MySql är en av de mest populära databaserna.
Vi har precis publicerat en MySQL-databaskurs på freeCodeCamp.org YouTube-kanalen.
Bharath Ram Manoharan från Execute on Command skapade den här kursen. Han är en senior databasingenjör och bra lärare.
Den här kursen börjar med SQL-grunderna. Sedan går det också in på viktiga databaskoncept som datamodellering, lås, index, SQL Explain och mer.
Här är de ämnen som behandlas i den här kursen:
- Hur man skapar en AWS EC2-instans
- Hur man installerar MySQL-databasen
- Datamodellering
- SQL Basics - Skapa en tabell
- SQL Basics - Infoga data
- SQL Basics - Uppdatera och ta bort data
- SQL Basics - Läsa data (Välj uttalanden)
- SQL-anslutningar
- Transaktionsisoleringsnivåer
- Bordsnivålås
- Lås på radnivå
- Databaslåsningar
- Klustrade index
- SQL Explain
Titta på hela kursen nedan eller på freeCodeCamp.org YouTube-kanalen (2 timmars visning).
Transkription
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MySQL är en av de mest populära databaserna, lär dig hur du använder den i den här kursen av en senior databasingenjör.
Välkommen till denna grundläggande MySQL-kurs. Jag vill börja med att uppskatta dig för att du försöker lära dig en ny färdighet.
Låt mig presentera mig själv.
Jag heter Barbara och jag arbetar för Salesforce som senior databasingenjör, jag har över 12 års erfarenhet av en mängd olika databaser som Oracle är den huvudsakliga, jag har erfarenhet av att arbeta med företag som Chase, PayPal, Wells, Fargo, StubHub, etc.
Låt mig svara på några grundläggande frågor för dig först, och det är vem, vad och varför.
Så vem ska ta den här kursen den här kursen är avsedd för databasproffs som vill utöka sin kompetens.
Om du är en mjukvaruingenjör eller en fullstack-utvecklare och du vill få en djup förståelse av MySQL-databasen är den här kursen för dig.
Och om du är collegestudent, datavetenskapsstudent eller nyutexaminerad, kommer den här kursen att ge dig lite kunskap om interna databaser.
Så varför ska du lära dig MySQL, MySQL är den mest populära databasen med öppen källkod och naturligtvis Postgres.
SQL finns definitivt där uppe.
När företag flyttar sin data från on prem till moln vill de vanligtvis migrera till en molnbaserad databas eller en öppen källkodsdatabas, som MySQL eller Postgres uppföljare, för att spara kostnader.
Så låt oss säga att du är en Oracle Database-expert.
Om du får kunskap om en databas som MySQL kan du hjälpa företag att migrera sin data från Oracle till MySQL, och det kan vara mycket värdefullt.
Låt oss nu titta på vad som tas upp i den här kursen.
Nu, först och främst, med MySQL, menar jag, MySQL InnoDB-lagringsmotorn under hela kursen, som används bakom alla handelswebbplatser, eller en bank eller en finansiell institution, och så vidare.
Och MySQL erbjuder en mängd olika lagringsmotorer, min I Sam, i minneslagringsmotor, eller några populära lagringsmotorer, som är tillgängliga, vi ska lära oss om MySQL InnoDB, jag täcker inte någon annan typ av lagringsmotorer.
Nu, det här är de ämnen som jag ska ta upp i den här kursen.
Och observera att detta är en databasadministrationskurs.
Så det är 80 % databasadministration.
Och för personer som är helt nya med databaser har jag inkluderat SQL-grunderna.
Så du kommer att lära dig om databasinstallation, MySQL Workbench, databasindex, databasloggar, du kommer också att lära dig lite prestandajustering, det vill säga SQL förklara.
Så det här är några intressanta ämnen som jag ska ta upp.
Så vad behöver du exakt för att komma igång med den här kursen, du behöver en PC eller en Mac.
Så om du använder en PC rekommenderar jag att du faktiskt tittar i arbetsbladen eller det kompletterande materialet som bifogas i beskrivningen.
