This document appears to be notes from experimenting with different approaches to implementing a domain service and repository architecture based on domain-driven design principles. It mentions trying different options for handling ordering of entities and handling errors. The notes cover multiple iterations denoted as TRY-00 through TRY-13 where different techniques for the domain service and interaction with the database repository were attempted. It also references a GitHub repository for code related to these experiments.
2022 COSCUP - Let's speed up your PostgreSQL services!.pptxJosé Lin
This document discusses ways to speed up PostgreSQL services. It begins with an introduction of the speaker and covers three main topics: architecture design, query optimization, and parameter configuration. For architecture design, it recommends using connection pools like PgBouncer and read-write separation with Pgpool-II. For query optimization, it suggests finding slow queries, diagnosing abnormal SQL, using EXPLAIN, and parallel queries. For parameter configuration, it provides guidance on tuning background writer settings and memory-related parameters. The goal is to avoid bottlenecks being discovered only during emergencies and optimize performance without focusing on hardware.
The document discusses concepts related to domain-driven design (DDD) including entities, value objects, services, factories, repositories, and aggregates. It provides examples of implementing these concepts in Java and Scala code and references Flickr photos that illustrate DDD concepts visually.
PostgreSQL Performance Tables Partitioning vs. Aggregated Data TablesSperasoft
Table partitioning and aggregated data tables (such as materialized views) are two approaches to improve PostgreSQL database performance as data volumes grow large over time. Table partitioning involves splitting a large table into multiple smaller tables (partitions) based on a partition function and key, while aggregated data tables pre-compute query results to avoid repeated computation. Both can improve query performance but come with caveats such as increased planning time for partitions or expensive refresh costs for materialized views. The best approach depends on each unique situation and data access patterns.
Solr - Indexação e Busca com ferramenta Open SourceMarcelo Rodrigues
O documento fornece uma introdução ao Apache Solr, cobrindo sua instalação e configuração, conceitos-chave como indexação, busca e análise de texto, e exemplos práticos de indexação e consulta de documentos usando a biblioteca SolrJ no Java.
此簡報為 Will 保哥 於 2015/6/25 (四) 接受 SQL PASS Taiwan 邀請演講的內容。
現場錄影: http://www.microsoftvirtualacademy.com/training-courses/sql-server-realase-management?mtag=MVP4015686
[ Will 保哥的部落格 - The Will Will Web ]
http://blog.miniasp.com
[ Will 保哥的技術交流中心 ] (Facebook 粉絲專頁)
https://www.facebook.com/will.fans
[ Will 保哥的噗浪 ]
http://www.plurk.com/willh/invite
[ Will 保哥的推特 ]
https://twitter.com/Will_Huang
[ Will 保哥的 G+ 頁面 ]
http://gplus.to/willh
1. A research study examined how university students' well-being during the COVID-19 pandemic is influenced by their satisfaction of basic psychological needs for competence, autonomy, and relatedness.
2. The study collected self-report data from over 6,000 students in Austria and over 1,600 students in Finland.
3. The findings revealed that competence was the strongest predictor of positive emotion, while competence and autonomy were significant predictors of intrinsic learning motivation in both countries. Relatedness played a smaller role in positive emotion.
This document discusses TextRank, a graph-based algorithm for automatic text summarization. TextRank was inspired by PageRank and works by representing sentences as nodes in a graph and calculating similarity between sentences. It segments the text into sentences, represents each sentence as a vector, calculates similarity between sentences to construct a graph, runs the graph-based algorithm to score sentences, and selects the top sentences as the summary. TextRank is an unsupervised extractive summarization approach that does not require domain or linguistic knowledge.
This document outlines the steps of a problem-based learning (PBL) process using an identify-design-solve-reflect (IDSR) scaffold. It includes 4 main steps: 1) identification, where students define the problem; 2) design, where they collect resources and draft a project plan; 3) solve problem, where they organize resources and complete the project; and 4) reflection, where they demonstrate and review the project with peers. It also discusses using social network analysis and grouping algorithms to form heterogeneous student groups based on their interaction types like being a hub, source, sink or island. The overall goal is to help implement information technology integration into instruction through a PBL approach.
KALS (Knowledge-based Annotation Learning System) is a digital reading support tool that allows for annotation, collaboration with peers, external cognitive support, reading behavior analysis, and recommendation features. It can analyze reading sequences, provide reading guides, and even diagnose reading anxiety. KALS aims to enhance digital reading through annotation, collaboration, and utilizing cognitive support and data to recommend annotations and understand reading behaviors.