless an explicit ORDER BY is added, which then incurs sorting costs.
Union All is widely supported across all major SQL databases, including MySQL, PostgreSQL, SQL Server, and Oracle, ensuring portability.	Union All cannot be used with SELECT INTO in some databases to create a new table directly, requiring a workaround with CREATE TABLE.

Similarities Between Union and Union All

Shared Aspect	How Union and Union All Are Alike
Core Purpose	Both Union and Union All combine rows from two or more SELECT statements into a single result set.
Required Syntax	Both Union and Union All require each SELECT statement to have the same number of columns in the same order.
Data Type Matching	Both Union and Union All require corresponding columns in each SELECT to have compatible or implicitly convertible data types.
Column Names	Both Union and Union All use the column names from the first SELECT statement as the headers for the combined output.
Result Structure	Both Union and Union All return a single flat table, not nested sets, with rows stacked vertically from each query.
Query Execution	Both Union and Union All execute all underlying SELECT statements fully before merging their respective row sets.
SQL Standard	Both Union and Union All are defined in the ANSI SQL standard and work across major relational databases like MySQL, PostgreSQL, SQL Server, and Oracle.
Use with WHERE	Both Union and Union All allow individual WHERE clauses inside each SELECT to filter rows before combination.
Use with GROUP BY	Both Union and Union All can combine results from queries that use GROUP BY and aggregate functions like SUM or COUNT.
Use with ORDER BY	Both Union and Union All allow a single ORDER BY clause at the end of the entire statement to sort the final combined result.
Use with LIMIT	Both Union and Union All support a final LIMIT or TOP clause to restrict the number of rows returned in the merged output.
Column Aliases	Both Union and Union All accept column aliases in the first SELECT, which become the display names for the entire result set.
Subquery Support	Both Union and Union All can be used inside subqueries, derived tables, or common table expressions (CTEs) for complex logic.
Join Compatibility	Both Union and Union All can combine results from queries that use INNER JOIN, LEFT JOIN, or other join types internally.
NULL Handling	Both Union and Union All treat NULL values as regular data; NULLs are preserved and not automatically eliminated or replaced.
Performance Baseline	Both Union and Union All require the database to execute each SELECT separately, so the total work is at least the sum of all queries.
Index Usage	Both Union and Union All can leverage indexes on the underlying tables during each individual SELECT execution.
Read-Only Operation	Both Union and Union All are read-only operations; they do not modify, insert, update, or delete any data in the source tables.
Result Cardinality	Both Union and Union All produce a result set whose row count is at least the maximum number of rows from any single SELECT.
Cross-Database Use	Both Union and Union All can combine data from different tables, schemas, or even different databases on the same server instance.
Dynamic SQL	Both Union and Union All can be constructed dynamically in application code or stored procedures to build flexible queries.
Error Behavior	Both Union and Union All fail with the same error types if column counts mismatch or data types are incompatible.
Transaction Scope	Both Union and Union All operate within the current transaction context and respect the isolation level of the session.
Concurrency Safety	Both Union and Union All are safe for concurrent execution; they do not lock tables beyond what the individual SELECTs require.
Portability	Both Union and Union All have identical syntax across MySQL, PostgreSQL, SQL Server, Oracle, and SQLite, making code portable.
Debugging Ease	Both Union and Union All allow you to run each SELECT independently to verify row sets before combining them.
Use in Views	Both Union and Union All can be embedded inside a CREATE VIEW statement to define a virtual table from multiple queries.
Use in Reporting	Both Union and Union All are common tools for merging monthly sales, regional data, or log segments into a single report.
Maintenance Pattern	Both Union and Union All require the same maintenance discipline: updating all SELECT statements whenever the column list changes.
Long-Term Stability	Both Union and Union All have been stable SQL features for decades, with no deprecation plans in any major database vendor.
Cost Estimation	Both Union and Union All have similar query plan costs for the underlying SELECTs; the only difference is the deduplication step.

Union or Union All: Which Should You Choose?

Choose Union All for raw performance and Union for deduplicated results. The single deciding variable is whether duplicate rows matter to your final output. If duplicates are acceptable or required, Union All wins; if you need a clean, unique dataset, Union is mandatory.