Om du har en bärbar Mac är du i den bästa positionen för att lära dig den här kursen.
För då kan du bara se vad jag skriver.
Och du kan bara skriva samma kommandon och bara följa med från ände till slut.
Och framför allt är detta huvudkravet, jag vill att du skapar ett AWS-konto, det är korrekt och förbluffa på Web Services-kontot.
Så om du inte vet vad jag pratar om, vänligen titta i mitt arbetsblad som finns i beskrivningen, jag har bifogat några resurser som visar dig hur du skapar ett AWS-konto, jag kommer att använda en AWS EC två instans under hela kursen.
Och jag ska visa dig hur du skapar en.
Men en viktig sak som jag vill att du ska komma ihåg är att efter varje studietillfälle kan du stänga av din EC två-instans.
På så sätt behöver du inte betala några onödiga kostnader.
Och kom ihåg att du inte behöver hålla din EM två-instans igång 24 sju.
Så när du har skapat ett AWS-konto och loggat in kommer du att landa på den här instrumentpanelen eller på den här sidan.
Och du kan gå till tjänstmenyn här.
Och sedan under beräkning kan du enkelt välja så här på vänster sida kan du välja instanser.
Och sedan här ska vi skapa en instans som kommer att vara vår labbmiljö.
Så klicka på startinstans.
Och låt oss sedan välja en bild för vårt exempel.
Så jag kommer att välja Red Hat Enterprise Linux version 864 bit och min instanstyp kommer att vara T two micro som är kvalificerad för gratis nivå.
Och du måste välja ett lämpligt undernät.
Om du precis skapat ett AWS-konto kan du bara lämna.
Lämna det vilket standardundernät som helst som dyker upp för dig.
För mig kommer jag att välja mig Var en specifik.
Och se sedan till att aktivera det här alternativet för automatisk tilldelning av offentlig IP.
Eftersom det kommer att tilldela en offentlig IP till din instans med hjälp av vilken du kan SSH till din instans, från din bärbara dator, och lämna alla andra inställningar som de är.
Och låt oss allokera som 25 spelningar, för databasen eller för hela instansen.
Och du kan bara lämna resten ISIS, gå till nästa sida läs, du kan skapa en tagg för din instans.
Så jag kommer att kalla det min SQL-instans.
En, gå till säkerhetsgruppens sida.
Och här, det som är viktigt är att du måste kunna SSH in i instansen.
Och du måste, du måste skapa ordentliga brandväggsregler för att vem som helst, i princip vem som helst i världen, kan logga in på den här instansen, via port 22.
Och det är inte säkert alls.
Och jag kommer att ta hand om det här exemplet.
För när jag väl är klar med inspelningen brukar jag ta bort den.
Så jag vet hur jag ska hantera det här.
Men när du skapar regler, se till att lägga in din bärbara dators IP där, bara så att den är säkrare, så kan du nu granska din konfiguration och sedan klicka på start.
Men om du vill kan du skapa ett nytt nyckelpar och sedan bara ge det ett namn.
Och ladda ner den innan du skapar instansen.
För mig kommer jag bara att välja ett befintligt nyckelpar.
Kanske den här, och jag säger kunskap, kanske en annan.
Okej, den här.
Och starta instans.
Nu skapas din instans, det tar förmodligen ett par minuter att skapa den här instansen.
Okej, det är så du skapar en EC två-instans.
Och nu när min instans är igång och jag kan se den offentliga IP-adressen.
Senare kommer jag att installera MySQL på den här instansen.
Så det var vad jag ville visa dig i den här lektionen.
Så det du ser är i princip min uppföljningsdokumentation som visar alla dessa olika installationsguider, som Windows och Mac, och så vidare.
Så vi är intresserade av Linux-installation är i grunden MySQL-installation på Linux.
Och det finns ett par guider faktiskt.
Så den här installerar i princip jannettek binär, vi hoppar över det och går hit.
