Return-Path: X-Original-To: apmail-hadoop-mapreduce-user-archive@minotaur.apache.org Delivered-To: apmail-hadoop-mapreduce-user-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 92A0F10ED4 for ; Sun, 19 Jan 2014 13:27:42 +0000 (UTC) Received: (qmail 99848 invoked by uid 500); 19 Jan 2014 13:27:34 -0000 Delivered-To: apmail-hadoop-mapreduce-user-archive@hadoop.apache.org Received: (qmail 99505 invoked by uid 500); 19 Jan 2014 13:27:34 -0000 Mailing-List: contact user-help@hadoop.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@hadoop.apache.org Delivered-To: mailing list user@hadoop.apache.org Received: (qmail 99492 invoked by uid 99); 19 Jan 2014 13:27:32 -0000 Received: from athena.apache.org (HELO athena.apache.org) (140.211.11.136) by apache.org (qpsmtpd/0.29) with ESMTP; Sun, 19 Jan 2014 13:27:32 +0000 X-ASF-Spam-Status: No, hits=1.7 required=5.0 tests=FREEMAIL_ENVFROM_END_DIGIT,HTML_MESSAGE,RCVD_IN_DNSWL_LOW,SPF_PASS X-Spam-Check-By: apache.org Received-SPF: pass (athena.apache.org: domain of chandler605@gmail.com designates 209.85.214.195 as permitted sender) Received: from [209.85.214.195] (HELO mail-ob0-f195.google.com) (209.85.214.195) by apache.org (qpsmtpd/0.29) with ESMTP; Sun, 19 Jan 2014 13:27:27 +0000 Received: by mail-ob0-f195.google.com with SMTP id vb8so1456996obc.2 for ; Sun, 19 Jan 2014 05:27:07 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:from:date:message-id:subject:to:content-type; bh=X+0cKrM5T/CegiQc745nAsvPYHCsXkiZ3C5cpBe/35I=; b=M0FgmIwcOcp8JpNG/m/ZKFnZnIkO1AB64ZLstvXasMlESWuiw6VlsMph+djy8HOCK0 qYa1ehFjp03+rUGa275+Qcs6SvEWz04zj8V73iCh44d0idoYbeCKghvPVX8Ms3wbIaWn Yz4ChJVhHlowqeD+sRSLUtU6LbyT9zoL5aHy9tw4sJe5BNDPQSKP99jqtvvp7VZWDcg5 YTsKL2c7XF77yhNSXxl0/YESKRH+XYtyRHzevEFYBJsvy4YYdhrWgjcvx/UhZK6T/41C xFHl1yq9DDDuoho2MO8V6IEZaktdLRvqF2P/Hb3RYVc+S1iglsMOajVIVB7bHk5A3o1g 3dWA== X-Received: by 10.182.65.36 with SMTP id u4mr10916234obs.31.1390138027069; Sun, 19 Jan 2014 05:27:07 -0800 (PST) MIME-Version: 1.0 Received: by 10.76.70.168 with HTTP; Sun, 19 Jan 2014 05:26:46 -0800 (PST) From: chandler song Date: Sun, 19 Jan 2014 21:26:46 +0800 Message-ID: Subject: Question about Yarn To: user@hadoop.apache.org Content-Type: multipart/alternative; boundary=047d7b604cbc4ad3c504f052bba3 X-Virus-Checked: Checked by ClamAV on apache.org --047d7b604cbc4ad3c504f052bba3 Content-Type: text/plain; charset=ISO-8859-1 hi all I have some question about yarn when I read the tutorial on the website. 1) the contain is physical or logic? for example, there are three PCs(A,B,C) on Cluster. if I allocate one container. it will run on one PC all the time. or the container will work on different PC at different time. 2) the contain, I can think it's a virtual PC which can run java application? is my correct? 3)about mapreduce, how mapreduce run on yarn? after reading the tutorial, I think yarn and mapreduce is totally different thing. I think the basic unit of yarn is container. map and reduce's basic unit is map and reduce. or how yarn handle concurrent? I know in mapreduce, I don't need to think too much about concurrent. because mapreduce will do this for you. it will split data into a small unit and you can do what you do. but I don't find yarn has same thing. --047d7b604cbc4ad3c504f052bba3 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable
hi all

=A0 I ha= ve some question about yarn when I read the tutorial on the website.
=A0=
=A01) the contain is physical or logic? for example, there are th= ree PCs(A,B,C) on Cluster.=A0 if I allocate one container. it will run on o= ne PC all the time. or the container will work on different PC at different= time.

2) the contain, I can think it's a virtual PC which can run j= ava application? is my correct?

3)about mapreduce, how mapredu= ce run on yarn? after reading the tutorial, I think yarn and mapreduce is t= otally different thing. I think the basic unit of yarn is container. map an= d reduce's basic unit is map and reduce.

or how yarn handle concurrent? I know in mapreduce, I don't n= eed to think too much about concurrent. because mapreduce will do this for = you. it will split data into a small unit and you can do what you do. but I= don't find yarn has same thing.

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