我们可以通过以下代码创建一个示例DataFrame用于写入: frompyspark.sqlimportSparkSession# 创建SparkSessionspark=SparkSession.builder \.appName("partition_write_to_table")\.getOrCreate()# 创建示例DataFramedata=[("Alice",34),("Bob",45),("Cathy",28),("David",52)]columns=["name","age"]df=spark...
# 5. 读取和写入mysql # 5.1 将mysql驱动放到pyspark/jars下 options = {'user':'xxxx','password':'xxx'} df.write.options(**options)\ .jdbc(url='jdbc:mysql://host_ip/database?useSSL=false&useUnicode=true' ,table='test_stu',mode='overwrite') # 5.2 读取mysql表 spark.read.options(**op...
write.jdbc(url="jdbc:mysql://***:3306/dbname", # dbname为库名,必须已存在(该语句不会创建库) # mode="overwrite", # 模式分为overwrite 重写表 append表内内容追加 # table="hive_mysql", # 表名,表不需要去创建,可以自己生成 # properties={'driver':'com.mysql.jdbc.Driver', 'user':'*',...
py:340(read) 48 0.022 0.000 0.022 0.000 {method 'write' of 'cStringIO.StringO' objects} 13 0.014 0.001 0.014 0.001 {method 'getvalue' of 'cStringIO.StringO' objects} 1 0.000 0.000 0.013 0.013 {method 'to_pandas' of 'pyarrow.lib.Table' objects} 1 0.000 0.000 0.013 0.013 pandas_compat....
与spark.read属性类似,.write则可用于将DataFrame对象写入相应文件,包括写入csv文件、写入数据库等 3)数据类型转换。DataFrame既然可以通过其他类型数据结构创建,那么自然也可转换为相应类型,常用的转换其实主要还是DataFrame=>rdd和DataFrame=>pd.DataFrame,前者通过属性可直接访问,后者则需相应接口: 数据读写及类型转换。
df.createOrReplaceTempView("tableA") df2 = spark.sql("SELECT count(*) from tableA") #存储计算结果 df2.write.csv('data.csv', header=True) df2.show() 有了它,我们可以通过SQL的join拼接数据(替代Pig join的功能),也可以执行复杂的SQL逻辑(类似Hive SQL)并将最终的计算结果存储成不同格式的数据...
defdump_stream(self,iterator,stream):importpyarrowaspawriter=Nonetry:forbatchiniterator:ifwriterisNone:writer=pa.RecordBatchStreamWriter(stream,batch.schema)writer.write_batch(batch)finally:ifwriterisnotNone:writer.close()defload_stream(self,stream):importpyarrowaspareader=pa.ipc.open_stream(stream)fo...
python.html#spark.udf.register("udf_squared", udf_squared) spark.udf.register("udf_numpy", udf_numpy) tableName ="test_pyspark1"df = spark.sql("""select id, udf_squared(age) age1, udf_squared(age) age2, udf_numpy() udf_numpy from %s """% tableName)print("rdf count, %s\n"%...
1. df.write.parquet('bar.parquet')2. spark.read.parquet('bar.parquet').show() +---+---+---+---+ |color| fruit| v1| v2| +---+---+---+---+ |black|carrot| 6| 60| | blue|banana| 2| 20| | blue| grape| 4| 40| | red|carrot| ...
df.write.parquet(os.path.join(tempfile.mkdtemp(),'data')) df.write.txt(os.path.join(tempfile.mkdtemp(),'data'))#wirte data to external database via jdbcdf.write.jdbc(url, table, mode=None, properties=None) 把DataFrame内容存储到源中: ...