When to Use Union

Choose Union when duplicate rows must be eliminated from your combined result set. Use it for reporting dashboards, customer lists, or financial consolidations where a row appearing twice would skew counts or totals. Expect slower execution on large tables because Union performs a distinct sort. This cost is justified only when data integrity depends on uniqueness.

When to Use Union All

Choose Union All when duplicates are acceptable or logically meaningful, such as appending transactional logs or audit trails. It is significantly faster because it skips the sorting and deduplication step entirely. Use it for high-volume ETL jobs, data warehousing loads, or any process where preserving every source row is the priority. This is the default choice for performance-critical queries.

Common Misconceptions About Union and Union All

Common Myth	The Reality
UNION and UNION ALL are completely interchangeable in every query.	UNION removes duplicate rows; UNION ALL preserves them. Swapping them changes result sets and can break aggregations.
UNION ALL is just a faster version of UNION with no other differences.	UNION ALL skips the sorting and deduplication step, making it faster, but it also returns duplicate rows that UNION filters out.
UNION automatically sorts the final result set in ascending order.	UNION performs a DISTINCT operation that may sort internally, but the output order is not guaranteed unless you add an explicit ORDER BY clause.
You can use UNION to combine columns from tables with different data types.	Both UNION and UNION ALL require matching column counts and compatible data types; mismatches cause conversion errors.
UNION ALL is always faster than UNION, so you should always choose it.	UNION ALL is faster only when duplicates are acceptable; choosing it when you need unique rows produces incorrect results.
UNION and UNION ALL work the same way in every SQL database system.	Behavior is consistent for deduplication, but syntax variations exist for column aliases and ORDER BY placement across MySQL, SQL Server, and Oracle.
UNION removes duplicates across all columns in the combined result set.	UNION removes rows only when every selected column has identical values; partial matches on a single column are kept.
UNION ALL requires more memory than UNION because it stores more rows.	UNION typically uses more memory for sorting and deduplication; UNION ALL streams rows directly without that overhead.
You cannot use ORDER BY with UNION ALL in any SQL dialect.	You can add ORDER BY after the last SELECT in both UNION and UNION ALL; it applies to the entire combined result set.
UNION is the same as JOIN; both combine data from two tables.	UNION stacks rows vertically, while JOIN combines columns horizontally based on a matching key; they serve different purposes.
UNION ALL returns only unique values from the first table.	UNION ALL returns every row from every SELECT statement, including all duplicates from each source table.
Using UNION with large tables always causes a performance disaster.	UNION performance depends on indexes and data distribution; with proper indexing, deduplication can be efficient on large datasets.
UNION and UNION ALL require the same number of columns in each SELECT.	Both require equal column counts; a mismatch triggers an error regardless of which operator you use.
UNION ALL ignores NULL values when combining rows.	UNION ALL treats NULL as a regular value; it preserves NULLs and duplicates exactly as they appear in source tables.
UNION automatically eliminates NULL values from the final result.	UNION treats NULLs as duplicates of each other; it keeps one NULL row but does not remove non-NULL duplicates.
You can use UNION to merge two queries with different column aliases without issues.	Column names come from the first SELECT; aliases in subsequent SELECTs are ignored, which confuses beginners expecting custom headers.
UNION ALL is only useful for small tables; it has no benefit on large datasets.	UNION ALL is often preferred for large datasets because it avoids the expensive sort operation that UNION requires for deduplication.
UNION removes duplicates only if you select all columns from both tables.	UNION checks duplicates on the selected columns only; unselected columns do not affect deduplication logic.
ORDER BY in a subquery before UNION affects the final ordering of results.	ORDER BY inside a subquery is ignored unless combined with LIMIT; only the final ORDER BY after the last SELECT controls output order.
UNION and UNION ALL produce identical execution plans in modern databases.	Execution plans differ; UNION includes a DISTINCT sort operator, while UNION ALL uses a concatenation operator without sorting.
You can combine more than two SELECT statements with UNION ALL.	Both UNION and UNION ALL support multiple SELECT statements; you can chain them indefinitely, subject to query length limits.
UNION ALL is the same as UNION DISTINCT in every SQL implementation.	UNION is equivalent to UNION DISTINCT in most databases, but UNION ALL is the opposite; they are never the same operation.
Using UNION with text columns automatically trims trailing spaces.	Deduplication behavior for trailing spaces varies by database; SQL Server ignores them, while PostgreSQL treats them as significant.
UNION ALL cannot be used with GROUP BY or aggregate functions.	You can wrap UNION ALL in a subquery and apply GROUP BY or aggregates to the combined result set without restrictions.
UNION is always slower than UNION ALL, so professionals never use it.	UNION is necessary when you need unique rows; using UNION ALL to avoid the cost produces incorrect data, which is worse than slower performance.
Column data types must be exactly identical for UNION to work.	Data types must be implicitly convertible; for example, INT and VARCHAR can combine, but conversion rules apply per database.
UNION ALL preserves the order of rows from the first SELECT statement.	Without an explicit ORDER BY, the database determines row order; UNION ALL does not guarantee source order preservation.
UNION removes duplicates only when the full row is an exact match.	UNION compares all selected columns; if any column differs, the row is kept, even if other columns are identical.
You cannot use UNION ALL with queries that contain subqueries in the SELECT list.	Subqueries in the SELECT list are allowed with both UNION and UNION ALL, as long as they return scalar values.
UNION and UNION ALL are only used for combining two tables, not for other purposes.	They can combine results from CTEs, subqueries, and views; they are general-purpose set operators, not limited to base tables.

