This not only increases the cost but also reduces productivity. However, as the data grows, organizations are required to expand their On-Premise Data Warehouses to enhance usability. Today, one of the core elements for effective Business Intelligence in organizations is Data Warehousing. Examples of Redshift Alter Table Command.Syntax and Parameters for Redshift Alter Table Command.Moreover, the syntax, parameters, and examples of the Redshift Alter Table command for various use cases are also discussed. As Redshift uses SQL, the article also introduces readers to the fundamentals of SQL commands. This article gives an overview of the Redshift Alter Table command. To streamline the entire process of data storage and retrieval, Redshift uses the power of Structured Query Language (SQL), making it simple for companies to leverage Redshift. It also supports various Business Intelligence (BI) tools to provide real-time updates on the dashboard. However, Amazon introduced Redshift, a Cloud-Based Data Warehouse, that enables Data Analysts to query their data faster with parallel processing and data compression. I’m open to suggestions.With the increasing data collection, Traditional Data Warehouses have failed to keep up with the desired processing pace of organizations. I’m not sure what the best solution is in this case. This is happening because the original exception is masked by another exception that happens due to the DROP TABLE IF EXISTS in the finally block, which fails because the transaction is in a bad state at this point, giving the error message Invalid operation: current transaction is aborted, commands ignored until end of transaction block. I was hoping that spark-redshift would let this error (which is the actual culprit) bubble up when it happens, but instead I get the error I mentioned in the beginning. When trying this transaction manually in SQL Workbench, I get the following error: (500310) Invalid operation: cannot drop table myschema.mytable because other objects depend on it JdbcWrapper.executeInterruptibly(conn.prepareStatement(s"DROP TABLE IF EXISTS $tempTable")) I tracked this error down to the following code in RedshiftWriter.scala: try ")) a view depends on the table): : (500310) Invalid operation: current transaction is aborted, commands ignored until end of transaction block Īt .(ErrorResponse.java:1830)Īt .PGMessagingContext.handleErrorResponse(PGMessagingContext.java:804)Īt .PGMessagingContext.handleMessage(PGMessagingContext.java:642)Īt .InboundMessagesPipeline.getNextMessageOfClass(InboundMessagesPipeline.java:312)Īt .PGMessagingContext.doMoveToNextClass(PGMessagingContext.java:1062)Īt .PGMessagingContext.getParameterDescription(PGMessagingContext.java:978)Īt .PGClient.prepareStatement(PGClient.java:1844)Īt .PGQueryExecutor.(PGQueryExecutor.java:106)Īt .PGDataEngine.prepare(PGDataEngine.java:211)Īt .SPreparedStatement.(Unknown Source)Īt 41.S41PreparedStatement.(Unknown Source)Īt .jdbc41.PGJDBC41PreparedStatement.(PGJDBC41PreparedStatement.java:49)Īt .(PGJDBC41ObjectFactory.java:119)Īt .SConnection.prepareStatement(Unknown Source)Īt .RedshiftWriter.withStagingTable(RedshiftWriter.scala:137)Īt .RedshiftWriter.saveToRedshift(RedshiftWriter.scala:369)Īt .DefaultSource.createRelation(DefaultSource.scala:106)Īt .$.apply(ResolvedDataSource.scala:222)Īt .DataFrameWriter.save(DataFrameWriter.scala:148)Īt 0(Native Method)Īt (NativeMethodAccessorImpl.java:57)Īt (DelegatingMethodAccessorImpl.java:43)Īt .invoke(Method.java:606)Īt (MethodInvoker.java:231)Īt (ReflectionEngine.java:381)Īt (AbstractCommand.java:133)Īt (CallCommand.java:79)Īt py4j.Gatewa圜n(Gatewa圜onnection.java:209)Ĭaused by: .ErrorException: (500310) Invalid operation: current transaction is aborted, commands ignored until end of transaction block When using overwrite mode to save data to a table, and also leaving usestagingtable to its default value of true, the operation fails with the following error when the target table already has dependencies (e.g.
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