HUAWEI CLOUD Microservice Tool Improves Development Efficiency Department: Application Platform Service Author: Wang Qijun Date: 2019-09-20 Security Level: Contents 1. Tool for Splitting Monolithic Process-level Overall availability Low High Continuous evolution Difficult Easy Communication efficiency Low High Technology stack selection Restricted Flexible Scalable Restricted Flexible Reusability verification Tool for Splitting Monolithic Applications into Microservices Improves Development Efficiency Supported processes Methodology • ThoughtWorks 5 Steps and 1 Phase • DDD aggregation • Event

bring significant efficiency gains during the training phase, which is the focus of this chapter. We start this chapter with an introduction to sample efficiency and label efficiency, the two criteria Our journey of learning techniques also continues in the later chapters. Learning Techniques and Efficiency Data Augmentation and Distillation are widely different learning techniques. While data augmentation breadth as efficiency? To answer this question, let’s break down the two prominent ways to benchmark the model in the training phase namely sample efficiency and label efficiency. Sample Efficiency Sample

indexes">Global indexes for partitioned tables (GA) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must scanning tasks based on node scale and hardware specifications. This �→ improves statistics collection efficiency by fully utilizing system �→ resources, reduces manual tuning, and ensures stable cluster �→ performance observe high CPU �→ consumption operations from multiple perspectives, and improving �→ diagnostic efficiency. This is especially useful for diagnosing �→ scenarios such as CPU spikes in instances or read/write

indexes">Global indexes for partitioned tables (GA) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must scanning tasks based on node scale and hardware specifications. This �→ improves statistics collection efficiency by fully utilizing system �→ resources, reduces manual tuning, and ensures stable cluster �→ performance observe high CPU �→ consumption operations from multiple perspectives, and improving �→ diagnostic efficiency. This is especially useful for diagnosing �→ scenarios such as CPU spikes in instances or read/write

partitioned tables (experimental) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must mance for high NDV data #9196 @guo-shaoge Before v8.3.0, TiFlash has low aggregation calculation efficiency during the first stage of HashAgg aggregation when handling data with high NDV (number of distinct statement to create SQL execution plan bindings from multiple historical execution plans to improve the efficiency of creating bindings. • The SQL execution plan binding supports more optimizer hints, and optimizes

In clusters �→ with many TiKV nodes, this algorithm significantly improves cluster �→ resource efficiency by more evenly distributing load across nodes and �→ better utilizing per-node network bandwidth and the query includes LIKE, the optimizer generates an IndexRangeScan to improve the execution efficiency #48181 #49138 @time-and-fate • Enhance the ability to convert OUTER JOIN to INNER JOIN in specific with TiDB. You will use TiDB Cloud to create a TiDB Serverless cluster, connect to it, and run a sample application on it. If you need to run TiDB on your local machine, see Starting TiDB Locally. 4

loading �→ efficiency by up to 10 times For clusters with a large number of tables and partitions, such as SaaS �→ or PaaS services, improvement in statistics loading efficiency can �→ solve release. For more information, see documentation. 2.2.1.2 Reliability • Improve statistics loading efficiency by up to 10 times #52831 @hawkingrei SaaS or PaaS applications can have a large number of data validation, which increases the complexity of development and maintenance, and reduces development efficiency. Starting from v8.2.0, the JSON_SCHEMA_VALID() func- tion is introduced. Using JSON_SCHEMA_VALID()

target="_blank">Distributed eXecution Framework �→ (DXF) for parallel import, which improves import efficiency �→ during large-scale imports. Specify 0 码力 | 6020 页 | 106.82 MB | 1 年前

3