Bu derste neler öğreneceksiniz?¶
SQL'de Tablo Birleştirme¶
SQL'de Tablo Birleştirme İşlemleri¶
Giriş¶
Veritabanlarında birden fazla tablodan gelen veriyle işlem yapmaya özellikle astronomide sıkça ihtiyaç duyulur. Aynı cisimlerin farklı katalog, teleskop arşivi ve tablolarda bulunan farklı dalgaboylarında, farklı gözlemsel nitelikte (tayf, fotometri, konum ölçüm, polarimetre) ve farklı zaman ölçeklerindeki verinin biraraya getirilmesiyle çalışılan cisimlere ilişkin daha bütünsel bir kavrayışa sahip olmak mümkün olabilmektedir. Astronomlar günümüzde uzaydan ve yerden yapılan pek çok gözlemle bu olanağa sahip olmakla birlikte bu olanağı etkin kullanmak için gerekli araçlara da hakim olmalıdırlar. Bu araçlardan biri de Yapılandırılmış Sorgu Dili (SQL)'nin fonksiyonalitesidir.
Ders 5a Yapılandırılmış Sorgu Dili SQL'e Giriş dersinde ele alınan örneklerde gezegenler (Planet) ve barınak yıldızlarına (Star) ilişkin bilgiler ayrı tablolarda bulunmaktadır; zira bu cisimler farklı gözlemsel karakteristiklere sahiptirler. Ayrıca böyle bir strateji, örneğin her bir gezegeni için barınak yıldızın tüm özelliklerin kopyalanması gibi tekrarları da önler. Bu durum barınak yıldızın tek bir özelliğinde yapılması gereken bir güncellemenin bir satırda ve tek bir kez yapılmasını da mümkün kılar.
Ancak bazen gezegen özelikleri ile barınak yıldızınkiler arasında korelasyonlar bakmak (gezegen büyüklüğü ile yıldız kütlesi ya da metal bolluğu arasındaki ilişkiler gibi) istenebilir. Bu durumda bu tabloları birleştirmeye ihtiyaç duyulur.
Tablo Birleştirme Türleri¶
Daha önce pandas modülüyle görüldüğü gibi birden fazla tabloyu birbirine bağlamak için çeşitli yöntemler bulunmaktadır. "Birebir eşleştirme" (one-to-one join ya da equi-join) en sık kullanılan birleştirme yöntemidir.
Örnek olarak Planet tablosu ile Star tablosu kepler_id sütunu üzerinden birebir bağlanabilir. Ancak öncelikle veritabanı bağlantılarının yapılması gerekir. Daha önce oluşturulmadıysa Star ve Planet tablolarının da oluşturularak verinin bulunduğu dosyalardan tablolara verinin alınması gerekir. Bu kez Planet tablosu oluşturulurken bu tabloyu kepler_id sütunundan Star tablosuna bağlamak için kepler_id sütununun tanımına REFERENCES Star(kepler_id) eklendiğine dikkat ediniz. Bu ek sonradan da yapılabilir ancak oluşturulmadan önce öngörülmüş olabilir.
In [1]:
# import the connect library for psycopg2
import psycopg2
def connect(conn_params_dic):
conn = None
try:
print('PostgreSQL sunucuya baglaniliyor...........')
conn = psycopg2.connect(**conn_params_dic)
print("Baglanti basarili..................")
except OperationalError as err:
# passing exception to function
show_psycopg2_exception(err)
# set the connection to 'None' in case of error
conn = None
return conn
conn_params_dic={ 'user':'myuser', 'password':'PostGRE:sql','host':'localhost','dbname':'mydatabase'}
conn = connect(conn_params_dic)
# We set autocommit=True so every command we execute will produce results immediately.
conn.autocommit = True
cursor = conn.cursor()
PostgreSQL sunucuya baglaniliyor........... Baglanti basarili..................
In [2]:
# Define function to create table
def create_table_Star(cursor):
try:
# Dropping table iris if exists
cursor.execute("DROP TABLE IF EXISTS Star CASCADE;")
sql = '''CREATE TABLE Star (
kepler_id INTEGER NOT NULL,
koi_name VARCHAR(20) NOT NULL,
t_eff INTEGER NOT NULL,
radius FLOAT NOT NULL,
PRIMARY KEY (kepler_id)
);'''
# Creating a table
cursor.execute(sql);
print("Star tablosu basariyla olusturuldu...............")
except psycopg2.OperationalError as err:
# pass exception to function
show_psycopg2_exception(err)
# set the connection to 'None' in case of error
conn = None
def create_table_Planet(cursor):
try:
# Dropping table iris if exists
cursor.execute("DROP TABLE IF EXISTS Planet CASCADE;")
sql = '''CREATE TABLE Planet (
kepler_id INTEGER NOT NULL REFERENCES Star(kepler_id),
koi_name VARCHAR(20) NOT NULL,
kepler_name VARCHAR(20),
status VARCHAR(20) NOT NULL,
period FLOAT NOT NULL,
radius FLOAT NOT NULL,
t_eq INTEGER NOT NULL,
PRIMARY KEY (koi_name)
)'''
# Creating a table
cursor.execute(sql);
print("Planet tablosu basariyla olusturuldu...............")
except psycopg2.OperationalError as err:
# pass exception to function
show_psycopg2_exception(err)
# set the connection to 'None' in case of error
conn = None
create_table_Star(cursor)
create_table_Planet(cursor)
Star tablosu basariyla olusturuldu............... Planet tablosu basariyla olusturuldu...............
In [3]:
fn = open("veri/stars.csv")
for line in fn:
f1 = line.split(",")
cursor.execute(
"INSERT INTO Star (kepler_id, koi_name, t_eff, radius) VALUES (%s, %s, %s, %s);",
(f1[0], f1[1], f1[2], f1[3])
)
In [4]:
fn = open("veri/planets.csv")
for line in fn:
f1 = line.split(",")
cursor.execute('''
INSERT INTO Planet (kepler_id, koi_name, kepler_name, status, period, radius, t_eq)
VALUES (%s, %s, %s, %s, %s, %s, %s)
''',(f1[0], f1[1], f1[2], f1[3], f1[4], f1[5], f1[6])
)
Örnek: WHERE İle Tabloların Birleştirilmesi¶
Aşağıdaki sorgu Star ve Planet tablolarını her ikisinde de ortak olan kepler_id sütunu üzerinden birleştirmekte ve Planet tablosunda yer alan her gezegen için barınak yıldızın koi_name sütununda bulunan KOI numarasını döndürmektedir. Bir barınak yıldıza bağlı birden fazla gezegen olabileceğinden kepler_id için birden fazla koi_name olabilir.
