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[neutymes]

Wrinkloss

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[birpaylass]

Ürün Mağazası Veritabanı Şeması

BirPaylaş Paylaşım Platformu https://birpaylas.com/urun-magazasi-veritabani-semasi.html

Ürün Mağazası Veritabanı Şeması

Ürün Mağazası Veritabanı Şeması Çevrimiçi bir ürün mağazası için bir veritabanı şeması tasarlamak, sistemdeki önemli varlıkların tanımlanmasını ve aralarındaki ilişkilerin tanımlanmasını içerir. Çevrimiçi bir ürün mağazasına yönelik basitleştirilmiş bir veritabanı şeması örneğini burada bulabilirsiniz:

Reklam

Ürün Mağazası Veritabanı Şeması

Yeni Yılda Alacağınız 3 Önemli Karar

Sırt Ağrısı Nedir ve Nedenleri

Sağlık Nedir Nasıl Sağlıklı Olabiliriz

Kullanıcı Tablosu:

UserID(Birincil anahtar)

Username

Password

Email

FirstName

LastName

Address

PhoneNumber

Ürün Tablosu:

ProductID(Birincil anahtar)

ProductName

Description

Price

StockQuantity

Kategori Tablosu:

CategoryID(Birincil anahtar)

CategoryName

Product_Category Tablosu (çoka-çok ilişkisini uygulamaya yönelik İlişkisel Tablo):

ProductID(Yabancı Anahtara referans veren Product.ProductID)

CategoryID(Kategori.KategoriKimliğine referans veren Yabancı Anahtar)

Sipariş Tablosu:

OrderID(Birincil anahtar)

UserID(User.UserID’ye referans veren Yabancı Anahtar)

OrderDate

TotalAmount

OrderItem Tablosu (bir sipariş içindeki öğeleri depolamak için):

OrderItemID(Birincil anahtar)

OrderID(Order.OrderID’ye referans veren Yabancı Anahtar)

ProductID(Yabancı Anahtara referans veren Product.ProductID)

Quantity

ItemPrice

Ödeme Tablosu:

PaymentID(Birincil anahtar)

OrderID(Order.OrderID’ye referans veren Yabancı Anahtar)

PaymentDate

PaymentAmount

PaymentMethod

Sepet Tablosu (kullanıcının alışveriş sepetindeki ürünleri depolamak için):

CartID(Birincil anahtar)

UserID(User.UserID’ye referans veren Yabancı Anahtar)

ProductID(Yabancı Anahtara referans veren Product.ProductID)

Quantity

Bu temel bir şemadır ve özel gereksinimlerinize bağlı olarak onu genişletmeniz veya değiştirmeniz gerekebilir. Örneğin, müşteri incelemeleri, indirimler, gönderim bilgileri için tablolar eklemek veya ürün çeşitleriyle (bedenler, renkler vb.) ilgili daha karmaşık senaryoları ele almak isteyebilirsiniz. Ayrıca, kullandığınız veritabanı yönetim sistemine bağlı olarak dizinler, kısıtlamalar ve diğer veritabanı optimizasyon tekniklerini eklemeyi düşünün.

Ürün Pazarı

Elbette, bir ürün mağazası için veritabanı şeması tasarımına benzer bir şeyler düşünebiliriz. Örnek bir veritabanı yapısını aşağıda bulabilirsin:

Ürünler Tablosu:

ÜrünID (Birincil Anahtar)

ÜrünAdı

Açıklama

Fiyat

StokMiktarı

Kategori Tablosu:

KategoriID (Birincil Anahtar)

KategoriAdı

Ürün_Kategori Tablosu (Çoktan-Çoka İlişkiyi Uygulamak İçin Bağlantı Tablosu):

ÜrünID (Ürünler Tablosuna Yabancı Anahtar)

KategoriID (Kategori Tablosuna Yabancı Anahtar)

Siparişler Tablosu:

SiparişID (Birincil Anahtar)

