When set to true, result ˓→rows are displayed immediately as they are fetched, yielding lower ˓→latency and memory usage at the price of extra display column ˓→padding. Setting --incremental=true is recommended tasks to process. 64 Chapter 6. High Availability Kyuubi, Release 1.3.0 While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

When set to true, result ˓→rows are displayed immediately as they are fetched, yielding lower ˓→latency and memory usage at the price of extra display column ˓→padding. Setting --incremental=true is recommended tasks to process. 64 Chapter 6. High Availability Kyuubi, Release 1.3.0 While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding maximize the parallelism according to the number of tasks to process. While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding maximize the parallelism according to the number of tasks to process. While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

When set to true, result ˓→rows are displayed immediately as they are fetched, yielding lower ˓→latency and memory usage at the price of extra display column ˓→padding. Setting --incremental=true is recommended process. 68 Chapter 6. High Availability Kyuubi, Release 1.4.1-incubating While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

When set to true, result ˓→rows are displayed immediately as they are fetched, yielding lower ˓→latency and memory usage at the price of extra display column ˓→padding. Setting --incremental=true is recommended tasks to process. 68 Chapter 6. High Availability Kyuubi, Release 1.3.0 While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

When set to true, result rows␣ ˓→are displayed immediately as they are fetched, yielding lower␣ ˓→latency and memory usage at the price of extra display column␣ ˓→padding. Setting --incremental=true is process. 78 Chapter 6. High Availability Kyuubi, Release 1.5.0-incubating While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding maximize the parallelism according to the number of tasks to process. While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

When set to true, result rows␣ ˓→are displayed immediately as they are fetched, yielding lower␣ ˓→latency and memory usage at the price of extra display column␣ ˓→padding. Setting --incremental=true is process. 78 Chapter 6. High Availability Kyuubi, Release 1.5.1-incubating While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle

are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding maximize the parallelism according to the number of tasks to process. While this minimizes the latency of the job, but with small tasks, the default behavior can waste many resources due to executor allocation unnecessary compatibility testing work, and it is hard to have complete test coverage. Bootstrap latency For long-running Spark applications, the bootstrap time is negligible compared to the total lifecycle