(p_age=10,p_name='Tom'); --需要指定所有的分区,不能只是p_age或p_name;否则org.apache.spark.sql.execution.QueryExecutionException:doesn't contain all (2) partition columns -- 查看分区: show partitions tab_test; -- 删除分区 alter table tab_test drop if exists partition(p_age=10); --...
_, _) => orders.foreach { order => if (!RowOrdering.isOrderable(order.dataType)) { failAnalysis( s"sorting is not supported for columns of type ${order
Spark RDD VS MapReduce R/Pandas : one machine ==> DataFrame:让小伙伴们感觉像开发单机版应用程序一样来开发分布式应用程序 A DataFrame is a Dataset organized into named columns 以列(列名、列类型、列值)的形式构成分布式的数据集 面试题:RDD与DataFrame的区别 12345 ...
SELECT *, row_number() OVER (ORDER BY rand(2077)) as rn FROM your_table ) SELECT * FROM RankedData WHERE rn <= 1000 抽取固定比例 直接使用TABLESAMPLE函数,实现对整体的固定比例抽样 SELECT * FROM your_table TABLESAMPLE (10 PERCENT) 分层随机抽样 分层抽样通常在数据科学中使用较多,为了保证样本...
SQL that is used to combine rows of two or more tables by using common values. It takes into consideration the records from two or more tables in a database and combines them. SQL Join is typically used in situations where you want to access one or more tables through a select statement...
scala> val rdd6= rdd5.sql(“select * from rdd4”).show() 1-7)、显示表中的数据 scala> rdd4.show() +---+---+---+---+ | ID|PLACE_TYPE|PLACE_CODE|PLACE_NAME| +---+---+---+---+ | 1| 01| 110000| 北京市| | 2| 01| 120000| 天津市| | ...
sql("SELECT name FROM people") // The results of SQL queries are DataFrames and support all the normal RDD operations // The columns of a row in the result can be accessed by field index or by field name results.map(attributes => "Name: " + attributes(0)).show() // +---+ /...
udf.register("oneArgFilter", (n: Int) => { n > 5 }) spark.range(1, 10).createOrReplaceTempView("test") spark.sql("SELECT * FROM test WHERE oneArgFilter(id)").show() // +---+ // | id| // +---+ // | 6| // | 7| // | 8| // | 9| // +---+ 8.聚合函数...
# Quick examples of get list of all duplicate items# Select duplicate rows except first occurrence# Based on all columnsdf2=df[df.duplicated()]# Select duplicate row based on all columnsdf2=df[df.duplicated(keep=False)]# Get duplicate last rows based on all columnsdf2=df[df.duplicated(keep...
select(self.ratingsDF.columns) # append to ratingsDF self.ratingsDF = self.ratingsDF.union(userDF) def _create_inference_data(self, userId, movieIds): """ create a user with all movies except ones were rated for inferencing """ # filter movies other_movieIds = self.moviesDF \ ....