Och även inom installation av MySQL, på Linux, finns det ett gäng guider.
Så den rekommenderade installationsmetoden är att använda RPM-paket från Oracle.
Men vi kommer att använda denna MySQL yum repository-baserade installation.
Och det är faktiskt ganska okomplicerat.
Så för den här installationen måste vi gå till MySQL comm och nedladdningar.
Och vi laddar ner Community Edition och går till yum-förrådet.
Som ni vet kör instansen som vi skapade Archie l eight, Red Hat, Enterprise Linux åtta.
Så vi måste ladda ner det här varvtalet.
Men då måste vi ladda ner RPM på själva instansen, den som vi skapade.
Så låt oss faktiskt logga in på instansen.
Så jag kommer att använda SSH, och vi kommer att använda min privata nyckel.
Och loggning är lätt för användaren att välja standardanvändare och i princip få den offentliga IP-adressen för min instansinloggning och vi byter till en root okej.
Så en sak som vi behöver för att ladda ner denna RPM till den här Linux-instansen är W get package.
Så låt oss gå vidare och installera det först.
Okej, så nu när w gate är installerad måste vi ladda ner RPM som vi just såg.
Så för att få länken till denna RPM måste vi gå in på den här nedladdningen.
Och vi måste högerklicka här och kopiera länken.
Och om du installerar på ett annat OSS måste du klicka på lämplig knapp.
Okej, så vi har länken, och låt oss bara gå vidare och klistra in den länken här, som w get och länken.
Och det kommandot laddar ner det här paketet, nu ska vi använda ett RPM-kommando för att installera det här paketet.
Så det här paketet, som jag nämnde tidigare, kommer att lägga till denna MySQL yum repo till din lokala systemrepolista.
Med Red Hat Enterprise Linux-installation får du som en MySQL-modul som standard.
Så låt oss inaktivera den.
Om du inte inaktiverar det, kommer detta att störa vår MySQL-installation.
Så låt oss gå vidare och inaktivera det med det här kommandot.
Och oroa dig inte för att skriva dessa kommandon, jag kommer att lägga en länk till mitt Git-repo med alla dessa kommentarer i beskrivningen.
Så alla dessa har inaktiverats.
Låt oss nu gå vidare och installera MySQL community Server Edition med yum install MySQL community server.
Och låt oss sätta minus y där bara för att gå vidare och acceptera alla uppmaningar.
Och det är att installera alla dessa paket.
Okej, så min SQL har installerats.
Låt oss gå vidare och starta MySQL-databasen med systemet CTL.
Kommando.
Och låt oss kontrollera statusen.
Så nu är min SQL-databas igång.
Okej, så loggfilen för denna MySQL-databasprogramvara är under varlogg.
Och sedan om du tar tag i temp från den här loggfilen får du det tillfälliga lösenordet för root-användare.
Och du kan använda det för att logga in i MySQL-databasen.
Och hur loggar du in, du använder det här kommandot min uppföljare minus u, det kommer att vara root och minus P är för lösenordsbaserad inloggning.
Och sedan loggar vi in i MySQL-databasen.
Så låt oss använda det här lösenordet och se om det loggar in.
Och vi är med.
Och om du kör något kommando vid det här laget, kommer min uppföljare att be dig återställa lösenordet med hjälp av alter user statement.
Vi kan göra detta på ett annat sätt.
Så det finns en körbar fil som heter MySQL admin.
Och detta är kommandot för det.
MySQL admin, minus användarnamnet och minus p lösenord.
Vi kommer att återställa lösenordet för root-användaren.
Och låt oss först ange det aktuella lösenordet som är detta tillfälliga lösenord.
Och låt oss ange det nya lösenordet nr.
Okej, lösenordet har godkänts.
Låt mig nu försöka logga in med detta nya lösenord med det föregående kommandot min SQL minus u bevisad och minus p MySQL.
Låt mig ange lösenordet som jag sa just nu, vi är inne.
Så låt oss köra ett enkelt kommando för show databases som visar alla standarddatabaser som ingår i installationen.
