MySQLデータベースの使い方を学ぶ

（自動生成）

MySQLは最も人気のあるデータベースの1つです。このコースでは、上級データベースエンジニアからMySQLの使用方法を学びます。

この基本的なMySQLコースへようこそ。まず、新しいスキルを習得しようとしていることに感謝します。

自己紹介をさせてください。

私の名前はバーバラです。Salesforceでシニアデータベースエンジニアとして働いています。Oracleをメインとするさまざまなデータベースで12年以上の経験があり、Chase、PayPal、Wells、Fargo、StubHubなどの企業で働いた経験があります。など

最初にいくつかの基本的な質問に答えさせてください。それは、誰が、何を、そしてなぜかということです。

したがって、このコースを受講する必要があるのは、スキルセットを拡張したいデータベースの専門家を対象としています。

ソフトウェアエンジニアまたはフルスタック開発者であり、MySQLデータベースを深く理解したい場合は、このコースが最適です。

また、大学生、コンピュータサイエンスの学生、または新卒者の場合、このコースではデータベースの内部に関する知識が得られます。

では、なぜMySQLを学ぶ必要があるのでしょうか。MySQLは最も人気のあるオープンソースデータベースであり、もちろんPostgresです。

SQLは間違いなくそこにあります。

企業がデータをオンプレミスからクラウドに移行するとき、コストを節約するために、通常、クラウドネイティブデータベース、またはMySQLやPostgresの続編などのオープンソースデータベースに移行するのが好きです。

それで、あなたがOracleデータベースの専門家であるとしましょう。

MySQLのようなデータベースに関する知識を身に付ければ、企業がOracleからMySQLにデータを移行するのを支援でき、それは非常に価値があります。

それでは、このコースでカバーされている内容を見てみましょう。

さて、まず第一に、MySQLとは、このコース全体でMySQL InnoDBストレージエンジンを意味します。これは、コマースWebサイト、銀行、金融機関などの背後で使用されます。

また、MySQLはさまざまなストレージエンジンを提供しています。私のI Samは、メモリストレージエンジン、または利用可能ないくつかの一般的なストレージエンジンです。ここでは、MySQL InnoDBについて学習しますが、他のタイプのストレージエンジンについては説明しません。

さて、これらは私がこのコースでカバーしようとしているトピックです。

また、これはデータベース管理コースであることに注意してください。

つまり、80％のデータベース管理です。

そして、データベースにまったく慣れていない人のために、SQLの基本を含めました。

したがって、データベースのインストール、MySQL Workbench、データベースインデックス、データベースログについて学習し、パフォーマンスの調整についても少し学習します。つまり、SQLの説明です。

ですから、これらは私がカバーしようとしているいくつかの興味深いトピックです。

したがって、このコースを開始するために正確に必要なものは何ですか。PCまたはMacが必要です。

したがって、PCを使用している場合は、説明に添付されているワークシートまたは補足資料を実際に調べることをお勧めします。

Macラップトップをお持ちの場合は、このコースを学ぶのに最適な立場にあります。

なぜなら、私が入力している内容を確認できるからです。

また、同じコマンドを入力して、端から端までたどることができます。

そして何よりも、これが主な要件です。AWSアカウントを作成してください。これは正しく、Webサービスアカウントに驚かされます。

したがって、私が何について話しているのかわからない場合は、説明にある私のワークシートを調べてください。AWSアカウントの作成方法を示すいくつかのリソースを添付しました。これを使用します。コース全体でAWSEC2インスタンス。

そして、その作成方法を紹介します。

ただし、覚えておいてほしい主なことの1つは、各勉強会の後で、EC2インスタンスをシャットダウンできることです。

そうすれば、不要な費用を支払う必要がありません。

また、EC2インスタンスを247で実行し続ける必要はないことを忘れないでください。

したがって、AWSアカウントを作成してサインインすると、このダッシュボードまたはこのページが表示されます。

そして、ここからサービスメニューに移動できます。

そして、コンピューティングでは、簡単に選択できるので、ここの左側でインスタンスを選択できます。

次に、ラボ環境となるインスタンスを作成します。

したがって、インスタンスの起動をクリックします。

次に、インスタンスの画像を選択しましょう。

そのため、Red Hat Enterprise Linuxバージョン864ビットを選択し、インスタンスタイプはT two microになります。これは、無料利用枠の対象です。

そして、適切なサブネットを選択する必要があります。

AWSアカウントを作成したばかりの場合は、そのままにしておくことができます。

表示されるデフォルトのサブネットはそのままにしておきます。

私にとって、私は私を選ぶつもりです。特定の人になりましょう。

次に、この自動割り当てパブリックIPオプションを必ず有効にしてください。

これにより、インスタンスにパブリックIPが割り当てられ、ラップトップからインスタンスにSSHで接続し、他のすべての設定をそのままにしておくことができます。

そして、データベースまたはインスタンス全体に25ギガのように割り当てましょう。

そして、残りのISISをそのままにして、次のページを読んで、インスタンスのタグを作成できます。

だから私はそれを私のSQLインスタンスと呼ぶつもりです。

1つは、セキュリティグループのページに移動します。

そしてここで重要なのは、インスタンスにSSHで接続できる必要があるということです。

そして、必要なのは、誰でも、基本的には世界中の誰もがポート22を介してこのインスタンスにログインできるようにするための適切なファイアウォールルールを作成する必要があることです。

