Trying to execute this query but getting 'operation must use an updatable query'. Only problem I can see is that the SELECT may not return a row. UPDATE tblFlatPkgDates As FD. In table #1 columns address and phone2 is empty and columns gender and birthdate values is same as table #2. How can I read data from table #2 and update address and phone2 in table #1 with values from table #2 address and phone columns when gender and birthdate is the same in each row? For example: this is some data in Table #1.

Delta Lake supports several statements to facilitate deleting data from and updating data in Delta tables.

Delete from a table

You can remove data that matches a predicate from a Delta table. For instance, to delete all events from before 2017, you can run the following:

Note

The Python API is available in Databricks Runtime 6.1 and above.

Note

The Scala API is available in Databricks Runtime 6.0 and above.

Note

The Java API is available in Databricks Runtime 6.0 and above.

See the API reference for details.

Important

delete removes the data from the latest version of the Delta table but does not remove it from the physical storage until the old versions are explicitly vacuumed. See vacuum for details.

Tip

When possible, provide predicates on the partition columns for a partitioned Delta table as such predicates can significantly speed up the operation.

Update a table

You can update data that matches a predicate in a Delta table. For example, to fix a spelling mistake in the eventType, you can run the following:

Note

The Python API is available in Databricks Runtime 6.1 and above.

Note

The Scala API is available in Databricks Runtime 6.0 and above.

Note

The Scala API is available in Databricks Runtime 6.0 and above.

See the API reference for details.

Tip

Similar to delete, update operations can get a significant speedup with predicates on partitions.

Upsert into a table using merge

You can upsert data from a source table, view, or DataFrame into a target Delta table using the merge operation. This operation is similar to the SQL MERGEINTO command but has additional support for deletes and extra conditions in updates, inserts, and deletes.

Suppose you have a Spark DataFrame that contains new data for events with eventId. Some of these events may already be present in the events table. To merge the new data into the events table, you want to update the matching rows (that is, eventId already present) and insert the new rows (that is, eventId not present). You can run the following:

For syntax details, see

• Databricks Runtime 7.x: MERGE INTO (Delta Lake on Databricks)
• Databricks Runtime 5.5 LTS and 6.x: Merge Into (Delta Lake on Databricks)

See the API reference for Scala, Java, and Python syntax details.

Operation semantics

Here is a detailed description of the merge programmatic operation.

• There can be any number of whenMatched and whenNotMatched clauses.

  Note

  In Databricks Runtime 7.2 and below, merge can have at most 2 whenMatched clauses and at most 1 whenNotMatched clause.

• whenMatched clauses are executed when a source row matches a target table row based on the match condition. These clauses have the following semantics.

  • whenMatched clauses can have at most on update and one delete action. The update action in merge only updates the specified columns (similar to the updateoperation) of the matched target row. The delete action deletes the matched row.

  • Each whenMatched clause can have an optional condition. If this clause condition exists, the update or delete action is executed for any matching source-target row pair row only when when the clause condition is true.

  • If there are multiple whenMatched clauses, then they are evaluated in order they are specified (that is, the order of the clauses matter). All whenMatched clauses, except the last one, must have conditions.

  • If both whenMatched clauses have conditions and neither of the conditions are true for a matching source-target row pair, then the matched target row is left unchanged.

  • To update all the columns of the target Delta table with the corresponding columns of the source dataset, use whenMatched(...).updateAll(). This is equivalent to:

    for all the columns of the target Delta table. Therefore, this action assumes that the source table has the same columns as those in the target table, otherwise the query throws an analysis error.

    Note

    This behavior changes when automatic schema migration is enabled. See Automatic schema evolution for details.

• whenNotMatched clauses are executed when a source rows does not match any target row based on the match condition. These clauses have the following semantics.

  • whenNotMatched clauses can have only the insert action. The new row is generated based on the specified column and corresponding expressions. You do not need to specify all the columns in the target table. For unspecified target columns, NULL is inserted.

    Note

    In Databricks Runtime 6.5 and below, you must provide all the columns in the target table for the INSERT action.

  • Each whenNotMatched clause can have an optional condition. If the clause condition is present, a source row is inserted only if that condition is true for that row. Otherwise, the source column is ignored.

  • If there are multiple whenNotMatched clauses, then they are evaluated in order they are specified (that is, the order of the clauses matter). All whenNotMatched clauses, except the last one, must have conditions.

  • To insert all the columns of the target Delta table with the corresponding columns of the source dataset, use whenNotMatched(...).insertAll(). This is equivalent to:

    for all the columns of the target Delta table. Therefore, this action assumes that the source table has the same columns as those in the target table, otherwise the query throws an analysis error.