In [5]:
query = '''
SELECT {},{}
FROM {},{}
WHERE {} = {};
'''.format('Star.kepler_id','Planet.koi_name',
'Star','Planet',
'Star.kepler_id','Planet.kepler_id')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(11446443, 'K00001.01') (10666592, 'K00002.01') (6922244, 'K00010.01') (11904151, 'K00072.01') (10187017, 'K00082.04') (10187017, 'K00082.05') (10984090, 'K00112.02') (9579641, 'K00115.01') (9579641, 'K00115.02') (9579641, 'K00115.03') (8395660, 'K00116.01') (8395660, 'K00116.02') (8395660, 'K00116.03') (8395660, 'K00116.04') (10875245, 'K00117.02') (10480982, 'K00744.01') (10526549, 'K00746.01') (10583066, 'K00747.01') (10601284, 'K00749.01') (10601284, 'K00749.02') (10601284, 'K00749.03') (10662202, 'K00750.01') (10682541, 'K00751.01') (10797460, 'K00752.01') (10797460, 'K00752.02') (10811496, 'K00753.01') (10848459, 'K00754.01') (10854555, 'K00755.01') (10872983, 'K00756.01') (10872983, 'K00756.02') (10872983, 'K00756.03') (10910878, 'K00757.01') (10910878, 'K00757.02') (10910878, 'K00757.03') (10987985, 'K00758.01') (11018648, 'K00759.01') (11018648, 'K00759.02') (11138155, 'K00760.01') (11153539, 'K00762.01') (11304958, 'K00764.01') (11391957, 'K00765.01') (11403044, 'K00766.01') (11414511, 'K00767.01') (11460018, 'K00769.01') (11465813, 'K00771.01') (11493732, 'K00772.01') (11507101, 'K00773.01') (11754553, 'K00775.01') (11754553, 'K00775.02') (11754553, 'K00775.03') (11812062, 'K00776.01') (11818800, 'K00777.01') (11853255, 'K00778.01') (11918099, 'K00780.01') (11918099, 'K00780.02') (11923270, 'K00781.01') (11960862, 'K00782.01') (12020329, 'K00783.01') (12066335, 'K00784.01') (12066335, 'K00784.02') (12070811, 'K00785.01') (12110942, 'K00786.01') (12366084, 'K00787.01') (12366084, 'K00787.02') (12404086, 'K00788.01') (12470844, 'K00790.01') (12470844, 'K00790.02') (12644822, 'K00791.01') (2713049, 'K00794.01') (3114167, 'K00795.01') (3115833, 'K00797.01') (3246984, 'K00799.01') (3342970, 'K00800.01') (3342970, 'K00800.02') (3351888, 'K00801.01') (3453214, 'K00802.01') (3641726, 'K00804.01') (3832474, 'K00806.01') (3832474, 'K00806.02') (3832474, 'K00806.03') (3935914, 'K00809.01') (3940418, 'K00810.01') (4049131, 'K00811.01') (4139816, 'K00812.01') (4139816, 'K00812.02') (4139816, 'K00812.03') (4139816, 'K00812.04') (4275191, 'K00813.01') (4476123, 'K00814.01') (5358241, 'K00829.01') (5358241, 'K00829.02') (5358241, 'K00829.03') (5358624, 'K00830.01') (5456651, 'K00835.01') (5456651, 'K00835.02') (6862328, 'K00865.01')
Aynı isimde öznitelikleri (sütunları) paylaşan tablolar söz konusu olduğunda, belirsizliği önlemek için hangi tablodan bahsedildiği AS ifadesi kullanılarak bir lakapla kısaca belirtilebilir. Bu, sorguyu çok daha kısa hale getirir ve okunabilirliğine de yardımcı olur.
In [6]:
query = '''
SELECT {}, {}
FROM {} AS {}, {} AS {}
WHERE {} = {}
'''.format('s.kepler_id','p.koi_name',
'Star','s','Planet','p',
's.kepler_id','p.kepler_id')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(11446443, 'K00001.01') (10666592, 'K00002.01') (6922244, 'K00010.01') (11904151, 'K00072.01') (10187017, 'K00082.04') (10187017, 'K00082.05') (10984090, 'K00112.02') (9579641, 'K00115.01') (9579641, 'K00115.02') (9579641, 'K00115.03') (8395660, 'K00116.01') (8395660, 'K00116.02') (8395660, 'K00116.03') (8395660, 'K00116.04') (10875245, 'K00117.02') (10480982, 'K00744.01') (10526549, 'K00746.01') (10583066, 'K00747.01') (10601284, 'K00749.01') (10601284, 'K00749.02') (10601284, 'K00749.03') (10662202, 'K00750.01') (10682541, 'K00751.01') (10797460, 'K00752.01') (10797460, 'K00752.02') (10811496, 'K00753.01') (10848459, 'K00754.01') (10854555, 'K00755.01') (10872983, 'K00756.01') (10872983, 'K00756.02') (10872983, 'K00756.03') (10910878, 'K00757.01') (10910878, 'K00757.02') (10910878, 'K00757.03') (10987985, 'K00758.01') (11018648, 'K00759.01') (11018648, 'K00759.02') (11138155, 'K00760.01') (11153539, 'K00762.01') (11304958, 'K00764.01') (11391957, 'K00765.01') (11403044, 'K00766.01') (11414511, 'K00767.01') (11460018, 'K00769.01') (11465813, 'K00771.01') (11493732, 'K00772.01') (11507101, 'K00773.01') (11754553, 'K00775.01') (11754553, 'K00775.02') (11754553, 'K00775.03') (11812062, 'K00776.01') (11818800, 'K00777.01') (11853255, 'K00778.01') (11918099, 'K00780.01') (11918099, 'K00780.02') (11923270, 'K00781.01') (11960862, 'K00782.01') (12020329, 'K00783.01') (12066335, 'K00784.01') (12066335, 'K00784.02') (12070811, 'K00785.01') (12110942, 'K00786.01') (12366084, 'K00787.01') (12366084, 'K00787.02') (12404086, 'K00788.01') (12470844, 'K00790.01') (12470844, 'K00790.02') (12644822, 'K00791.01') (2713049, 'K00794.01') (3114167, 'K00795.01') (3115833, 'K00797.01') (3246984, 'K00799.01') (3342970, 'K00800.01') (3342970, 'K00800.02') (3351888, 'K00801.01') (3453214, 'K00802.01') (3641726, 'K00804.01') (3832474, 'K00806.01') (3832474, 'K00806.02') (3832474, 'K00806.03') (3935914, 'K00809.01') (3940418, 'K00810.01') (4049131, 'K00811.01') (4139816, 'K00812.01') (4139816, 'K00812.02') (4139816, 'K00812.03') (4139816, 'K00812.04') (4275191, 'K00813.01') (4476123, 'K00814.01') (5358241, 'K00829.01') (5358241, 'K00829.02') (5358241, 'K00829.03') (5358624, 'K00830.01') (5456651, 'K00835.01') (5456651, 'K00835.02') (6862328, 'K00865.01')
WHERE İfadesi Kullanmadan Birleştirme¶
WHERE ifadesi aynı zamanda birleştirme şartı (ing. join condition) olarak da bilinir. Bu şart kulanılmadığında aşağıdaki sonuç elde edilir. Bu da bir tür birleştirmedir (bileşke), ancak pandas verçeçevelerine uygulanan OUTER JOIN işlemi yapılmmaktadır. Bu tür birleştirmeye çapraz (cross) ya da kartezyen (cartesian join) birleştirme adı verilir ve her iki tablonun satırları arasındaki tüm kombinasyonları listeler.
In [7]:
query = '''
SELECT {}, {}
FROM {}, {}
'''.format('Star.kepler_id','Planet.koi_name','Star','Planet')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(2713049, 'K00001.01') (2713049, 'K00002.01') (2713049, 'K00010.01') (2713049, 'K00072.01') (2713049, 'K00082.04') (2713049, 'K00082.05') (2713049, 'K00112.02') (2713049, 'K00115.01') (2713049, 'K00115.02') (2713049, 'K00115.03') (2713049, 'K00116.01') (2713049, 'K00116.02') (2713049, 'K00116.03') (2713049, 'K00116.04') (2713049, 'K00117.02') (2713049, 'K00744.01') (2713049, 'K00746.01') (2713049, 'K00747.01') (2713049, 'K00749.01') (2713049, 'K00749.02') (2713049, 'K00749.03') (2713049, 'K00750.01') (2713049, 'K00751.01') (2713049, 'K00752.01') (2713049, 'K00752.02') (2713049, 'K00753.01') (2713049, 'K00754.01') (2713049, 'K00755.01') (2713049, 'K00756.01') (2713049, 'K00756.02') (2713049, 'K00756.03') (2713049, 'K00757.01') (2713049, 'K00757.02') (2713049, 'K00757.03') (2713049, 'K00758.01') (2713049, 'K00759.01') (2713049, 'K00759.02') (2713049, 'K00760.01') (2713049, 'K00762.01') (2713049, 'K00764.01') (2713049, 'K00765.01') (2713049, 'K00766.01') (2713049, 'K00767.01') (2713049, 'K00769.01') (2713049, 'K00771.01') (2713049, 'K00772.01') (2713049, 'K00773.01') (2713049, 'K00775.01') (2713049, 'K00775.02') (2713049, 'K00775.03') (2713049, 'K00776.01') (2713049, 'K00777.01') (2713049, 'K00778.01') (2713049, 'K00780.01') (2713049, 'K00780.02') (2713049, 'K00781.01') (2713049, 'K00782.01') (2713049, 'K00783.01') (2713049, 'K00784.01') (2713049, 'K00784.02') (2713049, 'K00785.01') (2713049, 'K00786.01') (2713049, 'K00787.01') (2713049, 'K00787.02') (2713049, 'K00788.01') (2713049, 'K00790.01') (2713049, 'K00790.02') (2713049, 'K00791.01') (2713049, 'K00794.01') (2713049, 'K00795.01') (2713049, 'K00797.01') (2713049, 'K00799.01') (2713049, 'K00800.01') (2713049, 'K00800.02') (2713049, 'K00801.01') (2713049, 'K00802.01') (2713049, 'K00804.01') (2713049, 'K00806.01') (2713049, 'K00806.02') (2713049, 'K00806.03') (2713049, 'K00809.01') (2713049, 'K00810.01') (2713049, 'K00811.01') (2713049, 'K00812.01') (2713049, 'K00812.02') (2713049, 'K00812.03') (2713049, 'K00812.04') (2713049, 'K00813.01') (2713049, 'K00814.01') (2713049, 'K00829.01') (2713049, 'K00829.02') (2713049, 'K00829.03') (2713049, 'K00830.01') (2713049, 'K00835.01') (2713049, 'K00835.02') (2713049, 'K00865.01') (3114167, 'K00001.01') (3114167, 'K00002.01') (3114167, 'K00010.01') (3114167, 'K00072.01') (3114167, 'K00082.04') (3114167, 'K00082.05') (3114167, 'K00112.02') (3114167, 'K00115.01') (3114167, 'K00115.02') (3114167, 'K00115.03') (3114167, 'K00116.01') (3114167, 'K00116.02') (3114167, 'K00116.03') (3114167, 'K00116.04') (3114167, 'K00117.02') (3114167, 'K00744.01') (3114167, 'K00746.01') (3114167, 'K00747.01') (3114167, 'K00749.01') (3114167, 'K00749.02') (3114167, 'K00749.03') (3114167, 'K00750.01') (3114167, 'K00751.01') (3114167, 'K00752.01') (3114167, 'K00752.02') (3114167, 'K00753.01') (3114167, 'K00754.01') (3114167, 'K00755.01') (3114167, 'K00756.01') (3114167, 'K00756.02') (3114167, 'K00756.03') (3114167, 'K00757.01') (3114167, 'K00757.02') (3114167, 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'K00767.01') (12470844, 'K00769.01') (12470844, 'K00771.01') (12470844, 'K00772.01') (12470844, 'K00773.01') (12470844, 'K00775.01') (12470844, 'K00775.02') (12470844, 'K00775.03') (12470844, 'K00776.01') (12470844, 'K00777.01') (12470844, 'K00778.01') (12470844, 'K00780.01') (12470844, 'K00780.02') (12470844, 'K00781.01') (12470844, 'K00782.01') (12470844, 'K00783.01') (12470844, 'K00784.01') (12470844, 'K00784.02') (12470844, 'K00785.01') (12470844, 'K00786.01') (12470844, 'K00787.01') (12470844, 'K00787.02') (12470844, 'K00788.01') (12470844, 'K00790.01') (12470844, 'K00790.02') (12470844, 'K00791.01') (12470844, 'K00794.01') (12470844, 'K00795.01') (12470844, 'K00797.01') (12470844, 'K00799.01') (12470844, 'K00800.01') (12470844, 'K00800.02') (12470844, 'K00801.01') (12470844, 'K00802.01') (12470844, 'K00804.01') (12470844, 'K00806.01') (12470844, 'K00806.02') (12470844, 'K00806.03') (12470844, 'K00809.01') (12470844, 'K00810.01') (12470844, 'K00811.01') (12470844, 'K00812.01') (12470844, 'K00812.02') (12470844, 'K00812.03') (12470844, 'K00812.04') (12470844, 'K00813.01') (12470844, 'K00814.01') (12470844, 'K00829.01') (12470844, 'K00829.02') (12470844, 'K00829.03') (12470844, 'K00830.01') (12470844, 'K00835.01') (12470844, 'K00835.02') (12470844, 'K00865.01') (12644822, 'K00001.01') (12644822, 'K00002.01') (12644822, 'K00010.01') (12644822, 'K00072.01') (12644822, 'K00082.04') (12644822, 'K00082.05') (12644822, 'K00112.02') (12644822, 'K00115.01') (12644822, 'K00115.02') (12644822, 'K00115.03') (12644822, 'K00116.01') (12644822, 'K00116.02') (12644822, 'K00116.03') (12644822, 'K00116.04') (12644822, 'K00117.02') (12644822, 'K00744.01') (12644822, 'K00746.01') (12644822, 'K00747.01') (12644822, 'K00749.01') (12644822, 'K00749.02') (12644822, 'K00749.03') (12644822, 'K00750.01') (12644822, 'K00751.01') (12644822, 'K00752.01') (12644822, 'K00752.02') (12644822, 'K00753.01') (12644822, 'K00754.01') (12644822, 'K00755.01') (12644822, 'K00756.01') (12644822, 'K00756.02') (12644822, 'K00756.03') (12644822, 'K00757.01') (12644822, 'K00757.02') (12644822, 'K00757.03') (12644822, 'K00758.01') (12644822, 'K00759.01') (12644822, 'K00759.02') (12644822, 'K00760.01') (12644822, 'K00762.01') (12644822, 'K00764.01') (12644822, 'K00765.01') (12644822, 'K00766.01') (12644822, 'K00767.01') (12644822, 'K00769.01') (12644822, 'K00771.01') (12644822, 'K00772.01') (12644822, 'K00773.01') (12644822, 'K00775.01') (12644822, 'K00775.02') (12644822, 'K00775.03') (12644822, 'K00776.01') (12644822, 'K00777.01') (12644822, 'K00778.01') (12644822, 'K00780.01') (12644822, 'K00780.02') (12644822, 'K00781.01') (12644822, 'K00782.01') (12644822, 'K00783.01') (12644822, 'K00784.01') (12644822, 'K00784.02') (12644822, 'K00785.01') (12644822, 'K00786.01') (12644822, 'K00787.01') (12644822, 'K00787.02') (12644822, 'K00788.01') (12644822, 'K00790.01') (12644822, 'K00790.02') (12644822, 'K00791.01') (12644822, 'K00794.01') (12644822, 'K00795.01') (12644822, 'K00797.01') (12644822, 'K00799.01') (12644822, 'K00800.01') (12644822, 'K00800.02') (12644822, 'K00801.01') (12644822, 'K00802.01') (12644822, 'K00804.01') (12644822, 'K00806.01') (12644822, 'K00806.02') (12644822, 'K00806.03') (12644822, 'K00809.01') (12644822, 'K00810.01') (12644822, 'K00811.01') (12644822, 'K00812.01') (12644822, 'K00812.02') (12644822, 'K00812.03') (12644822, 'K00812.04') (12644822, 'K00813.01') (12644822, 'K00814.01') (12644822, 'K00829.01') (12644822, 'K00829.02') (12644822, 'K00829.03') (12644822, 'K00830.01') (12644822, 'K00835.01') (12644822, 'K00835.02') (12644822, 'K00865.01')
Bu şekilde bir birleştirme sonucu Planet tablosundaki tüm barınak yıldız kepler_id leri ile Star tablosundaki tüm kepler_id 'ler tüm kombinasyonları içerecek şekilde tek tek eşleştirildiği için (yıldız sayısı) $\times$ (gezegen sayısı) tane kayıt (satır) döndürülmüştür.
In [8]:
query1 = '''
SELECT COUNT(*) FROM {};
'''.format('Star')
query2 = '''
SELECT COUNT(*) FROM {};
'''.format('Planet')
query3 = '''
SELECT COUNT(*) FROM {}, {}
'''.format('Star','Planet')
cursor.execute(query1)
print(cursor.fetchone())
cursor.execute(query2)
print(cursor.fetchone())
cursor.execute(query3)
print(cursor.fetchone())
(72,) (96,) (6912,)
Örnek 1: Küçük Gezegenli Sistemler¶
Yarıçapları oranı Dünya yarıçapının Güneş yarıçapına oranından küçük olan gezegenler ve barınak yıldız ikililerini döndüren bir sorgu yazınız. Sonuçlarınızı yıldız yarıçapına göre azalan sırada sununuz. Planet tablosunda gezegen, Star tablosunda barınak yıldız yarıçapları sırasıyla Dünya ve Güneş yarıçapı cinsinden verilmiştir.
In [9]:
query = '''
SELECT {} AS {}, {} AS {}
FROM {} AS {}, {} AS {}
WHERE {} = {} AND {} / {} > 1.0
ORDER BY {} DESC;
'''.format('s.radius','star_radius','p.radius','planet_radius ',
'Star','s','Planet','p',
's.kepler_id','p.kepler_id','s.radius','p.radius',
's.radius')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(1.332, 0.65) (1.029, 0.85) (1.029, 0.99) (0.755, 0.58) (0.755, 0.49)
JOIN Operatörü¶
Veritabanı uygulamalarında tabloların birleştirilmesi işlemi çok yoğun kullanıldığı için, SQL JOIN operatörünü sağlar. Bu operatörü, çeşitli birleştirme türleri için farklı şekillerde kullanmak gerekir. Birebir eşleştirmeler için JOIN aşağıdaki şekilde kullanılabilir.
Tabloların birleştirme için sütun adı (öznitelik) kullanılmak istendiğinde
JOIN ... USING:
Belirli bir şart dahilinde birleştirme yapılmak istendiğinde
JOIN ... ON:
kullanılır.
JOIN ... USING¶
İki tablo birleştirilirken her ikisinde de ortak olan sütunlar üzerinden bir birleştirme işlemi yapılmak isteniyorsa bu yapı aşağıdaki şekilde kullanılır:
In [10]:
query = '''
SELECT {}, {}
FROM {}
JOIN {} USING ({});
'''.format('Star.kepler_id','Planet.koi_name',
'Star',
'Planet','kepler_id')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(11446443, 'K00001.01') (10666592, 'K00002.01') (6922244, 'K00010.01') (11904151, 'K00072.01') (10187017, 'K00082.04') (10187017, 'K00082.05') (10984090, 'K00112.02') (9579641, 'K00115.01') (9579641, 'K00115.02') (9579641, 'K00115.03') (8395660, 'K00116.01') (8395660, 'K00116.02') (8395660, 'K00116.03') (8395660, 'K00116.04') (10875245, 'K00117.02') (10480982, 'K00744.01') (10526549, 'K00746.01') (10583066, 'K00747.01') (10601284, 'K00749.01') (10601284, 'K00749.02') (10601284, 'K00749.03') (10662202, 'K00750.01') (10682541, 'K00751.01') (10797460, 'K00752.01') (10797460, 'K00752.02') (10811496, 'K00753.01') (10848459, 'K00754.01') (10854555, 'K00755.01') (10872983, 'K00756.01') (10872983, 'K00756.02') (10872983, 'K00756.03') (10910878, 'K00757.01') (10910878, 'K00757.02') (10910878, 'K00757.03') (10987985, 'K00758.01') (11018648, 'K00759.01') (11018648, 'K00759.02') (11138155, 'K00760.01') (11153539, 'K00762.01') (11304958, 'K00764.01') (11391957, 'K00765.01') (11403044, 'K00766.01') (11414511, 'K00767.01') (11460018, 'K00769.01') (11465813, 'K00771.01') (11493732, 'K00772.01') (11507101, 'K00773.01') (11754553, 'K00775.01') (11754553, 'K00775.02') (11754553, 'K00775.03') (11812062, 'K00776.01') (11818800, 'K00777.01') (11853255, 'K00778.01') (11918099, 'K00780.01') (11918099, 'K00780.02') (11923270, 'K00781.01') (11960862, 'K00782.01') (12020329, 'K00783.01') (12066335, 'K00784.01') (12066335, 'K00784.02') (12070811, 'K00785.01') (12110942, 'K00786.01') (12366084, 'K00787.01') (12366084, 'K00787.02') (12404086, 'K00788.01') (12470844, 'K00790.01') (12470844, 'K00790.02') (12644822, 'K00791.01') (2713049, 'K00794.01') (3114167, 'K00795.01') (3115833, 'K00797.01') (3246984, 'K00799.01') (3342970, 'K00800.01') (3342970, 'K00800.02') (3351888, 'K00801.01') (3453214, 'K00802.01') (3641726, 'K00804.01') (3832474, 'K00806.01') (3832474, 'K00806.02') (3832474, 'K00806.03') (3935914, 'K00809.01') (3940418, 'K00810.01') (4049131, 'K00811.01') (4139816, 'K00812.01') (4139816, 'K00812.02') (4139816, 'K00812.03') (4139816, 'K00812.04') (4275191, 'K00813.01') (4476123, 'K00814.01') (5358241, 'K00829.01') (5358241, 'K00829.02') (5358241, 'K00829.03') (5358624, 'K00830.01') (5456651, 'K00835.01') (5456651, 'K00835.02') (6862328, 'K00865.01')
Bu şeklde daha önce özdeş sonuç veren sorguda olduğu gibi WHERE ifadesiyle bir şart testi yapılmasından da kaçınılmış olur.
