Lær at bruge MySQL-databasen

MySql er en af ​​de mest populære databaser.

Vi har netop offentliggjort et MySQL-databasekursus på freeCodeCamp.org YouTube-kanalen.

Bharath Ram Manoharan fra Execute on Command oprettede dette kursus. Han er en senior databaseingeniør og en fantastisk lærer.

Dette kursus starter med SQL grundlæggende. Derefter går det også ind på centrale databasekoncepter såsom datamodellering, låse, indekser, SQL Explain og mere.

Her er emnerne i dette kursus:

• Sådan opretter du en AWS EC2-instans
• Sådan installeres MySQL-database
• Datamodellering
• SQL Basics - Oprettelse af en tabel
• SQL Basics - Indsættelse af data
• SQL Basics - Opdatering og sletning af data
• SQL Basics - Læsning af data (Vælg udsagn)
• SQL-tilmeldinger
• Transaktionsisolationsniveauer
• Bordniveaulåse
• Rækkeniveaulåse
• Læsninger i databasen
• Klyngede indekser
• SQL-forklaring

Se hele kurset nedenfor eller på freeCodeCamp.org YouTube-kanalen (2-timers visning).

Transskription

(autogenereret)

MySQL er en af ​​de mest populære databaser, lær hvordan du bruger det på dette kursus af en senior databaseingeniør.

Velkommen til dette grundlæggende MySQL-kursus. Jeg vil gerne starte med at værdsætte dig, fordi du prøver at lære en ny færdighed.

Lad mig præsentere mig selv.

Mit navn er Barbara og jeg arbejder for Salesforce som senior databaseingeniør, jeg har over 12 års erfaring med en række forskellige databaser, som Oracle er den vigtigste, jeg har erfaring med at arbejde med virksomheder som Chase, PayPal, Wells, Fargo, StubHub, osv.

Lad mig først besvare et par grundlæggende spørgsmål til dig, og det er hvem, hvad og hvorfor.

Så hvem skal tage dette kursus, dette kursus er beregnet til databaseprofessionelle, der ønsker at udvide deres færdigheder.

Hvis du er softwareingeniør eller fuld stack-udvikler, og du ønsker at få en dyb forståelse af MySQL-databasen, er dette kursus for dig.

Og hvis du er universitetsstuderende, datalogistuderende eller nyuddannet, vil dette kursus give dig en vis viden om interne databaser.

Så hvorfor skulle du lære MySQL, MySQL er den mest populære open source-database og selvfølgelig Postgres.

SQL er helt klart deroppe.

Når virksomheder flytter deres data fra on prem til cloud, vil de normalt gerne migrere til en cloud-native database eller en open source-database, som MySQL eller Postgres-efterfølger, for at spare omkostninger.

Så lad os sige, at du er en Oracle Database-ekspert.

Hvis du får viden om en database som MySQL, så kan du hjælpe virksomheder med at migrere deres data fra Oracle til MySQL, og det kan være virkelig værdifuldt.

Lad os nu se på, hvad der bliver dækket i dette kursus.

Med MySQL mener jeg først og fremmest MySQL InnoDB-lagringsmotoren gennem hele dette kursus, som bruges bag ethvert handelswebsted, en bank eller en finansiel institution og så videre.

Og MySQL tilbyder en række forskellige lagringsmotorer, min I Sam, i hukommelseslagringsmotor eller nogle populære lagringsmotorer, som er tilgængelige, vi skal lære om MySQL InnoDB, jeg dækker ikke nogen anden type lagringsmotorer.

Det er nu disse emner, som jeg vil dække i dette kursus.

Og bemærk venligst, at dette er et databaseadministrationskursus.

Så det er 80 % databaseadministration.

Og for folk, der er helt nye til databaser, har jeg inkluderet SQL basics.

Så du vil lære om databaseinstallation, MySQL Workbench, databaseindekser, databaselogfiler, du vil også lære en lille smule justering af ydeevne, det er SQL-forklaring.

Så det er nogle interessante emner, som jeg vil dække.

Så hvad skal du helt præcist bruge for at komme i gang med dette kursus, du skal bruge en pc eller en Mac.