    Note

    This behavior changes when automatic schema migration is enabled. See Automatic schema evolution for details.

Important

A merge operation can fail if multiple rows of the source dataset match and attempt to update the same rows of the target Delta table. According to the SQL semantics of merge, such an update operation is ambiguous as it is unclear which source row should be used to update the matched target row. You can preprocess the source table to eliminate the possibility of multiple matches. See the Change data capture example—it preprocesses the change dataset (that is, the source dataset) to retain only the latest change for each key before applying that change into the target Delta table.

Note

Update Table Select Another Table Sql Unbound Rows

In Databricks Runtime 7.3 LTS and above, multiple matches are allowed when matches are unconditionally deleted (since unconditional delete is not ambiguous even if there are multiple matches).

Schema validation

merge automatically validates that the schema of the data generated by insert and update expressions are compatible with the schema of the table. It uses the following rules to determine whether the merge operation is compatible:

• For update and insert actions, the specified target columns must exist in the target Delta table.
• For updateAll and insertAll actions, the source dataset must have all the columns of the target Delta table. The source dataset can have extra columns and they are ignored.
• For all actions, if the data type generated by the expressions producing the target columns are different from the corresponding columns in the target Delta table, merge tries to cast them to the types in the table.

Automatic schema evolution

Note

Schema evolution in merge is available in Databricks Runtime 6.6 and above.

By default, updateAll and insertAll assign all the columns in the target Delta table with columns of the same name from the source dataset. Any columns in the source dataset that don’t match columns in the target table are ignored. However, in some use cases, it is desirable to automatically add source columns to the target Delta table. To automatically update the table schema during a merge operation with updateAll and insertAll (at least one of them), you can set the Spark session configuration spark.databricks.delta.schema.autoMerge.enabled to true before running the merge operation.

Note

• Schema evolution occurs only when there is either an updateAll or an insertAll action, or both.
• update and insert actions cannot explicitly refer to target columns that do not already exist in the target table (even it there are updateAll or insertAll as one of the clauses). See the examples below.

Note

In Databricks Runtime 7.4 and below, merge supports schema evolution of only top-level columns, and not of nested columns.

Here are a few examples of the effects of merge operation with and without schema evolution.

Performance tuning

You can reduce the time taken by merge using the following approaches:

• Reduce the search space for matches: By default, the merge operation searches the entire Delta table to find matches in the source table. One way to speed up merge is to reduce the search space by adding known constraints in the match condition. For example, suppose you have a table that is partitioned by country and date and you want to use merge to update information for the last day and a specific country. Adding the condition

  will make the query faster as it looks for matches only in the relevant partitions. Furthermore, it will also reduce the chances of conflicts with other concurrent operations. See Concurrency control for more details.

• Compact files: If the data is stored in many small files, reading the data to search for matches can become slow. You can compact small files into larger files to improve read throughput. See Compact files for details.

• Control the shuffle partitions for writes: The merge operation shuffles data multiple times to compute and write the updated data. The number of tasks used to shuffle is controlled by the Spark session configuration spark.sql.shuffle.partitions. Setting this parameter not only controls the parallelism but also determines the number of output files. Increasing the value increases parallelism but also generates a larger number of smaller data files.

• Enable optimized writes: For partitioned tables, merge can produce a much larger number of small files than the number of shuffle partitions. This is because every shuffle task can write multiple files in multiple partitions, and can become a performance bottleneck. You can optimize this by enabling Optimized Writes.

Merge examples

Here are a few examples on how to use merge in different scenarios.

In this section:

Update Table Select Another Table Sql Unbound Syntax

A common ETL use case is to collect logs into Delta table by appending them to a table. However, often the sources can generate duplicate log records and downstream deduplication steps are needed to take care of them. With merge, you can avoid inserting the duplicate records.

Note

The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted. Hence, deduplicate the new data before merging into the table.

If you know that you may get duplicate records only for a few days, you can optimized your query further by partitioning the table by date, and then specifying the date range of the target table to match on.

This is more efficient than the previous command as it looks for duplicates only in thelast 7 days of logs, not the entire table. Furthermore, you can use this insert-only merge with Structured Streaming to perform continuous deduplication of the logs.

• In a streaming query, you can use merge operation in foreachBatch to continuously write any streaming data to a Delta table with deduplication. See the following streaming example for more information on foreachBatch.
• In another streaming query, you can continuously read deduplicated data from this Delta table. This is possible because an insert-only merge only appends new data to the Delta table.