KullanıcıID (Kullanıcılar Tablosuna Yabancı Anahtar)

SiparişTarihi

ToplamTutar

SiparişÜrün Tablosu (Sipariş İçindeki Ürünleri Saklamak İçin):

SiparişÜrünID (Birincil Anahtar)

SiparişID (Siparişler Tablosuna Yabancı Anahtar)

ÜrünID (Ürünler Tablosuna Yabancı Anahtar)

Adet

ÜrünFiyatı

Ödeme Tablosu:

ÖdemeID (Birincil Anahtar)

SiparişID (Siparişler Tablosuna Yabancı Anahtar)

ÖdemeTarihi

ÖdemeMiktarı

ÖdemeYöntemi

Sepet Tablosu (Kullanıcının Sepetindeki Ürünleri Saklamak İçin):

SepetID (Birincil Anahtar)

KullanıcıID (Kullanıcılar Tablosuna Yabancı Anahtar)

ÜrünID (Ürünler Tablosuna Yabancı Anahtar)

Adet

Bu basit bir şema olup, belirli gereksinimlere bağlı olarak genişletilebilir veya değiştirilebilir. Örneğin, ürün varyasyonları (beden, renk vb.) ile ilgili daha karmaşık senaryoları ele almak için tablolar eklenebilir. Ayrıca, veritabanı yönetim sisteminize bağlı olarak indeksler, kısıtlamalar ve diğer veritabanı optimizasyon teknikleri eklemeyi düşünebilirsin.

[taiyos]

画像自動生成の準備(Mac mini[M1])

作業内容 ・画像自動生成のため、Core ML Stable Diffusionを導入する ・参考：https://namileriblog.com/python/coreml-stable-diffusion_macos13-1/

手順１：事前準備 1.OS確認 ・ターミナルから下記のコマンド操作を行う taiyo@Mac-mini ~ % sw_vers ProductName: macOS ProductVersion: 14.2.1 BuildVersion: 23C71

2.python確認 ・ターミナルから下記のコマンド操作を行う taiyo@Mac-mini ~ % python3 -V Python 3.11.7 taiyo@Mac-mini ~ % pip list Package Version

certifi 2022.9.24 chardet 5.0.0 idna 3.4 pip 23.3.2 setuptools 68.2.2 urllib3 1.26.12 wheel 0.41.3

3.pyenvをインストールする ・ターミナルから下記のコマンド操作を行う taiyo@Mac-mini ~ % brew install pyenv ==> Downloading https://formulae.brew.sh/api/formula.jws.json ################################################################################################################################# 100.0% ==> Downloading https://formulae.brew.sh/api/cask.jws.json ################################################################################################################################# 100.0% ==> Downloading https://ghcr.io/v2/homebrew/core/pyenv/manifests/2.3.35 ################################################################################################################################# 100.0% ==> Fetching pyenv ==> Downloading https://ghcr.io/v2/homebrew/core/pyenv/blobs/sha256:d19469261f788c09404f05872bd75357213ee58e98426e05edb962962d5e1a06 ################################################################################################################################# 100.0% ==> Pouring pyenv--2.3.35.arm64_sonoma.bottle.tar.gz 🍺 /opt/homebrew/Cellar/pyenv/2.3.35: 1,132 files, 3.4MB ==> Running brew cleanup pyenv… Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP. Hide these hints with HOMEBREW_NO_ENV_HINTS (see man brew).

