From hdfs-issues-return-225539-archive-asf-public=cust-asf.ponee.io@hadoop.apache.org Thu Jul 5 11:08:04 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 773DA180657 for ; Thu, 5 Jul 2018 11:08:03 +0200 (CEST) Received: (qmail 40470 invoked by uid 500); 5 Jul 2018 09:08:02 -0000 Mailing-List: contact hdfs-issues-help@hadoop.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list hdfs-issues@hadoop.apache.org Received: (qmail 40459 invoked by uid 99); 5 Jul 2018 09:08:02 -0000 Received: from pnap-us-west-generic-nat.apache.org (HELO spamd4-us-west.apache.org) (209.188.14.142) by apache.org (qpsmtpd/0.29) with ESMTP; Thu, 05 Jul 2018 09:08:02 +0000 Received: from localhost (localhost [127.0.0.1]) by spamd4-us-west.apache.org (ASF Mail Server at spamd4-us-west.apache.org) with ESMTP id DFEB5C00B1 for ; Thu, 5 Jul 2018 09:08:01 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd4-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 (spamd4-us-west.apache.org [10.40.0.11]) (amavisd-new, port 10024) with ESMTP id hRd-x9K4w3bJ for ; Thu, 5 Jul 2018 09:08:01 +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 F358C5F4A9 for ; Thu, 5 Jul 2018 09:08:00 +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 7DDF1E00FF for ; Thu, 5 Jul 2018 09:08:00 +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 1360427505 for ; Thu, 5 Jul 2018 09:08:00 +0000 (UTC) Date: Thu, 5 Jul 2018 09:08:00 +0000 (UTC) From: "Elek, Marton (JIRA)" To: hdfs-issues@hadoop.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Commented] (HDDS-225) Provide docker-compose files to check the scalability of KSM 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/HDDS-225?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16533422#comment-16533422 ] Elek, Marton commented on HDDS-225: ----------------------------------- I uploaded a WIP utility to generate ksm volumes/buckets/keys. I check an existing key (http://ksm/template/template/template) and use the containerid/localid of that key to generate more keys. I tested it with locally and in the cloud. Locally I can generate 18-20000 key/sec (tempfs!!!) in the coud I can generate 14-15000 key/sec (ramfs!!!). I would like to generate a huge number of the keys and upload the generated data to somewhere. Space requirement of 727 700 000 keys is ~12Gb. > Provide docker-compose files to check the scalability of KSM > ------------------------------------------------------------ > > Key: HDDS-225 > URL: https://issues.apache.org/jira/browse/HDDS-225 > Project: Hadoop Distributed Data Store > Issue Type: Improvement > Reporter: Elek, Marton > Priority: Major > Attachments: HDDS-225.001.patch > > > I open this jira to start a discussion. The main question: how can we prove the scalability of KSM with minimal effort? > 1. The goal is to prove that KSM could handle 1-10 billion of keys without any problem. > 2. 10 000 000 000 * 10 kbyte object = 10 Terrabyte space. But we need to test only the KSM part. > 3. With a low level data generator we can generate the volumes/buckets/keys directly to the ksm.db (rocksdb). We can fake the block allocation and use exactly the same containerid/localid for all the keys. With this method we can test the read/list methods without any problems (all of the keys could be downloaded. > 4. With this storage optimization we can test 10 billion keys locally with a specific docker-compose setup where the local db-s are mounted from the local directory > 5. The data could be generated (takes some time) or could be uploaded after a generation > -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: hdfs-issues-unsubscribe@hadoop.apache.org For additional commands, e-mail: hdfs-issues-help@hadoop.apache.org