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azure databricks 常用的JDBC连接

news 2025/8/31 4:53:37

做个笔记常用的spark-jdbc连接
1、mysql 的连接

def query_mysql(database,sqlstr):jdbcUsername=''jdbcHostname = " "jdbcDatabase = ""jdbcPort = 3306mysql_df = spark.read \.format("jdbc") \.option("driver","com.mysql.cj.jdbc.Driver") \.option("url","jdbc:mysql://{0}:{1}/{2}?useUnicode=true&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=Asia/Shanghai&rewriteBatchedStatements=true".format(jdbcHostname, jdbcPort, jdbcDatabase)) \.option("dbtable", sqlstr) \.option("user", jdbcUsername) \.option("password", jdbcPassword) \.load()return mysql_dfdef save__mysql(jdbcDF,database,action_text):jdbcUsername=''jdbcHostname = " "jdbcDatabase = ""jdbcPort = 3306jdbcDF.write \.format("jdbc") \.option("driver","com.mysql.cj.jdbc.Driver") \.option("url","jdbc:mysql://{0}:{1}/{2}?useUnicode=true&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=Asia/Shanghai".format(jdbcHostname, jdbcPort, jdbcDatabase)) \.option("dbtable", action_text) \.option("user", jdbcUsername) \.option("password", jdbcPassword) \.save()

2、oracle 的连接

def query_oracle(database,sql_str):user = ""pwd = ""jdbcHostname=""jdbcDatabase =""empDF = spark.read \.format("jdbc") \.option("url", "jdbc:oracle:thin:@//{0}:1521/{1}".format(jdbcHostname,jdbcDatabase) )\.option("dbtable", sql_str) \.option("user", user) \.option("password", pwd) \.option("driver", "oracle.jdbc.driver.OracleDriver") \.option("numpartitions",5)\.option("fetchsize",2000)\.load()return empDF

3、sqlservice的连接

def query_sqlservice(jdbcdatabase,sql_str):user = ""pwd = ""jdbcHostname=""jdbcDatabase =""empDF = spark.read \.format("com.microsoft.sqlserver.jdbc.spark") \.option("url", "jdbc:sqlserver://{0}:1433;database={1}".format(jdbcHostname,jdbcDatabase)) \.option("dbtable", sql_str) \.option("user", user) \.option("password", pwd) \.option("numPartitions",5)\.option("fetchsize",2000)\.load().cache()return empDF

4、posgresq的连接

def query_postgresql (database,sql_str):jdbcUsername = ''jdbcPassword = ''empDF = spark.read \.format("jdbc") \.option("driver", "org.postgresql.Driver") \.option("url","jdbc:postgresql://{0}:1433/{1}".format(jdbcHostname,jdbcDatabase)) \.option("dbtable", action_text) \.option("user", jdbcUsername) \.option("password", jdbcPassword) \.option("numpartitions",5)\.option("fetchsize",3000)\.load()return empDF