apache-spark-dataset

问题 I'm using spark-sql-2.4.1v, and I'm trying to do find quantiles i.e. percentile 0, percentile 25, etc, on each column of my given data. As I am doing multiple percentiles, how to retrieve each calculated percentile from the results? Here an example, having data as show below: +----+---------+-------------+----------+-----------+ | id| date|total_revenue|con_dist_1| con_dist_2| +----+---------+-------------+----------+-----------+ |3310|1/15/2018| 0.010680705| 6|0.019875458| |3310|1/15/2018| 0

问题 I have a spark dataframe which has a timestamp field and i want to convert this to long datatype. I used a UDF and the standalone code works fine but when i plug to to a generic logic where any timestamp will need to be converted i m not ble to get it working.Issue is how can i assing the return value from UDF back to the dataframe column Below is the code snippet val spark: SparkSession = SparkSession.builder().master("local[*]").appName("Test3").getOrCreate(); import org.apache.spark.sql

问题 I have a spark dataframe which has a timestamp field and i want to convert this to long datatype. I used a UDF and the standalone code works fine but when i plug to to a generic logic where any timestamp will need to be converted i m not ble to get it working.Issue is how can i assing the return value from UDF back to the dataframe column Below is the code snippet val spark: SparkSession = SparkSession.builder().master("local[*]").appName("Test3").getOrCreate(); import org.apache.spark.sql

问题 I have a spark dataframe which has a timestamp field and i want to convert this to long datatype. I used a UDF and the standalone code works fine but when i plug to to a generic logic where any timestamp will need to be converted i m not ble to get it working.Issue is how can i assing the return value from UDF back to the dataframe column Below is the code snippet val spark: SparkSession = SparkSession.builder().master("local[*]").appName("Test3").getOrCreate(); import org.apache.spark.sql

问题 I have a dataset which contains only header (id,name,age) and 0 rows. I want to write it into an hdfs location as a csv file using DataFrameWriter dataFrameWriter = dataset.write(); Map<String, String> csvOptions = new HashMap<>(); csvOptions.put("header", "true"); dataFrameWriter = dataFrameWriter.options(csvOptions); dataFrameWriter.mode(SaveMode.Overwrite).csv(location); In the hdfs location , the files are: 1. _SUCCESS 2. tempFile.csv If I go to that location and download the file

问题 I have a dataset which contains only header (id,name,age) and 0 rows. I want to write it into an hdfs location as a csv file using DataFrameWriter dataFrameWriter = dataset.write(); Map<String, String> csvOptions = new HashMap<>(); csvOptions.put("header", "true"); dataFrameWriter = dataFrameWriter.options(csvOptions); dataFrameWriter.mode(SaveMode.Overwrite).csv(location); In the hdfs location , the files are: 1. _SUCCESS 2. tempFile.csv If I go to that location and download the file

问题 I have a dataset which contains only header (id,name,age) and 0 rows. I want to write it into an hdfs location as a csv file using DataFrameWriter dataFrameWriter = dataset.write(); Map<String, String> csvOptions = new HashMap<>(); csvOptions.put("header", "true"); dataFrameWriter = dataFrameWriter.options(csvOptions); dataFrameWriter.mode(SaveMode.Overwrite).csv(location); In the hdfs location , the files are: 1. _SUCCESS 2. tempFile.csv If I go to that location and download the file

问题 This article claims that a DataFrame in Spark is equivalent to a Dataset[Row] , but this blog post shows that a DataFrame has a schema. Take the example in the blog post of converting an RDD to a DataFrame : if DataFrame were the same thing as Dataset[Row] , then converting an RDD to a DataFrame should be as simple val rddToDF = rdd.map(value => Row(value)) But instead it shows that it's this val rddStringToRowRDD = rdd.map(value => Row(value)) val dfschema = StructType(Array(StructField(

问题 This article claims that a DataFrame in Spark is equivalent to a Dataset[Row] , but this blog post shows that a DataFrame has a schema. Take the example in the blog post of converting an RDD to a DataFrame : if DataFrame were the same thing as Dataset[Row] , then converting an RDD to a DataFrame should be as simple val rddToDF = rdd.map(value => Row(value)) But instead it shows that it's this val rddStringToRowRDD = rdd.map(value => Row(value)) val dfschema = StructType(Array(StructField(

问题 This article claims that a DataFrame in Spark is equivalent to a Dataset[Row] , but this blog post shows that a DataFrame has a schema. Take the example in the blog post of converting an RDD to a DataFrame : if DataFrame were the same thing as Dataset[Row] , then converting an RDD to a DataFrame should be as simple val rddToDF = rdd.map(value => Row(value)) But instead it shows that it's this val rddStringToRowRDD = rdd.map(value => Row(value)) val dfschema = StructType(Array(StructField(