そして、それはまったく安全ではありません。

そして、私がこのインスタンスの世話をします。

録音が終わったら、通常は削除するからです。

だから私はこれを処理する方法を知っています。

ただし、ルールを作成するときは、ラップトップのIPをそこに配置して、より安全にするようにしてください。そうすれば、構成を確認して、[起動]をクリックできます。

ただし、必要に応じて、新しいキーペアを作成し、それに名前を付けることができます。

そして、インスタンスを作成する前にダウンロードしてください。

私の場合は、既存のキーペアを選択するだけです。

多分これ、そして私は知識を言う、多分別のもの。

よし、これ。

そしてインスタンスを起動します。

これでインスタンスが作成されます。このインスタンスを作成するには、おそらく数分かかります。

さて、それがEC2インスタンスを作成する方法です。

これで、インスタンスが稼働し、パブリックIPを確認できるようになりました。

後で、このインスタンスにMySQLをインストールします。

それがこのレッスンでお見せしたかったことです。

つまり、表示されているのは基本的に、WindowsやMacなどのさまざまなインストールガイドをすべて示した私の続編のドキュメントです。

したがって、Linuxのインストールは基本的にLinuxへのMySQLのインストールに関心があります。

実際には、いくつかのガイドがあります。

つまり、これは基本的にjannettekバイナリをインストールしているので、それをスキップしてここに移動します。

また、MySQLをインストールしている場合でも、Linuxにはたくさんのガイドがあります。

したがって、推奨されるインストール方法は、OracleのRPMパッケージを使用することです。

ただし、このMySQLyumリポジトリベースのインストールを使用します。

そして、それは実際には非常に簡単です。

したがって、このインストールでは、MySQL通信にアクセスしてダウンロードする必要があります。

そして、Community Editionをダウンロードして、yumリポジトリに移動します。

ご存知のように、私たちが作成したインスタンスには、Archie l 8、Red Hat、EnterpriseLinux8が実行されています。

したがって、このrpmをダウンロードする必要があります。

ただし、作成したインスタンス自体にRPMをダウンロードする必要があります。

それでは、実際にインスタンスにログインしてみましょう。

そこで、SSHを使用し、秘密鍵を使用します。

また、ロギングはユーザーがデフォルトユーザーを選択するのが簡単で、基本的にインスタンスログインのパブリックIPを取得し、ルートに切り替えます。

したがって、このRPMをこのLinuxインスタンスにダウンロードするために必要なものの1つは、Wgetパッケージです。

それでは、先に進んでそれを最初にインストールしましょう。

了解しました。wゲートがインストールされたので、今見たRPMをダウンロードする必要があります。

したがって、このRPMのリンクを取得するには、このダウンロードに入る必要があります。

そして、ここをクリックしてリンクをコピーする必要があります。

また、別のOSSにインストールする場合は、適切なボタンをクリックする必要があります。

さて、リンクを取得しました。先に進んで、w getやリンクのように、そのリンクをここに貼り付けましょう。

そして、そのコマンドがこのパッケージをダウンロードします。次に、RPMコマンドを使用してこのパッケージをインストールします。

したがって、このパッケージは、前述したように、このMySQLyumリポジトリをローカルシステムリポジトリリストに追加します。

Red Hat Enterprise Linuxをインストールすると、デフォルトでMySQLモジュールのようになります。

それでは、それを無効にしましょう。

無効にしないと、MySQLのインストールが妨げられます。

それでは、先に進んで、このコマンドを使用して無効にしましょう。

そして、これらのコマンドを書くことについて心配する必要はありません。説明にこれらすべてのコメントを含むGitリポジトリへのリンクを配置します。

したがって、これらはすべて無効になっています。

それでは、yuminstallMySQLコミュニティサーバーを使用してMySQLコミュニティサーバーエディションをインストールしましょう。

そして、先に進んですべてのプロンプトを受け入れるために、そこにマイナスyを入れましょう。

これで、これらすべてのパッケージがインストールされます。

了解しました。SQLがインストールされました。

先に進み、システムCTLを使用してMySQLデータベースを起動しましょう。

コマンド。

そして、ステータスを確認しましょう。

これで、SQLデータベースが稼働しています。

さて、このMySQLデータベースソフトウェアのログファイルはvarlogの下にあります。

そして、このログファイルからtempを取得すると、rootユーザーの一時パスワードを取得します。

そして、それを使用してMySQLデータベースにログインできます。

そして、どのようにログインしますか。このコマンドを使用します。私の続編からuを引いたものがルートになり、マイナスPはパスワードベースのログイン用です。

そして、MySQLデータベースにログインしています。

それでは、このパスワードを使用して、ログインするかどうかを確認しましょう。

そして、私たちは入っています。

そして、この時点でコマンドを実行すると、私の続編では、alteruserステートメントを使用してパスワードをリセットするように求められます。

これは別の方法で行うことができます。

したがって、MySQLadminという実行可能ファイルがあります。

そして、これはそのためのコマンドです。

MySQL管理者、マイナスユーザー名とマイナスpパスワード。

rootユーザーのパスワードをリセットします。

そして、最初にこの一時的なパスワードである現在のパスワードを提供しましょう。

そして、新しいパスワード番号を入力しましょう。

了解しました。パスワードは承認されました。

ここで、前のコマンドを使用して、この新しいパスワードでログインしてみましょう。私のSQLからuを差し引き、MySQLを差し引いたものです。

今言ったパスワードを入れさせてください。

それでは、先に進んで、インストールの一部として提供されるすべてのデフォルトデータベースを表示する簡単なshowdatabasesコマンドを実行してみましょう。

したがって、インストールを完了するためにもう1つ行う必要があるのは、ここに示すようにタイムゾーンファイルまたはタイムゾーンテーブルをロードすることです。

したがって、基本的にこのテーブルから読み取るSQLクエリである選択スターを実行すると、テーブルが現在空であることがわかります。

それでは、終了して別のコマンドを実行しましょう。

タイムゾーン関連のデータを読み込むには。

これがコマンドです。

そして、先に進んで実行しましょう。

そして、先に進んでパスワードを入力します。これにより、大量のデータが読み込まれるため、これらの警告はすべて無視してかまいません。

MySQLデータベースに戻りましょう。

したがって、MySQLドットタイムゾーンからselectスターとして実行すると、大量のデータが表示されます。

だから今、あなたは元気です。

これでMySQLデータベースのインストールは完了です。

さて、皆さん、このセクションでは、データモデリングについて説明します。

さて、データベース設計、データモデリング、スキーマ設計、これらはすべて交換可能な言葉です。データベース設計の用語は継続的なプロセスです。

したがって、アプリケーションを作成するような場合は、基本的な設計を思い付くことができます。

そして、アプリケーションが新しい機能、拡張機能、改善点を追加すると、基本的にこの設計を繰り返し、設計に新しいものを追加し続けます。

したがって、データベース設計またはデータモデリングを行うときに最初に行うことは、ビジネスデータを理解することです。

そして、ビジネスデータを理解したら、データベースの論理設計を考え出す必要があります。

それはどういう意味ですか？さて、基本的に、あなたはあなたのテーブル、それらのテーブルに入る列、インデックス、主キー制約のような制約、一意のキー制約、nullではない制約、デフォルト値、外部キーを設計する必要があります、これらはすべてあなたがするさまざまなものです作成する必要があります。

スキーマの論理設計を思いついたときに、この基本的なテーブル設計またはスキーマ設計が実際にできたら、データの冗長性を探すことができます。つまり、基本的に、データが繰り返される場所を確認できます。

そして、実際にテーブルを正規化することで、それを排除し始めます。

これは、データの冗長性がデータの異常を引き起こすためです。

つまり、同じデータが複数回出現する場合、たとえば特定のデータを更新する場合は、多くの場所で更新する必要があります。

また、1つの場所でも更新するのを忘れた場合は、データベースに同じデータの2つのバージョンがあります。

そして、それはデータの異常のように作成されますデータの不整合は実際には同じ線に沿っています。

そして、それはすべてデータの冗長性のために起こります。

つまり、私たちが見ているのは基本的にスプレッドシートであり、スプレッドシートは基本的に巨大なテーブルであり、大きなテーブルですよね？そして、私たちがやろうとしていることは、基本的にeコマースWebサイトのテーブルを設計することです。eコマースWebサイトは、基本的に素晴らしいゾーン、Eベイ、またはオンラインビジネスのようなもの、AlibabaのようなオンラインeコマースWebサイトのようなものです。 、または何でも、そうですか？たとえば、このデータベースにはテーブルが1つしかないということですよね？そして、注文が入ってくると、このWebサイトから、このテーブルにデータを入れ始めますよね？この表に記録するもののいくつかを見てみましょう。もちろん、注文数のように必要です。

それで、あなたはあなたの注文に番号を付けるのが好きかもしれません、そしてそれからそれがどのようになっているのか、あなたが知っている、それはデスクトップですか、それともモバイルですか？それともどのような製品ですか？ご存知のように、ここに数冊の本、本のタイトル、そして製品の価格があります。

そして、顧客の顧客の詳細、支払いの詳細、配達の詳細などは誰ですか。

つまり、これらはすべて、基本的にeコマースワードの一部です。

右？そして、1つの巨大なテーブルがあります。

そして、ここのデータを見ると、そうでしょう？ですから、ここでは、2つの異なる製品のように、2、3人の顧客があなたを購入しているようです。

つまり、データが繰り返されていることがわかります。

つまり、同じ商品を購入するたびに、このデータを繰り返す必要があります。たとえば、最初に注文したのは、誕生によってもたらされたWebデスクトップWebサイトでした。その後、このデータのすべての詳細を確認できます。顧客と、製品および支払いに関するすべての詳細、支払いの詳細も同様です。

次に、別の人からの2番目の注文でしたが、同じ商品の注文であり、商品情報を繰り返す必要がありました。

そうです。

3番目は前の顧客からのものでした。

しかし、今回、彼は別の製品を購入しました。彼の情報、顧客情報は実際に繰り返されています。

そのため、多くのデータ冗長性データが繰り返されてきました。

つまり、これは基本的に非正規化されたデータベースであり、1つのテーブルまたは少数のテーブルしかなく、Webサイトまたはビジネスからのすべての情報をこれらのいくつかのテーブルにバックアップします。

実際、これはデータベースの非正規化バージョンです。他に何ができるかを実際に見てみましょう。

つまり、基本的には、この基本的な非正規化テーブルから始めて、データベースまたはテーブルアクションからすべての冗長な情報を取り出し始めることができます。最初に行ったのは、顧客情報のように取り出したことです。 、彼らはそれを別のテーブルに入れましたね。

ここには顧客の詳細しかありません。

そして、私は、顧客IDまたは顧客番号ごとにID番号を付けるのが好きでした。

顧客情報を取得すると、最初のテーブルであるOrdersテーブルができました。これで、Ordersテーブルを次のように呼び出しています。そして、ここに顧客ID列のようなものがあることがわかります。

そして、この顧客ID列は何ですか？あなたの推測？右？したがって、この顧客ID列は、ここに表示されているものと同じです。

右？それで、これらの行を関連付ける方法が必要なため、なぜそれがあるのですか。

お分かりのように、これらは列、行、行です。私は顧客との関係を築く必要がありますよね？顧客情報を取り出す場合、どのように関連付けることができますか？このテーブルとそのテーブル？共通の列、または一連の列を介して行われます。

実際、この場合は1列だけです。

では、顧客IDですね。ここにID番号を入れるようなものです。

そして、実際にこのテーブルから他に何を取り出すことができますか。

つまり、これは正規化の1つのレベルです。

右？それでは、製品の詳細を取り出すような正規化を続けましょう。

右？そのため、製品の詳細も繰り返されています。

したがって、このテーブルには3つのレコードしかないため、ここではそれほど苦痛を感じることはありません。

テーブルに100万件のレコードがある場合はどうなりますか？これが、テーブルを正規化する必要がある理由です。

これで、実際に製品情報を取り出して、別のテーブルに移動します。

次に、実際に商品をIDするように、番号を付けるための商品ID列があります。注文テーブルは次のようになり、支払い情報を別のテーブルに取り出します。

すると、注文テーブルは次のようになります。

これは基本的に、非正規化されたスキーマまたはデータベースから正規化されたデータベースに移行するプロセスです。

1つのテーブルにデータがある場合は、ジョイントを実行する必要はありません。

したがって、SQLを使用するなどのクエリを実行している場合、SQLは言語として正しいのでしょうか？構造化照会言語。データベースでコマンドを実行しているときに、テーブルデータベースが完全に非正規化されている場合は、この1つのテーブルからすべてのデータを取得できます。