In [11]:
query = '''
SELECT {}, {}
FROM {}, {}
WHERE {} = {};
'''.format('Star.kepler_id','Planet.koi_name',
'Star', 'Planet',
'Star.kepler_id','Planet.kepler_id')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(11446443, 'K00001.01') (10666592, 'K00002.01') (6922244, 'K00010.01') (11904151, 'K00072.01') (10187017, 'K00082.04') (10187017, 'K00082.05') (10984090, 'K00112.02') (9579641, 'K00115.01') (9579641, 'K00115.02') (9579641, 'K00115.03') (8395660, 'K00116.01') (8395660, 'K00116.02') (8395660, 'K00116.03') (8395660, 'K00116.04') (10875245, 'K00117.02') (10480982, 'K00744.01') (10526549, 'K00746.01') (10583066, 'K00747.01') (10601284, 'K00749.01') (10601284, 'K00749.02') (10601284, 'K00749.03') (10662202, 'K00750.01') (10682541, 'K00751.01') (10797460, 'K00752.01') (10797460, 'K00752.02') (10811496, 'K00753.01') (10848459, 'K00754.01') (10854555, 'K00755.01') (10872983, 'K00756.01') (10872983, 'K00756.02') (10872983, 'K00756.03') (10910878, 'K00757.01') (10910878, 'K00757.02') (10910878, 'K00757.03') (10987985, 'K00758.01') (11018648, 'K00759.01') (11018648, 'K00759.02') (11138155, 'K00760.01') (11153539, 'K00762.01') (11304958, 'K00764.01') (11391957, 'K00765.01') (11403044, 'K00766.01') (11414511, 'K00767.01') (11460018, 'K00769.01') (11465813, 'K00771.01') (11493732, 'K00772.01') (11507101, 'K00773.01') (11754553, 'K00775.01') (11754553, 'K00775.02') (11754553, 'K00775.03') (11812062, 'K00776.01') (11818800, 'K00777.01') (11853255, 'K00778.01') (11918099, 'K00780.01') (11918099, 'K00780.02') (11923270, 'K00781.01') (11960862, 'K00782.01') (12020329, 'K00783.01') (12066335, 'K00784.01') (12066335, 'K00784.02') (12070811, 'K00785.01') (12110942, 'K00786.01') (12366084, 'K00787.01') (12366084, 'K00787.02') (12404086, 'K00788.01') (12470844, 'K00790.01') (12470844, 'K00790.02') (12644822, 'K00791.01') (2713049, 'K00794.01') (3114167, 'K00795.01') (3115833, 'K00797.01') (3246984, 'K00799.01') (3342970, 'K00800.01') (3342970, 'K00800.02') (3351888, 'K00801.01') (3453214, 'K00802.01') (3641726, 'K00804.01') (3832474, 'K00806.01') (3832474, 'K00806.02') (3832474, 'K00806.03') (3935914, 'K00809.01') (3940418, 'K00810.01') (4049131, 'K00811.01') (4139816, 'K00812.01') (4139816, 'K00812.02') (4139816, 'K00812.03') (4139816, 'K00812.04') (4275191, 'K00813.01') (4476123, 'K00814.01') (5358241, 'K00829.01') (5358241, 'K00829.02') (5358241, 'K00829.03') (5358624, 'K00830.01') (5456651, 'K00835.01') (5456651, 'K00835.02') (6862328, 'K00865.01')
JOIN ... ON¶
Eşitlik dışındaki şartlara bağlı karşılaştırmalar için ON boolean her ifade için birleştirme yapılmasına olanak veren bir esneklik sağlar. ON kullanılarak, boolean sonuç üretecek tüm diğer karşılaştırmalar ("<",">"...) gibi doğal olarak eşitlik karşılaştırması da yapılabilir. Bundan önce verilen ve özdeş sonuç üreten iki sorgunun bir diğer alternatifi aşağıda JOIN ... ON yapısıyla aşağıda verilmektedir.
In [12]:
query = '''
SELECT {}, {}
FROM {}
JOIN {} ON {} = {};
'''.format('Star.kepler_id','Planet.koi_name',
'Star',
'Planet','Star.kepler_id','Planet.kepler_id')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(11446443, 'K00001.01') (10666592, 'K00002.01') (6922244, 'K00010.01') (11904151, 'K00072.01') (10187017, 'K00082.04') (10187017, 'K00082.05') (10984090, 'K00112.02') (9579641, 'K00115.01') (9579641, 'K00115.02') (9579641, 'K00115.03') (8395660, 'K00116.01') (8395660, 'K00116.02') (8395660, 'K00116.03') (8395660, 'K00116.04') (10875245, 'K00117.02') (10480982, 'K00744.01') (10526549, 'K00746.01') (10583066, 'K00747.01') (10601284, 'K00749.01') (10601284, 'K00749.02') (10601284, 'K00749.03') (10662202, 'K00750.01') (10682541, 'K00751.01') (10797460, 'K00752.01') (10797460, 'K00752.02') (10811496, 'K00753.01') (10848459, 'K00754.01') (10854555, 'K00755.01') (10872983, 'K00756.01') (10872983, 'K00756.02') (10872983, 'K00756.03') (10910878, 'K00757.01') (10910878, 'K00757.02') (10910878, 'K00757.03') (10987985, 'K00758.01') (11018648, 'K00759.01') (11018648, 'K00759.02') (11138155, 'K00760.01') (11153539, 'K00762.01') (11304958, 'K00764.01') (11391957, 'K00765.01') (11403044, 'K00766.01') (11414511, 'K00767.01') (11460018, 'K00769.01') (11465813, 'K00771.01') (11493732, 'K00772.01') (11507101, 'K00773.01') (11754553, 'K00775.01') (11754553, 'K00775.02') (11754553, 'K00775.03') (11812062, 'K00776.01') (11818800, 'K00777.01') (11853255, 'K00778.01') (11918099, 'K00780.01') (11918099, 'K00780.02') (11923270, 'K00781.01') (11960862, 'K00782.01') (12020329, 'K00783.01') (12066335, 'K00784.01') (12066335, 'K00784.02') (12070811, 'K00785.01') (12110942, 'K00786.01') (12366084, 'K00787.01') (12366084, 'K00787.02') (12404086, 'K00788.01') (12470844, 'K00790.01') (12470844, 'K00790.02') (12644822, 'K00791.01') (2713049, 'K00794.01') (3114167, 'K00795.01') (3115833, 'K00797.01') (3246984, 'K00799.01') (3342970, 'K00800.01') (3342970, 'K00800.02') (3351888, 'K00801.01') (3453214, 'K00802.01') (3641726, 'K00804.01') (3832474, 'K00806.01') (3832474, 'K00806.02') (3832474, 'K00806.03') (3935914, 'K00809.01') (3940418, 'K00810.01') (4049131, 'K00811.01') (4139816, 'K00812.01') (4139816, 'K00812.02') (4139816, 'K00812.03') (4139816, 'K00812.04') (4275191, 'K00813.01') (4476123, 'K00814.01') (5358241, 'K00829.01') (5358241, 'K00829.02') (5358241, 'K00829.03') (5358624, 'K00830.01') (5456651, 'K00835.01') (5456651, 'K00835.02') (6862328, 'K00865.01')
Ancak JOIN .. ON yapısının esnekliği karşılaştırmanın boolean her türlü ifade üzerinde yapılabilmesindedir. Barınak yıldızı 1.5 $R_{\odot}$'tan denge sıcaklığı 2000 K'den büyük gezegenlerin koi_name ve barınak yıldızlarının kepler_id bilgilerini döndüren bir SQL sorgusu aşağıda örnek olarak sunulmuştur. Bu yapısıyla WHERE 'e benzer şekilde kullanılabilir ancak JOIN USING ifadesinden farklılaşır.
In [13]:
query = '''
SELECT {}, {}
FROM {}
JOIN {} ON {} > 1.5 AND {} > 2000;
'''.format('Star.kepler_id','Planet.koi_name',
'Star',
'Planet','Star.radius','Planet.t_eq')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(6721123, 'K00002.01') (10666592, 'K00002.01')
Örnek 2: Büyük Yıldızların Gezegen Sayıları¶
Yarıçapları $1 R_{\odot}$'ten büyük barınak yıldızların gezegen sayılarını listeleyen bir sorgu yazınız. Sorgunuz sadece çoklu gezegen sistemleri arasında bir sonuç üretmeli ve bu sonuçlar sistemdeki gezegen sayısına göre azalan sırada sunulmalıdır.
İpucu: GROUP BY operatöründen faydalanınız.
In [14]:
query = '''
SELECT {}, {} AS {}, COUNT({}) AS {}
FROM {} AS {}
JOIN {} AS {} ON {} = {} AND {} > 1.0
GROUP BY {}
HAVING COUNT({}) > 1
ORDER BY COUNT({}) DESC;
'''.format('s.koi_name','s.radius','radius','p.kepler_id','count',
'Star','s',
'Planet','p','s.kepler_id','p.kepler_id','s.radius',
's.kepler_id',
'p.kepler_id',
'p.kepler_id')
cursor.execute(query)
print("KOI Adi,", "R_yildiz,", "Gezegen Sayisi")
for record in cursor.fetchall():
print(record)
KOI Adi, R_yildiz, Gezegen Sayisi
('K00116.01', 1.029, 4)
('K00115.01', 1.332, 3)
('K00752.01', 1.04, 2)
('K00800.01', 1.064, 2)
Dıştan Birleştirmeler¶
Şimdiye kadar incelediğimiz birleştirme türleri, verilerin yalnızca belirtilen koşul doğru olduğunda birleştirme işleminin, yani koşula göre kesişimin uygulandığı içten birleştirme (inner join) kategorisine girer. JOIN operatörünün varsayılan çalışma şekli iç birleştirmedir (kesişim).
İçten birleştirmenin tamamlayıcısı, dıştan birleştirme (outer join) olarak adlandırılır. Bileşke işlemine benzer şekilde düşünülebilecek bu işlem üç farklı şekilde uygulanabilir:
- $tablo1$
LEFT OUTER JOIN$tablo2$
Bu tür birleştirmede $tablo1$'deki tüm değerler korunurken $tablo2$'de eşleşmeyen kayıtlar NULL değerini alır.
- $tablo1$
RIGHT OUTER JOIN$tablo2$
Bu tür birleştirmede $tablo2$'deki tüm değerler korunurken $tablo1$'de eşleşmeyen kayıtlar NULL değerini alır.
- $tablo1$
FULL OUTER JOIN$tablo2$
Her iki tablodan gelen ve eşleşmeyen kayıtların tamamı korunarak bileşke işlemi uygulanır.
Star tablosundaki tüm satırları korurken Planet tablosunda eşleşmeyenler için NULL değeri veren bir sorgu aşağıda LEFT OUTER JOIN için bir örnek olarak verilmiştir.
In [15]:
query = '''
SELECT {}, {}
FROM {} {}
LEFT OUTER JOIN {} {} USING({});
'''.format('S.kepler_id','P.koi_name',
'Star', 'S',
'Planet','P','kepler_id')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(11446443, 'K00001.01') (10666592, 'K00002.01') (6922244, 'K00010.01') (11904151, 'K00072.01') (10187017, 'K00082.04') (10187017, 'K00082.05') (10984090, 'K00112.02') (9579641, 'K00115.01') (9579641, 'K00115.02') (9579641, 'K00115.03') (8395660, 'K00116.01') (8395660, 'K00116.02') (8395660, 'K00116.03') (8395660, 'K00116.04') (10875245, 'K00117.02') (10480982, 'K00744.01') (10526549, 'K00746.01') (10583066, 'K00747.01') (10601284, 'K00749.01') (10601284, 'K00749.02') (10601284, 'K00749.03') (10662202, 'K00750.01') (10682541, 'K00751.01') (10797460, 'K00752.01') (10797460, 'K00752.02') (10811496, 'K00753.01') (10848459, 'K00754.01') (10854555, 'K00755.01') (10872983, 'K00756.01') (10872983, 'K00756.02') (10872983, 'K00756.03') (10910878, 'K00757.01') (10910878, 'K00757.02') (10910878, 'K00757.03') (10987985, 'K00758.01') (11018648, 'K00759.01') (11018648, 'K00759.02') (11138155, 'K00760.01') (11153539, 'K00762.01') (11304958, 'K00764.01') (11391957, 'K00765.01') (11403044, 'K00766.01') (11414511, 'K00767.01') (11460018, 'K00769.01') (11465813, 'K00771.01') (11493732, 'K00772.01') (11507101, 'K00773.01') (11754553, 'K00775.01') (11754553, 'K00775.02') (11754553, 'K00775.03') (11812062, 'K00776.01') (11818800, 'K00777.01') (11853255, 'K00778.01') (11918099, 'K00780.01') (11918099, 'K00780.02') (11923270, 'K00781.01') (11960862, 'K00782.01') (12020329, 'K00783.01') (12066335, 'K00784.01') (12066335, 'K00784.02') (12070811, 'K00785.01') (12110942, 'K00786.01') (12366084, 'K00787.01') (12366084, 'K00787.02') (12404086, 'K00788.01') (12470844, 'K00790.01') (12470844, 'K00790.02') (12644822, 'K00791.01') (2713049, 'K00794.01') (3114167, 'K00795.01') (3115833, 'K00797.01') (3246984, 'K00799.01') (3342970, 'K00800.01') (3342970, 'K00800.02') (3351888, 'K00801.01') (3453214, 'K00802.01') (3641726, 'K00804.01') (3832474, 'K00806.01') (3832474, 'K00806.02') (3832474, 'K00806.03') (3935914, 'K00809.01') (3940418, 'K00810.01') (4049131, 'K00811.01') (4139816, 'K00812.01') (4139816, 'K00812.02') (4139816, 'K00812.03') (4139816, 'K00812.04') (4275191, 'K00813.01') (4476123, 'K00814.01') (5358241, 'K00829.01') (5358241, 'K00829.02') (5358241, 'K00829.03') (5358624, 'K00830.01') (5456651, 'K00835.01') (5456651, 'K00835.02') (6862328, 'K00865.01') (6721123, None) (10419211, None) (4932348, None) (11152159, None) (10583180, None)
Görüldüğü gibi son 5 cismin Planet tablosunda karşılığı yoktur!
RIGHT OUTER JOIN de benzer şekilde çalışır. Bu nedenle
- $tablo1$
LEFT OUTER JOIN$tablo2$ - $tablo2$
RIGHT OUTER JOIN$tablo1$
aynı sonucu üretir.
FULL OUTER JOIN¶
FULL OUTER JOIN tıpkı iki tabloyu tüm satırlarını koruyarak birleştirir. Böylece eşleşmeyen satırlar da alınmış olur. Bu nedenle aşağıdaki sorgu Planet tablosundaki kayıt sayısı (96) + Star tablosunda olup Planet tablosuyla eşleşmeyen kayıt sayısı (5) kadar satır içeren bir tablo döndürmektedir.
In [16]:
query = '''
SELECT COUNT({})
FROM {} {}
FULL OUTER JOIN {} {} USING({});
'''.format('S.kepler_id',
'Star', 'S',
'Planet','P','kepler_id')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(101,)
Dışta birleştirmelerde bu türlerden hangisinin uygulanmak istendği (LEFT, RIGHT, FULL) mutlaka belirtilmelidir.
Örnek 3: "Gökyüzünde Yalnız Gezegen Yıldızlar"¶
Star tablosunda bulunup bir gezegene barınaklık etmeyen yıldızların kepler_id, t_eff ve radius bilgilerini döndüren bir sorgu yazınız. Sorgunuzun sonuçlarını etkin sıcaklığa göre azalan sırada sununuz.
In [17]:
query = '''
SELECT {} AS {}, {} AS {}, {} AS {}
FROM {} {}
LEFT OUTER JOIN {} {} USING({})
WHERE {} IS NULL
ORDER BY {} DESC;
'''.format('S.kepler_id','kepler_id','S.t_eff','t_eff','S.radius','radius',
'Star', 'S',
'Planet','P','kepler_id',
'P.koi_name',
'S.t_eff')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(6721123, 6227, 1.958) (10419211, 5795, 0.848) (4932348, 5575, 0.932) (11152159, 5550, 0.91) (10583180, 4889, 0.819)
Alt Sorgular¶
Star tablosundaki ortalama barınak yıldızı büyüklüğünden (yarıçapça) daha büyük yıldızlar listelenmek isteniyor olsun.
SELECT * FROM Star
WHERE Star.radius > AVG(Star.radius)
şeklinde yazılmış bir sorgu başlangıçta doğru görünebilir; ancak WHERE ifadesinden sonra gelecek şarta AVG gibi istatistik fonksiyonları yazılamaz.
Bunun için iki sorgu yazılarak, birincide ortalamanın hesaplaması yapılabilir. İkinci sorguda bu ortalama alt limit olarak alınıp ondan daha büyük yıldızlar listelenebilir. Ancak bu arada tabloda yapılacak bir değişiklik (örneğin dışarıdan yeni veri girilmesi, silinmesi ya da güncellenmesi) ortalamayı değiştireceğinden bu çok tercih edilmemesi gereken bir yöntemdir.
Bu tür birden fazla sorgunun arka arkaya çalıştırılmasını gerektiren durumlarda alt sorgular (sub-queries) kullanılır.
In [18]:
query = '''
SELECT * FROM {}
WHERE {} > (
SELECT AVG({}) FROM {}
);
'''.format('Star',
'Star.radius',
'radius','Star')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(2713049, 'K00794.01', 5996, 0.956) (3246984, 'K00799.01', 5735, 0.973) (3342970, 'K00800.01', 6167, 1.064) (3351888, 'K00801.01', 5717, 1.057) (4932348, '', 5575, 0.932) (5358241, 'K00829.01', 6079, 0.945) (6721123, '', 6227, 1.958) (6922244, 'K00010.01', 6225, 1.451) (8395660, 'K00116.01', 5881, 1.029) (9579641, 'K00115.01', 6391, 1.332) (10480982, 'K00744.01', 6117, 0.947) (10666592, 'K00002.01', 6350, 1.991) (10797460, 'K00752.01', 5850, 1.04) (10854555, 'K00755.01', 6031, 1.046) (10872983, 'K00756.01', 6046, 0.972) (10875245, 'K00117.02', 5851, 1.411) (10984090, 'K00112.02', 5803, 1.073) (11138155, 'K00760.01', 6117, 1.025) (11152159, '', 5550, 0.91) (11153539, 'K00762.01', 6075, 0.969) (11304958, 'K00764.01', 5468, 1.046) (11403044, 'K00766.01', 6174, 1.103) (11414511, 'K00767.01', 5653, 0.965) (11446443, 'K00001.01', 5820, 0.964) (11465813, 'K00771.01', 5520, 0.983) (11493732, 'K00772.01', 6144, 1.091) (11507101, 'K00773.01', 5957, 0.971) (11904151, 'K00072.01', 5627, 1.056) (11960862, 'K00782.01', 5992, 0.989) (12110942, 'K00786.01', 5880, 0.917) (12366084, 'K00787.01', 5841, 0.931) (12644822, 'K00791.01', 5795, 0.919)
Görüldüğü gibi öncelikle tablodaki yıldızların ortalaması bir alt sorguyla alınarak bir çıktı oluşturulmakta; bu çıktı üst sorguya girdi yapılarak bundan daha büyük yarıçaptaki yıldızlar listelenmektedir.
Benzer bir şekilde iki ya da daha fazla tablonun çapraz eşleştirilmesiyle oluşan bir tablodaki satır sayısını hesaplamak da birden fazla sorguyu gerektirir (deneyiniz!). Ancak aşağıdaki gibi alt sorgu yapısından faydalanarak düzenlenmiş bir sorguyla önce tablolardan satır sayıları çekilip, çarpıldıktan sonra SELECT ifadesiyle çarpımın sonucu seçilebilir.
