Return-Path: X-Original-To: apmail-flink-issues-archive@minotaur.apache.org Delivered-To: apmail-flink-issues-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 54CAE19B8F for ; Thu, 21 Apr 2016 20:31:14 +0000 (UTC) Received: (qmail 44068 invoked by uid 500); 21 Apr 2016 20:31:13 -0000 Delivered-To: apmail-flink-issues-archive@flink.apache.org Received: (qmail 43963 invoked by uid 500); 21 Apr 2016 20:31:13 -0000 Mailing-List: contact issues-help@flink.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@flink.apache.org Delivered-To: mailing list issues@flink.apache.org Received: (qmail 43745 invoked by uid 99); 21 Apr 2016 20:31:13 -0000 Received: from arcas.apache.org (HELO arcas) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Thu, 21 Apr 2016 20:31:13 +0000 Received: from arcas.apache.org (localhost [127.0.0.1]) by arcas (Postfix) with ESMTP id 0DE032C1F62 for ; Thu, 21 Apr 2016 20:31:13 +0000 (UTC) Date: Thu, 21 Apr 2016 20:31:13 +0000 (UTC) From: "Chenguang He (JIRA)" To: issues@flink.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Created] (FLINK-3802) Add Very Fast Reservoir Sampling MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 Chenguang He created FLINK-3802: ----------------------------------- Summary: Add Very Fast Reservoir Sampling Key: FLINK-3802 URL: https://issues.apache.org/jira/browse/FLINK-3802 Project: Flink Issue Type: Improvement Components: Java API Reporter: Chenguang He Assignee: Chenguang He Adding Very Fast Reservoir Sampling (http://erikerlandson.github.io/blog/2015/11/20/very-fast-reservoir-sampling/) An improvement version of Reservoir Sampling, it's used to deal with small sampling in large dataset, where the set of dataset is much larger than the size of sampling. It is a random sampling proved in the link. The average possibility is P(R/J), where R is size of sampling and J is index of streaming data Thanks Erik Erlandson who is the author of this algorithm help me with implementation. -- This message was sent by Atlassian JIRA (v6.3.4#6332)