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Update (SQL)
View on Wikipediafrom Wikipedia
An SQL UPDATE statement changes the data of one or more records in a table. Either all the rows can be updated, or a subset may be chosen using a condition.
The UPDATE statement has the following form:[1]
UPDATEtable_nameSETcolumn_name = value [, column_name = value ...] [WHEREcondition]
For the UPDATE to be successful, the user must have data manipulation privileges (UPDATE privilege) on the table or column and the updated value must not conflict with all the applicable constraints (such as primary keys, unique indexes, CHECK constraints, and NOT NULL constraints).
In some databases, such as PostgreSQL, when a FROM clause is present, what essentially happens is that the target table is joined to the tables mentioned in the fromlist, and each output row of the join represents an update operation for the target table. When using FROM, one should ensure that the join produces at most one output row for each row to be modified. In other words, a target row shouldn't join to more than one row from the other table(s). If it does, then only one of the join rows will be used to update the target row, but which one will be used is not readily predictable.[2]
Because of this indeterminacy, referencing other tables only within sub-selects is safer, though often harder to read and slower than using a join.
MySQL does not conform to ANSI standard.[3]
Examples
[edit]Set the value of column C1 in table T to 1, only in those rows where the value of column C2 is "a".
UPDATE T
SET C1 = 1
WHERE C2 = 'a'
In table T, set the value of column C1 to 9 and the value of C3 to 4 for all rows for which the value of column C2 is "a".
UPDATE T
SET C1 = 9,
C3 = 4
WHERE C2 = 'a'
Increase value of column C1 by 1 if the value in column C2 is "a".
UPDATE T
SET C1 = C1 + 1
WHERE C2 = 'a'
Prepend the value in column C1 with the string "text" if the value in column C2 is "a".
UPDATE T
SET C1 = 'text' || C1
WHERE C2 = 'a'
Set the value of column C1 in table T1 to 2, only if the value of column C2 is found in the sublist of values in column C3 in table T2 having the column C4 equal to 0.
UPDATE T1
SET C1 = 2
WHERE C2 IN ( SELECT C3
FROM T2
WHERE C4 = 0)
One may also update multiple columns in a single update statement:
UPDATE T
SET C1 = 1,
C2 = 2
Complex conditions and JOINs are also possible:
UPDATE T
SET A = 1
WHERE C1 = 1
AND C2 = 2
Some databases allow the non-standard use of the FROM clause:
UPDATE a
SET a.[updated_column] = updatevalue
FROM articles a
JOIN classification c
ON a.articleID = c.articleID
WHERE c.classID = 1
Or on Oracle systems (assuming there is an index on classification.articleID):
UPDATE
(
SELECT *
FROM articles
JOIN classification
ON articles.articleID = classification.articleID
WHERE classification.classID = 1
)
SET [updated_column] = updatevalue
With long name of table:
UPDATE MyMainTable AS a
SET a.LName = Smith
WHERE a.PeopleID = 1235
Potential issues
[edit]- See Halloween Problem. It is possible for certain kinds of
UPDATEstatements to become an infinite loop when theWHEREclause and one or moreSETclauses may utilize an intertwined index.
References
[edit]- ^ http://dev.mysql.com/doc/refman/5.0/en/update.html simplified from this page
- ^ "UPDATE". January 2012.
- ^ "SQL - Update a table column, then the other column with updated value of the former. MySQL / PostgreSQL differ".