一方、テーブルが多数ある場合は、厩舎を結合または結合してから、データを取得する必要があります。

つまり、これはテーブルの結合と呼ばれます。

したがって、非正規化データベースがある場合、多くのジョイントを実行する必要はありません。

データベースがデータを取得するためにそれほど多くのことを考える必要がないので、それはある意味で良いことです。

あなたは私がこのデータが欲しいと言います、そしてこのデータはこの表で利用可能です。

ですから、それは非常に簡単です。

一方、正規化されたデータベースの場合と同様に、多くのテーブルを結合すると、Oracleまたはほとんどのテープのほとんどのデータベースでオプティマイザを呼び出すデータベースエンジンが使用されます。

したがって、このデータベースエンジンは、どのテーブルを最初にスキャンする必要があるのか​​、というように考える必要があります。そして、このテーブルのデータをどのようにフィルタリングすればよいですか。

そして、わかりました。そのテーブルから結果セットを取得し、これらの他のテーブルと結合する必要があります。

したがって、このデータベースが実行されているサーバーで実行する必要のある処理が非常に多いと考えることがはるかに多くなります。

そうです。

そのため、パフォーマンスはある程度柔軟になります。

右下にすると、多くのリソースを消費し、多くの操作が同時に発生しているように、すべてが大規模に発生します。基本的に、実際にはパフォーマンスが低下するか、少なくともパフォーマンスが低下します。データベースを非正規化しますが、同時に、データの繰り返しを大幅に削除するか、データの冗長性が非常に低くなります。そのため、正規化されたデータベースに必要なストレージははるかに少なくなります。

したがって、実際に一般化するのが好きではなく、正規化されたデータベースは常に遅い、または非正規化されたデータベースは速いと言うことはできません。実際にデータを調べて、どのくらいの繰り返しが発生しているかなどを確認する必要があるようです。

ですが、一般的には、このように設計プロセスを進めていくと、実際に何をしたかがわかります。

そこで、必要なテーブルを決定しました。ご存知のように、注文があり、テーブル製品が顧客と支払いを受け取り、列名を決定します。

そして、それだけでなく、テーブルごとに、実際に主キーとなるものを決定する必要があります。

主キーとしての意味は一意のキーであり、実際にはnullにすることはできません。これは、非常に重要です。

したがって、この主キーを使用すると、このテーブルの任意のレコード、このテーブルの任意の行を識別できるはずです。

たとえば、ここで言うと、主キーは注文番号です。注文番号があればいつでも、このテーブルを検索できます。たとえば、注文番号が2の場合、このレコードを取得できます。私のデータベース。

そして、実際には、いくつかの固有のキーも必要です。

したがって、一意キーは主キーとほとんど同じです。

また、前述したように、一意キーを主キーにすることができ、nullにすることはできません。

また、テーブルにインデックスを付けることもできます。

したがって、インデックスは基本的に1日のデータをより速く選択する方法です。

顧客の電子メールに基づいてこのテーブルを検索することがよくあるとしましょう。次に、顧客の電子メール列にインデックスが必要です。そう、あなたはそれを決定する必要があります。

そして、どの列をnullにすることができるかを決定する必要があります。

右？ここでは、どの列もnullにすることはできません。

たとえば、どのような種類の配送、どのような種類の電話番号が優先されるかなど、顧客の好みと呼ばれる別の列があるとします。

つまり、それはnull列になる可能性がありますよね？したがって、列を指定することはできません。

それ以外の場合は、列をnullではないと定義します。

たとえば、注文テーブルに、顧客がWebサイトで製品を購入したときに注文が基本的に作成されるときに、この配信された列があるとします。

もちろん、すぐには配信されません。注文の作成時に、配信された列には常にnまたはn、nの値がありますよね？これらすべて、私たちが話しているこれらすべての決定は、スキーマ設計の一部です。

そして、これらすべてを理解したら、その情報を設計ツールのエンティティ関係設計ツールに入れることができます。

そして次のセクションでは、私の続編の世界のワークベンチである続編のワークベンチでそれをどのように行うかを示します。基本的には、データベースの論理設計を実際に絵で表現することができます。

そして、それは基本的にあなたがER図と呼ぶものです。

そしてもちろん、2つのテーブルの関係について話すこともできます。たとえば、このテーブルのこのテーブルには1対多の関係があり、たとえば、各顧客は多くの注文を行うことができます。

つまり、これは実際には1対多の関係です。

しかし、1つは、ご存知のとおり、1つの注文は1人の顧客のみが行うことができます。

右？だから、そういうことです。

つまり、テーブル間に1対1の関係、1対多の関係、または多対多の関係があります。

実際、これらはすべてデータモデリングの一部です。

ただし、データベースにどのデータが入ってくるかを明確に把握している限り、それほど心配する必要はありません。

その過程で、列のデータ型のように定義する必要があります。

実際、それは非常に重要です。

あなたの名前はワイルドチャーになります。

ご存知のとおり、電話番号は番号にすることができ、メールは再び時計のチャートのようになります。

また、ID列または数値列はintまたは数値になります。

これらはすべて、あなたが下す決定です。データモデリングタスクでは、実際、それは私がデータモデリングについて言いたいこととほぼ同じです。それから、私たちがそれについて話すことができることはもっとたくさんあります。

そして、アトミック性のように、あなたのように、あなたはすべてのアドレスを持っています、一種の属性が1つの列に詰め込まれています、私たちもそれを分割する必要があります。

これをアトミック性と呼びます。

住所は個別に、市区町村は個別に、郵便番号は個別に設定できます。

ですから、そのようなことには、データベースをますます効率的にするニュアンスがあります。

そしてもちろん、ここでは多くの詳細については説明しません。

ただし、これは理解する必要のある基本的なデータモデリングです。

前に述べたように、次のセクションでは、これを取得して、後続のワークベンチに入力する方法を示します。

前のセクションで行ったことを簡単に要約すると、基本的にeコマースWebサイトの論理的なデザインを作成しました。

だからあなたが見ているのは私が始めたテーブルです。

これは非正規化テーブルであり、基本的にこの非正規化テーブルを使用して正規化しました。

ご覧のとおり、このテーブルには4つのバージョンがあり、このテーブルをOrdersテーブルと呼んでいます。

したがって、4つの異なるバージョンがあります。

そして、反復するたびに、繰り返しデータを取り出しました。

最後に、元のOrdersテーブルとは別に、4つのテーブルを用意しました。

そのため、今では顧客の製品と支払いもあります。

このビデオでは、これからすべての厩舎を取り上げ、次に構造を取り上げ、論理的なデザインを作成します。

では、実際にMySQL Workbenchに移動して、すでにデータベースに接続しているので、ファイルに移動して新しいモデルに移動します。

したがって、ここで新しいER図である実体関連図を追加できます。

そして、このデータベースをeecomと呼びましょう。

店、そのようなもの。

それでは、先に進んでテーブルの作成を始めましょう。

さて、おそらくもっと時間や時間がかかる4つのテーブルすべてを作成するつもりはなく、いくつかのテーブルを作成するつもりです。

そして、それは私たちがこれをどのように行っているかを理解するのに十分なはずです。

それでは、Customersテーブルから始めましょう。

つまり、customerテーブルには5つの列があります。これは、新しいテーブルを作成するためのアイコンです。ドラッグアンドドロップするか、ダブルクリックしてcustomerというテーブルを作成し、ここでテーブルの列の配置を開始できます。顧客IDに名前を付けると、これにシーケンスが入力されます。

したがって、シーケンスはデータベースオブジェクトです。

そして、それは整数になります。

そのままにしておくことができます。

そして、それを主キーとして持つことができます。それは問題なく、主キーを入力する必要がありますが、今は入力できません。

自動的に選択されます。

次は顧客の名前です。これを名に分割して、時計を選択し、名前の長さ、名前、名前の長さのように少し余裕を持たせることができます。100を見ると、これらすべてを見ることができます。今はできないので、nullではない制約を選択できます。

したがって、これらは利用可能なさまざまな制約です。

次の1つのアドレスをもう一度アドレスに進めましょう。

そして、あなたが覚えているなら、私は原子性について話しました。

したがって、ここでは基本的にアドレス全体が1つの列にパックされているという意味で、列をアトミックに知っておく必要があります。

実際にそれを住所として個別にアトミック列に分割し、都市を個別に記述してから、郵便番号を個別に圧縮することをお勧めします。

もちろん、これらはすべて揃っていますが、現在はどれもあり得ず、他に何がありますか？したがって、顧客の電話番号、電話番号はすべての番号になります。

しかし、それでは、もちろんnullや顧客の電子メールではなく10個の番号を取得したいと思います。

だから私はメールID100と言うことができます。nullではありません。

ここでの主キーまたは顧客IDはIDであるため、顧客情報の繰り返しを避けるための制約があることを確認したいと思います。

たとえば、ID 1の顧客データが1つある場合、同じ顧客、たとえばID2の別のIDの顧客データを繰り返す必要はありません。

そこで、実際にメールIDをここのレコードごとに一意にするつもりです。

そして、電話番号かもしれませんし、電話番号かもしれません。

したがって、これらはすべて一意のキー制約、または一意の制約です。

それだけです。

これで、Customersテーブルが作成されました。

では、戻って他に何があるか見てみましょう。

それでは、作成しましょう。製品と言います。

そして、基本的に同じことを行い、それを選択して新しいテーブルを作成します。

そして今ここで、これを描画することができます。これを製品と呼びます。

そして、同じプロセスを経て、そこに製品の列名を入れたいと思います。

ご参考までに、これはここで追加したのと同じ顧客ID列です。

そして、それをすぐに外部キーにします。

それでは、先に進んで、それを複数の列に分割しましょう。

繰り返しになりますが、すべてが1つの列にまとめられているため、これは良い習慣ではありません。

それでは、クレジットカード番号と呼びましょう。

お客様がPayPalを使用している場合は、そのメールが必要です。

したがって、ここでメールIDを使用できます。

したがって、使用されている支払いタイプに基づいて、これはnullになる場合とnullにならない場合があります。

これで問題ありません。有効期限は日付列になります。

それでは実際にそれを変更しましょう。

したがって、よくわからない場合は、ドロップダウンを押して、それぞれに適切なデータ型を選択できます。

私が言及したもう1つのことは、基本的に外部キーに関するものですが、この顧客IDは、ここで追加したものと同じです。

それでは、実際にその顧客IDを外部キーにしましょう。

したがって、これを顧客ID外部キー1と呼ぶことができます。

そして、参照されるテーブルは顧客です。

そして、列は顧客IDになります。

以上です。

これで、これら2つのテーブル間に接続または関係ができたことがわかります。実際には、Ordersテーブルも追加します。

メインテーブルであるOrdersテーブルも作成しました。次に、注文の外部キーをいくつか作成します。

すべてが完了しました。

この時点でインデックスを作成したい場合は、それを行うことができます。

完了したと思います。

したがって、基本的に4つのテーブルを論理設計に追加し、これらの4つのテーブルを作成してから、列を作成し、それらのデータ型を定義します。

また、外部キーと、もちろん各テーブルの主キーと一意キーを作成しました。

And you can see the foreign key relationship you know clearly showing here and that You know, that's what you would do to create a data model.

Alright, so now actually, let's just go ahead and create a SQL script for this data model.

So you go to database, and then do forward engineer.

And then basically, you provide the database details where you want to create this, these tables are the schema.

So this is, these are my details continue, go to the next one, provide the password.

Right now we are connected, I had to try the password two, three times.

And this has basically created SQL script for us to create the schema and the tables with all the primary key unique key and foreign key constraints.

So what we can do is we can just continue and then now the database or the schema is created as it goes through and then executes that script.

And close.

And now you can see the stables are actually created.

So you can even go to your SQL editor, and then you can start reading your can start querying your tables.

There you go.

So you ready came back, of course, there is no data in it.

And you can now start using your database.

So we actually successfully created the basic schema, or designed the data model for this e commerce website.

Table creation or a CREATE TABLE command starts with CREATE TABLE keyword followed by the name of the table and followed by parenthesis.

So within the parenthesis, this pair of parentheses, you have all these column names, followed by the column data types, and followed by the constraints.

And you can also use this auto increment keyword, if you want your column value to be incremented.

Automatically, as you load values to the as you load records to this table, and after the column definition, you have the option of specifying the keys like primary keys, unique keys, foreign keys, and so on, you can also specify the storage engine type as part of your table creation.

And this is a very simple table.

You can also have partition tables or partition tables that have compressed tables, encrypted tables, and all these things require special keywords to be used in your table definition.

And please check my sequel documentation.

If you want more details about the syntax.

As mentioned, I'm using just integer watch char data types, the MySQL documentation shows like all these different data types like numeric, date, and time data types, string data types, like the ones I'm using, and JSON spatial.

So these are all available in MySQL for you to use.

So let's go ahead and create this table.

And before creating the table, I want to run this drop command just to make sure the table doesn't exist.

And I'm going to be creating that table and see if the table has been created.

Yes, the table has been created successfully, I'm going to be running a select star from the table name to see if I can successfully query from this table as well.

And then it returns.

Basically, it doesn't return anything that means no data exists in the stable.

And that's how you create a table using CREATE TABLE syntax.

Finally, there is actually a default keyword which helps you specify default values for a certain or for your columns.

So if you don't specify a value for this quantity column in your insert statements, or when you're loading through procedures, loading data through procedures, it will automatically take this default value.

That's pretty much it.

And I'll see you guys in my next MySQL SQL session.

So I just did a describe on the table that I created and it has product ID product name, product type, price and quantity.

And you can see that product ID is also an auto incrementing column right now there is no data in it a typical insert Statement looks like this, let's go to insert into keyword and the table name a bunch of columns within parenthesis, the ones that you want to populate, followed by the values keyword.

And followed by the actual column values.

If you can realize I haven't actually specified the product ID value because it's an auto incrementing column.

So let's go ahead and execute this and insert statement goes through.

So let me also run the select statement.

As you can see, the product ID table has taken the value one, and that's happened automatically ident supply the value one, so I'll go ahead and commit the change.

And then let's actually move on to the second variation.

So this time, I'm going to specify a value for product ID, nothing else is different.

So just want to show you that it is possible.

So it goes through and then a commit.

And then let's do a select to make sure the value has been inserted.

So let's go to this third variation of this insert statement.

So it's going to be pretty much the same, except Actually, I'm going to just jump some values and then insert the value 10.

For this product ID column, I'll go ahead and do that.

It goes through a comment and select again, then you can see that that is also fine.

So yeah, so that worked.

So you can actually jump a few values.

let's actually look at the next variation.

Again, I'm going to insert a record into the stable, with no product ID specified exclusively.

Or explicitly, the product ID column is missing over here, and I'm going to run the insert statement and commit.

And then I'm going to run the select statement.

So just wanted to show you that wherever the latest value is for this auto incrementing column, I inserted the value 10 for product ID last time, and then the next time I do an insert is auto increment kicks in and then you know increases, increases this value from 10 to 11, right picks up from the value that was inserted last time.

And I'm gonna just take another insert statement, and this time, it's actually insert into the table name.

And instead of the values keyword, specifying the column names, values, etc, we actually select from a different table.

Basically, if the products three table is exactly matching the structure of products, one table, then we can do even a select star from if the columns don't match exactly as in like products, one has a different set of columns, and product three has a different set of columns, then we need to make sure that we actually select the columns.

And then, for example, this product ID from products, three maps to this product ID and products one, and product name from products, three maps to product one in I'm sorry, Product Name and product one, and so on.

Let's go ahead and run this and see what happens.

And then that goes through.

And then if I now select the products, one table, you can see like take all these rows are inserted properly.

And basically the products one table is populated.

And then we got all the data from products three table.

So this time I want to show you the insert statement.

Again, this is kind of like a bulk insert or multiple insert just combined into one statement, you can see the insert into clause is specified only once but in the values spark in this clause, actually, we have two rows specified at the same time.

So we can even use such as syntax and a commit, and then do a select all good.

So these are a few variations of insert statement.

I hope you understood how this works.

I will see you guys in the next session.

Hey, my sequel learners.

So in this session, actually, I'm going to be talking about update and delete statements.

So as usual, I'm going to be using my eecom store schema.

And I will be using my products table to do this demo.

So just quickly, if we select products table, there's two rows right now.

So the first update is just to show you the syntax of update.

So you have the update keyword followed by the table name followed by set keyword.

And then you can have as many columns as your table contains, but in this case, I've got only one column and I can just run this update.

So let's actually add one more clause to it, which is the where clause and this is to just update the rows that Do you really want to update so we will be updating only the rows with product ID equals one.

So let's go ahead and do that.

And then I'll just come in and do a SELECT FROM products quantity has gone up by 50, it went from 299 to 349.

Now one more thing to realize is actually like you can, you know, you can specify literal values, when you are updating, you know, that happens all the time.

Or you can also like specify a formula, or you can have SQL functions like replace substring, length, and so on, you know, you can look at my SQL documentation to see what kind of functions are available in this update, like, I just wanted to show you the syntax, basically, to let you know that like you can have multiple, you can update multiple rows at once.

In this case, I put like, values one, two, and three, you know, you don't have to do one row at a time or anything.

So when you use the end keyword, and you can say product ID or whatever, call them in and then a bunch of values to select the rows that you want.

And there are other ways to do it.

But the point being, you can update multiple records at once.

And another interesting usage is using the case statement, you know, you can let's say you have a bunch of update statements, one for product ID equals one another for product ID equals two and another for the other product ID values.

And you can combine all that into one UPDATE statement using a case when then and clause or keyword basically, in this case, actually, for product ID equals one, I want to increment the quantity by 50.

And product ID equals two, I want to increment the quantity by 100, and so on.

So then I've got this similar WHERE clause are similar to the one that I showed you before, I'm going to run this you can see the columns are getting incremental, I'm not going to go back and check I'm pretty sure that it's done the right thing.

So the next one is basically when you want to delete records from a table or purge data from a table, then you can just use a simple delete statement.

And if you are wanting to delete a particular row, again, similar to the update, you can use a where clause to actually like narrow down the data that you want to delete this particular statement, which is delete from a table name, and then where column name equals or the column value.

And you can have multiple filters in here.

So here I don't have the row number three, I think I deleted it already.

Alright, so let's keep going products three table, I just wanted to show you it contains a lot more data than my other table.

You can see it contains data about 5849 rows, I wanted to also show you this particular parameter MySQL configuration parameter to basically enable and disable safe updates.

So let's say like if your delete statement or UPDATE statement is not using a primary key column in the where clause, you know, then basically if you enable this particular parameter, let's say by setting this one, and then if you run your delete, you will get like an error code 1175 it mean, and then it says you're using safe update mode, etc, etc.

It's not leading you to the run this kind of add delete statements, because it could be a good cause bad performance.

So if I disable the same thing, and then if I run the Delete statement, and then just run a select again, then it should go through because now the parameter is disabled.

And two more things.

One is actually like if you have a huge table and you want to delete only a few rows at a time, then you can use the limit keyword to limit the number of records that are deleted by the statement, you know, in this case, because I wanted to delete only 10 rows, let's go ahead and do that.

And it should work just fine.

And then if I do a select, you'll see the difference in the row count actually, now it's like 5839, before it was fired four nights.

So that's how the LIMIT clause helps you also in the limit floors, you can also specify the ORDER BY clause.

It basically sorts the data by these columns, first by quantity, then by product ID, then it deletes the top and or 100 or whatever value you put here, actually.

So let's go ahead and do it and then select again.

Yeah, Kearney, seven D is gone.

So the top 10 rows are gone.

And yeah, that's pretty much it.

Actually, those are all a few variations of update and delete statements.

And of course, there's lots of tangents we can get into but I will leave that task to you.

And I hope it was useful.

And if you have any questions, let me know in the comments.

I'll see you guys in the next session.

MySQL learners.

So in this session, we'll look at SELECT statements, not just the syntax But also like some ways you can actually like improve the performance of your queries, I'm going to be using the schema called income store to explain about this SELECT statement in its most simple form will look like this.

So you have the Select and from keywords and then after the Select, you specify the Select list, which is the columns that you want to select.

If you specify a star, or asterik, that actually selects all the column columns from this table, and then after the from keyword, you specify the table names where you want to select the data from.

So if I do a select star from products underscore three, it's going to return all the data from products underscore three table.

But do remember that anytime you are using a star after the Select, or in the Select list, you're basically querying all the columns in this table, you don't need to query all the columns in the table in most of the cases, so you only like specify the columns that you need to query.

So in this next query, let's go line by line and see what changes that have done to this query to make it better.

So let's say I want to select only these columns.

That's why I specified only these columns in the Select list.

In the from clause, I have specified products underscore three table very often you will be selecting from multiple tables, you need to join the tables and then retrieve useful data out of it.

And in the where clause, you specify all the filters, or the conditions based on which your data will be filtered out.

So here I am, including only the data which have quantity less than 25.

So this way, I'm able to actually filter most of the data out of this table, this is very useful in minimizing the amount of data that you retrieve from the database.

And your queries are going to be fast as ordered by is basically going to sort the data that is retrieved based on the columns that we specify here.

So here, I'm just like ordering by product name.

And of course, like when you're sorting data, especially when you're sorting a lot of data, the operation can be expensive, unless your source buffer size, that is actually the memory area where the slots happen.

Unless it is sized properly, the operation can be really slow.

So you need to pay attention to that configuration as well.

And I have this other query, which just goes to show you that like this is a very simple SELECT statement.

Again, in this select, actually, I have only the Select keyword and a function, I'm using the now function.

But there are several other SQL functions that you can use in the script, for example, I can use the database function to return the database that I am actually connected to.

And as you can see, I am actually able to invoke multiple functions in the same query.

So that's pretty much it.

I'll see you guys in the next session.

In this session, I will be teaching about SQL joints, let's dive straight into the demo, I will be using a schema called eecom store.

And I'm creating a table first called T one with one column, the column name is C one, and I'm inserting these two values in this table.

One and two, I'm creating another table called T two with a column called C one and inserting these two values again, into table D two, one and three.

So it's one and two here and one and three, here, we're going to go ahead and run a commit to make my changes permanent.

So I'm going to be just creating these two tables just to show you the records, D one has one and two, D two as one and three.

Of course, a join is an operation that joins two tables.

And we have all these different types of joints, we'll go one by one and understand what they are.

So this is the syntax, so select an a column list that you're selecting.

So we're joining T one and T two.

And then we're specifying the kind of join that we are making.

And then we also have this on keyword.

And then comes the condition on which the table is joined.

So I'm going to go ahead and run this query and see what happens.

As you can see, this query, this inner join has returned the value one, so that means actually, so it returns the values that exist in both the tables that match.

So that's what INNER JOIN does.

So let's just change it to a left to join, go ahead and run it.

Now the left join is gonna return all the values from your left to table which is T one.

So T one has values one and two are the rows one and two, and then T two, it's going to return only the matching values.

And then for this value, which only exists in table T one, it's going to return and now and then I'm going to change it to right join and as you might have guessed it's going to return all the way Use from table t to in the places where there is no matching value, it's going to return a null.

So let's see if that happens.

That's what we expected.

So we got all the rows from T two, and then for three, there is no matching value and T one, you know, that position has no value.

Now we'll jump quickly to a union.

And then we'll come back to a full join, a union is basically going to look like this.

So two queries, and then in between, we have the union keyword, let's see what it returns, you can see that it's written one, two, and three.

So that's actually the rows from both the tables, but it's kind of like combined the data and then smashes them together.

And then you have one, two, and three, and then let's run the same query with a slight difference.

We'll put union all and then we'll see what happens.

That's written one, two, and one, three.

So that's returned all the data from both the tables, but except this time, we have duplicate values, Union gets rid of all the duplicate values, it's almost like a set where you have a unique set of data, a union all returns all the values, including duplicate data.

Jumping back to full join, we don't have a full join keyword.

So rather, we do full join this way in my sequel.

So basically, you have the similar query where you're joining T one and T two, a left join first on this, there's one column that we have, and then you have another query, again, joining T one and T two on this, just one column, but then we are doing a union of these two, and that's going to return the data from both the tables, we have one, two, these two are matching, then for two, there is no matching value.

So it returns and now for three, there is no matching value in T one, it returns a null over here.

So this is a full joint, that's basically all the joints, all different types of joints that you can do in MySQL, I hope this example was clear.

And I'll see you guys in my next session.

All right, my SQL learners.

In this section, we're going to learn about locks.

More specifically, I want to talk about the isolation level section.

So the first thing is just see what I have here, I have two terminal sessions.

One is in black.

The other one is in slight maroon color.

So I'm actually going to log into the database as the root user.

And I'm going to do the same thing over here.

There you go.

I am logged into my SQL database.

So I have a little script here to create a dummy table called T one.

Okay, so let me show you the SQL script.

At this point, actually, you might not understand the SQL syntax and so on.

But then let me explain.

Briefly, first thing I'm doing is setting auto commit to zero or commit is basically a command that you use to save your work.

Basically, the data changes that you're doing is permanently stored in the database.

When you issue a commit command, in my sequel, you have this variable called auto commit, which is turned on by default, meaning all your commands will be automatically committed.

If you don't turn this off, I want to have more control over what I'm doing here.

So basically, I am doing an auto commit, disable first, so and then I'm starting a transaction.

And just to be safe, I'm dropping this table if I had already created it.

So this table doesn't exist.

So it says unknown table.

And the next thing is I'm creating a table called T one in eecom store schema.

And then the column name is C one.

And the data type is int and its primary key.

So and then I'm actually inserting value, just one row into this table called p one, right, the one that we just created.

And I issue a commit command, alter or the alternative to commit is rollback command.

So which basically rolls back rewards the changes that you just done in that session.

So if I just do a select star from the stable, then I'm going to see this value, so which is fine so far.

So this is pretty straightforward.

So far, we haven't talked about the isolation levels.

So what I mean by isolation level is when multiple sessions are trying to modify or access the same data data, then you need locking mechanism to make sure the data is not corrupted, or the database is behaving in a way that you expect to see how you actually set isolation levels.

And this is the command.

So this is the other session I had opened show session variables like isolation.

So that shows like the transaction isolation level is set to read committed.

Right.

So this is one of the possible options action.

So this is read committed, and you have read uncommitted, and you have repetative read, or repeatable read.

And then you have a serializable value, actually, so let's go one by one, right.

In this session, I already started a transaction.

So I'm going to actually try to update this value using an update command.

So basically, I am updating the same table, and I'm updating this column to to where the column value is currently one, right.

So I'm going to do that the auto commit is turned off.

So it's not committed yet for the start a transaction over here.

And let me run a query against the same table and just copy and paste the table name, want to type it, okay, so we see the value one, which is the previous value.

And if I ran the same query over here, in this session, I see the value two, because this is the session where we are modifying the data, right? So and I can see the changes before committing in the same session here.

Actually, since the value of this transaction isolation, or the isolation level is set to read committed, it is possible only to read the committed data.

In other words, when multiple sessions are accessing the same data, in this case, this column right here from this table, apart from the session that is actually modifying the data, the other sessions can only see committed data, any data that is committed just before this select is executed.

So I'm going to go here and run a commit, and come back over here and run a select.

So now you see the latest data because that commit happened before I ran this query.

Now let's talk about read uncommitted isolation, setting actually freshly log in again, because these things can get tricky.

So every time I want to just recreate the tables to remove any confusion.

So let's actually log in again, okay, in here, and I'm actually going to execute the same script that I showed you before.

So just disabling auto command, starting a transaction, dropping the stable and recreating it, inserting this value, and then running the command.

So now here, what we could do is go ahead and update this value to two.

But remember, I haven't committed this data yet.

Let's go to this session.

And here, go ahead and change the setting to the isolation setting to read uncommitted, because by default, it is always set to read committed action.

Right.

So you can see that here.

So and this is a session level setting.

And you can also change it at global level.

But for the purpose of this demo, we just need to change it at the session level.

So session level isolation initially read committed, then I ran the set session transaction, isolation level, read uncommitted, and then checking the value again.

Now it's changed to read uncommitted, if I ran a select star from this table, then I get the value two, and if you remember that I I only updated the value from one to two.

And you can already see this beta even though it is not committed over here.

So that is how read uncommitted works.

So there is not much locking going on here.

Because database is now letting the sessions do dirty reads because one session is able to read and other sessions changes even before the commits happen actually, right.

So those are dirty reads.

Yes, actually.

So let's go on to the next one.

So we have seen read, committed and read Committed so far.

So now let's move on to repeatable reads action.

右？ So exit.

So here I am going to just commit.

And I'm gonna re rerun my initial script just to clear the table.

So drop table and then recreated insert, value one again, and then commit.

So now, the table is back to how it looked before.

So here, let me log in again.

So this is repeatable read setting, right? So remember that the default value for this isolation setting is always read committed.

So if I change it to repeatable read, write, and then check the value again, then you can see this.

So and again, remember, or show variables is the command to check the current value.

And then set is the command to set the configuration right, so I will put all these commands in like a git GitHub repo file, then you can actually grab the commands from there, and then you can try them yourself.

Basically, I'm changing the I'm changing the setting from read committed to repeatable read, right, so I'm going to just start a new transaction over here in here, I'm going to update this value to two.

And over here, I'm going to run the Select query that we saw before just selecting everything from this table.

And you see that the value is currently one.

And that makes sense.

So let me go ahead and run commit.

And if I ran the same query, again, I see the value one.

And this is the same as the value that was read before, even though the data was changed by this other session.

And then committed within this transaction, the data that we are seeing is the same in in other terms, basically, we are, we are reading the same data, or the reads are being repeated.

Right.

So that is the third setting.

And the last one is the most strict locking configuration.

So which is called serializable.

So I'm going to, as usual, I'm going to drop the table and then just recreate them recreated, inserted value one again, they might come in.

So here, we're going to log in again.

And as usual, the default setting is read committed, right? So let's check that first, just to show you, and then I'm going to change it to serial serializable.

So what this means is, basically, I'm going to start a transaction.

So on the first session, I'm going to run an update, basically changing the value from one to two.

And here, I'm going to start a transaction, and I'm going to run a query on that table.

Right.

And now this query, even though it's just a select, select is just a read, it's not updating, it's not deleting or doing anything, it's just a read, it is waiting, because the update is basically updating this data.

And then it's not database, MySQL databases, not even letting this read or the Select query from the other session to see the data.

So this is the most strict setting action.

So if I do a commit over here, then on this other session, you will see that the Gradius return and it's seeing the latest value, right.

So if I go ahead and run another select, of course, it's returning the same thing.

But if I try to update this value from two to three, another update, that is basically going to wait on the Select, basically this transaction that is running right now because the Select again, select is just a read, it is just reading the data.

But still it is locking that row in the database, and it's not letting any updates or modifications to that data.

And then you can see that the update even failed because it waited for some time and then the timeout value exceeded so we don't have to go into those details.

But I'm going to try updating now.

And here.

I'm just going to exit out of this session, which will release all the locks.

And that will help the update to go through.

And then I can commit and exit as well, and how you hope it was clear to you guys.

And if you have any questions, please put it in the comments and reach out to me somehow I know you can figure it out, as you guys are next section.

Hey, MySQL learners.

So welcome back to this new section of my MySQL tutorial.

So in this video, or in this section, we're going to talk about locks.

So what are these locks?右？ So let's actually approach this kind of like logically.

So if you have a database, and if you're the only person working in this database, then you basically need not worry about anything, right? You know what you're doing.

So you will insert data, delete, or update data the way you want.

And there is no one else trying to intervene or interrupt your work.

But unfortunately, that's not the case.

In today's world.

If you think of a busy ecommerce database like Amazon, then then there's like, a lot going on on the on those websites.

There's like multiple people browsing is like, a lot of people buying stuff.

There's the people who are selling stuff on on these websites, they're updating data relevant to their products.

So that is basically concurrency, right? So you have many users trying to do something on this website at the same time, so how do you manage this concurrency, that's why we need locks.

So if I let everyone work on the same data at the same time, then there's going to be a lot of confusion.

And we might end up losing some data.

So let me actually show you a simple example of how that happens.

So I have a table, a product table.

So if you've been following my tutorial, thus far, we talked about this table called products.

So where we store all the product information, right.

So now, there's a couple of records over here.

And let's say that we have a seller and a buyer who are working on these records, especially like this particular record, the first one, which is a book, and the books, prizes, this and the quantity, the thing we didn't have quantity when we talked about it in my previous sections.

But then I added quantity here.

So there's this quantity column.

And there's a there's a seller and buying buyer interested in this record, let's look at this, right, so we have sort of like a time sequence here.

So what the seller of this particular product is trying to do is he's trying to update the quantity of this product at nine one, he is adding 60 more quantity to that product, which is you know, 40 plus 60, which 100.

And that's what we have over here.

So then a buyer comes and he looks at the quantity.

And then he basically wants to order two or these books, that's 100 minus 90 100 minus two, it's 98 and then you have the quantity 98 over here.

So this happened in a sequence.

So but we are worried about concurrency, right? concurrency is like when things happen at the same time.

But what if Okay, first the seller comes and then he reads the quantity of this item.

Initially it was 40 and then buyer comes and he also sees that the quantity is 40.

右？ And at 901.

So the first two operations happen at the same time at nine or one seller comes in he says I want to update I want to add 60 more quantity, like meaning I have 60 more books of this title, but then buyer comes and he says okay, I'm buying two items or two of these books.

So but while you that he saw before was 40.

So 40 minus two is 38.

So he updates the quantity 38.

So the seller updates at 200.

But then, because of this previous look up, the quantity is updated to 38.

due to which this whole thing, this whole operation is lost.

And we end up with sort of like corrupted data for this quantity column.

So this is a simple example of how concurrency when not managed well might cause issue data issues like this.

MySQL learners.

So in this video, we are going to look at basically how table locks works.

In the context of e commerce database, we created a simple database or schema called the column store.

And we created a bunch of tables or used another dummy table to explain our transaction isolation levels.

So if you haven't seen my previous material, go back and check it out.

And come back here.

But then yeah, you have four tables for main tables.

And the main table that we are interested in is products table here.

And in the products table, I inserted a couple of records.

These are dummy records.

So I don't have a front end or application running over here.

So we're just looking at database, right.

So what what's going to happen in this tutorial is, so we, we're going to basically simulate a situation where a seller is trying to update the quantity of the book that he is selling on this website, which is this first book actually, the common path to uncommon success.

And then the right now the quantity of this the quantity available.

You know, for this book is 40, right? So he wants to update this quantity 200.

And also, we'll have a couple of more users, or buyers, basically one buyer is trying to buy the same book, we'll have another buyer Hill, who tried to buy a different book, which is this book, tiny habits, and then the same buyer will also try to browse the website, like of course, like, we are going to have to imagine a little bit because I don't have a front end to show you everything.

So let's actually see how this goes.

So first of all, you know basics first, actually, let's actually turn off the auto commit.

Just so just so actually, we have more control over what's happening.

And let me do that in all the three sessions I have open and the first session is the seller session.

The second session is the buyer one session.

And the third session is the buyer to session, basically.

So I'm going to turn off the auto commit, which is basically a mechanism that commits automatically if it's enabled.

