What is the recommended way to store records that don't meet a filter?

Community , Given a dataset ds , what is the recommended way to store the records that don't meet a filter? For example : val ds = Seq(1,2,3,4).toDS val f = (i : Integer) => i < 2 val filtered = ds.filter(f(_)) I understand I can do this : val filterNotMet =

答复: Re: Re: How to get all input tables of a SPARK SQL 'select' statement

Thank you so much. I tried your suggestion and it really works! 发件人: "Ramandeep Singh Nanda" 收件人: l...@china-inv.cn 抄送: "Shahab Yunus" , "Tomas Bartalos" , "user @spark/'user @spark'/spark users/user@spark" 日期: 2019/01/26 05:42 主题: Re: Re: How to get all input tables of a SPARK SQL

CVE-2018-11760: Apache Spark local privilege escalation vulnerability

Severity: Important Vendor: The Apache Software Foundation Versions affected: All Spark 1.x, Spark 2.0.x, and Spark 2.1.x versions Spark 2.2.0 to 2.2.2 Spark 2.3.0 to 2.3.1 Description: When using PySpark , it's possible for a different local user to connect to the Spark application and

Silly Spark SQL query

Hi, How to do this when the column (malignant and prediction) names are stored in two respective variables? tp = test_transformed[(test_transformed.malignant == 1) & (test_transformed.prediction == 1)].count() Thanks, Aakash.

Re: Silly Spark SQL query

Well, it is done. Using: ma = "malignant" pre = "prediction" tp_test = test_transformed.filter((col(ma) == "1") & (col(pre) == "1")).count() On Mon, Jan 28, 2019 at 5:41 PM Aakash Basu wrote: > Hi, > > How to do this when the column (malignant and prediction) names are stored > in two

CfP: LASCAR 2019 - Workshop on Large Scale RDF Analytics || @ESWC 2019 || 2nd – 6th June 2019 || Portorož, Slovenia

Dear all **Apologies for cross-posting** Call for Papers & Posters ESWC - Workshop on Large Scale RDF Analytics *June 3, 2019* Website: http://lascar.sda.tech/ Twitter: @lascarworkshop ABSTRACT: LASCAR-19, Workshop on Large Scale RDF Analytics, at the