apache-spark

问题 Hi I'm new to pyspark and I'm trying to convert pyspark.sql.dataframe into list of dictionaries. Below is my dataframe, the type is <class 'pyspark.sql.dataframe.DataFrame'>: +------------------+----------+------------------------+ | title|imdb_score|Worldwide_Gross(dollars)| +------------------+----------+------------------------+ | The Eight Hundred| 7.2| 460699653| | Bad Boys for Life| 6.6| 426505244| | Tenet| 7.8| 334000000| |Sonic the Hedgehog| 6.5| 308439401| | Dolittle| 5.6| 245229088|

问题 Hi I'm new to pyspark and I'm trying to convert pyspark.sql.dataframe into list of dictionaries. Below is my dataframe, the type is <class 'pyspark.sql.dataframe.DataFrame'>: +------------------+----------+------------------------+ | title|imdb_score|Worldwide_Gross(dollars)| +------------------+----------+------------------------+ | The Eight Hundred| 7.2| 460699653| | Bad Boys for Life| 6.6| 426505244| | Tenet| 7.8| 334000000| |Sonic the Hedgehog| 6.5| 308439401| | Dolittle| 5.6| 245229088|

问题 Issue In EMR 5.21 , Spark - Hbase integration is broken. df.write.options().format().save() fails. Reason is json4s-jackson version 3.5.3 in spark 2.4 , EMR 5.21 it works fine in EMR 5.11.2 , Spark 2.2 , son4s-jackson version 3.2.11 Problem is this is EMR so i cant rebuild spark with lower json4s . is there any workaround ? Error py4j.protocol.Py4JJavaError: An error occurred while calling o104.save. : java.lang.NoSuchMethodError: org.json4s.jackson.JsonMethods$.parse(Lorg/json4s/JsonInput;Z

问题 Issue In EMR 5.21 , Spark - Hbase integration is broken. df.write.options().format().save() fails. Reason is json4s-jackson version 3.5.3 in spark 2.4 , EMR 5.21 it works fine in EMR 5.11.2 , Spark 2.2 , son4s-jackson version 3.2.11 Problem is this is EMR so i cant rebuild spark with lower json4s . is there any workaround ? Error py4j.protocol.Py4JJavaError: An error occurred while calling o104.save. : java.lang.NoSuchMethodError: org.json4s.jackson.JsonMethods$.parse(Lorg/json4s/JsonInput;Z

问题 AFAIK, the newest, best S3 implementation for Hadoop + Spark is invoked by using the "s3a://" url protocol. This works great on pre-configured Amazon EMR. However, when running on a local dev system using the pre-built spark-2.0.0-bin-hadoop2.7.tgz , I get Caused by: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2101) at org.apache.hadoop.conf.Configuration.getClass

问题 AFAIK, the newest, best S3 implementation for Hadoop + Spark is invoked by using the "s3a://" url protocol. This works great on pre-configured Amazon EMR. However, when running on a local dev system using the pre-built spark-2.0.0-bin-hadoop2.7.tgz , I get Caused by: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2101) at org.apache.hadoop.conf.Configuration.getClass

问题 PMML, Mleap, PFA currently only support row based transformations. None of them support frame based transformations like aggregates or groupby or join. What is the recommended way to export a spark pipeline consisting of these operations. 回答1: I see 2 options wrt Mleap: 1) implement dataframe based transformers and the SQLTransformer -Mleap equivalent. This solution seems to be conceptually the best (since you can always encapsule such transformations in a pipeline element) but also alot of

问题 PMML, Mleap, PFA currently only support row based transformations. None of them support frame based transformations like aggregates or groupby or join. What is the recommended way to export a spark pipeline consisting of these operations. 回答1: I see 2 options wrt Mleap: 1) implement dataframe based transformers and the SQLTransformer -Mleap equivalent. This solution seems to be conceptually the best (since you can always encapsule such transformations in a pipeline element) but also alot of

问题 PMML, Mleap, PFA currently only support row based transformations. None of them support frame based transformations like aggregates or groupby or join. What is the recommended way to export a spark pipeline consisting of these operations. 回答1: I see 2 options wrt Mleap: 1) implement dataframe based transformers and the SQLTransformer -Mleap equivalent. This solution seems to be conceptually the best (since you can always encapsule such transformations in a pipeline element) but also alot of

问题 I've got ETL-like scenario, in which I read data from multiple JDBC tables and files and perform some aggregations and join between sources. In one step I must join two JDBC tables. I've tried to do something like: val df1 = spark.read.format("jdbc") .option("url", Database.DB_URL) .option("user", Database.DB_USER) .option("password", Database.DB_PASSWORD) .option("dbtable", tableName) .option("driver", Database.DB_DRIVER) .option("upperBound", data.upperBound) .option("lowerBound", data