Så hvis du bruger en pc, så anbefaler jeg, at du rent faktisk kigger i arbejdsarkene eller det supplerende materiale, der er vedhæftet i beskrivelsen.

Hvis du har en bærbar Mac, er du i den bedste position til at lære dette kursus.

For så kan du bare se, hvad jeg skriver.

Og du kan bare skrive de samme kommandoer og bare følge med fra ende til anden.

Og mest af alt er dette hovedkravet, jeg vil have dig til at oprette en AWS-konto, det er korrekt og forbløffende på Web Services-kontoen.

Så hvis du ikke ved, hvad jeg taler om, så kig venligst i mit arbejdsark, som kan findes i beskrivelsen, jeg har vedhæftet nogle ressourcer, som vil vise dig, hvordan du opretter en AWS-konto, jeg vil bruge en AWS EC to instans gennem hele kurset.

Og jeg viser dig, hvordan du opretter en.

Men en hovedting, som jeg vil have dig til at huske, er, at du efter hver din studiesession kan lukke din EC to-instans ned.

På den måde skal du ikke betale unødvendige omkostninger.

Og husk venligst, at du ikke behøver at holde din EM to-instans kørende 24.7.

Så når du har oprettet en AWS-konto og logger ind, vil du lande på dette dashboard eller denne side.

Og du kan gå til servicemenuen lige her.

Og så under beregning kan du nemt vælge, så her i venstre side kan du vælge forekomster.

Og så skal vi her lave en instans, som vil være vores laboratoriemiljø.

Så klik på startinstans.

Og lad os så vælge et billede til vores eksempel.

Så jeg vil vælge Red Hat Enterprise Linux version 864 bit, og min instanstype vil være T two micro, som er gratis tier berettiget.

Og du skal vælge et passende undernet.

Hvis du lige har oprettet en AWS-konto, kan du bare forlade.

Lad det være, uanset hvilket standardundernet, der dukker op for dig.

For mig vil jeg vælge mig Vær en specifik.

Og sørg derefter for at aktivere denne mulighed for automatisk tildeling af offentlig IP.

Fordi det vil tildele en offentlig IP til din instans, ved hjælp af hvilken du kan SSH ind i din instans fra din bærbare computer og lade alle de andre indstillinger være som de er.

Og lad os allokere 25 gigs til databasen eller hele forekomsten.

Og du kan bare lade resten være ISIS, gå til næste side læst, du kan oprette et tag til din instans.

Så jeg vil kalde det min SQL-instans.

Ét, gå til sikkerhedsgruppesiden.

Og så her, det vigtige er, at du skal være i stand til at SSH ind i instansen.

Og du skal, du skal oprette ordentlige firewall-regler for, at enhver, dybest set, alle i verden kan logge ind på denne instans via port 22.

Og det er slet ikke sikkert.

Og jeg vil tage mig af dette tilfælde.

For når jeg er færdig med at optage, plejer jeg at fjerne det.

Så jeg ved, hvordan jeg skal håndtere dette.

Men når du opretter regler, skal du sørge for at placere din bærbare computers IP der, bare så den er mere sikker, så kan du nu gennemgå din konfiguration og derefter klikke på start.

Men hvis du vil, kan du oprette et nyt nøglepar og så bare give det et navn.

Og download den derefter, før du opretter forekomsten.

For mig vil jeg bare vælge et eksisterende nøglepar.

Måske denne, og jeg siger viden, måske en anden.

Okay, denne her.

Og start forekomst.

Nu er din instans ved at blive oprettet, det tager sandsynligvis et par minutter at oprette denne instans.

Okay, det er sådan, du opretter en EC to-instans.

Og nu hvor min instans er oppe at køre, og jeg kan se den offentlige IP.

Senere vil jeg installere MySQL på denne instans.

Så det var det, jeg ville vise dig i denne lektion.

Så det, du ser, er dybest set min efterfølgerdokumentation, der viser alle disse forskellige installationsvejledninger, såsom Windows og Mac, og så videre.

Så vi er interesseret i Linux installation er dybest set MySQL installation på Linux.

Og der er faktisk et par guider.