4.miniforgeをインストールする ・ターミナルから下記のコマンド操作を行う taiyo@Mac-mini ~ % brew install miniforge ==> Downloading https://formulae.brew.sh/api/formula.jws.json ################################################################################################################################# 100.0% ==> Downloading https://formulae.brew.sh/api/cask.jws.json ################################################################################################################################# 100.0% ==> Caveats Please run the following to setup your shell: conda init "$(basename "${SHELL}")"

==> Downloading https://github.com/conda-forge/miniforge/releases/download/23.3.1-1/Miniforge3-23.3.1-1-MacOSX-arm64.sh

[中略]

no change /opt/homebrew/Caskroom/miniforge/base/etc/profile.d/conda.csh modified /Users/taiyo/.zshrc

==> For changes to take effect, close and re-open your current shell. <==

5.ターミナルを閉じた後に再度開く

手順２：Stable Diffusionの設定 1.conda仮想環境を準備する ・ターミナルから下記のコマンド操作を行う (base) taiyo@Mac-mini ~ % conda create -n coreml_sd python=3.9 -y Collecting package metadata (current_repodata.json): done Solving environment: done

[中略]

Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate coreml_sd # # To deactivate an active environment, use # # $ conda deactivate

2.conda仮想環境を有効にする ・ターミナルから下記のコマンド操作を行う (base) taiyo@Mac-mini ~ % conda activate coreml_sd (coreml_sd) taiyo@Mac-mini ~ % (coreml_sd) taiyo@Mac-mini ~ % python -V Python 3.9.18

3.Stable Diffusionを取得する ・ターミナルから下記のコマンド操作を行う (coreml_sd) taiyo@Mac-mini ~ % mkdir ~/coreml_sd (coreml_sd) taiyo@Mac-mini ~ % cd ~/coreml_sd (coreml_sd) taiyo@Mac-mini coreml_sd % git clone https://github.com/apple/ml-stable-diffusion.git Cloning into 'ml-stable-diffusion'… remote: Enumerating objects: 640, done. remote: Counting objects: 100% (392/392), done. remote: Compressing objects: 100% (175/175), done. remote: Total 640 (delta 300), reused 232 (delta 216), pack-reused 248 Receiving objects: 100% (640/640), 23.56 MiB | 9.20 MiB/s, done. Resolving deltas: 100% (354/354), done.

4.ライブラリを取得する ・ターミナルから下記のコマンド操作を行う (coreml_sd) taiyo@Mac-mini coreml_sd % cd ml-stable-diffusion (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % pip install --upgrade pip Requirement already satisfied: pip in /opt/homebrew/Caskroom/miniforge/base/envs/coreml_sd/lib/python3.9/site-packages (23.3.2) (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % pip install -e . Obtaining file:///Users/taiyo/coreml_sd/ml-stable-diffusion Preparing metadata (setup.py) … done Collecting coremltools>=7.0b2 (from python_coreml_stable_diffusion==1.1.0) Downloading coremltools-7.1-cp39-none-macosx_11_0_arm64.whl.metadata (2.4 kB)

[以下略]

5.ライブラリをアップグレードする ・ターミナルから下記のコマンド操作を行う (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % pip install --upgrade diffusers transformers scipy Requirement already satisfied: diffusers in /opt/homebrew/Caskroom/miniforge/base/envs/coreml_sd/lib/python3.9/site-packages (0.25.0) Requirement already satisfied: transformers in /opt/homebrew/Caskroom/miniforge/base/envs/coreml_sd/lib/python3.9/site-packages (4.36.2)

[以下略]

6.git-lfsをインストールする ・ターミナルから下記のコマンド操作を行う (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % brew install git-lfs ==> Downloading https://formulae.brew.sh/api/formula.jws.json ################################################################################################################################# 100.0% ==> Downloading https://formulae.brew.sh/api/cask.jws.json ################################################################################################################################# 100.0% ==> Downloading https://ghcr.io/v2/homebrew/core/git-lfs/manifests/3.4.1 ################################################################################################################################# 100.0% ==> Fetching git-lfs ==> Downloading https://ghcr.io/v2/homebrew/core/git-lfs/blobs/sha256:63461c3fbf6ddab9d90c8c6dcb51748a68c4446f222709b970dd688dd78a77f6 ################################################################################################################################# 100.0% ==> Pouring git-lfs--3.4.1.arm64_sonoma.bottle.tar.gz ==> Caveats Update your git config to finish installation: # Update global git config $ git lfs install # Update system git config $ git lfs install --system

==> Summary 🍺 /opt/homebrew/Cellar/git-lfs/3.4.1: 78 files, 13.1MB ==> Running brew cleanup git-lfs… Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP. Hide these hints with HOMEBREW_NO_ENV_HINTS (see man brew). (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % git lfs install Updated Git hooks. Git LFS initialized.