And I don't want that.

So I'm disabling it.

So next is I want to show you the transaction isolation level.

And we talked about it in my previous session.

So right now it's a repeatable read.

And it's the same for all.

So we are going to change that to read committed, because read committed is isolation is the right isolation level for OLTP databases.

So now let's actually start with the first seller session.

So three sessions.

So the first seller session is going to update the quantity of this book that he's interested in or his selling action.

But we are going to take this aggressive approach and log the whole table.

Right.

So let's say the application is returned in a way that it logs the whole product stable for right.

And then the other session, let's say by one second session, buyer one comes and he is going to try to buy two books and and how actually we're going To do that is by running an update.

So we are basically updating the products table and we are subtracting the quantity by two, which means actually the we are buying two books.

And which book is there in the book? Where are the record where product ID equals one, right? So if you remember the data, product ID one is this book, let's go ahead and run this update in the second session.

And it's going to obviously, wait, because the table itself has been locked for right by the seller session, the buyer, one session is waiting.

And let's go to the buyer to session the buyer to Australia trying to buy a different book, which book is it this other book, which is tiny habits book where product ID equals two.

And we're gonna do that.

Of course, even that is hanging or waiting.

And that is actually a little bit crazy, isn't it.

So just sellers trying to update the quantity of this one record with just one book.

And everything is tanking.

And the buyer, too, was trying to buy a different book, he kind of gives up.

So he moves to a different session.

And instead of buying or trying to buy a book, he just tries to browse the website, which is a select query or read query, read a select query, which is also hanging.

So the buyer too is getting frustrated right now.

So you can see how restricted this kind of sequences.

So if someone's using table logs, that's going to basically reduce the concurrency of the operations that can happen in this database.

So that's the main point here in this demo.

Hey, my sequel learners.

So in this session, we are going to take a brief look at row level locks.

In my sequel, I have three sessions, I'm already connected to my ecommerce database, MySQL database, and this is how the data looks now.

So we have a products table which holds you know, this data, only two books now, just dummy data that I created this, this is the price and you have the quantity column showing you how many, how much quantity is left for each of these books.

So the first session is seller session.

The second session is buyer session, we can call this buyer one session.

And the third session is a buyer to session.

So this is the data.

And just for clarity, actually, I wanted to show you the transaction isolation setting, which is read committed.

And the auto commit is turned to turned off basically, it's disabled.

So unless I commit explicitly, my transactions will not be permanent.

So let's actually start with a seller.

He's going on the website or a portal that he has available to update the inventory of, let's say the book one, it or the product one, which is this book.

And so he is going to click some buttons, which is going to translate to an update statement being executed in this database, right? So let's say he wants to increase the number of books available in the inventory.

So that will mean quantity is going to be increased incremented by 50.

So that's the UPDATE statement.

And he's going to run that update.

And we can look at the buyer one session, let's say buyer one is trying to buy the same book.

And and then, so he's going to go on the website and then click on buy now or whatever and then is going to translate into this UPDATE statement in the database, choose quantity equals quantity minus one.

So reducing the quantity by one, meaning he's buying a, buying a book.

And of course, there's going to be, you know, other statements updating other tables.

But then to keep it simple, I'm just showing you the product table changes section.

So as you can see, this is going to wait because seller is updating this particular row action.

And that can be seen using acquittee.

On data locks, so if you're under this greddy, of course, you can modify this query as per your needs.

But then if you query this, you will see that there's bunch of sessions and is, is the lock mode column.

And then the table on which the database on which the locks are happening the table, so it gives you a lot of details.

So, so if you want to understand what's going on here.

So we have products table, and then we have ix lock, which is intention, exclusive lock on the table itself, meaning like a transaction is about to get an exclusive lock.

And this is at the table level, but don't get tricked by that.

There is also another row indicating there is a record level or a row level lock.

And, and that is logging only this data equals one.

So if you remember that UPDATE statement, we are using product ID.

So and data for which is one, actually, so product ID equals one.

So that's what we are seeing over here.

And if you see here, this buyer session has actually timed out already, so he's going to attempt to buy again.

So that's how like, you can actually look at the locking details in this table.

Let's try, let's say like buyer two comes in at this point.

And then he just tries to browse the inventory on this ecommerce website.

So that would mean a select query or read query.

And he's, he's able to do this happy reaction.

Right.

So there is no problem.

So while the rollouts are happening, other sessions can read this table, they can even look at the data for the same product.

But they they just cannot buy this book, because that is being blocked by the seller.

So again, it timed out.

So at this point, buyer two wants to buy a different book, you know, I'm not able to buy this book, let me try buying a different book, that's going to translate to, you know, product ID ID equals two, which is not being locked by the seller.

And then that update goes through.

And at this point, let's say the seller has completed updating the inventory.

And, of course, if you look at the data, now, it's going to look different, because this has been updated to 150.

And of course, this hasn't gone down because buyer, buyer, one is still in the process of buying the book, because the commit has not happened yet in the application.

And then if we look at the data, again, the data has gone down, or the quantity has gone down, then via two, let's say wants to buy the first book that buyer one wanted to buy.

At this point, there are no no locks in this table.

Because everyone's committed, and let's say buyer, who is trying to buy this, this book, and then he goes through with that date, and then commits and look at data.

And then the data is changing actually.

So this is how row level log basically allows for high concurrency.

So only the rows which are logged by your transactions are not available for these other sessions to modify.

Right So the other records which are not touched by your transactions are available for updating, deleting, etc.

and all, of course, you can add new books, that means inserting new records in this table.

So I just wanted to show you the difference between table level logs and row level locks.

So this session and my previous session will, will be useful in understanding that difference.

Thank you, I'll see you in my next session.

In this session, we're going to be talking about deadlocks.

And I just want to show you how deadlocks happen, they do happen in in a busy ecommerce or B.

database often, so it's good to know what they are.

So it's going to be a very short and sweet session.

So here, we have a couple of sessions again, so connecting a connected to the same database has two sessions, two different sessions.

So let's say that we are working with products table, right.

So we have seen the stable before in my previous sessions.

Basically, this table has information about the products that are being sold on, you know, an e commerce website.

So we have a couple of records over here, you know, we're going to first let's say, you know, I seller comes to actually update the quantity of this product, basically, let's say if he wants to increase the quantity by 25.

For this first book, this is the command that he's, you know, that's going to be executed, you know, whatever buttons he is clicking, will be translated to an update command like this.

Right.

And let's say like a different person from the same company wants to update the price of this book, not this book, let's say we have it the other book, I'm just actually using the product ID to update the right product, right.

So we have one session where seller, one is updating the quantity of this item, we have another session where we are updating the price of this item.

And then if you see the prices incremented by two, let's say $2.

And this is fine, right? So now we have row level locks.

So this guy is holding a row level lock on this row.

And this guy is holding a row level lock on this row.

So this is fine, right? So we are operating on two different records, two different locks are independent of each other.

All good.

So now let's say the same seller, the second person who is updating with price, wants to update the price of this other book to actually like he is actually increasing the price.

Again, by $2 of this book, the product ID equals one, which book, this one right here, let's go ahead and try to increment the price.

By running this command, you know, he's waiting on waiting for the lock ECI exclusive lock.

And that's not available, because this seller has not committed actually is not committed.

So let's actually go back here and, and this seller at the same time price to update the price of or quantity of this book.

So two sessions are fighting for pretty much the same resource, you know, we ended up in a deadlock situation.

So my sequel was smart, smart enough to just kill the session.

Otherwise, we would have two sessions waiting for each other endlessly.

右？ So here you can see the error code that is thrown, it says deadlock found when trying to get locks and try restarting that transaction.

So let's go ahead and query the products table and see how it looks.

You can see this, this whole transaction was rolled back.

Correct.

Both the transactions were rolled back.

There's even this one was rolled back.

So I think that Locke was also killed.

So that's why this this one went through.

If you can see the prices have increased by $2.

right because initially For 1699 and 2039, and here 8099 and 20 to 39.

Okay, so that's how it works.

This is a typical deadlock situation, I hope this explanation was clear.

And I will see you guys in my next session.

All right, my sequel learners.

So in this session, we're going to talk about clustered indexes.

So, so clustered index is not a different index type as in, like, you can, you know, directly create a create a clustered index yourself.

So it is a type of index that, that MySQL kind of maintains in the, you know, behind the scenes actually.

So, in also your table data, the data that you insert into your tables or load into your tables are maintained in these indexes.

indexes only what I mean by that is, so let's say this is a B tree index, right, so this is a B tree index.

So you have my sequel, creating this B tree index, as you load the data into these tables.

And then, you know, in the leaf nodes, what you have is actually the data, the data that you're loading into these tables, right? In the clustering, the sorting is based on the primary key that you define, or, you know, in this table, actually, so if you don't define a primary key, MySQL will automatically pick up a non nullable index key, what that means is, so let's say that, in fact, actually, let's jump straight into the example that I have prepared for you guys.

So so this is my MySQL Workbench.