Update (SQL)
View on Grokipediafrom Grokipedia
The UPDATE statement in SQL is a fundamental Data Manipulation Language (DML) command used to modify existing records in a relational database table by altering the values of specified columns in selected rows.[1] It allows database administrators and applications to perform targeted updates based on conditions, ensuring data integrity while avoiding blanket changes to the entire dataset unless explicitly intended.[2] As part of the ANSI/ISO SQL standard since SQL-86, the UPDATE statement is universally supported across major relational database management systems (RDBMS), including PostgreSQL, MySQL, Oracle Database, and Microsoft SQL Server, though implementations may include vendor-specific extensions.[1][3]
The basic syntax of the UPDATE statement is structured as
This operation sets the
or to confirm the count:
This returns 150, matching the update count and ensuring the operation succeeded as intended.[1]
A more targeted example involves updating with literal values under a simple condition. Consider a
Here, only rows matching the WHERE condition receive the new price value, leaving others unchanged. For example, in PostgreSQL, the system outputs the affected row count as
which displays the updated prices for those rows, confirming the selective application.[1]
This query increases the salary by 10% and sets a fixed bonus for qualifying employees, ensuring atomic updates to maintain data integrity across columns.[1]
For updates involving data from another table—simulating a join—standard SQL employs subqueries in the WHERE or SET clauses rather than direct JOIN syntax, which is a vendor extension in systems like PostgreSQL and SQL Server. This approach leverages SELECT subquery concepts to filter or derive values conditionally. A practical example updates order statuses based on customer regions:
Here, the subquery identifies eligible customers, enabling targeted bulk modifications without altering the core UPDATE structure.[8]
Such advanced updates are particularly valuable in scenarios like data migration, where synchronizing multiple attributes across tables ensures consistency during transfers, or in reporting pipelines, where bulk adjustments to metrics (e.g., recalibrating sales figures and timestamps) streamline ETL processes without multiple passes. These techniques scale well for large datasets, reducing transaction overhead compared to sequential single-column operations.[13]
UPDATE table_name SET column1 = expression1, column2 = expression2, ... [WHERE condition];, where the SET clause defines the columns to modify and their new values—derived from literals, variables, or expressions—and the optional WHERE clause filters the rows affected, often using predicates like equality checks or joins.[4] Without a WHERE clause, the statement updates all rows in the table, which can lead to significant data alterations and is typically used with caution in production environments.[2] Expressions in the SET clause can reference other tables via subqueries or, in extended syntax supported by many RDBMS, through a FROM clause for multi-table updates, enabling complex operations like synchronizing data across related entities.[1][3]
In practice, the UPDATE statement plays a critical role in database maintenance, application data synchronization, and transactional processing, often combined with transactions to ensure atomicity and rollback capabilities in case of errors.[4] Security considerations include requiring appropriate privileges, such as UPDATE on the target table and SELECT on any referenced columns, to prevent unauthorized modifications.[1] Advanced features, like the RETURNING clause in PostgreSQL and Oracle, allow retrieval of updated values immediately after modification, facilitating audit trails or chained operations without additional queries.[1][2] While the core functionality adheres to SQL standards, variations such as support for updatable views or common table expressions (CTEs) highlight the statement's adaptability to diverse database ecosystems.[4]
Fundamentals
Definition and Purpose
The UPDATE statement in SQL is a Data Manipulation Language (DML) statement designed to modify existing data by altering the values in one or more rows of a specified table.[5] It targets updatable base tables or views, replacing column values while adhering to database constraints and integrity rules.[5] The primary purpose of the UPDATE statement is to enable the revision of data after its initial insertion, facilitating ongoing data maintenance, error corrections, and adjustments aligned with evolving business logic or requirements.[6] Unlike the INSERT statement, which adds new rows to a table, or the DELETE statement, which removes existing rows, UPDATE focuses exclusively on modifying content within pre-existing records without changing the row count.[6] This distinction ensures precise control over data lifecycle management in relational databases.[5] At its core, the UPDATE statement operates solely on existing records, necessitating the identification of a target table and the specification of assignments via a SET mechanism to define new column values.[5] It is commonly paired with a SELECT statement to first query and verify the data intended for modification, promoting accurate and intentional updates.[6]Role in SQL Data Manipulation
