From issues-return-194126-archive-asf-public=cust-asf.ponee.io@spark.apache.org Fri Jun 15 01:08:04 2018 Return-Path: X-Original-To: archive-asf-public@cust-asf.ponee.io Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by mx-eu-01.ponee.io (Postfix) with SMTP id 85E9B180600 for ; Fri, 15 Jun 2018 01:08:03 +0200 (CEST) Received: (qmail 73235 invoked by uid 500); 14 Jun 2018 23:08:02 -0000 Mailing-List: contact issues-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list issues@spark.apache.org Received: (qmail 73226 invoked by uid 99); 14 Jun 2018 23:08:02 -0000 Received: from pnap-us-west-generic-nat.apache.org (HELO spamd1-us-west.apache.org) (209.188.14.142) by apache.org (qpsmtpd/0.29) with ESMTP; Thu, 14 Jun 2018 23:08:02 +0000 Received: from localhost (localhost [127.0.0.1]) by spamd1-us-west.apache.org (ASF Mail Server at spamd1-us-west.apache.org) with ESMTP id 1DC74C94A9 for ; Thu, 14 Jun 2018 23:08:02 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd1-us-west.apache.org X-Spam-Flag: NO X-Spam-Score: -109.501 X-Spam-Level: X-Spam-Status: No, score=-109.501 tagged_above=-999 required=6.31 tests=[ENV_AND_HDR_SPF_MATCH=-0.5, KAM_ASCII_DIVIDERS=0.8, RCVD_IN_DNSWL_MED=-2.3, SPF_PASS=-0.001, USER_IN_DEF_SPF_WL=-7.5, USER_IN_WHITELIST=-100] autolearn=disabled Received: from mx1-lw-us.apache.org ([10.40.0.8]) by localhost (spamd1-us-west.apache.org [10.40.0.7]) (amavisd-new, port 10024) with ESMTP id ixWLSUkUT_aD for ; Thu, 14 Jun 2018 23:08:01 +0000 (UTC) Received: from mailrelay1-us-west.apache.org (mailrelay1-us-west.apache.org [209.188.14.139]) by mx1-lw-us.apache.org (ASF Mail Server at mx1-lw-us.apache.org) with ESMTP id 233AC5F1F7 for ; Thu, 14 Jun 2018 23:08:01 +0000 (UTC) Received: from jira-lw-us.apache.org (unknown [207.244.88.139]) by mailrelay1-us-west.apache.org (ASF Mail Server at mailrelay1-us-west.apache.org) with ESMTP id B4174E0101 for ; Thu, 14 Jun 2018 23:08:00 +0000 (UTC) Received: from jira-lw-us.apache.org (localhost [127.0.0.1]) by jira-lw-us.apache.org (ASF Mail Server at jira-lw-us.apache.org) with ESMTP id 7ABC021832 for ; Thu, 14 Jun 2018 23:08:00 +0000 (UTC) Date: Thu, 14 Jun 2018 23:08:00 +0000 (UTC) From: "Apache Spark (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Commented] (SPARK-24565) Add API for in Structured Streaming for exposing output rows of each microbatch as a DataFrame MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/SPARK-24565?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16513110#comment-16513110 ] Apache Spark commented on SPARK-24565: -------------------------------------- User 'tdas' has created a pull request for this issue: https://github.com/apache/spark/pull/21571 > Add API for in Structured Streaming for exposing output rows of each microbatch as a DataFrame > ---------------------------------------------------------------------------------------------- > > Key: SPARK-24565 > URL: https://issues.apache.org/jira/browse/SPARK-24565 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming > Affects Versions: 2.3.0 > Reporter: Tathagata Das > Assignee: Tathagata Das > Priority: Major > > Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have a better sense of building a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the micro-batch output as a dataframe is useful. > - Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning). > - Reuse batch data sources for output whose streaming version does not exist (e.g. redshift data source). > - Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice. > The proposal is to add a method {{foreachBatch(f: Dataset[T] => Unit)}} to Scala/Java/Python `DataStreamWriter`. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org For additional commands, e-mail: issues-help@spark.apache.org