Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to Execution (AQE) in Kyuubi The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying

Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to Execution (AQE) in Kyuubi The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying

Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to (AQE) in Kyuubi 2.2.1. The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying

Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to Execution (AQE) in Kyuubi 2.1. The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying

Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to Execution (AQE) in Kyuubi The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying

Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to Execution (AQE) in Kyuubi The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying

as Spark, and Flink, is no longer necessary. That is, most work related to deployment, runtime optimization, etc., should be done by professionals on the Kyuubi server side. It is suitable for the following throughput • Sharable execution runtime for low latency • Server-side global and continuous optimization • Auxiliary performance plugins, such as Z-Ordering, Query Optimizer, and so on Another goal goal of Serverless SQL is to make end users need not or rarely care about tricky performance optimization issues. 6 Chapter 2. Serverless SQL and More CHAPTER THREE WHAT’S NEXT 3.1 Quick Start Note:

as Spark, and Flink, is no longer necessary. That is, most work related to deployment, runtime optimization, etc., should be done by professionals on the Kyuubi server side. It is suitable for the following throughput • Sharable execution runtime for low latency • Server-side global and continuous optimization • Auxiliary performance plugins, such as Z-Ordering, Query Optimizer, and so on Another goal goal of Serverless SQL is to make end users need not or rarely care about tricky performance optimization issues. 6 Chapter 2. Serverless SQL and More CHAPTER THREE WHAT’S NEXT 3.1 Quick Start Note:

as Spark, and Flink, is no longer necessary. That is, most work related to deployment, runtime optimization, etc., should be done by professionals on the Kyuubi server side. It is suitable for the following throughput • Sharable execution runtime for low latency • Server-side global and continuous optimization • Auxiliary performance plugins, such as Z-Ordering, Query Optimizer, and so on Another goal goal of Serverless SQL is to make end users need not or rarely care about tricky performance optimization issues. 6 Chapter 2. Serverless SQL and More CHAPTER THREE WHAT’S NEXT 3.1 Quick Start Note:

as Spark, and Flink, is no longer necessary. That is, most work related to deployment, runtime optimization, etc., should be done by professionals on the Kyuubi server side. It is suitable for the following throughput • Sharable execution runtime for low latency • Server-side global and continuous optimization • Auxiliary performance plugins, such as Z-Ordering, Query Optimizer, and so on Another goal goal of Serverless SQL is to make end users need not or rarely care about tricky performance optimization issues. 6 Chapter 2. Serverless SQL and More CHAPTER THREE WHAT’S NEXT 3.1 Quick Start Note: