\nKullan\u0131m Alan\u0131<\/td>\n Primary Key, aral\u0131k sorgular\u0131<\/td>\n Filtreleme, birle\u015ftirme, s\u0131ralama<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\nIndex Olu\u015fturma ve Kullan\u0131m Senaryolar\u0131<\/h3>\n Peki, indeksleri ne zaman ve nas\u0131l olu\u015fturmal\u0131y\u0131z? \u0130\u015fte basit bir \u00f6rnek:<\/p>\n
\n -- \u00d6rnek bir tablo olu\u015ftural\u0131m\n CREATE TABLE Musteriler (\n MusteriID INT PRIMARY KEY,\n Ad VARCHAR(50) NOT NULL,\n Soyad VARCHAR(50) NOT NULL,\n Email VARCHAR(100) UNIQUE,\n KayitTarihi DATETIME DEFAULT GETDATE()\n );\n\n -- Tabloya \u00f6rnek veri ekleyelim (b\u00fcy\u00fck bir tablo sim\u00fclasyonu i\u00e7in)\n DECLARE @i INT = 1;\n WHILE @i <= 100000\n BEGIN\n INSERT INTO Musteriler (MusteriID, Ad, Soyad, Email)\n VALUES (@i, 'Ad' + CAST(@i AS VARCHAR), 'Soyad' + CAST(@i AS VARCHAR), 'email' + CAST(@i AS VARCHAR) + '@example.com');\n SET @i = @i + 1;\n END;\n\n -- Index olmadan bir sorgu \u00e7al\u0131\u015ft\u0131ral\u0131m (KayitTarihi \u00fczerinde)\n -- Bu sorgu, t\u00fcm tabloyu taramak zorunda kalaca\u011f\u0131 i\u00e7in yava\u015f olacakt\u0131r.\n SELECT * FROM Musteriler WHERE KayitTarihi < '2023-01-01';\n\n -- 'KayitTarihi' s\u00fctununa bir Non-Clustered Index ekleyelim\n CREATE INDEX IX_Musteriler_KayitTarihi ON Musteriler (KayitTarihi);\n\n -- Index ile ayn\u0131 sorguyu tekrar \u00e7al\u0131\u015ft\u0131ral\u0131m\n -- \u015eimdi sorgu, indeks sayesinde \u00e7ok daha h\u0131zl\u0131 \u00e7al\u0131\u015facakt\u0131r.\n SELECT * FROM Musteriler WHERE KayitTarihi < '2023-01-01';\n <\/pre>\n<\/code><\/p>\n
G\u00f6rd\u00fc\u011f\u00fcn\u00fcz gibi, yaln\u0131zca bir indeks ekleyerek sorgu performans\u0131nda b\u00fcy\u00fck bir fark yaratabiliriz. \u0130ndeksler \u00f6zellikle a\u015fa\u011f\u0131daki durumlarda faydal\u0131d\u0131r:<\/p>\n
\nWHERE<\/code> ko\u015fulunda s\u0131k\u00e7a kullan\u0131lan s\u00fctunlar.<\/li>\nJOIN<\/code> operasyonlar\u0131nda kullan\u0131lan s\u00fctunlar (birle\u015ftirme anahtarlar\u0131).<\/li>\nORDER BY<\/code> veya GROUP BY<\/code> ile s\u0131ralama veya gruplama yap\u0131lan s\u00fctunlar.<\/li>\nDISTINCT<\/code> ile benzersiz de\u011ferler aranan s\u00fctunlar.<\/li>\n<\/ul>\nAncak, her s\u00fctuna indeks eklemek performans\u0131 her zaman art\u0131rmaz. \u0130ndeksler disk alan\u0131 kaplar ve veri ekleme, silme, g\u00fcncelleme i\u015flemlerinin maliyetini art\u0131r\u0131r. Bu nedenle, indeksleri ak\u0131ll\u0131ca kullanmak ve sadece ger\u00e7ekten ihtiya\u00e7 duyulan yerlere eklemek \u00f6nemlidir.<\/p>\n
\u26a1 Hashing ile Veri Eri\u015fimini Radikal \u015eekilde \u0130yile\u015ftirme Yollar\u0131<\/h2>\n Hashing, bilgisayar bilimlerinde verileri sabit boyutlu bir de\u011fere (hash de\u011feri veya hash kodu) d\u00f6n\u00fc\u015ft\u00fcrme i\u015flemidir. Bu d\u00f6n\u00fc\u015f\u00fcm, genellikle b\u00fcy\u00fck veri k\u00fcmelerinde h\u0131zl\u0131 arama ve kar\u015f\u0131la\u015ft\u0131rma yapmak i\u00e7in kullan\u0131l\u0131r. Veritaban\u0131 ba\u011flam\u0131nda hashing, \u00f6zellikle join (birle\u015ftirme) i\u015flemlerinin performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde etkileyebilir.<\/p>\n
Hashing Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n Bir hash fonksiyonu, ald\u0131\u011f\u0131 veriyi belirli bir algoritmaya g\u00f6re i\u015fler ve benzersiz veya neredeyse benzersiz bir \u00e7\u0131kt\u0131 (hash de\u011feri) \u00fcretir. \u00d6rne\u011fin, \"Ahmet\" ad\u0131n\u0131 bir hash fonksiyonundan ge\u00e7irdi\u011finizde \"X1Y2Z3\" gibi bir de\u011fer elde edebilirsiniz. \"Mehmet\" i\u00e7in ise tamamen farkl\u0131 bir de\u011fer \u00e7\u0131kar. Veritaban\u0131, bu hash de\u011ferlerini kullanarak verileri h\u0131zl\u0131ca bulabilir veya kar\u015f\u0131la\u015ft\u0131rabilir.<\/p>\n
Hashing'in veritabanlar\u0131nda en yayg\u0131n uygulama alanlar\u0131ndan biri \"Hash Join\" algoritmas\u0131d\u0131r. Bu algoritma, iki b\u00fcy\u00fck tabloyu birle\u015ftirmek i\u00e7in kullan\u0131l\u0131r ve \u00f6zellikle birle\u015ftirme ko\u015fulundaki s\u00fctunlarda indeks bulunmad\u0131\u011f\u0131nda veya tablolar \u00e7ok b\u00fcy\u00fck oldu\u011funda performans\u0131 art\u0131rabilir. Hash Join, birle\u015ftirilecek tablolardan birini (genellikle daha k\u00fc\u00e7\u00fck olan\u0131) bellek i\u00e7i bir hash tablosuna y\u00fckler. Daha sonra di\u011fer tabloyu tarayarak her bir sat\u0131r i\u00e7in ayn\u0131 hash fonksiyonunu uygular ve hash tablosunda e\u015fle\u015fen de\u011feri arar. Bu y\u00f6ntem, birle\u015ftirme i\u015flemini do\u011frusal bir arama yerine sabit zamanl\u0131 (ortalama) bir arama haline getirerek b\u00fcy\u00fck verilerde \u00e7ok daha h\u0131zl\u0131 sonu\u00e7lar verir.<\/p>\n
Hash Join ve Di\u011fer Join T\u00fcrleri Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h3>\n Veritaban\u0131 optimizasyoncusu (optimizer), sorgu plan\u0131n\u0131 olu\u015ftururken farkl\u0131 join algoritmalar\u0131 aras\u0131nda se\u00e7im yapar:<\/p>\n
\n\n Nested Loop Join:<\/strong> \u0130\u00e7 i\u00e7e d\u00f6ng\u00fcler \u015feklinde \u00e7al\u0131\u015f\u0131r. Birinci tablonun her sat\u0131r\u0131 i\u00e7in ikinci tablo taran\u0131r. K\u00fc\u00e7\u00fck tablolarda veya indeksli join s\u00fctunlar\u0131nda etkilidir.\n <\/li>\n\n Merge Join:<\/strong> \u0130ki tabloyu birle\u015ftirme s\u00fctunlar\u0131 \u00fczerinden s\u0131ralar ve ard\u0131ndan s\u0131ralanm\u0131\u015f listeleri birle\u015ftirir. \u00d6nceden s\u0131ralanm\u0131\u015f tablolarda veya indekslenmi\u015f s\u00fctunlarda iyi performans g\u00f6sterir.\n <\/li>\n\n Hash Join:<\/strong> Bir tabloyu hash tablosuna d\u00f6n\u00fc\u015ft\u00fcr\u00fcr ve di\u011fer tabloyu bu hash tablosuyla e\u015fle\u015ftirir. B\u00fcy\u00fck, s\u0131ral\u0131 olmayan tablolarda, \u00f6zellikle e\u015fitlik (=<\/code>) tabanl\u0131 birle\u015ftirmelerde \u00e7ok etkilidir.\n <\/li>\n<\/ul>\nVeritaban\u0131, mevcut istatistiklere ve sorgu yap\u0131s\u0131na g\u00f6re bu join t\u00fcrlerinden hangisinin daha uygun oldu\u011funa karar verir. Bir s\u00fctun \u00fczerinde indeks olmasa bile, veritaban\u0131 hash join'i kullanarak h\u0131zl\u0131 birle\u015ftirme yapabilir. Ancak bu, bellekte yeterli alan\u0131n olmas\u0131 durumunda ge\u00e7erlidir; aksi takdirde disk I\/O maliyetleri artabilir.<\/p>\n
\n -- Hash Join \u00f6rne\u011fi (Genellikle veritaban\u0131 optimizer'\u0131 taraf\u0131ndan otomatik se\u00e7ilir)\n -- Varsayal\u0131m ki 'Siparisler' ve 'Urunler' tablolar\u0131m\u0131z var.\n -- ve her ikisi de b\u00fcy\u00fck, 'UrunID' s\u00fctununda indeks yok veya \u00e7ok faydal\u0131 de\u011fil.\n\n CREATE TABLE Siparisler (\n SiparisID INT PRIMARY KEY,\n MusteriID INT,\n UrunID INT,\n Adet INT,\n SiparisTarihi DATETIME\n );\n\n CREATE TABLE Urunler (\n UrunID INT PRIMARY KEY,\n UrunAdi VARCHAR(100),\n Fiyat DECIMAL(10, 2)\n );\n\n -- \u00d6rnek veri ekleme (b\u00fcy\u00fck veri sim\u00fclasyonu)\n -- (Bu b\u00f6l\u00fcm atlanabilir veya daha k\u00fc\u00e7\u00fck veri setleri kullan\u0131labilir testler i\u00e7in)\n -- INSERT INTO Siparisler ...\n -- INSERT INTO Urunler ...\n\n -- UrunID \u00fczerinden bir Hash Join olas\u0131 bir senaryo\n -- SQL Server'da birle\u015ftirme ipu\u00e7lar\u0131 kullan\u0131labilir, ancak genellikle optimizasyoncu karar verir.\n -- SELECT S.SiparisID, U.UrunAdi, S.Adet\n -- FROM Siparisler S\n -- INNER JOIN Urunler U ON S.UrunID = U.UrunID;\n\n -- MySQL\/PostgreSQL'de EXPLAIN ANALYZE \u00e7\u0131kt\u0131s\u0131, Hash Join kullan\u0131l\u0131p kullan\u0131lmad\u0131\u011f\u0131n\u0131 g\u00f6sterir.\n -- Bu sorgunun nas\u0131l \u00e7al\u0131\u015faca\u011f\u0131na dair Optimizer'\u0131n plan\u0131n\u0131 g\u00f6rmek i\u00e7in EXPLAIN kullan\u0131r\u0131z.\n -- \u00d6rne\u011fin (ger\u00e7ek \u00e7\u0131kt\u0131lar DB'ye g\u00f6re de\u011fi\u015fir):\n -- EXPLAIN SELECT S.SiparisID, U.UrunAdi, S.Adet FROM Siparisler S INNER JOIN Urunler U ON S.UrunID = U.UrunID;\n <\/pre>\n<\/code><\/p>\n\n Uzman \u0130pucu: B\u00fcy\u00fck veritabanlar\u0131nda Hash Join'ler, \u00f6zellikle veri ambar\u0131 (data warehouse) ortamlar\u0131nda veya karma\u015f\u0131k raporlama sorgular\u0131nda \u00e7ok yayg\u0131nd\u0131r. Sorgu planlar\u0131n\u0131 analiz ederek (EXPLAIN komutuyla), veritaban\u0131n\u0131z\u0131n hangi join algoritmalar\u0131n\u0131 kulland\u0131\u011f\u0131n\u0131 g\u00f6rebilir ve buna g\u00f6re optimizasyon stratejileri geli\u015ftirebilirsiniz.\u2699\ufe0f SQL Sorgu Optimizasyonu: Performans Katili Hatalardan Nas\u0131l Ka\u00e7\u0131n\u0131r\u0131z?<\/h2>\n Veritaban\u0131 performans\u0131 sadece indeksler ve hashing ile s\u0131n\u0131rl\u0131 de\u011fildir. Sorgular\u0131n kendisi de performans\u0131 do\u011frudan etkileyen en \u00f6nemli fakt\u00f6rd\u00fcr. \u0130yi yaz\u0131lm\u0131\u015f bir sorgu, do\u011fru indekslerle birle\u015fti\u011finde mucizeler yaratabilir. Ancak k\u00f6t\u00fc yaz\u0131lm\u0131\u015f bir sorgu, en g\u00fc\u00e7l\u00fc indeksleri bile i\u015fe yaramaz hale getirebilir.<\/p>\n
En S\u0131k Yap\u0131lan Hatalar ve \u00c7\u00f6z\u00fcmleri<\/h3>\n\n\n SELECT *<\/code> Kullanmaktan Ka\u00e7\u0131n\u0131n:<\/strong> T\u00fcm s\u00fctunlar\u0131 se\u00e7mek yerine, yaln\u0131zca ihtiyac\u0131n\u0131z olan s\u00fctunlar\u0131 belirtin. Bu, a\u011f trafi\u011fini azalt\u0131r, veritaban\u0131 sunucusunun daha az veri i\u015flemesini sa\u011flar ve indeks kullan\u0131m\u0131n\u0131 iyile\u015ftirebilir (covering indexes).<\/p>\n\n -- K\u00f6t\u00fc \u00d6rnek\n SELECT * FROM BuyukTablo WHERE Durum = 'Aktif';\n\n -- \u0130yi \u00d6rnek\n SELECT ID, Ad, Soyad, Email FROM BuyukTablo WHERE Durum = 'Aktif';\n <\/pre>\n<\/code>\n <\/li>\n
\n WHERE<\/code> Ko\u015fulunda Fonksiyon Kullan\u0131m\u0131ndan Sak\u0131n\u0131n:<\/strong> \u0130ndeksli bir s\u00fctun \u00fczerinde bir fonksiyon kullanmak, veritaban\u0131n\u0131n indeksi kullanmas\u0131n\u0131 engelleyebilir ve tam tablo taramas\u0131na yol a\u00e7abilir (SARGable durum).<\/p>\n\n -- K\u00f6t\u00fc \u00d6rnek (Indeksi kullanamaz)\n SELECT * FROM Siparisler WHERE YEAR(SiparisTarihi) = 2023;\n\n -- \u0130yi \u00d6rnek (Indeksi kullanabilir)\n SELECT * FROM Siparisler WHERE SiparisTarihi >= '2023-01-01' AND SiparisTarihi < '2024-01-01';\n <\/pre>\n<\/code>\n <\/li>\n
\n LIKE<\/code> Operat\u00f6r\u00fcn\u00fcn Do\u011fru Kullan\u0131m\u0131:<\/strong> LIKE '%deger'<\/code> \u015feklindeki aramalar genellikle indeksi kullanamazken, LIKE 'deger%'<\/code> \u015feklindeki aramalar indeksi kullanabilir (buna \"prefix matching\" denir).<\/p>\n\n -- K\u00f6t\u00fc \u00d6rnek (Yava\u015f olabilir)\n SELECT * FROM Urunler WHERE UrunAdi LIKE '%telefon%';\n\n -- \u0130yi \u00d6rnek (Indeks varsa daha h\u0131zl\u0131 olabilir)\n SELECT * FROM Urunler WHERE UrunAdi LIKE 'Ak\u0131ll\u0131%';\n <\/pre>\n<\/code>\n <\/li>\n
\n Subquery (Alt Sorgu) Yerine JOIN<\/code> Kullan\u0131m\u0131:<\/strong> Bir\u00e7ok durumda, alt sorgular yerine JOIN<\/code> operasyonlar\u0131 kullanmak daha performansl\u0131 olabilir. Alt sorgular, her ana sorgu sat\u0131r\u0131 i\u00e7in tekrar \u00e7al\u0131\u015ft\u0131r\u0131labilece\u011fi i\u00e7in maliyetli olabilir.<\/p>\n\n -- K\u00f6t\u00fc \u00d6rnek (Korele alt sorgu)\n SELECT Ad, Soyad FROM Musteriler M\n WHERE EXISTS (SELECT 1 FROM Siparisler S WHERE S.MusteriID = M.MusteriID AND S.SiparisTarihi > '2023-01-01');\n\n -- \u0130yi \u00d6rnek (JOIN ile)\n SELECT DISTINCT M.Ad, M.Soyad FROM Musteriler M\n JOIN Siparisler S ON M.MusteriID = S.MusteriID\n WHERE S.SiparisTarihi > '2023-01-01';\n <\/pre>\n<\/code>\n <\/li>\n
\n HAVING<\/code> Yerine WHERE<\/code> Kullan\u0131m\u0131:<\/strong> WHERE<\/code> ko\u015fulu, verileri gruplamadan \u00f6nce filtreler ve bu genellikle daha verimlidir. HAVING<\/code> ise verileri gruplad\u0131ktan sonra filtreleme yapar.<\/p>\n\n -- K\u00f6t\u00fc \u00d6rnek\n SELECT MusteriID, SUM(ToplamFiyat) FROM Siparisler GROUP BY MusteriID HAVING SUM(ToplamFiyat) > 1000;\n\n -- \u0130yi \u00d6rnek (\u00d6nce filtreleyip sonra gruplama)\n SELECT MusteriID, SUM(ToplamFiyat) FROM Siparisler WHERE ToplamFiyat > 1000 GROUP BY MusteriID;\n <\/pre>\n<\/code>\n <\/li>\n<\/ol>\n
Sorgu Plan\u0131 Analizi: EXPLAIN<\/code> ile Sorgular\u0131n\u0131z\u0131 G\u00f6zlemleyin<\/h3>\nBir SQL sorgusunun nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 anlaman\u0131n en g\u00fc\u00e7l\u00fc yolu, veritaban\u0131n\u0131n sorgu plan\u0131n\u0131 incelemektir. Hemen hemen t\u00fcm modern RDBMS'ler (MySQL<\/code>, PostgreSQL<\/code>, SQL Server<\/code>, Oracle<\/code>), bir sorgunun nas\u0131l y\u00fcr\u00fct\u00fclece\u011fine dair bir plan \u00fcretebilir. Bu plana \"Execution Plan\" (Y\u00fcr\u00fctme Plan\u0131) denir ve EXPLAIN<\/code> (veya EXPLAIN ANALYZE<\/code> \/ SET SHOWPLAN_ALL ON<\/code>) komutuyla g\u00f6r\u00fcnt\u00fclenir.<\/p>\n\n -- PostgreSQL'de bir sorgunun plan\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleme\n EXPLAIN ANALYZE SELECT M.Ad, S.SiparisID\n FROM Musteriler M\n JOIN Siparisler S ON M.MusteriID = S.MusteriID\n WHERE S.SiparisTarihi >= '2023-01-01' AND S.SiparisTarihi < '2024-01-01'\n ORDER BY M.Ad;\n <\/pre>\n<\/code><\/p>\n
Bu komutun \u00e7\u0131kt\u0131s\u0131, hangi tablolar\u0131n tarand\u0131\u011f\u0131n\u0131, hangi indekslerin kullan\u0131ld\u0131\u011f\u0131n\u0131, hangi birle\u015ftirme algoritmalar\u0131n\u0131n uyguland\u0131\u011f\u0131n\u0131 ve her ad\u0131m\u0131n ne kadar maliyetli oldu\u011funu g\u00f6sterir. Plan\u0131 okumay\u0131 \u00f6\u011frenmek, performans\u0131 d\u00fc\u015f\u00fcren darbo\u011fazlar\u0131 tespit etmenin anahtar\u0131d\u0131r. \u00d6rne\u011fin, \"Full Table Scan\" (Tam Tablo Taramas\u0131) veya \"Sort\" (S\u0131ralama) gibi maliyetli operasyonlar g\u00f6rd\u00fc\u011f\u00fcn\u00fczde, bu genellikle bir indeks eksikli\u011fi veya yanl\u0131\u015f yaz\u0131lm\u0131\u015f bir sorgu oldu\u011funa i\u015faret eder.<\/p>\n\n Uzman \u0130pucu: Karma\u015f\u0131k sorgularda ad\u0131m ad\u0131m optimizasyon yap\u0131n. \u00d6nce en maliyetli k\u0131sm\u0131 tespit edin (EXPLAIN ile), sonra o k\u0131sm\u0131 optimize edin ve tekrar \u00f6l\u00e7\u00fcm yap\u0131n. Her zaman en b\u00fcy\u00fck etkiyi yaratacak de\u011fi\u015fikliklere odaklan\u0131n.\ud83d\udca1 Geli\u015fmi\u015f Teknikler ve Modern Yakla\u015f\u0131mlar: B\u00fcy\u00fck Veritabanlar\u0131 \u0130\u00e7in \u00c7\u00f6z\u00fcmler<\/h2>\n Temel indeksleme, hashing ve sorgu optimizasyon tekniklerinin \u00f6tesinde, b\u00fcy\u00fck ve yo\u011fun kullan\u0131lan veritabanlar\u0131 i\u00e7in daha ileri d\u00fczey yakla\u015f\u0131mlar mevcuttur. Bu teknikler, genellikle daha fazla planlama ve altyap\u0131 de\u011fi\u015fikli\u011fi gerektirse de, ola\u011fan\u00fcst\u00fc performans art\u0131\u015flar\u0131 sa\u011flayabilir.<\/p>\n
Materialized Views (Somutla\u015ft\u0131r\u0131lm\u0131\u015f G\u00f6r\u00fcn\u00fcmler)<\/h3>\n Normal g\u00f6r\u00fcn\u00fcmler (views), her \u00e7a\u011fr\u0131ld\u0131klar\u0131nda temel tablolar\u0131ndan verileri dinamik olarak \u00e7ekerler. Ancak Materialized Views, sorgunun sonucunu \u00f6nceden hesaplay\u0131p diske kaydeder. \u00d6zellikle karma\u015f\u0131k join'ler veya aggregation'lar (toplama fonksiyonlar\u0131) i\u00e7eren raporlama sorgular\u0131 i\u00e7in idealdir. Veriler \u00f6nceden haz\u0131rland\u0131\u011f\u0131 i\u00e7in, sorgu an\u0131nda hesaplama y\u00fck\u00fc ortadan kalkar ve \u00e7ok daha h\u0131zl\u0131 sonu\u00e7 verir. Dezavantaj\u0131, temel tablolar de\u011fi\u015fti\u011finde Materialized View'\u0131n yenilenmesi (refresh) gerekmesidir, bu da bir maliyet getirir.<\/p>\n
\n -- PostgreSQL \u00f6rne\u011fi: Sat\u0131\u015f raporlar\u0131 i\u00e7in Materialized View\n CREATE MATERIALIZED VIEW AylikSatisRaporu AS\n SELECT\n TO_CHAR(SiparisTarihi, 'YYYY-MM') AS Ay,\n COUNT(SiparisID) AS ToplamSiparis,\n SUM(Adet * Fiyat) AS ToplamCiro\n FROM Siparisler s\n JOIN Urunler u ON s.UrunID = u.UrunID\n GROUP BY Ay\n ORDER BY Ay;\n\n -- Materialized View'\u0131 sorgula (\u00e7ok h\u0131zl\u0131)\n SELECT * FROM AylikSatisRaporu WHERE Ay = '2023-10';\n\n -- Temel veriler de\u011fi\u015fti\u011finde Materialized View'\u0131 g\u00fcncelle\n REFRESH MATERIALIZED VIEW AylikSatisRaporu;\n <\/pre>\n<\/code><\/p>\n
Tablo B\u00f6l\u00fcmleme (Partitioning)<\/h3>\n \u00c7ok b\u00fcy\u00fck tablolar\u0131 y\u00f6netmek ve optimize etmek zorla\u015fabilir. B\u00f6l\u00fcmleme, mant\u0131ksal olarak tek bir tablo gibi g\u00f6r\u00fcnen veriyi, fiziksel olarak daha k\u00fc\u00e7\u00fck ve y\u00f6netilebilir par\u00e7alara (partition) ay\u0131rma tekni\u011fidir. Bu par\u00e7alar farkl\u0131 disklerde veya dosya gruplar\u0131nda depolanabilir. B\u00f6l\u00fcmleme, \u00f6zellikle b\u00fcy\u00fck aral\u0131k sorgular\u0131nda (\u00f6rne\u011fin belirli bir tarih aral\u0131\u011f\u0131ndaki veriler) performans\u0131 art\u0131r\u0131r \u00e7\u00fcnk\u00fc veritaban\u0131 sadece ilgili b\u00f6l\u00fcmleri tarar, t\u00fcm tabloyu de\u011fil. Ayr\u0131ca, eski verileri ar\u015fivlemek veya silmek de kolayla\u015f\u0131r.<\/p>\n
Veritaban\u0131 D\u00fczeyinde \u00d6nbellekleme (Caching)<\/h3>\n Bir\u00e7ok veritaban\u0131 sistemi, s\u0131k\u00e7a eri\u015filen verileri veya sorgu sonu\u00e7lar\u0131n\u0131 \u00f6nbellekte (cache) tutar. Bu, ayn\u0131 verilere veya sorgu sonu\u00e7lar\u0131na tekrar eri\u015fildi\u011finde disk I\/O'sundan ka\u00e7\u0131n\u0131lmas\u0131n\u0131 ve bellekteki daha h\u0131zl\u0131 eri\u015fimi sa\u011flar. Do\u011fru yap\u0131land\u0131r\u0131lm\u0131\u015f bir \u00f6nbellek, uygulaman\u0131z\u0131n performans\u0131n\u0131 \u00e7arp\u0131c\u0131 \u015fekilde art\u0131rabilir. Uygulama taraf\u0131nda da kendi \u00f6nbellekleme mekanizmalar\u0131n\u0131z\u0131 (Redis, Memcached gibi) kullanarak veritaban\u0131 y\u00fck\u00fcn\u00fc azaltabilirsiniz.<\/p>\n
Ba\u011flant\u0131 Havuzu (Connection Pooling)<\/h3>\n Her veritaban\u0131 ba\u011flant\u0131s\u0131 olu\u015fturmak, maliyetli ve zaman al\u0131c\u0131 bir i\u015flemdir. \u00d6zellikle web uygulamalar\u0131 gibi s\u0131k\u00e7a ba\u011flant\u0131 a\u00e7\u0131p kapatan sistemlerde bu, bir performans darbo\u011faz\u0131 olu\u015fturabilir. Ba\u011flant\u0131 havuzu, belirli say\u0131da veritaban\u0131 ba\u011flant\u0131s\u0131n\u0131 \u00f6nceden a\u00e7ar ve yeniden kullan\u0131lmak \u00fczere haz\u0131r tutar. Uygulama yeni bir ba\u011flant\u0131ya ihtiya\u00e7 duydu\u011funda, havuzdan mevcut bir ba\u011flant\u0131y\u0131 al\u0131r ve i\u015fi bitti\u011finde havuza geri verir. Bu, ba\u011flant\u0131 olu\u015fturma ve kapatma maliyetini ortadan kald\u0131r\u0131r ve performans\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r.<\/p>\n\n Uzman \u0130pucu: Uygulaman\u0131z\u0131n ve veritaban\u0131n\u0131z\u0131n mimarisini bir b\u00fct\u00fcn olarak d\u00fc\u015f\u00fcn\u00fcn. Performans darbo\u011fazlar\u0131 sadece SQL sorgular\u0131nda de\u011fil, ayn\u0131 zamanda a\u011f katman\u0131nda, uygulama sunucusunda veya hatta istemci taraf\u0131nda da olabilir. Kapsaml\u0131 bir izleme ve analiz stratejisi geli\u015ftirin.\ud83d\udcf1 Mobil Uygulamalarda SQL Optimizasyonu: H\u0131zl\u0131 ve Ak\u0131c\u0131 Deneyimler \u0130\u00e7in P\u00fcf Noktalar\u0131<\/h2>\n Mobil uygulamalar\u0131n ba\u015far\u0131s\u0131, b\u00fcy\u00fck \u00f6l\u00e7\u00fcde h\u0131zl\u0131 ve sorunsuz bir kullan\u0131c\u0131 deneyimine ba\u011fl\u0131d\u0131r. Mobil cihazlar genellikle s\u0131n\u0131rl\u0131 bant geni\u015fli\u011fine, daha y\u00fcksek gecikme s\u00fcrelerine ve daha az i\u015flem g\u00fcc\u00fcne sahip oldu\u011fu i\u00e7in, veritaban\u0131 performans\u0131n\u0131n \u00f6nemi burada katlanarak artar. Optimize edilmi\u015f SQL sorgular\u0131 ve iyi tasarlanm\u0131\u015f bir veritaban\u0131 mimarisi, mobil uygulaman\u0131z\u0131n h\u0131z\u0131n\u0131 ve duyarl\u0131l\u0131\u011f\u0131n\u0131 do\u011frudan etkiler.<\/p>\n
Mobil Uygulama Performans\u0131na SQL'in Etkisi<\/h3>\n Bir mobil uygulama genellikle sunucuda \u00e7al\u0131\u015fan bir API arac\u0131l\u0131\u011f\u0131yla veritaban\u0131yla ileti\u015fim kurar. SQL sorgular\u0131n\u0131z ne kadar h\u0131zl\u0131 \u00e7al\u0131\u015f\u0131rsa, API yan\u0131t s\u00fcreleri o kadar k\u0131sal\u0131r. Daha k\u0131sa yan\u0131t s\u00fcreleri ise mobil uygulamadaki y\u00fckleme ekranlar\u0131n\u0131 azalt\u0131r, veri yenilemelerini h\u0131zland\u0131r\u0131r ve genel olarak daha ak\u0131c\u0131 bir kullan\u0131c\u0131 deneyimi sunar. \u00d6rne\u011fin, bir e-ticaret uygulamas\u0131nda \u00fcr\u00fcn listeleme veya sipari\u015f ge\u00e7mi\u015fini g\u00f6r\u00fcnt\u00fcleme gibi i\u015flemlerin milisaniyeler i\u00e7inde ger\u00e7ekle\u015fmesi, kullan\u0131c\u0131 memnuniyetini do\u011frudan etkiler.<\/p>\n
\n\n Azalt\u0131lm\u0131\u015f Veri Y\u00fck\u00fc:<\/strong> Mobil cihazlara g\u00f6nderilen veri miktar\u0131n\u0131 minimumda tutmak \u00e7ok \u00f6nemlidir. SELECT *<\/code> kullanmak yerine, yaln\u0131zca mobil uygulaman\u0131n ihtiyac\u0131 olan s\u00fctunlar\u0131 se\u00e7in. Gerekirse, karma\u015f\u0131k nesneleri k\u00fc\u00e7\u00fck, hafif veri yap\u0131lar\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fcn.\n <\/li>\n\n Etkin API Tasar\u0131m\u0131:<\/strong> Mobil uygulamalar i\u00e7in tasarlanm\u0131\u015f API'ler, veritaban\u0131 sorgular\u0131n\u0131n sonu\u00e7lar\u0131n\u0131 filtreleme, s\u0131ralama ve sayfalama yetenekleri sunmal\u0131d\u0131r. Bu, mobil cihaz\u0131n t\u00fcm veriyi \u00e7ekip sonra filtrelemesi yerine, veritaban\u0131n\u0131n sadece gerekli veriyi g\u00f6ndermesini sa\u011flar.\n <\/li>\n\n \u00d6nbellekleme Stratejileri:<\/strong> S\u0131k\u00e7a eri\u015filen ve nadiren de\u011fi\u015fen verileri mobil cihaz\u0131n kendisinde veya API katman\u0131nda \u00f6nbelle\u011fe almak, veritaban\u0131 sorgu say\u0131s\u0131n\u0131 ve a\u011f trafi\u011fini azalt\u0131r.\n <\/li>\n<\/ul>\nHTML Veri Sunumu ve Duyarl\u0131 Tasar\u0131m\u0131n \u00d6nemi<\/h3>\n Mobil uygulamalar genellikle kendi UI\/UX katmanlar\u0131na sahip olsa da, bazen webview i\u00e7inde HTML i\u00e7eri\u011fi g\u00f6stermeleri veya veri taban\u0131ndan gelen listelerin web tabanl\u0131 g\u00f6r\u00fcnt\u00fcs\u00fc gerekebilir. \u0130\u015fte bu noktada, veritaban\u0131ndan gelen verilerin mobil uyumlu bir \u015fekilde sunulmas\u0131 devreye girer. Optimize edilmi\u015f SQL sorgular\u0131ndan elde edilen veriler, kullan\u0131c\u0131ya do\u011fru \u015fekilde ula\u015ft\u0131r\u0131lmal\u0131d\u0131r.<\/p>\n
A\u015fa\u011f\u0131daki gibi basit bir HTML tablosu, mobil cihazlarda kolayca okunabilir ve y\u00f6netilebilir hale getirilmelidir. Bu, CSS ile duyarl\u0131 tasar\u0131m prensipleri (responsive design) kullan\u0131larak yap\u0131l\u0131r:<\/p>\n
\n \n
\n \n \n \u00dcr\u00fcn ID<\/th>\n Ad\u0131<\/th>\n Fiyat<\/th>\n Stok<\/th>\n <\/tr>\n <\/thead>\n \n \n 101<\/td>\n Ak\u0131ll\u0131 Saat<\/td>\n 2500 TL<\/td>\n 50<\/td>\n <\/tr>\n \n 102<\/td>\n Kablosuz Kulakl\u0131k<\/td>\n 1200 TL<\/td>\n 120<\/td>\n <\/tr>\n \n 103<\/td>\n Diz\u00fcst\u00fc Bilgisayar<\/td>\n 15000 TL<\/td>\n 30<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n <\/pre>\n<\/code><\/p>\n
Bu HTML yap\u0131s\u0131, CSS medya sorgular\u0131 (media queries) kullan\u0131larak mobil cihazlarda farkl\u0131 \u015fekillerde g\u00f6r\u00fcnt\u00fclenebilir. \u00d6rne\u011fin, k\u00fc\u00e7\u00fck ekranlarda tablo s\u00fctunlar\u0131 alt alta s\u0131ralanabilir, kayd\u0131r\u0131labilir hale getirilebilir veya baz\u0131 daha az \u00f6nemli s\u00fctunlar gizlenebilir. Bu sayede, SQL'den gelen optimize edilmi\u015f veri, her cihazda en iyi \u015fekilde sunulur. <\/p>\n
\ud83d\udcc8 Vaka Analizi: Ger\u00e7ek Bir E-ticaret Senaryosu \u00dczerinden Performans \u0130yile\u015ftirmeleri<\/h2>\n Bir e-ticaret platformu, kullan\u0131c\u0131 say\u0131s\u0131n\u0131n artmas\u0131yla birlikte ciddi performans sorunlar\u0131 ya\u015famaya ba\u015flad\u0131. \u00d6zellikle \u00fcr\u00fcn arama, kategori filtreleme ve m\u00fc\u015fteri sipari\u015f ge\u00e7mi\u015fi sayfalar\u0131 \u00e7ok yava\u015f yan\u0131t veriyordu. M\u00fc\u015fteri \u015fikayetleri artarken, sat\u0131\u015flar da d\u00fc\u015f\u00fc\u015f e\u011filimindeydi. Geli\u015ftirme ekibi, kapsaml\u0131 bir veritaban\u0131 performans analizi yapmaya karar verdi.<\/p>\n
Problem Tespiti<\/h3>\n\n\n Eksik \u0130ndeksler:<\/strong> \u00dcr\u00fcn tablosunda (10 milyon kay\u0131t), UrunAdi<\/code> ve KategoriID<\/code> s\u00fctunlar\u0131nda indeks yoktu. Arama ve filtreleme sorgular\u0131, tam tablo taramas\u0131na neden oluyordu.\n <\/li>\n\n Ineffective JOINs:<\/strong> M\u00fc\u015fteri sipari\u015f ge\u00e7mi\u015fi sorgusu, Musteriler<\/code>, Siparisler<\/code> ve SiparisDetay<\/code> tablolar\u0131n\u0131 birle\u015ftirirken, birle\u015ftirme ko\u015fullar\u0131nda indekslenmemi\u015f s\u00fctunlar kullan\u0131yordu.\n <\/li>\n\n SELECT *<\/code> Kullan\u0131m\u0131:<\/strong> T\u00fcm sorgularda gereksiz yere SELECT *<\/code> kullan\u0131l\u0131yordu, bu da hem veri i\u015fleme hem de a\u011f trafi\u011fi y\u00fck\u00fcn\u00fc art\u0131r\u0131yordu.\n <\/li>\n\n Korele Alt Sorgular:<\/strong> Baz\u0131 pop\u00fcler \u00fcr\u00fcnleri listelerken, stok kontrol\u00fc i\u00e7in korele alt sorgular kullan\u0131lm\u0131\u015ft\u0131, bu da her ana sorgu sat\u0131r\u0131 i\u00e7in binlerce alt sorgu \u00e7al\u0131\u015ft\u0131rmaya neden oluyordu.\n <\/li>\n<\/ol>\nUygulanan \u00c7\u00f6z\u00fcmler<\/h3>\n\n\n \u0130ndeks Eklemeleri:<\/strong><\/p>\n\nUrunler<\/code> tablosuna UrunAdi<\/code> (non-clustered) ve KategoriID<\/code> (non-clustered) i\u00e7in indeksler eklendi.<\/li>\nSiparisler<\/code> tablosunda MusteriID<\/code> ve SiparisTarihi<\/code> s\u00fctunlar\u0131 i\u00e7in non-clustered indeksler olu\u015fturuldu.<\/li>\nSiparisDetay<\/code> tablosunda SiparisID<\/code> ve UrunID<\/code> i\u00e7in indeksler eklendi.<\/li>\n<\/ul>\n<\/li>\n\n Sorgular\u0131n Yeniden Yaz\u0131m\u0131:<\/strong><\/p>\n\nSELECT *<\/code> ifadeleri, sadece gerekli s\u00fctunlar\u0131 i\u00e7eren SELECT Kolon1, Kolon2...<\/code> ifadelerine d\u00f6n\u00fc\u015ft\u00fcr\u00fcld\u00fc.<\/li>\nKorele alt sorgular, INNER JOIN<\/code> veya LEFT JOIN<\/code> yap\u0131lar\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fclerek \u00e7ok daha verimli hale getirildi.<\/li>\nLIKE '%keyword%'<\/code> tarz\u0131 aramalar, e\u011fer m\u00fcmk\u00fcnse LIKE 'keyword%'<\/code> veya Full-Text Search \u00f6zellikleriyle de\u011fi\u015ftirildi.<\/li>\n<\/ul>\n<\/li>\n\n Sorgu Plan\u0131 Analizi:<\/strong> Her kritik sorgu i\u00e7in EXPLAIN<\/code> kullan\u0131larak, indekslerin do\u011fru kullan\u0131l\u0131p kullan\u0131lmad\u0131\u011f\u0131 ve birle\u015ftirme algoritmalar\u0131n\u0131n uygun olup olmad\u0131\u011f\u0131 kontrol edildi. Gerekirse veritaban\u0131 istatistikleri g\u00fcncellendi.\n <\/li>\n\n \u00d6nbellekleme:<\/strong> S\u0131k\u00e7a eri\u015filen statik \u00fcr\u00fcn verileri (kategori listeleri, pop\u00fcler \u00fcr\u00fcnler) Redis gibi bir \u00f6nbellek sistemine al\u0131nd\u0131.\n <\/li>\n<\/ol>\nSonu\u00e7lar ve Etki<\/h3>\n Bu optimizasyonlar sonucunda, platformda g\u00f6zle g\u00f6r\u00fcl\u00fcr bir performans art\u0131\u015f\u0131 ya\u015fand\u0131:<\/p>\n
\n\u00dcr\u00fcn arama ve filtreleme s\u00fcreleri %80 oran\u0131nda azald\u0131.<\/li>\n M\u00fc\u015fteri sipari\u015f ge\u00e7mi\u015fi sayfalar\u0131 10 saniyeden 1 saniyenin alt\u0131na d\u00fc\u015ft\u00fc.<\/li>\n Veritaban\u0131 sunucusunun CPU kullan\u0131m\u0131 %50 oran\u0131nda azald\u0131, bu da daha fazla e\u015fzamanl\u0131 kullan\u0131c\u0131ya hizmet verilebilece\u011fi anlam\u0131na geliyordu.<\/li>\n Kullan\u0131c\u0131 memnuniyeti artt\u0131 ve platformun genel ak\u0131\u015fkanl\u0131\u011f\u0131 iyile\u015fti.<\/li>\n<\/ul>\nBu vaka analizi, indeksleme, hashing ve sorgu optimizasyonunun sadece teorik kavramlar olmad\u0131\u011f\u0131n\u0131, ayn\u0131 zamanda ger\u00e7ek d\u00fcnya uygulamalar\u0131nda somut i\u015f de\u011ferleri yaratabilece\u011fini a\u00e7\u0131k\u00e7a g\u00f6stermektedir. Do\u011fru yakla\u015f\u0131mla, veritaban\u0131 performans sorunlar\u0131n\u0131n \u00fcstesinden gelmek ve daha iyi bir kullan\u0131c\u0131 deneyimi sunmak m\u00fcmk\u00fcnd\u00fcr.<\/p>\n
\ud83c\udfaf Sonu\u00e7 ve S\u0131k\u00e7a Sorulan Sorular: Performans Yolculu\u011funuz Ba\u015fl\u0131yor!<\/h2>\n SQL veritaban\u0131 performans\u0131, bir uygulaman\u0131n ba\u015far\u0131s\u0131 i\u00e7in hayati \u00f6neme sahiptir. Bu makale boyunca, indeksleme, hashing ve sorgu optimizasyonunun temel prensiplerini ve ileri d\u00fczey tekniklerini derinlemesine inceledik. G\u00f6rd\u00fck ki, do\u011fru indeksleri se\u00e7mek, hash algoritmalar\u0131n\u0131n nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 anlamak ve sorgular\u0131 en iyi pratiklere g\u00f6re yazmak, yava\u015f \u00e7al\u0131\u015fan bir sistem ile \u0131\u015f\u0131k h\u0131z\u0131nda bir uygulama aras\u0131ndaki fark\u0131 yaratabilir. Unutmay\u0131n, performans optimizasyonu tek seferlik bir i\u015flem de\u011fil, s\u00fcrekli bir izleme, test etme ve iyile\u015ftirme d\u00f6ng\u00fcs\u00fcd\u00fcr. Veritaban\u0131n\u0131z b\u00fcy\u00fcd\u00fck\u00e7e ve kullan\u0131m \u015fekilleri de\u011fi\u015ftik\u00e7e, optimizasyon ihtiya\u00e7lar\u0131n\u0131z da evrilecektir. D\u00fczenli olarak sorgu planlar\u0131n\u0131 kontrol etmek, indeksleri g\u00f6zden ge\u00e7irmek ve yeni teknolojilere a\u00e7\u0131k olmak, veritabanlar\u0131n\u0131z\u0131n her zaman zirvede kalmas\u0131n\u0131 sa\u011flayacakt\u0131r. Bu yolculukta edindi\u011finiz bilgilerle, art\u0131k veritaban\u0131 performans\u0131n\u0131z\u0131 kendi ellerinize alma g\u00fcc\u00fcne sahipsiniz. Uygulamalar\u0131n\u0131z\u0131n h\u0131z\u0131n\u0131 art\u0131r\u0131n, kullan\u0131c\u0131lar\u0131n\u0131z\u0131 mutlu edin ve i\u015finize de\u011fer kat\u0131n!<\/p>\n
\u2753 S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n\n\n Her s\u00fctuna indeks eklemeli miyim?<\/strong><\/p>\nHay\u0131r, kesinlikle eklememelisiniz. Her indeks disk alan\u0131 kaplar ve veri ekleme, silme, g\u00fcncelleme i\u015flemlerinin maliyetini art\u0131r\u0131r. \u0130ndeksler sadece WHERE<\/code>, JOIN<\/code>, ORDER BY<\/code> veya GROUP BY<\/code> gibi clause'larda s\u0131k\u00e7a kullan\u0131lan ve veritaban\u0131 performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde etkileyen s\u00fctunlara eklenmelidir. Ayr\u0131ca, \u00e7ok az benzersiz de\u011feri olan s\u00fctunlara indeks eklemek de genellikle faydas\u0131zd\u0131r.<\/p>\n<\/li>\n\n EXPLAIN komutunun \u00e7\u0131kt\u0131s\u0131n\u0131 nas\u0131l yorumlamal\u0131y\u0131m?<\/strong><\/p>\nEXPLAIN \u00e7\u0131kt\u0131s\u0131, sorgunun hangi ad\u0131mlardan ge\u00e7ti\u011fini (tablo taramas\u0131, indeks aramas\u0131, join t\u00fcr\u00fc, s\u0131ralama vb.) ve her ad\u0131m\u0131n tahmini maliyetini (sat\u0131r say\u0131s\u0131, s\u00fcre) g\u00f6sterir. Y\u00fcksek maliyetli ad\u0131mlara odaklan\u0131n. \"Full Table Scan\" veya \"Sort\" gibi ibareler genellikle bir indeks eksikli\u011fine veya yanl\u0131\u015f yaz\u0131lm\u0131\u015f bir sorguya i\u015faret eder. \"Using Index\" veya \"Index Scan\" g\u00f6rmek genellikle iyi bir i\u015farettir.<\/p>\n<\/li>\n
\n Hangi durumlarda Hashing, Indexleme'den daha iyi bir se\u00e7enek olabilir?<\/strong><\/p>\nHashing (\u00f6zellikle Hash Join), e\u015fitlik (=<\/code>) tabanl\u0131 birle\u015ftirme i\u015flemlerinde, \u00f6zellikle birle\u015ftirilecek tablolar\u0131n \u00e7ok b\u00fcy\u00fck oldu\u011fu ve birle\u015ftirme s\u00fctunlar\u0131nda uygun indekslerin bulunmad\u0131\u011f\u0131 veya kullan\u0131lamad\u0131\u011f\u0131 durumlarda \u00e7ok verimli olabilir. \u0130ndeksleme ise aral\u0131k sorgular\u0131, s\u0131ralama ve genel veri filtreleme i\u00e7in daha uygundur. \u00c7o\u011fu RDBMS'de, hashing genellikle dahili bir i\u015flem olarak, yani bir join algoritmas\u0131 olarak kullan\u0131l\u0131rken, indeksleme daha do\u011frudan sorgu h\u0131zland\u0131rma arac\u0131d\u0131r.<\/p>\n<\/li>\n\n Sorgu optimizasyonu yaparken en \u00e7ok hangi hatay\u0131 g\u00f6zden ka\u00e7\u0131r\u0131yorum?