7.mlpackageを取得する ・ターミナルから下記のコマンド操作を行う (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % cd ~/coreml_sd (coreml_sd) taiyo@Mac-mini coreml_sd % git clone https://huggingface.co/apple/coreml-stable-diffusion-2-base Cloning into 'coreml-stable-diffusion-2-base'… remote: Enumerating objects: 193, done. remote: Total 193 (delta 0), reused 0 (delta 0), pack-reused 193 Receiving objects: 100% (193/193), 552.85 KiB | 4.16 MiB/s, done. Resolving deltas: 100% (40/40), done. Filtering content: 100% (72/72), 16.07 GiB | 20.78 MiB/s, done.

8.動作確認 ・ターミナルから下記のコマンド操作を行う (coreml_sd) taiyo@Mac-mini coreml_sd % cd ml-stable-diffusion (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % python -m python_coreml_stable_diffusion.pipeline \ --prompt "a_high_quality_photo_of_an_astronaut_riding_a_horse_in_space" \ -i ../coreml-stable-diffusion-2-base/original/packages \ -o ~ --compute-unit CPU_AND_GPU --seed 11 \ --model-version stabilityai/stable-diffusion-2-base

9.conda仮想環境を無効にする ・ターミナルから下記のコマンド操作を行う (coreml_sd) taiyo@Mac-mini ml-stable-diffusion % conda deactivate (base) taiyo@Mac-mini ml-stable-diffusion %

[govindhtech]

Mastering Kafka Schema Basics: A Comprehensive Guide

The popular open-source event store and stream processing platform Apache Kafka has developed into the industry standard for data streaming. With IBM Event Streams on IBM Cloud, a fully managed Kafka service, developer Michael Burgess offers an understanding of schemas and schema management to enhance your event-driven applications.

Define a schema

A schema explains how data is organized.

For case:

A basic Java class that models a product order from an internet retailer might include the following fields at the beginning:

public class Order{ private String productName private String productCode private int quantity

A schema like this Avro schema might be used to define the structure of records created by order objects formed with this class and submitted to a Kafka topic

Why is using a schema recommended?

Apuana Kafka transmits data without first verifying the contents of the messages. It is unable to see what kinds of data are being sent or received or what kinds of data it might contain. Kafka doesn’t go into your messages’ metadata.

Decoupling producing and consuming applications so they interact through a Kafka topic rather than directly is one of Kafka’s features. As a result, they can each operate at their own pace. Nevertheless, they must decide on the same data format; otherwise, the consuming applications won’t be able to reassemble the data that they get into a meaningful form. The apps must all operate under the same presumptions regarding the data’s structure.

Within the context of Kafka, a schema explains the data structure of a message. It outlines the types of fields that are required in every message as well as the fields that must be present.

In other words, a schema creates a clear contract between a producing application and a consuming application, enabling the latter to accurately parse and understand the contents in the messages they get.

A schema registry: what is it?

By acting as a repository for managing and validating schemas inside your Kafka cluster, a schema registry helps your cluster. It serves as a database to hold your schemas and offers an interface for retrieving and managing schemas. The evolution of schemas is also verified by a schema registry.

Use a schema registry to make your Kafka system more efficient

In your Kafka context, a schema registry is effectively an agreement about the structure of your data. You may prevent typical mistakes in application development, like inconsistent data across your generating and consuming applications that could eventually result in data corruption, and poor data quality by maintaining a consistent store of the data formats in your applications. In addition to being technically essential, having a well-managed schema registry supports the strategic objectives of considering data as a valuable commodity and greatly facilitates the process of using data as a product.