Så en sak till som vi behöver göra för att slutföra installationen är att ladda en tidszonsfil eller tidszonstabell som visas här.
Så om jag gör en vald stjärna, vilket i grunden är en SQL-fråga att läsa från den här tabellen, kan du se att tabellen är tom just nu.
Så låt oss avsluta och köra ett nytt kommando.
För att ladda tidszonrelaterad data.
Så detta är kommandot.
Och låt oss köra det.
Och jag ska gå vidare och lägga mitt lösenord, och det laddar en massa data, du kan ignorera alla dessa varningar.
Låt oss gå tillbaka till vår MySQL-databas.
Så om du gör som att välja stjärna, från MySQL dot-tidszon, igen, visar den en massa data.
Så nu är du bra.
Och det slutför installationen av MySQL-databasen.
Okej, gott folk, i det här avsnittet kommer vi att prata om datamodellering.
Okej, så databasdesign, datamodellering, schemadesign, dessa är alla utbytbara ord, termer för databasdesign är en pågående process.
Så du kommer på en grundläggande design när du liksom skapar din applikation.
Och när applikationen, du vet, får nya funktioner, förbättringar, förbättringar, upprepar du i princip den här designen, eller hur, du fortsätter att lägga till nya saker till din design, och så vidare.
Så det första du gör när du gör databasdesign eller datamodellering är att förstå affärsdata.
Och när du väl förstår affärsdata måste du komma med en logisk design av din databas.
Vad menar jag med det? Tja, i grund och botten måste du designa dina tabeller, kolumnerna, som går in i dessa tabeller, index, begränsningar, som primärnyckelbegränsning, unik nyckelbegränsning, inte nollbegränsningar, standardvärden, främmande nycklar, dessa är alla olika saker som du behöver skapa.
När du kommer med en logisk design av ditt schema, när du faktiskt har den här grundläggande tabelldesignen eller schemadesignen, kan du leta efter dataredundans, det vill säga att du i princip ser var din data är repeterande.
Och sedan börjar du eliminera det genom att normalisera dina tabeller faktiskt.
Och det beror på att dataredundans orsakar dataavvikelser.
Vad jag menar med det är att när du har liknande flera förekomster av samma data, när du, låt oss säga, uppdaterar viss data, måste du uppdatera på många ställen.
Och om du glömmer att uppdatera en enda plats, nu har du två versioner av samma data i din databas.
Och det skapar precis som dataavvikelser att datainkonsekvens är på samma linje faktiskt.
Och allt det händer på grund av dataredundans.
Så vad vi tittar på är i princip ett kalkylblad och kalkylarket är i grunden ett stort bord och ett stort bord, eller hur? Och vad vi ska göra är att designa en tabell för en e-handelswebbplats, en e-handelswebbplats är i grunden som en fantastisk zon, eller E Bay, eller vad som helst, som online-affärer, som e-handelswebbplats online, som Alibaba , eller vad som helst, eller hur? Låt oss säga att du bara har en tabell i den här databasen, eller hur? Och du börjar, när beställningarna kommer in, genom den här webbplatsen, börjar du lägga in data i den här tabellen, eller hur? Du har, du vet, låt oss titta på några av de saker som du kommer att spela in i den här tabellen, eller hur behöver du uppenbarligen en räkning av dina beställningar.
Så du kanske gillar att numrera dina beställningar och sedan hur det kommer, du vet, är det stationär eller mobil? Eller vad är det för produkt? Som du vet, här har jag som ett par böcker, boktitlarna och sedan priset på produkterna.
Och sedan vem är kundens kunduppgifter, betalningsdetaljer, leveransdetaljer och så vidare.
Så dessa är alla en del av liknande, e-handelsord, i princip.
Rätt? Och du har ett gigantiskt bord.
Och om du tittar på uppgifterna här, eller hur? Så, du vet, här, jag har som ett par kunder som köper du vet, ungefär som två olika produktprodukter, eller hur.
Så, och du kan se att data har varit repetitiva.
