![]() If MySQL applies them atomically, the connector takes the DDL statements in order, groups them by database, and creates a schema change event for each group. Multiple DDL statements appear in the order in which they were applied to the database.Ĭlients can submit multiple DDL statements that apply to multiple databases. The ddl field can contain multiple DDL statements.Įach statement applies to the database in the databaseName field. This field contains the DDL that is responsible for the schema change. The value of the databaseName field is used as the message key for the record. Identifies the database and the schema that contains the change. By comparing the value for _ms with the value for payload.ts_ms, you can determine the lag between the source database update and Debezium. In the source object, ts_ms indicates the time that the change was made in the database. The time is based on the system clock in the JVM running the Kafka Connect task. Optional field that displays the time at which the connector processed the event. This field is useful to correlate events on different topics. The source field is structured exactly as standard data change events that the connector writes to table-specific topics. Descriptions of fields in messages emitted to the schema change topic Item "ddl": "ALTER TABLE customers ADD middle_name varchar(255) AFTER first_name", (4) Integration Testing with Testcontainers.Using SMT Predicates to Selectively Apply Transformations.(responseText, checkConditionInt) <- Tx.singleRowQueryE defaultMSSQLT圎rrorHandler (finalSelectQuery `withQueryTags` queryTags) Execute SELECT query to fetch mutation response and check constraint result let finalSelectQuery = toQueryFlat $ TQ.fromSelect finalSelect MutationOutputCheckConstraintSelect = selectMutationOutputAndCheckCondition withAlias mutationOutputSelect checkBoolExpįinalSelect = mutationOutputCheckConstraintSelect SelectFrom = Just $ FromTempTable $ Aliased tempTableNameUpdated "updated_alias " The check constraint is translated to boolean expressionĬheckBoolExp <- runFromIr $ runReaderT (fromGBoolExp checkCondition) ( EntityAlias withAlias) Let checkCondition = _auCheck updateOperation MutationOutputSelect <- runFromIr $ mkMutationOutputSelect stringifyNum withAlias $ _auOutput updateOperation ![]() Execute UPDATE statement Tx.unitQueryE mutationMSSQLT圎rrorHandler (updateQueryValidated `withQueryTags` queryTags) UpdateQueryValidated runFromIr updateQuery Let updateQuery = TQ.fromUpdate omUpdate updateOperation ToQueryFlat $ TQ.fromSelectIntoTempTable $ TSQL.toSelectIntoTempTable tempTableNameUpdated (_auTable updateOperation) (_auAllCols updateOperation) RemoveConstraints - Create a temp table Tx.unitQueryE defaultMSSQLT圎rrorHandler (createInsertedTempTableQuery `withQueryTags` queryTags) buildUpdateTx :: AnnotatedUpdate 'MSSQL -> Options.StringifyNumbers -> QueryTagsComment -> Tx.T圎T QErr IO EncJSONīuildUpdateTx updateOperation stringifyNum queryTags = do let withAlias = "with_alias " ![]() constructs the query from the temporary table, including - relationships with other tables. SET FROM with - updates the rows from the table and inserts the - updated rows to the temporary table from (1) - 3. INTO WHERE - creates a temporary table - with the same schema as the original table in which we'll store the updated rows - from the table we are deleting - 2. (Potentially) returns the updated rows (including relationships) as JSON - In order to complete these 2 things we need 3 SQL statements: - 1. Update rows in a table according to some predicate - 2. ![]() A GraphQL update mutation does two things: - 1. | Converts an Update IR AST to a transaction of three update sql statements.
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