The UPDATE statement serves as a core component of SQL's Data Manipulation Language (DML), enabling the modification of existing data in relational database tables at the row level. It complements other DML commands—such as SELECT for querying and retrieving data, INSERT for adding new rows, and DELETE for removing rows—to provide a comprehensive set of operations for managing data content without affecting the underlying database structure. This integration allows developers and administrators to perform complete data lifecycle operations within relational systems, aligning with the principles of the relational model where data is treated as relations subject to algebraic manipulations.[7] In practical workflows, such as extract-transform-load (ETL) processes or transactional updates, the UPDATE statement is frequently employed following data extraction via SELECT to identify records needing revision, ensuring changes are applied atomically before committing the transaction. For instance, in a transactional context, multiple DML operations including UPDATE can be grouped to maintain data consistency across a sequence of changes, with the transaction boundary defined by COMMIT or ROLLBACK to guarantee durability and isolation. This sequencing supports reliable data processing in applications ranging from business intelligence pipelines to real-time system updates.[6] Unlike Data Definition Language (DDL) statements, which handle schema modifications like creating or altering tables, UPDATE focuses exclusively on content alterations within existing structures, preserving the database's architectural integrity while enabling dynamic data evolution. This distinction ensures that data modifications remain non-structural, avoiding impacts on table definitions, constraints, or indexes that DDL operations might enforce.Syntax
Basic Syntax Structure
The UPDATE statement in standard SQL follows a foundational syntax that specifies modifications to rows in a single table. The core structure is defined as<update statement: searched> ::= UPDATE <table name> [ [ AS ] <correlation name> ] SET <set clause list> [ WHERE <search condition> ], where the UPDATE clause identifies the target table, the SET clause assigns new values to columns, and the WHERE clause is optional for selecting specific rows.[5]
The UPDATE clause names the table to be modified, optionally with a correlation name (alias) for reference within the statement; table names follow SQL identifier rules, such as being delimited if containing special characters. The SET clause consists of a comma-separated list of assignments in the form <set clause list> ::= <set clause> [ , <set clause> ... ], where each <set clause> is <object column> = <update source>; here, <object column> refers to the column being updated, and <update source> provides the new value. This source supports <value expression> for computations, such as arithmetic operations (e.g., column = column + 1), as well as literals, NULL, DEFAULT, parameters, or contextually typed specifications, ensuring the expression result is assignable to the target column.[5]
Assignments in the SET clause must respect data type compatibility, meaning the <update source> must yield a value that matches the declared type of the <object column>, including precision and constraints; for instance, numeric literals cannot be assigned to string columns without explicit casting where permitted by the standard. Subqueries are allowable as <value expression> in the SET clause provided they return a compatible scalar type, enabling dynamic value derivation from other tables. If the WHERE clause is omitted, the update applies to all rows in the specified table.[5]
Conditional and Advanced Clauses
The WHERE clause is a fundamental component of the UPDATE statement in SQL, enabling selective modification of rows based on specified conditions, as defined in the ISO/IEC 9075 SQL standard.[1] Without the WHERE clause, the UPDATE operation affects all rows in the target table, which can lead to unintended widespread changes.[8] The condition in the WHERE clause is a Boolean expression that evaluates to true for rows to be updated, supporting comparison operators such as =, >, <, >=, <=, and !=, as well as pattern matching with LIKE and NULL checks using IS NULL or IS NOT NULL.[9] Logical operators like AND, OR, and NOT allow combination of multiple predicates to form complex conditions, drawing on standard SQL Boolean logic for predicate evaluation. Advanced conditional updates incorporate subqueries within the WHERE clause or the SET clause to derive dynamic values or filters based on other tables or computations. In the SET clause, a subquery can assign values to columns, such as setting a field to the result of an aggregate like an average from a related table, provided the subquery returns a single value per updated row.