And, you know, I'll show you this table definition.

So this is called products underscore one.

And it's basically a products table that is typically used in a ecommerce store.

And if you've been following my lessons, this is what I've been using, I just changed the name of the table for, you know, demonstrating this concept, this clustering, clustered index concept.

So you have all these like columns, and I'm defining a primary key.

Okay, so let's just start by, you know, I'm just going to switch to a database called eecom, store our schema called the econ store, I'm going to drop, you know, these tables if they exist already, by any chance.

So the table doesn't exist, which is okay, so I'm going to create this table, which I just talked about, called products.

And then this table has primary key in a primary key is product ID.

So product ID is sort of like an integer column.

So this is an auto increment, right? So you don't even have to provide value for this column, actually, when you load the data, so you can just put all this information and load it and then we are good, MySQL will automatically increment the value of this column action.

So and then, of course, like I said, like there is isbm column, which is over here, sort of leg book iasb.

And information if you are, you know, if you remember your school days like this, this is be a number attached with any book, so something like that.

So some kind of ISDN alphanumeric number.

So I'm going to call that like a unique key or a unique constraint.

And let's go ahead and create the stable and this constraint.

So that was successfully created.

And I'm going to create a procedure, which I can use to kind of like populate the stable, right, so don't worry about the details of this procedure.

This is something that I wrote to populate this table.

And then that is successfully created and change the delimiter back to a semi colon.

And then I'm going to call this procedure and which is going to throw some warnings, which is okay with me.

As long as as long as the data gets populated, I'm fine.

So it's going to probably generate some, you know, load some 6000 plus rows into this table.

So we'll see how much we get this awesome.

So it's actually loading a lot of data.

It seems to be done.

So let's go ahead and commit the data and Now Actually, I'm going to select the data in this table, right? Just select all the data, and you will see that the data by default, or the data is actually sorted based on the primary key, which is product ID.

And you can see, we know, I haven't like specified any ordering.

So this is, you know, this is the default ordering of data, right.

And so basically, your table data is sorted based on your clustered index, which is primary key over here, because you have the primary key in the table section.

Right.

So now the next thing is actually, I'm going to create a similar table, which is, you know, so I'm going to call it products too.

But in this case, I'm going to basically not define a primary key, I'm still going to have a unique key called, again, the same thing, you know, it's isbm, it's a unique key.

And let's just give it a different name, just so we have kind of like, we have different names for different constraints.

So let's actually go ahead and create this table.

And so this table is created, I'm going to copy the data from the first table that you know, where I loaded a lot of data.

So I'm going to copy the data from that table into this table, right.

So just very simple.

And then I'm going to commit, right, so that's a board 6455 6455 number of rows inserted into this table.

And I'm going to select all the rows from this table.

And you can see that now, the data is not sorted by product Id rather it is sorted by this iasb.

And it is sorting based on first character first, and then Initially, the first and second characters are the same, then 010 true.

And that keeps going 05 and then 090, a BCD of GE hedge and then having after the zeros, you know, see one, so it is basically sorting data based on iasb.

And and why is being because because of the absence of primary key, it's going to choose this iasb and column, as are the it's going to choose this non nullable unique index key, which is based on iasb and column, right.

So it's starting based on this, but this is actually a terrible, terrible idea.

Because if you're generating random, alphanumeric strings for iasb.

And, you know, then you're not going to be generating the string in sort of like an ascending order or in any type of order, actually.

So in that case, actually, you know, when you're, as you're inserting data into the stable, this B tree is going to be created behind the scenes.

And then my sequel, like whatever program is creating or maintaining this data structure behind the scenes has to work really, really hard to manage this Bre B tree index, actually, right.

That's why this is a terrible idea to have like a you UID or some kind of alphanumeric string as a primary key actually, or in the absence of primary key.

Well, my sequel is going to use this this key for clustering.

And again, it is very bad.

So keep that in mind when you're creating tables actually.

Right.

So finally, what I'm going to do is create another table called product three.

And before that, I'm going to show you the output of this query, which is basically going to come up empty or no, no, no road rows returned.

All I'm doing is actually checking whether this index the index with named Jen flushed index is there in this database, actually.

And then I'm checking the InnoDB tables and information schema I'm joining in odb tables and in odb indexes.

And I'm checking whether this index indeed exists, right? Saying it doesn't exist, which is where the this, this credit, return no rows, and I'm going to create this table and this time, I'm not even going to create the create a unique key.

And I'm going to make all these columns as nullable columns, you know.

So I just want to show you what happens when you have a scenario where you're creating a table with all nullable columns and no primary key index no unique, not nullable index and you know Then I'm going to insert data into this table.

Again, six, the 400 plus rows inserted, commit.

And then I'm going to select from this product three table right now.

And when the data comes up, you can see that there is still some ordering that's happening.

And, you know, we don't have any of these options primary key or a not nullable, unique key available, then how is MySQL able to sort data? What is it using, so it actually uses a hidden, hidden key actually, right, a hidden primary key.

So if you run the same query, again, is ready, you can see that this index has been created on products three table, which is maintained internally by my SQL, for just the purpose of clustering this table, actually.

Okay, so that's a lot of information.

I hope you found this useful.

And I will see you guys in my next video.

Hello, my SQL learners.

So in this session, I want to teach you the basics of using explain or explained plan in MySQL.

Alright, so now let's just let me just show you the table that I'm going to be working with, I'm going to be working with that table called products underscore one.

And it's got some net in a product name, product type price.

And if the product is a book, it will have an ISP a number attached with it.

And then there is a quantity column.

So these are some basic columns that you would see in an e commerce online store.

So let's get started by just looking at the indexes of this table.

So this basically has two indexes.

One is a primary key index, which is on the product ID.

And the other one is an index on the iasb and column.

And this is a unique index, actually.

So let's get started by picking a simple query that we are going to kind of like optimize using explain.

So the query that I'm going to be using is this.

So I'm going to be selecting iasb.

And from this products underscore one table where product name has cat in it.

So the product name is cat.

Okay, so And before I run this query, I'm going to look at the explained plan of it.

And I'm going to put a slash g at the end.

So I get that we'll put in there in a readable format.

So first of all, it gives this output, right, and selectors, just one straights, simple select.

That's what this is showing.

But the main thing is we are working with our this particular row is referring to this table.

And apart from that, actually, you have all these columns, and then they are all null right now, like they don't make much sense apart from this.

So this is a tight column and all means that it is doing a full table scan.

Basically, MySQL is doing a full table scan, it's scanning the whole table.

And how many rows is that it's these many rows.

And we are using a filter over here, it gets all those rows and then it filters the output.

And basically a you know, there's about 600 rows with product name equals cat, right, so the filtered person ages like 10%, basically, and then there is some extra information.

Let's go ahead and create an index on this table.

Create index called, you know, we can give an arbitrary name.

And, and I'm going to create add on products, one table and the column is product name, of course.

This is the column on which I'm creating the index.

Actually, let's just go ahead and run the explain again.

So this is the explained plan.

And that's how it looks.

So basically, you can see that the Again, it's pretty much the same kind of output, but this time, it is also showing some data for all these columns.

So first of all possible keys column shows like all the indexes that this query can use.

And, and out of which, like this is the key or index that it is, you know, it is going to use this particular execution is going to use, and this is the key length in bytes actually write the number of rows that is being scanned in this key, which is 589.

And, you know, since this is index based, we're not really filtering data, rather, we're just going to the index and getting the data.

So there is no filtering over there.

Let's actually create another index, which also includes iasb.

And, and see, like, what happens, actually, we're gonna create the other index and give it a different name.

So let's go ahead and run the explain plan again.

So now, again, the possible keys are these two indexes, but it still chooses to go with this particular index, and the index, key length is the same, and then grows, and etc, etc.

So there's no filtering that happened, right? Because we're choosing an index.

So you might be wondering, like, you know, why it's not using the covering index, right.

So this is supposed to be the covering index and covering indexes are supposed to be better than normal, non clustered index or a secondary index.

So you can actually like, use a format like JSON format to get more information.

So how you can do that is by just specifying like format equals JSON, and use the use that.

And so that's going to give you the output in JSON format.

And you can see that the you know, it gives you a little bit more information as then like the query cost, you know, this is how much it's going to cost for my sequel to execute this query.

And this is a representation of the amount of work MySQL has to do to run this query actually.

So the cost for this one is 7690, right.

And then again, it says these are the possible keys.

And used key is used key parts is product name, which was not given over here.

And then there is a cost and for which is a split of where the cost is going.

So you can read my SQL documentation on all these fields.

You know, you might be wondering why the covering index is not being used.

And we can actually force that index by using this use index.

Syntax or use index keyword.

And then I'm going to put the index name that I want to force which is this one.

And when I ran it, this ran the explained plan this time, it shows the cost of this one is going to be 109 point two seven, you know in comparison to the previous explain plan, where the cost is only 76.

And this is why my sequel is going with this particular plan instead of this guy.

Okay, I hope this session was useful.