architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation true # spark.eventLog.longForm.enabled true # spark.eventLog.rolling.enabled true # spark.yarn.historyServer.address http://historyserver:18080

architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation true # spark.eventLog.longForm.enabled true # spark.eventLog.rolling.enabled true # spark.yarn.historyServer.address http://historyserver:18080

architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation true # spark.eventLog.longForm.enabled true # spark.eventLog.rolling.enabled true # spark.yarn.historyServer.address http://historyserver:18080

architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation true # spark.eventLog.longForm.enabled true # spark.eventLog.rolling.enabled true # spark.yarn.historyServer.address http://historyserver:18080

architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation true # spark.eventLog.longForm.enabled true # spark.eventLog.rolling.enabled true # spark.yarn.historyServer.address http://historyserver:18080

architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation true # spark.eventLog.longForm.enabled true # spark.eventLog.rolling.enabled true # spark.yarn.historyServer.address http://historyserver:18080

architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation true # spark.eventLog.longForm.enabled true # spark.eventLog.rolling.enabled true # spark.yarn.historyServer.address http://historyserver:18080

engine registers an SQLOperationListener before executing the statement, logging a few summary statistics when each stage completes. bool ean 1.6.0 kyuubi.operation.st atus.polling.timeou t PT5S Timeout(ms) architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation

engine registers an SQLOperationListener before executing the statement, logging a few summary statistics when each stage completes. bool ean 1.6.0 kyuubi.operation.st atus.polling.timeou t PT5S Timeout(ms) architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation

engine registers an SQLOperationListener before executing the statement, logging a few summary statistics when each stage completes. bool ean 1.6.0 kyuubi.operation.st atus.polling.timeou t PT5S Timeout(ms) architecture, the AQE is a framework of dynamic planning and replanning of queries based on runtime statistics, which supports a variety of optimizations such as, Dynamically Switch Join Strategies Dynamically autoBroadcastJoinThreshold=10M Without AQE, the estimated size of join relations comes from the statistics of the original table. It can go wrong in most real-world cases. For example, the join relation