[8] Similarly, subqueries in the WHERE clause enable updates conditioned on existence checks (e.g., using EXISTS) or comparisons with derived results, enhancing the precision of row targeting while adhering to SQL standard syntax for nested queries.[10] These subqueries must comply with scoping rules to avoid referencing the target table in ways that cause mutual dependencies, as specified in the standard.[1] Some database systems extend the standard with non-standard clauses to limit the number of rows affected, such as LIMIT in MySQL or TOP in SQL Server, allowing capped updates for safety or performance in large datasets.[11] These features are not part of the core ISO/IEC 9075 specification and vary in implementation, often requiring an ORDER BY to determine which rows are selected when a limit is applied.[1] Regarding error handling, the SQL standard implicitly supports diagnostics through transaction mechanisms, where UPDATE operations return the count of affected rows in compliant systems, allowing verification of the operation's scope post-execution.[1] Updates are typically executed within transactions, enabling rollback via the ROLLBACK statement if errors occur or conditions are not met, as mandated by the ANSI/ISO SQL standards for transaction control.[12] This ensures data integrity by reverting changes without permanent alteration to the database.Examples
Simple Single-Table Updates
The UPDATE statement in SQL enables the modification of data within a single table by assigning new values to specified columns, with an optional WHERE clause to target specific rows; this core functionality adheres to the ANSI/ISO SQL standard for data manipulation.[1] In its simplest form, it updates all rows when no condition is provided, making it suitable for bulk changes across an entire table.[1] For instance, assume anemployees table with a department column containing employee records. To reassign all employees to the 'HR' department, the following statement can be used:
sql
UPDATE employees
SET department = 'HR';
UPDATE employees
SET department = 'HR';
department value to 'HR' for every row in the table. For example, in PostgreSQL, the database reports the number of affected rows as UPDATE 150 if the table has 150 records, indicating the scale of the modification.[1] To verify the changes, a subsequent SELECT query can retrieve the updated data, for example:
sql
SELECT * FROM employees;
SELECT * FROM employees;
sql
SELECT COUNT(*) FROM employees WHERE department = 'HR';
SELECT COUNT(*) FROM employees WHERE department = 'HR';
products table with price and category columns. To adjust prices for electronics only, execute:
sql
UPDATE products
SET price = 19.99
WHERE category = 'Electronics';
UPDATE products
SET price = 19.99
WHERE category = 'Electronics';
UPDATE 45 if 45 products are in that category.[1] Verification follows similarly via SELECT, such as:
sql
SELECT price, category FROM products WHERE category = 'Electronics';
SELECT price, category FROM products WHERE category = 'Electronics';
Multi-Column and Joined Updates
Multi-column updates in SQL allow modification of several fields within the same statement, enhancing efficiency for related attribute changes in a single operation. This is achieved by listing multiple column-value pairs in the SET clause, separated by commas, following the standard SQL syntax defined in the ANSI/ISO SQL standards. For instance, to adjust employee compensation based on tenure, one might execute:sql
UPDATE employees
SET salary = salary * 1.1, bonus = 1000
WHERE years > 5;
UPDATE employees
SET salary = salary * 1.1, bonus = 1000
WHERE years > 5;
sql
UPDATE orders
SET status = 'Shipped'
WHERE customer_id IN (
SELECT id FROM customers
WHERE region = 'EU'
);
UPDATE orders
SET status = 'Shipped'
WHERE customer_id IN (
SELECT id FROM customers
WHERE region = 'EU'
);
Considerations
Common Issues and Errors
One of the most frequent errors in SQL UPDATE statements occurs when the WHERE clause is omitted, resulting in unintended modifications to every row in the target table. This can lead to catastrophic data changes, such as overwriting all records with the same value, and is a well-known risk highlighted in database client configurations. For instance, in MySQL, the safe-updates mode is designed to prevent such accidents by rejecting UPDATE statements without a limiting WHERE clause or key-based condition, ensuring that only intended rows are affected. To mitigate this, developers should always include a WHERE clause and verify queries in a transaction before committing, especially in production environments. Another common pitfall involves data type mismatches or violations of table constraints during updates, which trigger runtime errors and prevent the statement from