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幕后:智能工作负载管理.pdf

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1、Intelligent Workload Management in Databricks SQLUnder the H2023 Databricks Inc.All rights reservedConfidential and ProprietaryOverview Background Workload Management in Databricks SQL Load based workload management Query Costing Whats next2023 Databricks Inc.All rights reservedConfidential and Prop

2、rietaryBackground32023 Databricks Inc.All rights reservedConfidential and ProprietaryWhat is Workload Management Workload Management-efficient compute utilization in Databricks SQLWhen and where to run a queryWhen to Scale up or down2023 Databricks Inc.All rights reservedConfidential and Proprietary

3、Databricks SQL Logical Architecture52023 Databricks Inc.All rights reservedConfidential and ProprietaryWhen and where to run a query Whether to run the query or to put it in a queue Which compute resource to run the query on2023 Databricks Inc.All rights reservedConfidential and ProprietaryWhen to S

4、cale up/down Upscale when we see queueing we see high utilization Downscale when we see idle compute we see low utilization2023 Databricks Inc.All rights reservedConfidential and ProprietaryHow to do this rightOptimize For Latency?Keep the latency same even if we increase the cost Throughput?Process

5、 as many queries as possible Cost?Use as few resources as possible2023 Databricks Inc.All rights reservedConfidential and ProprietaryHow to do this rightPrinciples Latency is important for short queries Throughput is important for longer queries Both of the above should be optimized against cost2023

6、 Databricks Inc.All rights reservedConfidential and ProprietaryDatabricks SQL:Workload Management102023 Databricks Inc.All rights reservedConfidential and ProprietaryWorkload Management TodayQuery Concurrency based Allows a static concurrency Autoscaling based on query throughput,rate of incoming qu

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标记中的内容详细介绍了Databricks SQL中的智能工作负载管理。主要内容包括: 1. 工作负载管理:旨在提高Databricks SQL中的计算利用率,决定何时运行查询以及何时扩展或缩减资源。 2. 工作负载管理原则:对于短查询,优化延迟;对于长查询,优化吞吐量;同时尽量减少成本。 3. 当前工作负载管理:基于查询并发性,允许静态并发,根据查询吞吐量、传入查询率和排队查询进行自动扩展,每2分钟评估一次自动扩展决策。 4. 智能工作负载管理:通过查询优先级、集群利用率和自适应工作负载管理,提高查询执行速度和系统观测性。 5. 查询优先级:预先拒绝模型根据查询大小和AI模型估计的成本进行优先级排序;后拒绝模型在查询被接纳后,根据并发查询的数量动态分配计算资源。 6. 查询成本估计: Databricks SQL使用基于历史数据的成本分类和机器学习模型,以及基于查询计划统计的本地模型,预测查询成本。 7. 未来计划:智能工作负载管理功能正在逐步推出,包括查询优先级(已全面推出)、智能/快速自动扩展(公共预览)、基于负载的调度(公共预览)等。 综上所述,Databricks SQL通过智能工作负载管理,优化查询执行速度和计算利用率,同时尽量减少成本,以满足不同查询的需求。
"Databricks SQL如何实现智能工作负载管理?" "如何通过查询成本估算优化Databricks SQL的工作负载管理?" "Databricks SQL的智能工作负载管理有哪些特点和优势?"
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