In [19]:
query = '''
SELECT
(SELECT COUNT(*) FROM {}) *
(SELECT COUNT(*) FROM {});
'''.format('Star','Planet')
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(6912,)
Birbirine Bağlı ve Bağlı Olmayan Alt Sorgular¶
SQL'de temel olarak iki tür alt sorgu yapısı vardır.
Bağlı Alt Sorgular (co-related): Üst seviyedeki sorgunun her bir elemanı için alt sorgunun çaıştırıldığı sorgular;
Bağlı Olmayan Alt Sorgular (non-co-related)): Alt sorgunun sadece bir kez çalıştırıldığı alt sorgular.
İki örnek üzerinden aradaki fark vurgulanabilir:
In [20]:
query = '''
SELECT s.kepler_id
FROM Star s
WHERE EXISTS (
SELECT * FROM Planet p
WHERE s.kepler_id = p.kepler_id
AND p.radius < 1
);
'''
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(8395660,) (9579641,) (10187017,)
Yukarıdaki sorguda Star tablosunda her bir satırdaki kepler_id 'sinin Planet tablosunda karşılık geldiği barınak yıldızlardan $1 R_{\oplus}$'den küçük yarıçapa sahip gazegen barındıranların Kepler ID'leri listelenmektedir. Bu yapıda Star tablosundaki her bir yıldız için Planet tablosu tekrar sorgulanmakta ve şartları sağlayan (s.kepler_id = p.kepler_id ve p.radius < 1) gezegenler varsa onların barınak yıldızları listelenmektedir. Bunun için EXISTS ifadesinden faydalanılmaktadır. Bu nedenle bu bir bağlı alt sorgu örneğidir.
In [21]:
query = '''
SELECT s.kepler_id
FROM Star s
WHERE s.kepler_id IN (
SELECT p.kepler_id FROM Planet p
WHERE p.radius < 1
);
'''
cursor.execute(query)
for record in cursor.fetchall():
print(record)
(8395660,) (9579641,) (10187017,)
Bu sorguda ise yarıçapları $1 R_{\oplus}$'den küçük tüm gezegenlerin barınak yıldızları alt sorgu ile bulunmakta, bunlardan kepler_id değeri Star tablosunda olanlar listelenmektedir. Böylece Planet tablosu Star tablosundaki yıldızlar için sadece bir kez sorgulanmaktadır.
Bu iki örneğe bakılarak üst sorgudaki bir özniteliğin (sütun adı) alt sorgu yapısının içinde de kullanılması durumunda (örnekte s.kepler_id), bu alt sorguların bağlı alt sorgular (co-related sub-queries) olduğu söylenebilir.
Alt Sorgular ve Birleştirme İşlemleri¶
Programcılıkta olduğu gibi veritabanı sorgularında da istenen çıktıya ulaşmanın birden fazla yolu vardır. Bazı sorgu problemleri hem alt sorgularla hem de birleştirme işlemleriyle çözülebilir. Bu durumlarda hangisinin tercih edileceğini belirlemek için çalışma süreleri temel alınabilir. PostgreSQL'in \timing komutu bu amaçla kullanılabilir. (psql 'de sorgunuzun başına yazarak deneyiniz!)
\timing
Aşağıdaki örnekte Python'ın time modülü fonksiyonlarından faydalanılmıştır.
In [22]:
import time
start = time.perf_counter()
query_subq = '''
SELECT s.kepler_id
FROM Star s
WHERE s.kepler_id IN (
SELECT p.kepler_id FROM Planet p
WHERE p.radius < 1);
'''
end = time.perf_counter() - start
print("Sorgu toplam {:g} saniyede tamamlandi.".format(end))
cursor.execute(query_subq)
for record in cursor.fetchall():
print(record)
Sorgu toplam 0.0001284 saniyede tamamlandi. (8395660,) (9579641,) (10187017,)
In [23]:
start = time.perf_counter()
query_join = '''
SELECT DISTINCT(s.kepler_id)
FROM Star s
JOIN Planet p USING (kepler_id)
WHERE p.radius < 1;
'''
end = time.perf_counter() - start
print("Sorgu toplam {:g} saniyede tamamlandi.".format(end))
cursor.execute(query_subq)
for record in cursor.fetchall():
print(record)
Sorgu toplam 6.2754e-05 saniyede tamamlandi. (8395660,) (9579641,) (10187017,)
JOIN sorgusunun alt sorgu (sub-query) kullanımına göre daha yavaş olduğu görülmektedir.
Örnek 4: Sıcak Barınak Yıldızların Ötegezegenleri¶
Aşağıdaki nicelikleri listelemek üzere Star ve Planet tablolarını birleştiren sorgular yazınız. Sorgularınız sadece Star tablosundaki barınak yıldızların ortalama sıcaklığından daha sıcak yıldızlar için sonuç döndürmelidir.
Ortalamadan sıcak barınak yıldızların gezegenlerinin ortalama denge sıcaklıkları (
t_eq);Ortalamadan sıcak yıldızlardan en soğuğunun etkin sıcaklığı (
t_eff);Ortalamadan sıcak yıldızlardan en sıcağının etkin sıcaklığı (
t_eff);
Ortalama gezegen denge sıcaklığını 1 basamağa yuvarlayınız.
İpucu:¶
Yuvarlama işlemi doğal olarak Python'daki fonksiyonlar kullanılarak da yapılabilir ancak SQL'deki ROUND fonksiyonu da aşağıdaki şekilde kullanılabilir:
In [24]:
cursor.execute('SELECT ROUND(3.1415926, 1);')
cursor.fetchone()
Out[24]:
(Decimal('3.1'),)
In [25]:
query = '''
SELECT ROUND(AVG({}),1), MIN({}), MAX({})
FROM {} {}, {} {}
WHERE {} = {} AND {} > (
SELECT AVG({}) FROM {}
);
'''.format('p.t_eq','s.t_eff','s.t_eff',
'Planet','p','Star','s',
's.kepler_id','p.kepler_id','s.t_eff',
't_eff','Star')
cursor.execute(query)
for record in cursor.fetchall():
for r in record:
print("{:g}".format(r))
1004.9 5520 6391
Örnek 5: Büyük Yıldızların Gezegenleri de Büyük Müdür?¶
Star tablosundaki en büyük 5 yıldızın etrafında dolanan gezegenlerin yarıçaplarını (radius) ve KOI isimlerini (koi_name) listeleyiniz.
In [26]:
query = '''
SELECT p.koi_name, p.radius, s.radius
FROM Star s
JOIN Planet p USING(kepler_id)
WHERE s.kepler_id IN (
SELECT kepler_id
FROM Star
ORDER BY radius DESC
LIMIT 5);
'''
cursor.execute(query)
for record in cursor.fetchall():
print(record)
('K00002.01', 16.39, 1.991)
('K00010.01', 14.83, 1.451)
('K00115.01', 3.28, 1.332)
('K00115.02', 1.88, 1.332)
('K00115.03', 0.65, 1.332)
('K00117.02', 1.84, 1.411)