Så denne installerer grundlæggende jannettek binær, det springer vi over og går her.

Og selv inden for installation af MySQL, på Linux, er der en masse guider.

Så den anbefalede installationsmetode er at bruge RPM-pakker fra Oracle.

Men vi kommer til at bruge denne MySQL yum repository-baserede installation.

Og det er faktisk ret ligetil.

Så for denne installation skal vi gå til MySQL comm og downloads.

Og vi downloader Community Edition og går til yum repository.

Som du ved, kører den instans, vi oprettede, Archie l 8, Red Hat, Enterprise Linux otte.

Så vi skal downloade denne rpm.

Men så skal vi downloade RPM på selve instansen, den som vi oprettede.

Så lad os faktisk logge ind på instansen.

Så jeg vil bruge SSH, og vi skal bruge min private nøgle.

Og logning er let for brugeren at vælge standardbrugeren og dybest set få den offentlige IP for mit instans login, og vi skifter til en root okay.

Så en ting, vi har brug for for at downloade denne RPM til denne Linux-instans, er W get-pakken.

Så lad os gå videre og installere det først.

Okay, så nu hvor w gate er installeret, skal vi downloade den RPM, vi lige har set.

Så for at få linket til denne RPM, skal vi gå ind i denne download.

Og vi skal højreklikke her og kopiere linket.

Og hvis du installerer på et andet OSS, skal du klikke på den relevante knap.

Okay, så vi har linket, og lad os bare gå videre og indsætte det link her, ligesom w get og linket.

Og den kommando downloader denne pakke, nu skal vi bruge en RPM-kommando til at installere denne pakke.

Så denne pakke, som jeg nævnte før, vil tilføje denne MySQL yum repo til din lokale systemrepo liste.

Med Red Hat Enterprise Linux-installation bliver du som et MySQL-modul som standard.

Så lad os deaktivere den.

Hvis du ikke deaktiverer det, vil dette forstyrre vores MySQL-installation.

Så lad os gå videre og deaktivere det ved hjælp af denne kommando.

Og du skal ikke bekymre dig om at skrive disse kommandoer, jeg vil sætte et link til min Git-repo med alle disse kommentarer i beskrivelsen.

Så alle disse er blevet deaktiveret.

Lad os nu gå videre og installere MySQL community Server Edition ved hjælp af yum install MySQL community server.

Og lad os sætte minus y derind bare for at gå videre og acceptere alle anvisningerne.

Og det er at installere alle disse pakker.

Okay, så min SQL er blevet installeret.

Lad os gå videre og starte MySQL-databasen ved hjælp af systemet CTL.

Kommando.

Og lad os tjekke status.

Så nu er min SQL-database oppe og køre.

Okay, så logfilen for denne MySQL-databasesoftware er under var log.

Og hvis du så griber temp fra denne logfil, får du den midlertidige adgangskode til root-brugeren.

Og du kan bruge det til at logge ind på MySQL-databasen.

Og hvordan logger du ind, du bruger denne kommando min efterfølger minus u, det vil være root og minus P er til adgangskodebaseret login.

Og så logger vi på MySQL-databasen.

Så lad os bruge denne adgangskode og se, om den logger ind.

Og vi er med.

Og hvis du kører en kommando på dette tidspunkt, vil min efterfølger bede dig om at nulstille adgangskoden ved at bruge alter user statement.

Vi kan gøre dette på en anden måde.

Så der er en eksekverbar fil kaldet MySQL admin.

Og dette er kommandoen til det.

MySQL admin, minus dig brugernavnet og minus p adgangskode.

Vi vil nulstille adgangskoden for root-brugeren.

Og lad os først angive den aktuelle adgangskode, som er denne midlertidige adgangskode.

Og lad os give det nye kodeord nr.

Okay, adgangskoden er blevet accepteret.

Lad mig nu prøve at logge ind med denne nye adgangskode ved at bruge den forrige kommando min SQL minus u bevist og minus p MySQL.

Lad mig indsætte adgangskoden, jeg sagde lige nu, vi er i.

Så lad os gå videre og køre en simpel show databases-kommando, som viser alle de standarddatabaser, der følger med som en del af installationen.