Vad jag menar med det är att varje gång jag köper samma produkt måste jag upprepa denna information, som att den första beställningen som kom in var via webbsidan för skrivbordet som kom från födseln och sedan kan du se alla detaljer om detta kund och all information om produkten och betalningen, betalningsinformation också.
Sedan var den andra beställningen som kom från en annan person, men då var det, du vet, beställningen gällde samma produkt och du var tvungen att upprepa produktinformationen.
Precis.
Den tredje var från den tidigare kunden.
Men så den här gången köpte han en annan produkt, hans information, kundinformationen har faktiskt upprepats.
Så det finns en hel del dataredundans som har upprepats.
Så, detta är i grunden en denormaliserad databas, där du bara har en tabell eller en handfull tabeller, vi backar all information från din webbplats eller ditt företag till dessa få tabeller.
Egentligen är detta en denormaliserad version av din databas, låt oss titta på vad mer du kan göra.
Så vad du kan göra är i princip att du kan börja med den här grundläggande denormaliseringstabellen, och sedan kan du börja ta ut all redundant information från din databas eller din tabellåtgärd, det första jag gjorde var att jag tog ut som kundinformationen , de lägger det i en separat tabell, eller hur.
Och jag har bara kunduppgifter här.
Och jag började som att sätta ett ID-nummer för varje kund-ID eller kundnummer, vad man än kallar det.
När jag tar ut kundinformationen har jag ordertabellen, den initiala tabellen, jag ringer ordertabellen som ser ut så här nu, eller hur? Och du kan se att jag har en liknande kund-ID-kolumn här.
Och vad är denna kund-ID-kolumn? Dina gissningar? Rätt? Så den här kund-ID-kolumnen är densamma som du ser här.
Rätt? Så och varför har jag det eftersom jag behöver ett sätt att relatera dessa rader.
Som du kan se, som du vet, det här är kolumner, det här är rader, de här raderna, jag måste kunna relatera till en kund, eller hur? Om jag tar ut kundinformation, hur kan jag då relatera? Du vet, det här bordet och det där bordet? Det är genom en gemensam kolumn, eller ett gäng kolumner.
I det här fallet är det faktiskt bara en kolumn.
Så kund-ID, eller hur? Jag är precis som att lägga ID-numret här.
Och vad mer kan vi egentligen ta ur det här bordet.
Så detta är en nivå av normalisering.
Rätt? Så låt oss fortsätta att normalisera vilket är som att ta bort produktdetaljerna.
Rätt? Så produktdetaljerna upprepas också.
Så här känner du inte så mycket smärta, eftersom det bara finns tre poster i den här tabellen.
Tänk om bordet har miljoner poster, eller hur? Det är därför vi måste normalisera tabellen.
Nu tar du faktiskt ut produktinformationen och flyttar den till en annan tabell.
Och sedan har jag en produkt-ID-kolumn bara för att numrera, som ID-produkterna faktiskt, och din ordertabell kommer att se ut så här, sedan tar du ut betalningsinformationen till en annan tabell.
Och din ordertabell kommer då att se ut så här.
Detta är i grunden processen att gå från ett denormaliserat schema eller en databas till en normaliserad databas.
När du har dina data i en enda tabell behöver du inte göra några skarvar.
Så du kanske frågar faktiskt, vad är joints egentligen, när du kör frågor, som att använda SQL, är SQL ett språk eller hur? Ett strukturerat frågespråk, när du kör kommandon i din databas kan du hämta all din data från den här tabellen om din tabelldatabas är helt avnormaliserad.
Medan om du har många tabeller måste du liksom kombinera eller gå med i stallet och sedan måste du få ut data.
Så det kallas att gå med i borden.
Så när du har en denormaliseringsdatabas behöver du inte göra många kopplingar.
Och det är ganska bra på ett sätt eftersom din databas inte behöver tänka så mycket för att få data.
Du säger att jag vill ha denna information och den här informationen är tillgänglig i den här tabellen.