Note

Insert-only merge is optimized to only append data in Databricks Runtime 6.2 and above. In Databricks Runtime 6.1 and below, writes from insert-only merge operations cannot be read as a stream.

Another common operation is SCD Type 2, which maintains history of all changes made to each key in a dimensional table. Such operations require updating existing rows to mark previous values of keys as old, and the inserting the new rows as the latest values. Given a source table with updates and the target table with the dimensional data, SCD Type 2 can be expressed with merge.

Here is a concrete example of maintaining the history of addresses for a customer along with the active date range of each address. When a customer’s address needs to be updated, you have to mark the previous address as not the current one, update its active date range, and add the new address as the current one.

Similar to SCD, another common use case, often called change data capture (CDC), is to applyall data changes generated from an external database into a Delta table. In other words, a setof updates, deletes, and inserts applied to an external table needs to be applied to a Delta table.You can do this using merge as follows.

You can use a combination of merge and foreachBatch (see foreachbatch for more information) to write complex upserts from a streaming query into a Delta table. For example:

• Write streaming aggregates in Update Mode: This is much more efficient than Complete Mode.
• Write a stream of database changes into a Delta table: The merge query for writing change data can be used in foreachBatch to continuously apply a stream of changes to a Delta table.
• Write a stream data into Delta table with deduplication: The insert-only merge query for deduplication can be used in foreachBatch to continuously write data (with duplicates) to a Delta table with automatic deduplication.

Note

• Make sure that your merge statement inside foreachBatch is idempotent as restartsof the streaming query can apply the operation on the same batch of data multiple times.
• When merge is used in foreachBatch, the input data rate of the streaming query(reported through StreamingQueryProgress and visible in the notebook rate graph) may be reportedas a multiple of the actual rate at which data is generated at the source. This is because merge reads the input data multiple times causing the input metrics to be multiplied. If this is a bottleneck, you can cache the batch DataFrame before merge and then uncache it after merge.

Syntax

Single-table syntax:

Multiple-table syntax:

Contents

1. Description

Description

For the single-table syntax, the UPDATE statement updatescolumns of existing rows in the named table with new values. TheSET clause indicates which columns to modify and the valuesthey should be given. Each value can be given as an expression, or the keywordDEFAULT to set a column explicitly to its default value. TheWHERE clause, if given, specifies the conditions that identifywhich rows to update. With no WHERE clause, all rows areupdated. If the ORDER BY clause is specified, the rows areupdated in the order that is specified. The LIMIT clauseplaces a limit on the number of rows that can be updated.

MariaDB starting with 10.0

The PARTITION clause was introduced in MariaDB 10.0. See Partition Pruning and Selection for details.

Until MariaDB 10.3.2, for the multiple-table syntax, UPDATE updates rows in eachtable named in table_references that satisfy the conditions. In this case,ORDER BY and LIMIT cannot be used. This restriction was lifted in MariaDB 10.3.2 and both clauses can be used with multiple-table updates. An UPDATE can also reference tables which are located in different databases; see Identifier Qualifiers for the syntax.

where_condition is an expression that evaluates to true foreach row to be updated.

table_references and where_condition are asspecified as described in SELECT.

For single-table updates, assignments are evaluated in left-to-right order, while for multi-table updates, there is no guarantee of a particular order. If the SIMULTANEOUS_ASSIGNMENTsql_mode (available from MariaDB 10.3.5) is set, UPDATE statements evaluate all assignments simultaneously.

You need the UPDATE privilege only for columns referenced inan UPDATE that are actually updated. You need only theSELECT privilege for any columns that are read butnot modified. See GRANT.

The UPDATE statement supports the following modifiers:

• If you use the LOW_PRIORITY keyword, execution of the UPDATE is delayed until no other clients are reading from the table. This affects only storage engines that use only table-level locking (MyISAM, MEMORY, MERGE). See HIGH_PRIORITY and LOW_PRIORITY clauses for details.
• If you use the IGNORE keyword, the update statement does not abort even if errors occur during the update. Rows for which duplicate-key conflicts occur are not updated. Rows for which columns are updated to values that would cause data conversion errors are updated to the closest valid values instead.

UPDATE Statements With the Same Source and Target

MariaDB starting with 10.3.2

From MariaDB 10.3.2, UPDATE statements may have the same source and target.

For example, given the following table:

Until MariaDB 10.3.1, the following UPDATE statement would not work:

From MariaDB 10.3.2, the statement executes successfully:

Example

Single-table syntax:

Update Table Select Another Table Sql Unbound Server

Multiple-table syntax:

See Also

Update Table Select Another Table Sql Unbound Pdf

Comments

Update Table Select Another Table Sql Unbound Tutorial