<\/strong><\/p>\nEn s\u0131k g\u00f6zden ka\u00e7an hatalardan biri, k\u00fc\u00e7\u00fck tablolar \u00fczerindeki performans sorunlar\u0131n\u0131 g\u00f6z ard\u0131 etmektir. K\u00fc\u00e7\u00fck bir tablo zamanla b\u00fcy\u00fcyebilir ve optimize edilmemi\u015f bir sorgu birdenbire b\u00fcy\u00fck bir darbo\u011faza d\u00f6n\u00fc\u015febilir. Ayr\u0131ca, WHERE<\/code> ko\u015fullar\u0131nda fonksiyon kullanmak veya LIKE '%keyword%'<\/code> gibi ifadelerle indeksleri devre d\u0131\u015f\u0131 b\u0131rakmak da s\u0131k\u00e7a yap\u0131lan ve kolayca g\u00f6zden ka\u00e7\u0131r\u0131lan hatalard\u0131r.<\/p>\n<\/li>\n\n Mobil uygulamalar i\u00e7in veritaban\u0131 optimizasyonunda en kritik ad\u0131m nedir?<\/strong><\/p>\nMobil uygulamalar i\u00e7in en kritik ad\u0131m, a\u011f trafi\u011fini ve veri y\u00fck\u00fcn\u00fc minimumda tutmakt\u0131r. Bu, SELECT *<\/code> kullanmaktan ka\u00e7\u0131narak yaln\u0131zca gerekli s\u00fctunlar\u0131 \u00e7ekmek, sunucu taraf\u0131nda veri filtreleme ve sayfalama yapmak ve s\u0131k\u00e7a kullan\u0131lan verileri API veya istemci taraf\u0131nda \u00f6nbelle\u011fe alarak sa\u011flan\u0131r. H\u0131zl\u0131 API yan\u0131tlar\u0131, mobil uygulaman\u0131z\u0131n genel ak\u0131\u015fkanl\u0131\u011f\u0131n\u0131 do\u011frudan etkiler.<\/p>\n<\/li>\n<\/ol>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Veritaban\u0131 performans\u0131, modern uygulamalar\u0131n en kritik ba\u015far\u0131 fakt\u00f6rlerinden biridir. Yava\u015f y\u00fcklenen sayfalar, tak\u0131lan uygulamalar veya yan\u0131t vermeyen sistemler,…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1505],"tags":[],"class_list":{"0":"post-30984","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-sql-2","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
SQL Performans\u0131n\u0131 U\u00e7urmak: Indexleme, Hashing & Sorgu Optimizasyonu<\/title>\n<meta name=\"description\" content=\"Veritaban\u0131 performans\u0131, modern uygulamalar\u0131n en kritik ba\u015far\u0131 fakt\u00f6rlerinden biridir. Yava\u015f y\u00fcklenen sayfalar, tak\u0131lan uygulamalar veya yan\u0131t vermeyen sistemler, kullan\u0131c\u0131 deneyimini do\u011frudan olumsuz etkileyerek m\u00fc\u015fteri kayb\u0131na ve i\u015f s\u00fcreklili\u011fi sorunlar\u0131na yol a\u00e7abilir. G\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda, veritabanlar\u0131 s\u00fcrekli b\u00fcy\u00fcyor, karma\u015f\u0131kla\u015f\u0131yor ve bu durum, sorgu s\u00fcrelerinin artmas\u0131na neden oluyor. Peki, bu ka\u00e7\u0131n\u0131lmaz bir kader mi? 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Yava\u015f y\u00fcklenen sayfalar, tak\u0131lan uygulamalar veya yan\u0131t vermeyen sistemler, kullan\u0131c\u0131 deneyimini do\u011frudan olumsuz etkileyerek m\u00fc\u015fteri kayb\u0131na ve i\u015f s\u00fcreklili\u011fi sorunlar\u0131na yol a\u00e7abilir. G\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda, veritabanlar\u0131 s\u00fcrekli b\u00fcy\u00fcyor, karma\u015f\u0131kla\u015f\u0131yor ve bu durum, sorgu s\u00fcrelerinin artmas\u0131na neden oluyor. Peki, bu ka\u00e7\u0131n\u0131lmaz bir kader mi? 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Yava\u015f y\u00fcklenen sayfalar, tak\u0131lan uygulamalar veya yan\u0131t vermeyen sistemler, kullan\u0131c\u0131 deneyimini do\u011frudan olumsuz etkileyerek m\u00fc\u015fteri kayb\u0131na ve i\u015f s\u00fcreklili\u011fi sorunlar\u0131na yol a\u00e7abilir. G\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda, veritabanlar\u0131 s\u00fcrekli b\u00fcy\u00fcyor, karma\u015f\u0131kla\u015f\u0131yor ve bu durum, sorgu s\u00fcrelerinin artmas\u0131na neden oluyor. Peki, bu ka\u00e7\u0131n\u0131lmaz bir kader mi? Elbette hay\u0131r!","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fatihsoysal.com\/blog\/sql-performansini-ucurmak-indexleme-hashing-sorgu-optimizasyonu\/","og_locale":"tr_TR","og_type":"article","og_title":"SQL Performans\u0131n\u0131 U\u00e7urmak: Indexleme, Hashing & Sorgu Optimizasyonu","og_description":"Veritaban\u0131 performans\u0131, modern uygulamalar\u0131n en kritik ba\u015far\u0131 fakt\u00f6rlerinden biridir. Yava\u015f y\u00fcklenen sayfalar, tak\u0131lan uygulamalar veya yan\u0131t vermeyen sistemler, kullan\u0131c\u0131 deneyimini do\u011frudan olumsuz etkileyerek m\u00fc\u015fteri kayb\u0131na ve i\u015f s\u00fcreklili\u011fi sorunlar\u0131na yol a\u00e7abilir. G\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda, veritabanlar\u0131 s\u00fcrekli b\u00fcy\u00fcyor, karma\u015f\u0131kla\u015f\u0131yor ve bu durum, sorgu s\u00fcrelerinin artmas\u0131na neden oluyor. Peki, bu ka\u00e7\u0131n\u0131lmaz bir kader mi? 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