Så det är bara väldigt enkelt.
Medan som i en normaliserad databas, när du sammanfogar många tabeller, kommer din databasmotor med vilken de anropar optimizer i, i Oracle eller i de flesta av bandet de flesta databaserna.
Så den här databasmotorn måste tänka mer som då, okej, vilken tabell ska jag skanna först? Och hur ska jag filtrera data i den här tabellen.
Och sedan Okej, jag tar resultatuppsättningen från den tabellen och måste gå med i dessa andra tabeller.
Så det finns så mycket mer att tänka på att det är så mycket mer bearbetning som måste ske på vilken server denna databas än körs på.
Precis.
Och på grund av det kommer prestandan att vara ganska flexibel.
Nedre höger och det kommer att förbruka mycket resurser och du har allt det som händer i stor skala eftersom som många operationer händer samtidigt, då har du i princip långsam prestanda faktiskt, eller åtminstone lägre än vad det skulle ha varit i en avnormalisera databasen, men samtidigt tar vi bort så mycket dataupprepning eller så är dataredundansen väldigt låg, på grund av det är lagringen som behövs i en normaliserad databas mycket lägre.
Så du kan faktiskt inte gilla att generalisera och säga att en normaliserad databas alltid kommer att vara långsam eller att denormalisera databas kommer att vara snabb, det är som att det beror på att du faktiskt måste titta på data och se hur mycket upprepning som händer, etc, etc.
Så, men i allmänhet är det så här det går, när du går igenom den här designprocessen, okej, du vet, se vad vi har gjort faktiskt, eller hur.
Så vi har bestämt tabellerna som vi behöver, som ni vet, vi har beställningar, bordsprodukter fick kunderna och betalningarna, och vi bestämmer kolumnnamnen.
Och inte bara det för varje tabell, du måste bestämma vad som ska vara den primära nyckeln.
Vad jag menar med det som primärnyckel är en unik nyckel och som faktiskt inte kan vara null, vilket är väldigt viktigt.
Så med den här primärnyckeln bör du kunna identifiera vilken post som helst i den här tabellen, vilken rad som helst i den här tabellen.
Till exempel, om jag säger här, den primära nyckeln är ordernummer, då kan jag, när som helst om jag har ett ordernummer, då kan jag slå upp den här tabellen, låt oss säga att ordernummer är lika med två, jag kan bara hämta denna post från min databas.
Och då måste du också ha som några unika nycklar faktiskt, eller hur.
Så unika nycklar är ungefär som primärnyckeln.
Och en unik nyckel kan nu vara en primärnyckel kan inte vara null, som jag nämnde.
Och då kan du också ha index på ditt bord.
Så index är sätt att i princip välja din dagdata snabbare.
Låt oss säga att jag ofta söker i den här tabellen baserat på en kunds e-post, då behöver jag ett index på kolumnen för kunde-post, eller hur måste du bestämma det.
Och du måste bestämma vilka kolumner som kan vara null.
Rätt? Här kan ingen av kolumnerna vara null.
Låt oss säga att du har en annan kolumn som heter preferens och kundens preferens som i vilken typ av frakt eller vilken typ av eller vilket telefonnummer som är att föredra, eller något liknande.
Så det kan vara en nollkolumn, eller hur? Så du kan inte ha några kolumner.
Annars definierar du dina kolumner som inte null.
Låt oss säga att i din ordertabell har du den här levererade kolumnen, när en beställning i princip skapas när en kund köper en produkt på din webbplats.
Naturligtvis levereras det inte omedelbart, vid tidpunkten för orderskapandet kommer den levererade kolumnen alltid att ha no eller n, ett n värde, eller hur? Alla dessa saker, alla dessa beslut som vi fattar vi pratar om är en del av schemadesign.
Och när du har räknat ut allt detta kan du lägga in informationen i ditt designverktyg Entity Relationship-designverktyg.
Och i nästa avsnitt kommer jag att visa dig hur jag gör det på sequel workbench, min sequel world workbench, i princip, då kan du faktiskt få en bildrepresentation av din logiska design av din databas, eller hur.