By imposing guidelines for schema evolution, a schema registry improves the quality of your data and guarantees data consistency. Thus, a schema registry guarantees that your messages will continue to work together even if schema versions change over time, in addition to guaranteeing data consistency between generated and consumed messages. It is highly likely that during a firm’s existence, the messages that the apps supporting the business exchange will need to alter in format.

For instance, the Order class in the previously used sample schema might get a new status field; the product code field might be changed to a field that combines the department and product numbers, or something similar. As a result, the schema of the objects in our business domain is always changing, thus at any given time, you must be able to guarantee agreement on the schema of messages related to any given topic.

The evolution of schemas follows several patterns:

Forward Compatibility: While awaiting migration to the new version of the schema, all consuming apps can continue to receive messages thanks to forward compatibility, which allows producing applications to upgrade to a new version of the schema.

Backward compatibility refers to the ability of consuming programs to be moved to a new schema version ahead of generating applications, allowing the former to continue consuming messages created in the latter format.

Full Compatibility: When a schema is fully compatible, it can function both forwards and backwards.

By guaranteeing the forward, backward, or full compatibility of new schema versions and restricting the introduction of incompatible schema versions, a schema registry can impose rules for schema evolution.

A schema registry, which offers an easy way to track and audit changes to your topic data formats, makes it easier to comply with data governance and data quality requirements by acting as a store of versions of schemas used inside a Kafka cluster, both past and present.

Next, what?

In conclusion, a schema registry is essential for controlling versioning, schema evolution, and data consistency in distributed systems, all of which help to ensure interoperability between various components. An Enterprise subscription for Event Streams on IBM Cloud includes access to a Schema Registry. Make sure your environment is optimized by taking advantage of this capability on IBM Cloud’s fully managed Kafka service to create intelligent, responsive apps that respond instantly to events.

Read more on Govindhtech.com

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[imemon-blog]

Social Flow Machine Review + Bonus ($997 value)

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[codehunter]

Iterating multiple lists in parallel with Python inside HTML (Flask)

I am building a python web app hosted on pythonanywhere following this tutorial loosely. I am modifying the resulting application to fit my own goal.

Here is my python code that I am using to pass variables to a HTML document in order for them to be added to a table using a for loop:

from flask import Flask, redirect, render_template, request, url_forapp = Flask(__name__)app.config["DEBUG"] = Trueproductnames = []reviews = []@app.route("/", methods=["GET", "POST"])def index():if request.method == "GET": return render_template("main.html", reviews=reviews, productnames=productnames)reviews.append(request.form["review"])productnames.append(request.form["products"])return redirect(url_for('index'))

Using the following code within my HTML, I am looping through that list and adding each item to the table:

{% for review in reviews %} <tr> <td></td> <td>{{ review }}</td> <td></td> </tr>{% endfor %}

And this works, however, I am trying to iterate through multiple lists and found various statements saying that the zip function was what I was looking for so I changed my HTML code to the following segment and it no longer works.

{% for review, product in zip(reviews, productname) %} <tr> <td>{{ product }}</td> <td>{{ review }}</td> <td></td></tr>{% endfor %}

From python anywhere, the error page says "Error code: Unhandled Exception", and the error log through the pythonanywhere dashboard says:

2018-04-24 12:57:23,957: File "/home/FdScGroup/cloudapp/templates/main.html", line 43, in top-level template code2018-04-24 12:57:23,957: {% for review, product in zip(reviews, productnames) %}

How do I get this to work?