Så en ting mere, vi skal gøre for at fuldføre installationen, er at indlæse en tidszonefil eller tidszonetabel som vist her.

Så hvis jeg laver en select star, som dybest set er en SQL-forespørgsel, der skal læses fra denne tabel, kan du se, at tabellen er tom lige nu.

Så lad os gå ud og køre endnu en kommando.

For at indlæse tidszonerelaterede data.

Så dette er kommandoen.

Og lad os gå videre og køre det.

Og jeg vil gå videre og indsætte min adgangskode, og det indlæser en masse data, du kan ignorere alle disse advarsler.

Lad os gå tilbage til vores MySQL-database.

Så hvis du gør som vælg stjerne, fra MySQL dot-tidszone, igen, viser den en masse data.

Så nu har du det godt.

Og det fuldender MySQL-databaseinstallationen.

Okay folkens, i dette afsnit skal vi tale om datamodellering.

Okay, så databasedesign, datamodellering, skemadesign, disse er alle udskiftelige ord, termer for databasedesign er en løbende proces.

Så du kommer med et grundlæggende design, når du ligesom laver din applikation.

Og efterhånden som applikationen, du ved, får tilføjet nye funktioner, forbedringer, forbedringer, gentager du dybest set dette design, ja, du bliver ved med at tilføje nye ting til dit design, og så videre.

Så det første, du gør, når du laver databasedesign eller datamodellering, er at forstå forretningsdata.

Og når du først forstår forretningsdata, skal du finde på et logisk design af din database.

Hvad mener jeg med det? Nå, dybest set skal du designe dine tabeller, de kolonner, der går ind i disse tabeller, indekser, begrænsninger, som primær nøgle begrænsning, unik nøgle begrænsning, ikke nul begrænsninger, standardværdier, fremmednøgler, disse er alle forskellige ting, du skal oprette.

Når du, når du kommer med et logisk design af dit skema, når du faktisk har dette grundlæggende tabeldesign eller skemadesign, så kan du kigge efter dataredundans, det vil sige, at du dybest set kan se, hvor dine data er repeterende.

Og så begynder du at eliminere det ved at normalisere dine tabeller faktisk.

Og det er fordi dataredundans forårsager dataanomalier.

Hvad jeg mener med det er, at når du har ligesom flere forekomster af de samme data, når du, lad os sige, opdaterer visse data, skal du opdatere mange steder.

Og hvis du glemmer at opdatere selv ét sted, har du nu to versioner af de samme data i din database.

Og det skaber ligesom dataanomali datainkonsistens er faktisk på samme måde.

Og alt det sker på grund af dataredundans.

Så det, vi kigger på, er dybest set et regneark, og regnearket er dybest set et stort bord og et stort bord, ikke? Og hvad vi vil gøre, er dybest set at designe en tabel til et e-handelswebsted, et e-handelswebsted er dybest set som en fantastisk zone, eller E Bay, eller hvad som helst, som online forretning, som online e-handelswebsted, som Alibaba , eller hvad, ikke? Lad os sige, at du kun har én tabel i denne database, ikke? Og du begynder, efterhånden som ordrerne kommer ind, gennem denne hjemmeside, begynder du at lægge data ind i denne tabel, ikke? Du har, du ved, lad os se på nogle af de ting, du vil optage i denne tabel, ikke sandt, du har selvfølgelig brug for en optælling af dine ordrer.

Så du kunne godt tænke dig at nummerere dine ordrer, og hvordan det så kommer, du ved, er det desktop eller mobil? Eller hvilket produkt er det? Som du ved, her har jeg ligesom et par bøger, bogtitlerne og så prisen på produkterne.

Og så hvem er kundens kunders detaljer, betalingsoplysninger, leveringsdetaljer og så videre.

Så disse er i bund og grund alle en del af lignende, e-handelsord.

Ret? Og du har et kæmpe bord.

Og hvis du ser på dataene her, ikke? Så, du ved, her, jeg har ligesom et par kunder, der køber, du ved, ligesom to forskellige produktprodukter, ikke.

Så, og du kan se, at dataene har været gentagne.

Hvad jeg mener med det er, at hver gang jeg køber det samme produkt, skal jeg gentage disse data, ligesom den første ordre, der kom ind, var gennem web-desktop-webstedet, der blev bragt ved fødslen, og så kan du se alle detaljerne i dette kunde og alle de detaljer om produktet og betalingen, også betalingsoplysninger.

Så var den anden ordre, der kom, fra en anden person, men så var det, du ved, ordren var for det samme produkt, og du skulle gentage produktinformationen.

Højre.

Den tredje var fra den tidligere kunde.

Men så denne gang købte han et andet produkt, hans oplysninger, kundeoplysningerne har faktisk gentaget sig.

Så der er en masse dataredundans, der har været gentagende.

Så dette er dybest set en denormaliseret database, hvor du kun har én tabel eller en håndfuld tabeller, vi bakker alle oplysninger fra dit websted eller din virksomhed ind i disse få tabeller.

Faktisk er dette en denormaliseret version af din database, lad os faktisk se på, hvad du ellers kunne gøre.

Så hvad du kan gøre er dybest set, du kan starte med denne grundlæggende denormaliseringstabel, og så kan du begynde at tage alle de overflødige oplysninger ud af din database eller din tabelhandling, det første jeg gjorde var, at jeg tog ud som kundeoplysningerne , de lægger det i en separat tabel, ikke sandt.

Og jeg har kun kundeoplysninger her.

Og jeg begyndte med at sætte et ID-nummer for hvert kunde-ID eller kundenummer, hvad man nu kalder det.

Når jeg har udtaget kundeoplysningerne, har jeg ordretabellen, den indledende tabel, jeg ringer til ordretabellen og ser sådan ud nu, ikke? Og du kan se, at jeg har en lignende kunde-id-kolonne herovre.

Og hvad er denne kunde-id-kolonne? Dine gæt? Ret? Så denne kunde-id-kolonne er den samme som den, du ser her.

Ret? Så og hvorfor har jeg det, fordi jeg har brug for en måde at relatere disse rækker på.

Som du kan se, som du ved, det er kolonner, det er rækker, disse rækker, jeg skal være i stand til at relatere til en kunde, ikke? Hvis jeg udtager kundeoplysninger, hvordan kan jeg så forholde mig? Du ved, dette bord og det bord? Det er gennem en fælles kolonne eller en masse kolonner.

Faktisk er det i dette tilfælde kun én kolonne.

Så kunde-id, ikke? Jeg er ligesom at sætte id-nummeret her.

Og hvad kan vi ellers tage ud af dette bord.

Så dette er et niveau af normalisering.

Ret? Så lad os fortsætte med at normalisere, hvilket er som at fjerne produktdetaljerne.

Ret? Så produktdetaljerne gentager sig også.

Så her føler du ikke så meget smerte, for der er kun tre poster i denne tabel.

Hvad hvis bordet har millioner poster, ikke? Det er derfor, vi er nødt til at normalisere tabellen.

Nu tager du faktisk produktinformationen ud og flytter den til en anden tabel.

Og så har jeg en produkt-id-kolonne bare for at nummerere, ligesom ID-produkterne faktisk, og din ordretabel vil se sådan ud, så tager du betalingsoplysningerne ud til en anden tabel.

Og din ordretabel vil så se sådan ud.

Dette er dybest set processen med at gå fra et denormaliseret skema eller en database til en normaliseret database.

Når du har dine data i en enkelt tabel, så behøver du ikke lave nogen joints.

Så du kan faktisk spørge, hvad er joints faktisk, når du kører forespørgsler, som at bruge SQL, er SQL et sprog ikke? Et struktureret forespørgselssprog, når du kører kommandoer i din database, kan du få alle dine data fra denne ene tabel, hvis din tabeldatabase er fuldstændig denormaliseret.

Hvorimod hvis du har mange borde, skal du på en måde kombinere eller slutte dig til staldene, og så skal du få dataene ud.

Så det kaldes at deltage i bordene.

Så når du har en denormaliseringsdatabase, behøver du ikke lave mange joints.

Og det er på en måde godt, fordi din database ikke behøver at tænke så meget for at få dataene.

Du siger, at jeg vil have disse data, og disse data er tilgængelige i denne tabel.

Så det er bare meget ligetil.

Hvorimod som i en normaliseret database, når du forbinder mange tabeller, så din databasemotor, som de kalder optimizer med i, i Oracle eller i det meste af båndet de fleste databaser.

Så denne databasemotor skal tænke mere som dengang, okay, hvilken tabel skal jeg scanne først? Og hvordan skal jeg filtrere dataene i denne tabel.

Og så Okay, jeg tager resultatsættet fra den tabel og skal slutte mig til disse andre tabeller.

Så der er så meget mere at tænke på, at der er så meget mere behandling, der skal ske på hvilken server denne database kører på.

Højre.

Og på grund af det vil ydelsen være en smule fleksibel.

Nederst til højre, og det vil forbruge en masse ressourcer, og du har alt det, der sker i skala, som i ligesom mange operationer sker på samme tid, så har du dybest set langsom ydeevne, eller i det mindste lavere end, hvad det ville have været i en denormalisere databasen, men på samme tid fjerner vi så meget datagentagelse, eller dataredundansen er meget lav, på grund af det er den nødvendige lagring i en normaliseret database meget lavere.

Så du kan faktisk ikke lide at generalisere og sige, at en normaliseret database altid vil være langsom, eller at denormalisering af databasen vil være hurtig, det er som om, det afhænger faktisk af, at du skal se på dataene og se, hvor meget gentagelse der sker, osv. osv.

Så, men generelt er det sådan, det går, mens du går gennem denne designproces, ja, du ved, se hvad vi faktisk har gjort, ikke sandt.

Så vi har besluttet, hvilke tabeller vi skal bruge, som du ved, vi har ordrer, bordprodukter fik kunderne og betalinger, og vi bestemmer kolonnenavnene.

Og så ikke kun det for hver tabel, du skal faktisk beslutte, hvad der skal være den primære nøgle.

Hvad jeg mener med det som en primær nøgle, er en unik nøgle, og som faktisk ikke kan være nul, hvilket er meget vigtigt.

Så ved at bruge denne primærnøgle bør du være i stand til at identificere enhver post i denne tabel, enhver række i denne tabel.

For eksempel, hvis jeg siger her, den primære nøgle er ordrenummer, så kan jeg, når som helst, hvis jeg har et ordrenummer, så kan jeg slå op i denne tabel, lad os sige, at ordrenummer er lig med to, jeg kan bare få denne post ud af min database.

Og så skal du også have nogle unikke nøgler faktisk, ikke.

Så unikke nøgler er stort set ligesom primær nøgle.

Og en unik nøgle kan nu være en primær nøgle kan ikke være nul, som jeg nævnte.

Og så kan du også have indekser på dit bord.

Så indekser er måder til grundlæggende at vælge dine dagsdata hurtigere.

Lad os sige, at jeg ofte søger i denne tabel baseret på en kundes e-mail, så har jeg brug for et indeks på kunde-e-mail-kolonnen, ikke sandt, det skal du bestemme.

Og du skal beslutte dig for, hvilke kolonner der kan være nul.

Ret? Her kan ingen af ​​kolonnerne være nul.

Lad os sige, at du har en anden kolonne kaldet præference og kundens præference som i hvilken slags forsendelse eller hvilken slags eller hvilket telefonnummer der foretrækkes, eller sådan noget.

Så det kan være en nulkolonne, ikke? Så du kan ikke have nogen kolonner.

Ellers definerer du dine kolonner som ikke null.

Lad os sige, at du i din ordretabel har denne leverede kolonne, når en ordre grundlæggende oprettes, når en kunde køber et produkt på dit websted.

Selvfølgelig bliver det ikke leveret med det samme, på tidspunktet for ordreoprettelse vil den leverede kolonne altid have ingen eller n, en n værdi, ikke? Alle disse ting, alle disse beslutninger, som vi tager, vi taler om, er en del af skemadesign.

Og når du har fundet ud af alt dette, kan du lægge oplysningerne i dit designværktøj Entity Relationship-designværktøj.

