在做spark开发过程中,时不时的就有可能遇到租户的hive库目录下的文件个数超出了最大限制问题. 一般情况下通过hive的参数设置: val conf = new SparkConf().setAppName("MySparkJob") //.setMaster("local[1]").setMaster("spark://172.21.7.10:7077").setJars(List("xxx.jar")).set(&qu
https://blog.csdn.net/nihaoxiaocui/article/details/95060906 https://xuexiyuan.cn/article/detail/173.html from etlsdk.lib.datasources.datasource_factory import DatasourceFactory from data_pipeline.df_transform.transform import DataframeTransform from
看很多资料,很少有讲怎么去操作读写csv文件的,我也查了一些.很多博客都是很老的方法,还有好多转来转去的,复制粘贴都不能看.下面我在这里归纳一下,以免以后用到时再费时间去查.前端实现文件下载和拖拽上传 通过sc.textFile val input = sc.textFile("test.csv") val result = input.map { line => val reader = new CSVReader(new StringReader(line)); reader
//prepare csv year,make,model,comment,blank "2012","Tesla","S","No comment", "1997","Ford,E350","Go get one now they are going fast", "2015","Chevy","Volt"
#_*_coding:utf-8_*_ # spark读取csv文件 #指定schema: schema = StructType([ # true代表不为null StructField("column_1", StringType(), True), # nullable=True, this field can not be null StructField("column_2", StringType(), True), StructField("
1. 练习 数据: (1)需求1:统计有过连续3天以上销售的店铺有哪些,并且计算出连续三天以上的销售额 第一步:将每天的金额求和(同一天可能会有多个订单) SELECT sid,dt,SUM(money) day_money FROM v_orders GROUP BY sid,dt 第二步:给每个商家中每日的订单按时间排序并打上编号 SELECT sid,dt,day_money, ROW_NUMBER() OVER(PARTITION BY sid ORDER BY dt) rn FROM
http://blog.csdn.net/pipisorry/article/details/53320669 pyspark.sql.SQLContext Main entry point for DataFrame and SQL functionality. [pyspark.sql.SQLContext] 皮皮blog pyspark.sql.DataFrame A distributed collection of data grouped into named columns. sp