table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits

table. It can go wrong in most real-world cases. For example, the join relation is a convergent but composite operation rather than a single table scan. In this case, Spark might not be able to switch the join-strategy join-strategy to BroadcastHash Join. While with AQE, we can runtime calculate the size of the composite operation accurately. And then, Spark now can replan the join strategy unmistakably if the size fits