executing. Attempting to assign a value of incompatible type to a column, such as inserting a string into an integer field, raises errors like "invalid input syntax for type integer" in PostgreSQL or "ORA-01722: invalid number" in Oracle Database. Similarly, updates that breach constraints—such as setting a foreign key to a non-existent referenced value—result in violations, for example, "new row for relation violates foreign key constraint" in PostgreSQL. Prevention requires validating input types and ensuring compliance with referential integrity rules defined in the schema before issuing the UPDATE. Ambiguous column references, particularly in UPDATE statements using subqueries or joins, can cause parsing errors where the database cannot determine which table's column is intended. This issue arises when unqualified column names appear in multiple tables, leading to errors such as ORA-00918 in Oracle ("column ambiguously defined") or similar ambiguity complaints in other systems. In concurrent environments, transaction isolation levels may exacerbate problems like lost updates, where two transactions read the same row, modify it independently, and one overwrites the other's changes without detection; SQL's READ COMMITTED level permits this, but higher levels like SERIALIZABLE prevent it by restricting visibility of uncommitted changes. To avoid these, qualify all column names with table aliases and use appropriate isolation levels for multi-user scenarios.Performance and Best Practices
Optimizing UPDATE operations in SQL is crucial for maintaining efficiency in database systems, particularly as datasets grow larger and queries become more frequent. Poorly structured UPDATE statements can lead to full table scans, prolonged lock durations, and resource contention, impacting overall system performance. By adhering to established best practices, developers can minimize these issues, ensuring faster execution times and reliable data manipulation. One fundamental best practice is to use indexed columns in the WHERE clause to avoid full table scans, which occur when the database must examine every row in a table. Indexing relevant columns, such as primary keys or frequently filtered attributes, allows the query optimizer to leverage index seeks or scans, significantly reducing I/O operations and execution time. For instance, updating rows where an indexed user_id equals a specific value will perform much faster than a non-indexed condition. Indexing is a core element of database design that enhances query efficiency across operations.[14][15] For large-scale updates affecting many rows, batching the operation into smaller chunks is recommended to prevent excessive locking and resource exhaustion. This can be achieved by using DBMS-specific mechanisms to limit the rows affected, such as a LIMIT clause in MySQL or CTEs in PostgreSQL, along with range conditions in the WHERE clause, processing subsets iteratively until completion. Such an approach reduces the duration of table-level locks, minimizes transaction log growth, and allows for better concurrency with other database activities.[14][16] To ensure data integrity and atomicity, UPDATE statements should be wrapped within explicit transactions using BEGIN TRANSACTION, followed by the UPDATE, and concluded with COMMIT upon success or ROLLBACK on failure. This grouping treats the operation as a single unit, preventing partial updates that could leave the database in an inconsistent state, especially in multi-statement scenarios like fund transfers. Additionally, implementing audit logs to record changes—capturing details such as the user, timestamp, affected rows, and old/new values—facilitates compliance, debugging, and security monitoring. Logs should be reviewed regularly to detect anomalies.[17][18] Another key performance tip is to avoid applying functions to indexed columns in the WHERE clause, as this prevents the database from utilizing the index effectively, often reverting to a full scan. For example, prefer direct comparisons likeWHERE date_column = '2025-01-01' over WHERE YEAR(date_column) = 2025, which would negate index benefits. In complex scenarios involving conditional logic across multiple tables, consider the MERGE statement as an alternative to traditional UPDATE with JOINs; it efficiently handles insert, update, and delete actions based on a source-target join, reducing code complexity and potential errors.[14][19][20]
Standards and Implementations
Evolution in SQL Standards
The UPDATE statement was first introduced in the SQL-86 standard, formally known as ANSI X3.135-1986, as a fundamental component of the Data Manipulation Language (DML) within the relational database model.[21] This initial specification defined the core syntax—UPDATE <table> SET <column> = <value> [WHERE <condition>]—allowing modifications to existing rows in a table based on specified criteria, a structure that has remained stable across subsequent standards to ensure backward compatibility and portability.