Og i næste afsnit vil jeg vise dig, hvordan jeg gør det på sequel workbench, min sequel world workbench, dybest set kan du faktisk så have en billedlig repræsentation af dit logiske design af din database, ikke sandt.

Og det er dybest set, hvad du kalder et ER-diagram.

Og selvfølgelig kan du tale om forholdet mellem de to borde, lad os sige, at du kan sige åh, denne tabel i denne tabel, de har et til mange forhold, for eksempel kan hver kunde placere mange ordrer.

Så det er faktisk et en til mange forhold, ikke sandt.

Men én, du ved, én ordre kan kun udføres af én kunde.

Ret? Så den slags ting.

Så du har et til en forhold, et til mange forhold eller mange til mange forhold mellem tabeller.

Faktisk er disse alle en del af datamodellering.

Men det behøver du ikke at bekymre dig så meget om, så længe du har en klar idé om, hvilke data der kommer ind i din database.

Og undervejs skal du definere som datatypen for dine kolonner.

Det er faktisk meget vigtigt.

Dine navne bliver en vild ørred.

Du ved, telefonnumre kan være numre, og så er e-mail igen som et vagtdiagram.

Og din ID-kolonne eller nummerkolonner vil være int eller nummer.

Det er alle nogle beslutninger, du ville tage dig. I en datamodelleringsopgave er det faktisk stort set det, jeg vil sige om datamodellering, så er der meget mere, vi kan tale om det.

Og ligesom atomicitet, ligesom dig, har du hele adressen, ligesom attributter pakket ind i én kolonne, vi skal også opdele det.

Så det kaldes atomicitet.

Du kan have adresse separat, byer separat stater separat, og du ved, postnummer separat, ikke.

Så den slags ting, der er nuancer, der gør din database mere og mere effektiv.

Og selvfølgelig skal vi ikke gå ind i mange detaljer der.

Men dette er den grundlæggende datamodellering, som du skal forstå.

Og som jeg sagde før, i det næste afsnit, vil jeg vise dig, hvordan du tager dette og derefter indtaste det på mit efterfølger-arbejdsbord.

hurtig opsummering af, hvad jeg gjorde i det sidste afsnit, jeg skabte grundlæggende et logisk design af et e-handelswebsted.

Så det, du ser på, er en tabel, som jeg startede med.

Det er en denormaliseringstabel, og vi tog dybest set denne denormaliserede tabel, og vi normaliserede den.

Som du kan se, er der fire versioner af denne tabel, jeg kalder denne tabel Ordretabellen.

Så der er fire forskellige versioner.

Og for hver gentagelse tog jeg gentagne data ud.

Så endelig landede vi med fire borde, bortset fra det originale ordrebord.

Så nu har vi også kunders produkter og betaling.

I denne video vil jeg tage dette nu alle staldene, og så tager jeg strukturen, og jeg vil skabe et logisk design.

Okay, så lad os faktisk gå til MySQL Workbench, og jeg er allerede forbundet til en database. Det, jeg skal gøre, er at gå til Filer og gå til nye modeller.

så her kan vi tilføje et nyt ER-diagram, et entitetsrelationsdiagram.

Og lad os kalde denne database eecom.

Butik, sådan noget.

Så lad os gå videre og begynde at skabe vores tabeller.

Nu vil jeg ikke oprette alle fire tabeller, der sandsynligvis ville tage længere tid eller lang tid, og jeg vil oprette et par tabeller.

Og det burde være nok til, at du forstår, hvordan vi gør dette.

Så lad os bare starte med tabellen Kunder.

Så kundetabellen har fem kolonner, dette er ikonet for at oprette en ny tabel, du kan trække og slippe, eller du kan prøve at tegne nu dobbeltklik og derefter oprette en tabel kaldet kunde og her kan vi begynde at sætte tabellen kolonnen navngiver kunde-id, og så vil dette blive udfyldt af din sekvens.

Så sekvens er et databaseobjekt.

Og det bliver et heltal.

Så vi kan lade det være som det er.

