spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")spark.sql("LOAD DATA LOCAL INPATH 'data/kv1.txt' INTO TABLE src")df=spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")df.show(5)#5.2读取mysql数据 url="jdbc:mysql://localhost:3306/t...
importpyspark # importing sparksession from # pyspark.sql module frompyspark.sqlimportSparkSession # creating sparksession and giving # an app name spark=SparkSession.builder.appName('sparkdf').getOrCreate() # list of college data with two lists data=[["node.js","dbms","integration"], ["...
从pandasdf转换:spark_df = SQLContext.createDataFrame(pandas_df) 另外,createDataFrame支持从list转换sparkdf,其中list元素可以为tuple,dict,rdd 1.6. index索引 pandas 自动创建 pyspark 没有index索引,若需要则要额外创建该列 1.7. 行结构 pandas Series结构,属于Pandas DataFrame结构 pyspark Row结构,属于Spark Dat...
frompyspark.sqlimportSparkSessionif__name__ =='__main__': spark = SparkSession.builder.appName("spark sql").getOrCreate() spark.sql("DROP TABLE IF EXISTS spark_sql_test_table") spark.sql("CREATE TABLE spark_sql_test_table(name STRING, num BIGINT)") spark.sql("INSERT INTO spark_sql...
df.select(df.age.alias('age_value'),'name') 查询某列为null的行: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from pyspark.sql.functionsimportisnull df=df.filter(isnull("col_a")) 输出list类型,list中每个元素是Row类: 代码语言:javascript ...
df=spark.createDataFrame([('p1',56),('p2',23),('p3',11),('p4',40),('p5',29)],['name','age']) df.show() ===>> +---+---+ |name|age| +---+---+ | p1| 56| | p2| 23| | p3| 11| | p4| 40| | p5| 29| +-...
from datetime import datetime, date import pandas as pd from pyspark.sql import Row df = spark.createDataFrame([ Row(a=1, b=2., c='string1', d=date(2000, 1, 1), e=datetime(2000, 1, 1, 12, 0)), Row(a=2, b=3., c='string2', d=date(2000, 2, 1), e=datetime(2000,...
.getOrCreate() df=spark.sql(“select * from hive_tb_name”) df.show() 2.9.从hdfs读取 直接使用read.csv的方法即可。 直接读取,不需要指定ip和port data= spark.read.csv(‘hdfs:///tmp/_da_exdata_path/data.csv’, header=True) data.show() 有些情况下是需要指定ip和端口的 data= spark.rea...
from pyspark.sql import SQLContext import pandas as pd localIpAddress = socket.gethostbyname(socket.gethostname()) # 创建Spark配置 sparkConf = SparkConf() # 初始化我们的Spark集群,这实际上会生成工作节点。 spark = SparkSession.builder.config(conf=sparkConf).getOrCreate() ...
from pyspark.sql import SparkSession # 创建spark会话(连接) spark = SparkSession.builder.appName('Basics').getOrCreate() # 获取people.json里的数据 # option("multiline","true") 是为了能解析json数组 df = spark.read.option("multiline","true").json("people.json") # 打印整个dataframe df.sh...