Och det är i princip vad man kallar ett ER-diagram.
Och naturligtvis kan du prata om förhållandet mellan de två borden, låt oss säga att du kan säga åh, den här tabellen i den här tabellen, de har en till många relation, till exempel kan varje kund göra många beställningar.
Så det är faktiskt en en till många-relation, eller hur.
Men en, du vet, en beställning kan bara göras av en kund.
Rätt? Alltså, sånt där.
Så du har en till en relation, en till många relation, eller många till många relationer mellan tabeller.
Egentligen är dessa alla en del av datamodellering.
Men du behöver inte oroa dig så mycket för det, så länge du har en klar uppfattning om vilken data som kommer in i din databas.
Och längs vägen måste du definiera som datatypen för dina kolumner.
Det är faktiskt väldigt viktigt.
Dina namn kommer att bli en vild röding.
Du vet, telefonnummer kan vara nummer, och då är e-post igen som ett klockdiagram.
Och din ID-kolumn eller nummerkolumner kommer att vara int eller nummer.
Det här är alla några beslut som du skulle ta dig. I en datamodelleringsuppgift är det faktiskt i stort sett vad jag vill säga om datamodellering, sedan finns det mycket mer vi kan prata om det.
Och som atomicitet, precis som du, har du alla adresser, typ av liknande attribut packade i en kolumn, vi måste dela upp det också.
Så det kallas atomicitet.
Du kan ha adress separat, stad separat stater separat, och, du vet, postnummer separat, eller hur.
Så den typen av saker, det finns nyanser som gör din databas mer och mer effektiv.
Och, naturligtvis, vi kommer inte att gå in på en massa detaljer där.
Men det här är den grundläggande datamodelleringen som du behöver förstå.
Och som jag sa tidigare, i nästa avsnitt kommer jag att visa dig hur du tar detta och sedan mata in det på min uppföljares arbetsbänk.
snabb sammanfattning av vad jag gjorde i det förra avsnittet, jag skapade i princip en logisk design av en e-handelswebbplats.
Så det du tittar på är en tabell som jag började med.
Det är en denormaliseringstabell, och vi tog i princip den här denormaliserade tabellen och vi normaliserade den.
Som du kan se finns det fyra versioner av den här tabellen, jag kallar den här tabellen för ordertabell.
Så det finns fyra olika versioner.
Och jag för varje iteration tog jag ut upprepande data.
Så äntligen landade vi med fyra bord, förutom det ursprungliga orderbordet.
Så nu har vi även kunders produkter och betalning.
I den här videon ska jag ta det här nu alla stallar och sedan ska jag ta strukturen och jag ska skapa en logisk design.
Okej, så låt oss faktiskt gå till MySQL Workbench och jag är redan ansluten till en databas, vad jag ska göra är att gå till Arkiv och gå till nya modeller.
så här kan vi lägga till ett nytt ER-diagram, ett entitetsrelationsdiagram.
Och låt oss kalla denna databas för eecom.
Butik, något sådant.
Så låt oss gå vidare och börja skapa våra tabeller.
Nu kommer jag inte att skapa alla fyra tabellerna som förmodligen skulle ta längre tid eller lång tid, och jag kommer att skapa ett par tabeller.
Och det borde vara tillräckligt för att du ska förstå hur vi gör det här.
Så låt oss bara börja med tabellen Kunder.
Så kundtabellen har fem kolumner, detta är ikonen för att skapa en ny tabell, du kan dra och släppa, eller så kan du försöka rita nu dubbelklicka och sedan skapa en tabell som heter kund och här kan vi börja lägga tabellen kolumnen namnger kund-ID, och sedan kommer detta att fyllas i av din sekvens.
Så sekvens är ett databasobjekt.
Och det kommer att bli ett heltal.
Så vi kan lämna det som det är.
Och vi kan ha den som en primärnyckel, det är bra och en primärnyckel måste fyllas i, det kan det inte vara nu.
