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 more than the deepCleanFileExpiredTime. FREE_SPACE_THRESHOLD 60 % After first clean, if free Space low than threshold trigger deep clean. SCHEDULE_INTERVAL 3600 seconds Cleaner sleep between cleaning

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 more than the deepCleanFileExpiredTime. FREE_SPACE_THRESHOLD 60 % After first clean, if free Space low than threshold trigger deep clean. SCHEDULE_INTERVAL 3600 seconds Cleaner sleep between cleaning

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 session Large-scale ETL Ad hoc High Low USER One engine per user Ad hoc Small-scale ETL Medium Medium GROUP One engine per primary group Ad hoc Small-scale ETL Low High SERVER One engine per cluster

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 session Large-scale ETL Ad hoc High Low USER One engine per user Ad hoc Small-scale ETL Medium Medium GROUP One engine per primary group Ad hoc Small-scale ETL Low High SERVER One engine per cluster

are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding session Large-scale ETL Ad hoc High Low USER One engine per user Ad hoc Small-scale ETL Medium Medium GROUP One engine per primary group Ad hoc Small-scale ETL Low High SERVER One engine per cluster 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

are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding session Large-scale ETL Ad hoc High Low USER One engine per user Ad hoc Small-scale ETL Medium Medium GROUP One engine per primary group Ad hoc Small-scale ETL Low High SERVER One engine per cluster 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

are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding session Large-scale ETL Ad hoc High Low USER One engine per user Ad hoc Small-scale ETL Medium Medium GROUP One engine per primary group Ad hoc Small-scale ETL Low High SERVER One engine per cluster 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

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

State-of-the-art query engines Multiple application for high throughput Sharable execution runtime for low latency Server-side global and continuous optimization Auxiliary performance plugins, such as Z-Ordering High Low USER One engine per user Ad hoc Small-scale ETL Medium Medium Share Level Syntax Scenario Isolation Degree Shareability GROUP One engine per primary group Ad hoc Small-scale ETL Low High 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