From issues-return-183620-archive-asf-public=cust-asf.ponee.io@spark.apache.org Wed Jan 31 18:38:11 2018 Return-Path: X-Original-To: archive-asf-public@eu.ponee.io Delivered-To: archive-asf-public@eu.ponee.io Received: from cust-asf.ponee.io (cust-asf.ponee.io [163.172.22.183]) by mx-eu-01.ponee.io (Postfix) with ESMTP id 2A57E180662 for ; Wed, 31 Jan 2018 18:38:11 +0100 (CET) Received: by cust-asf.ponee.io (Postfix) id 1A862160C55; Wed, 31 Jan 2018 17:38:11 +0000 (UTC) Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by cust-asf.ponee.io (Postfix) with SMTP id 6AA32160C42 for ; Wed, 31 Jan 2018 18:38:10 +0100 (CET) Received: (qmail 74541 invoked by uid 500); 31 Jan 2018 17:38:04 -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 74532 invoked by uid 99); 31 Jan 2018 17:38:04 -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; Wed, 31 Jan 2018 17:38:04 +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 06FEDDB4FD for ; Wed, 31 Jan 2018 17:38:04 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd1-us-west.apache.org X-Spam-Flag: NO X-Spam-Score: -109.511 X-Spam-Level: X-Spam-Status: No, score=-109.511 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, T_RP_MATCHES_RCVD=-0.01, 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 XW1Hzk-yk0Uv for ; Wed, 31 Jan 2018 17:38:03 +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 397F35FBD8 for ; Wed, 31 Jan 2018 17:38:03 +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 09ECDE01D8 for ; Wed, 31 Jan 2018 17:38:02 +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 AB8D02410A for ; Wed, 31 Jan 2018 17:38:00 +0000 (UTC) Date: Wed, 31 Jan 2018 17:38:00 +0000 (UTC) From: "Xiao Li (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Updated] (SPARK-14098) Generate Java code to build CachedColumnarBatch and get values from CachedColumnarBatch when DataFrame.cache() is called 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-14098?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-14098: ---------------------------- Labels: releasenotes (was: ) > Generate Java code to build CachedColumnarBatch and get values from CachedColumnarBatch when DataFrame.cache() is called > ------------------------------------------------------------------------------------------------------------------------ > > Key: SPARK-14098 > URL: https://issues.apache.org/jira/browse/SPARK-14098 > Project: Spark > Issue Type: Umbrella > Components: SQL > Reporter: Kazuaki Ishizaki > Priority: Major > Labels: releasenotes > > [Here|https://docs.google.com/document/d/1-2BnW5ibuHIeQzmHEGIGkEcuMUCTk87pmPis2DKRg-Q/edit?usp=sharing] is a design document for this change (***TODO: Update the document***). > This JIRA implements a new in-memory cache feature used by DataFrame.cache and Dataset.cache. The followings are basic design based on discussions with Sameer, Weichen, Xiao, Herman, and Nong. > * Use ColumnarBatch with ColumnVector that are common data representations for columnar storage > * Use multiple compression scheme (such as RLE, intdelta, and so on) for each ColumnVector in ColumnarBatch depends on its data typpe > * Generate code that is simple and specialized for each in-memory cache to build an in-memory cache > * Generate code that directly reads data from ColumnVector for the in-memory cache by whole-stage codegen. > * Enhance ColumnVector to keep UnsafeArrayData > * Use primitive-type array for primitive uncompressed data type in ColumnVector > * Use byte[] for UnsafeArrayData and compressed data > Based on this design, this JIRA generates two kinds of Java code for DataFrame.cache()/Dataset.cache() > * Generate Java code to build CachedColumnarBatch, which keeps data in ColumnarBatch > * Generate Java code to get a value of each column from ColumnarBatch > ** a Get a value directly from from ColumnarBatch in code generated by whole stage code gen (primary path) > ** b Get a value thru an iterator if whole stage code gen is disabled (e.g. # of columns is more than 100, as backup path) -- 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