Conclusion

Difference Between Union and Union All comes down to duplicate handling. Union removes duplicate rows, while Union All preserves every row for faster performance. Choose Union for clean, distinct result sets. Choose Union All when you need maximum speed and don't mind duplicates. This simple rule guides your SQL query decisions.

FAQs on Difference Between Union and Union All

What is the difference between Union and Union All in SQL?: Union removes duplicate rows from the combined result set, while Union All keeps every row, including duplicates. Union performs a distinct sort operation, which makes it slower on large datasets, whereas Union All simply concatenates results without extra processing.
Which is better for performance: Union or Union All?: Union All is better for performance because it skips the duplicate-removal step, avoiding an expensive sort or hash operation. Union requires extra CPU and memory to deduplicate rows, so Union All returns results faster, especially on large tables.
Does Union require the same number of columns as Union All?: Yes, both Union and Union All require the same number of columns and compatible data types in each SELECT statement. The column names come from the first query, and the database checks positional compatibility, not column names, for both operators.
Can I use Union All instead of Union to get the same result?: You can use Union All instead of Union only if you are certain the result sets contain no duplicate rows. If duplicates exist, Union All will return them, changing your final output, so verify your data or add a DISTINCT clause to match Union's behavior.
What is the main risk of using Union instead of Union All?: The main risk of using Union is performance degradation on large datasets because it performs an implicit DISTINCT operation. This can cause significant memory and CPU spikes, leading to slower queries or even tempdb overflow in SQL Server environments.
Are Union and Union All compatible with all SQL databases?: Yes, Union and Union All are standard SQL operators supported by all major databases, including MySQL, PostgreSQL, SQL Server, Oracle, and SQLite. However, syntax variations exist for edge cases, such as ORDER BY placement, so check your specific database documentation.
What is a common beginner mistake when using Union or Union All?: A common beginner mistake is forgetting that both operators require identical column counts and compatible data types, causing conversion errors. Another mistake is assuming Union preserves the first query's column names, which it does, but beginners often expect the second query's names to appear.
When should I use Union instead of Union All in a real-world query?: Use Union when you need a clean, deduplicated list from multiple sources, such as combining customer lists from different regions. Use Union All when you need to preserve all records for reporting or when you know duplicates are impossible, like merging transaction logs by date.
Can I switch from Union to Union All without changing my output?: You can switch from Union to Union All without changing output only when your combined datasets are guaranteed to have zero overlapping rows. To verify, run a quick count comparison: if the row count from Union equals the row count from Union All, the switch is safe.
Does Union All affect the ORDER BY clause differently than Union?: No, ORDER BY applies to the final result set for both Union and Union All, and you cannot use ORDER BY inside individual SELECT statements. However, you can use column aliases in the first SELECT to reference in the final ORDER BY, which works identically for both operators.