Any help appreciated, thank you.

https://codehunter.cc/a/flask/iterating-multiple-lists-in-parallel-with-python-inside-html-flask

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[taiyos]

X68000エミュレータの準備(Mac mini[M1])

作業内容

X68000ソフトのメディアをイメージファイル化するため、エミュレータ「XEiJ」を準備する

参考：https://stdkmd.net/xeij/

参考：https://zenn.dev/tantangh/articles/1b86b788812028

手順１：事前準備

OS確認

ターミナルから下記のコマンド操作を行う

taiyo@Mac-mini ~ % sw_vers ProductName: macOS ProductVersion: 13.3.1 ProductVersionExtra: (a) BuildVersion: 22E772610a

java確認

ターミナルから下記のコマンド操作を行う

taiyo@Mac-mini ~ % brew list --version openjdk openjdk 20.0.1 taiyo@Mac-mini ~ % java -version The operation couldn’t be completed. Unable to locate a Java Runtime. Please visit http://www.java.com for information on installing Java.

OpenJDKのリンク更新

ターミナルから下記のコマンド操作を行う

taiyo@Mac-mini ~ % sudo ln -sfn $HOMEBREW_PREFIX/opt/openjdk/libexec/openjdk.jdk /Library/Java/JavaVirtualMachines/openjdk.jdk Password:

java再確認

ターミナルから下記のコマンド操作を行う

taiyo@Mac-mini ~ % java -version openjdk version "20.0.1" 2023-04-18 OpenJDK Runtime Environment Homebrew (build 20.0.1) OpenJDK 64-Bit Server VM Homebrew (build 20.0.1, mixed mode, sharing)

手順２：XEiJ準備

XEiJをダウンロードする

ターミナルから下記のコマンド操作を行う

taiyo@Mac-mini ~ % curl https://stdkmd.net/xeij/XEiJ_0230503.zip -s -o XEiJ_0230503.zip

XEiJを展開する

ターミナルから下記のコマンド操作を行う

taiyo@Mac-mini ~ % mkdir -p ~/Emulator/X68k/XEiJ/0230503 taiyo@Mac-mini ~ % cd ~/Emulator/X68k/XEiJ/0230503 taiyo@Mac-mini 0230503 % unzip ~/XEiJ_0230503.zip Archive: /Users/taiyo/XEiJ_0230503.zip inflating: atcmonitor.png inflating: class/xeij/AbstractUnit$1.class inflating: class/xeij/AbstractUnit$2.class inflating: class/xeij/AbstractUnit.class inflating: class/xeij/ADPCM$PIP$1.class [以下略]

XEiJを起動する

ターミナルから下記のコマンド操作を行う

taiyo@Mac-mini 0230503 % java -jar XEiJ.jar

------------------------------------------------- XEiJ (X68000 Emulator in Java) version 0.23.05.03 -------------------------------------------------

日本語が選択されました java.vendor は Homebrew です java.version は 20.0.1 です os.arch は aarch64 です os.name は Mac OS X です 設定ファイルは /Users/taiyo/XEiJ.ini です /Users/taiyo/XEiJ.ini がありません CGROM_XEiJ.DAT を読み込みました IPLROM.DAT を読み込みました IPLROMXV.DAT を読み込みました IPLROMCO.DAT を読み込みました IPLROM30.DAT を読み込みました HUMAN.SYS を読み込みました FLOAT2.X を読み込みました DB.X を読み込みました IPLROM 1.6 $00FC0000-$00FC1A23 SCSIINROM 16 $00FC2000-$00FCFDB1 ROM Human 2.60 $00FD0000-$00FD5657 ROM FLOAT 2.03 $00FD5800-$00FE9A49 ROM Debugger 3.60 $00FEA000-$00FEEC03 IPL/BIOS 1.6 2nd $00FEF400-$00FEFFFF ANK6x12 $00FF0000-$00FFFFFF IPL/BIOS 1.6 1st X68030/Xellent30 のハイメモリはありません 060turbo のローカルメモリのサイズは 128MB です 060turbo のローカルメモリをゼロクリアします メインメモリのサイズは 12MB です メインメモリをゼロクリアします SRAM のサイズは 16KB です SRAM をゼロクリアします /Users/taiyo を hf0 に接続しました MC68000 を起動します SRAM の容量は 16KB ($00ED0000-$00ED3FFF) です SRAM にあるメモリサイズを 4MB から 12MB に変更しました