Så det väljs automatiskt.
Nästa är kundnamn, vi skulle kunna dela upp det i förnamn, och sedan kan vi välja titta på vår och kanske ge lite mer utrymme som i längden på namnet och sedan efternamnet, jag kan se 100 och sedan alla dessa kan inte vara nu så vi kan välja inte null-begränsning.
Så det här är olika begränsningar som är tillgängliga.
Låt oss gå vidare med nästa adress igen.
Och om du kommer ihåg att jag pratade om atomicitet.
Så du vill att dina kolumner ska vara du vet, atomära i den meningen att här är i princip hela adressen packad i en kolumn.
god praxis att faktiskt dela upp det i atomkolumner som en adress separat, stad separat stat separat och sedan postnummer separat.
Så vi har alla dessa, naturligtvis, ingen av dessa kan vara nu och vad finns det mer? Så kundens telefonnummer, telefonnummer kommer att vara alla nummer.
Men då vill jag få 10 nummer, naturligtvis inte null och kundmail.
Så jag kan bara säga e-post-id 100 Okej, inte null.
Så eftersom I-ID är den primära nyckeln här eller kund-ID, vill jag se till att vi har en begränsning för att undvika att upprepa kundinformation.
Till exempel, om du har en kunddata för ID ett, vill jag inte att samma kund, kunddata ska upprepas för ett annat ID, till exempel ID två.
Så jag ska faktiskt göra e-post-ID unikt för varje post här borta.
Och sedan kan eller kan vara telefonnummer också.
Så dessa är alla unika nyckelbegränsningar, eller unika begränsningar.
Det var allt.
Så vi har skapat tabellen Kunder.
Så låt oss gå tillbaka och se vad mer vi har.
Så låt oss nu skapa, skulle jag säga produkt.
Och sedan gör du i princip samma sak, väljer det för att skapa en ny tabell.
Och nu här, du kan bara rita in den här, jag ska kalla den produkt.
Och vi vill gå igenom samma process och sedan lägga in produktkolumnnamnen där.
Om du undrar är det här samma kund-ID-kolumn som vi lade till här.
Och vi kommer att göra det till en främmande nyckel på en minut.
Så låt oss gå vidare och dela upp det i flera kolumner.
För, återigen, allt är packat i en kolumn, vilket inte är en bra praxis.
Så låt oss säga att vi kallar det kreditkortsnummer.
Om kunden använder PayPal behöver vi den e-postadressen.
Så kan använda e-post-ID här.
Så detta kan vara null eller inte null baserat på vilken betalningstyp som används.
Så det är okej, så utgångsdatum kommer att vara en datumkolumn.
Så låt oss faktiskt ändra på det.
Så om du inte är säker kan du klicka på den rullgardinsmenyn och sedan välja rätt datatyp för varje.
Den andra saken som jag nämnde, som i grund och botten handlar om främmande nyckel, detta kund-ID är samma som det vi lade till här.
Så låt oss faktiskt göra detta kund-ID till en främmande nyckel.
Så vi kan bara kalla det kund-ID främmande nyckel ett.
Och sedan är den tabell som kommer att refereras till kunderna.
Och kolumnen kommer att vara kund-ID.
Och det är allt.
Så du kan se att nu vi har en koppling eller en relation mellan dessa två tabeller, jag tänker faktiskt bara lägga till tabellen Order också.
Jag har också skapat ordertabellen, som är huvudtabellen och jag ska nu skapa några främmande nycklar för orderna.
Allt är klart.
Om du vill skapa några index vid det här laget kan du göra det.
Så jag antar att vi är klara.
Så vi har i princip lagt till fyra tabeller till vår logiska design dessa fyra tabeller och sedan har vi skapat kolumner och sedan definierar deras datatyper.
Och även vi skapade främmande nycklar och naturligtvis primärnyckeln och unik nyckel för var och en av tabellerna.
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.
Rätt? 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? Rätt? 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.
Rätt? 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.
Rätt? 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.