The evolution of SQL standards, governed by the ISO/IEC 9075 series under the joint efforts of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), has progressively refined the UPDATE statement while preserving its foundational role. Adopted internationally in 1987 following the ANSI release, SQL-86's UPDATE was expanded in SQL-92 (ISO/IEC 9075:1992), which added support for subqueries in the WHERE clause, enabling more sophisticated conditional logic such as correlated subqueries to target rows dynamically from related tables.[22][23][24]
Further enhancements appeared in SQL:2003 (ISO/IEC 9075:2003), which introduced the MERGE statement as a versatile extension that can perform upsert operations—combining conditional UPDATE, INSERT, and DELETE in a single atomic statement—serving as a partial alternative for scenarios requiring synchronized data modification across sources.[25][26] The most recent standard, SQL:2023 (ISO/IEC 9075:2023), maintains the core UPDATE syntax without significant changes, redirecting innovations toward areas like JSON table functions and property graph querying to address contemporary data needs.[23][27]
Variations Across Database Systems
Different relational database management systems (RDBMS) implement the UPDATE statement with variations that extend beyond the ANSI SQL standard, particularly in handling joins and returning affected row data. These extensions enhance functionality but introduce compatibility challenges when porting code across systems. While the core ANSI syntax using subqueries for conditional updates is universally supported, vendor-specific features like direct joins in UPDATE clauses require dialect-aware adaptations.[1][4][3] PostgreSQL and Microsoft SQL Server support a non-standard FROM clause in UPDATE statements to facilitate joins with other tables, allowing direct reference to columns from joined tables in the SET clause. For instance, in PostgreSQL, the syntaxUPDATE table1 SET col = t2.col FROM table2 t2 WHERE table1.id = t2.id; updates table1 based on matching rows in table2, treating the FROM clause as an implicit join. This extension simplifies updates involving multiple tables compared to the standard subquery approach. Similarly, SQL Server uses UPDATE table1 SET col = t2.col FROM table1 JOIN table2 t2 ON table1.id = t2.id;, enabling explicit JOIN syntax within the UPDATE. Both implementations are proprietary and not part of ANSI SQL, originating from PostgreSQL's early design choices and SQL Server's Transact-SQL enhancements.[1][4]
In contrast, MySQL employs a multiple-table UPDATE syntax that names multiple tables in the FROM clause without explicit JOIN keywords, as in UPDATE table1, table2 SET table1.col = table2.col WHERE table1.id = table2.id;. This updates rows across the specified tables based on the WHERE condition, but it applies changes to all named tables simultaneously, differing from the single-target focus in PostgreSQL and SQL Server. Oracle traditionally avoids direct joins in UPDATE, relying instead on correlated subqueries (e.g., UPDATE table1 SET col = (SELECT t2.col FROM table2 t2 WHERE table1.id = t2.id) WHERE EXISTS (SELECT 1 FROM table2 t2 WHERE table1.id = t2.id);) or the MERGE statement for conditional updates and inserts. The MERGE statement serves as an upsert alternative, combining UPDATE and INSERT logic in one operation, such as MERGE INTO table1 USING table2 ON (table1.id = table2.id) WHEN MATCHED THEN UPDATE SET table1.col = table2.col;, which is particularly useful for synchronizing data from a source table. Recent Oracle versions (23c and later) introduce limited direct join support in UPDATE for improved readability.[3][2][28]
Regarding output from UPDATE operations, PostgreSQL and Oracle provide a RETURNING clause to retrieve values from affected rows, avoiding a separate SELECT query. For example, PostgreSQL's UPDATE table1 SET col = new_value WHERE id = 1 RETURNING *; returns the updated row, while Oracle's equivalent uses UPDATE table1 SET col = new_value WHERE id = 1 RETURNING * INTO variables;, binding results to PL/SQL variables. SQL Server offers a similar OUTPUT clause, as in UPDATE table1 SET col = new_value OUTPUT inserted.*, deleted.* WHERE id = 1;, which exposes both pre- and post-update values. MySQL lacks a native RETURNING or OUTPUT mechanism for UPDATE, requiring developers to use ROW_COUNT() for the number of affected rows or a follow-up query for details. These features, introduced in PostgreSQL 8.2 (2006), Oracle 9i (2001), and SQL Server 2005, stem from practical needs in application development but are non-standard. As of PostgreSQL 18 (released September 2025), the RETURNING clause has been enhanced to support references to both OLD (pre-update) and NEW (post-update) row values in UPDATE statements, further improving capabilities for auditing and data processing.[1][2][29][3][30]
Compatibility issues arise primarily from these extensions, as code relying on UPDATE FROM or RETURNING will fail in systems without them, necessitating rewrites for portability. For example, an application using PostgreSQL's UPDATE FROM for joined updates must be refactored to subqueries for Oracle deployment, potentially impacting performance or maintainability in cross-database environments. Developers often use abstraction layers like ORM tools to mitigate dialect-specific code, ensuring the ANSI-compliant core remains the fallback for universal support.[1][2]