emr

I have some pyspark program running on AWS cluster. I am monitoring the job through Spark UI (see attached). However, I noticed that unlike the scala or Java spark program, which shows each Stage is corresponding to which line of code, I can't find which Stage is corresponding to which line of code in the pyspark code. Is there a way I can figure out which Stage is corresponding to which line of the pyspark code? Thanks! 来源： https://stackoverflow.com/questions/38315344/sparkui-for-pyspark-corresponding-line-of-code-for-each-stage

I have a 17.7GB file on S3. It was generated as the output of a Hive query, and it isn't compressed. I know that by compressing it, it'll be about 2.2GB (gzip). How can I download this file locally as quickly as possible when transfer is the bottleneck (250kB/s). I've not found any straightforward way to compress the file on S3, or enable compression on transfer in s3cmd, boto, or related tools. Michel Feldheim S3 does not support stream compression nor is it possible to compress the uploaded file remotely. If this is a one-time process I suggest downloading it to a EC2 machine in the same

In one of my spark job (2.0 on EMR 5.0.0) where I had about 5GB of data that was crossed joined with 30 rows(data size few MBs). I further needed to group by it. What I noticed that I was taking lot of time (Approximately 4 hours with one m3.xlarge master and six m3.2xlarge core nodes). In total time 2 hour was taken by processing and another 2 hour was taken to write data to s3. The time taken was not very impressive to me. I tried searching over net and found this link that says groupBy leads lot of shuffling. It also suggests that for avoiding lot of shuffling ReduceByKey should be used

I am creating clusters on EMR and configure Zeppelin to read the notebooks from S3. To do that I am using a json object that looks like that: [ { "Classification": "zeppelin-env", "Properties": { }, "Configurations": [ { "Classification": "export", "Properties": { "ZEPPELIN_NOTEBOOK_STORAGE":"org.apache.zeppelin.notebook.repo.S3NotebookRepo", "ZEPPELIN_NOTEBOOK_S3_BUCKET":"hs-zeppelin-notebooks", "ZEPPELIN_NOTEBOOK_USER":"user" }, "Configurations": [ ] } ] } ] I am pasting this object in the Stoftware configuration page of EMR: My question is, how/where I can configure the Spark interpreter

阿里文档: EMR里可以通过 Ranger组件来实现 https://help.aliyun.com/document_detail/66410.html?spm=a2c4g.11186623.3.4.1a685b78iZGjgK 4.AWS S3迁移到阿里OSS https://help.aliyun.com/document_detail/95130.html?spm=a2c4g.11186623.2.8.73cf48fayabm5m#concept-igj-s12-qfb 5.UFile迁移到阿里OSS 在线迁移服务目前暂时未包括UFile，可以通过UFile提供的工具将文件下到NAS或本地 https://docs.ucloud.cn/storage_cdn/ufile/tools/tools/tools_file 再通过在线迁移服务从本地NAS弄到OSS https://help.aliyun.com/document_detail/98476.html?spm=a2c4g.11174283.6.617.480251ccL3tHG2 目前UFile应该也是兼容了S3 API的，所以可以尝试用迁移S3的方式先试试看能否迁UFile的数据 1. binlog 写到 HDFS https://help.aliyun.com/document_detail/71539.html

I am developing a code to read data and write it into HDFS using mapreduce . However when I have multiple files I don't understand how it is processed . The input path to the mapper is the name of the directory as evident from the output of String filename = conf1.get("map.input.file"); So how does it process the files in the directory ? In order to get the input file path you can use the context object, like this: FileSplit fileSplit = (FileSplit) context.getInputSplit(); String inputFilePath = fileSplit.getPath().toString(); And as for how it multiple files are processed: Several instances