Og vi kan have det som en primær nøgle, det er fint, og en primær nøgle skal udfyldes, det kan det ikke være nu.

Så det vælges automatisk.

Den næste er kundenavn, vi kunne dele det op i fornavn, og så kan vi vælge se vores og måske give lidt mere plads som i længden af ​​navnet og så efternavnet, jeg kan se 100 og så alle disse kan ikke være nu, så vi kan vælge ikke null constraint.

Så det er forskellige begrænsninger, som er tilgængelige.

Lad os gå videre med den næste adresse igen.

Og hvis du husker, jeg talte om atomicitet.

Så du vil have dine kolonner til at være du kender, atomare i den forstand, at her er stort set hele adressen pakket i én kolonne.

god praksis faktisk at opdele det i atomsøjler som en adresse separat, by separat stat separat og derefter postnummer separat.

Så vi har alle disse, selvfølgelig, ingen af ​​disse kan være nu, og hvad er der ellers? Så kundetelefonnummer, telefonnummer vil være alle numre.

Men så vil jeg få 10 numre, selvfølgelig ikke null og kunde e-mail.

Så jeg kan bare sige e-mail-id 100 Okay, ikke null.

Så da I-id er den primære nøgle her eller kunde-id, vil jeg gerne sikre mig, at vi har en begrænsning for at undgå at gentage kundeoplysninger.

Hvis du f.eks. har én kundedata for ID én, vil jeg ikke have, at den samme kunde, kundedata gentages for et andet ID, for eksempel ID to.

Så jeg vil faktisk gøre e-mail-id'et unikt for hver post herovre.

Og så kan eller kan være telefonnummer også.

Så disse er alle unikke nøglebegrænsninger eller unikke begrænsninger.

Det var det.

Så vi har oprettet kundetabellen.

Så lad os gå tilbage og se, hvad vi ellers har.

Så lad os nu skabe, vil jeg sige produkt.

Og så gør du stort set det samme, vælg det for at oprette en ny tabel.

Og så nu her, du kan bare tegne i denne, jeg vil kalde det produkt.

Og vi vil gennemgå den samme proces og derefter sætte produktkolonnenavnene ind der.

Hvis du undrer dig, er dette den samme kunde-id-kolonne, som vi tilføjede her.

Og vi vil gøre det til en fremmednøgle om et minut.

Så lad os gå videre og dele det op i flere kolonner.

For igen er alt pakket i én kolonne, hvilket ikke er en god praksis.

Så lad os sige, lad os kalde det kreditkortnummer.

Hvis kunden bruger PayPal, har vi brug for den e-mail.

Så kan bruge e-mail-id herovre.

Så dette kan være null eller ikke null baseret på hvilken betalingstype der bruges.

Så det er okay, så udløbsdatoen bliver en datokolonne.

Så lad os faktisk ændre det.

Så hvis du ikke er sikker, kan du trykke på rullemenuen og derefter vælge din rigtige datatype for hver.

Den anden ting, som jeg nævnte, som dybest set handler om fremmednøgle, dette kunde-id er det samme som det, vi tilføjede her.

Så lad os faktisk gøre det kunde-id til en fremmednøgle.

Så vi kan bare kalde det kunde-id udenlandsk nøgle en.

Og så er den tabel, der skal refereres til, kunderne.

Og kolonnen bliver kunde-id.

Og det er det.

Så du kan se, at nu vi har forbindelse eller en relation mellem disse to tabeller, vil jeg faktisk også bare tilføje ordretabellen.

Jeg har også oprettet tabellen Ordrer, som er hovedtabellen, og jeg vil nu oprette nogle fremmednøgler til ordrerne.

Alt er gjort.

Hvis du vil oprette nogle indekser på dette tidspunkt, kan du gøre det.

Så vi er vel færdige.

Så vi er grundlæggende tilføjet fire tabeller til vores logiske design, disse fire tabeller, og så har vi oprettet kolonner og derefter defineret deres datatyper.

Og vi skabte også fremmednøglerne og selvfølgelig primærnøgle og unik nøgle til hver af tabellerne.

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.

Ret? 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? Ret? 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.

Ret? 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.

Ret? 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.