mdonkers

#!/usr/bin/env python3
"""
License: MIT License
Copyright (c) 2023 Miel Donkers

Very simple HTTP server in python for logging requests
Usage::
    ./server.py [<port>]
"""
from http.server import BaseHTTPRequestHandler, HTTPServer

PeterAttardo

set hivevar:start_date=0000-01-01;
set hivevar:days=1000000;
set hivevar:table_name=[INSERT YOUR TABLE NAME HERE];

-- If you are running a version of HIVE prior to 1.2, comment out all uses of date_format() and uncomment the lines below for equivalent functionality

CREATE TABLE IF NOT EXISTS ${table_name} AS
WITH dates AS (
    SELECT date_add("${start_date}", a.pos) as date

wdullaer

# Ask for the user password
# Script only works if sudo caches the password for a few minutes
sudo true

# Install kernel extra's to enable docker aufs support
# sudo apt-get -y install linux-image-extra-$(uname -r)

# Add Docker PPA and install latest version
# sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9
# sudo sh -c "echo deb https://get.docker.io/ubuntu docker main > /etc/apt/sources.list.d/docker.list"

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

Pyrolistical

function map() {
	emit(1, {
		sum: this.value, // the field you want stats for
		min: this.value,
		max: this.value,
		count: 1,
		diff: 0
	});
}

jpountz

// Set codec, dir and segmentName accordingly to the segment you are trying to restore
Codec codec = new Lucene42Codec();
Directory dir = FSDirectory.open(new File("/tmp/test"));
String segmentName = "_0";

IOContext ioContext = new IOContext();
SegmentInfo segmentInfos = codec.segmentInfoFormat().getSegmentInfoReader().read(dir, segmentName, ioContext);
Directory segmentDir;
if (segmentInfos.getUseCompoundFile()) {
	segmentDir = new CompoundFileDirectory(dir, IndexFileNames.segmentFileName(segmentName, "", IndexFileNames.COMPOUND_FILE_EXTENSION), ioContext, false);

miketheman

Adding 2 nodes to an existing 3-node ZooKeeper ensemble without losing the Quorum

Since many deployments may start out with 3 nodes and so little is known about how to grow a cluster from 3 memebrs to 5 members without losing the existing Quorum, here is an example of how this might be achieved.

In this example, all 5 nodes will be running on the same Vagrant host for the purpose of illustration, running on distinct configurations (ports and data directories) without the actual load of clients.

YMMV. Caveat usufructuarius.

Step 1: Have a healthy 3-node ensemble

nathanlws

//
// See: https://github.com/foundationdb/sql-parser
//

import com.foundationdb.sql.parser.*;
import com.foundationdb.sql.parser.SQLParserContext.*;

public class CustomCase {
    public static class ColumnNamePrinter implements Visitor {
        @Override

dadoonet

# Script to be placed in elasticsearch/bin
# Launch it from elasticsearch dir
# bin/backup indexname
# We suppose that data are under elasticsearch/data
# It will create a backup file under elasticsearch/backup

if [ -z "$1" ]; then
  INDEX_NAME="dummy"
else
  INDEX_NAME=$1

radu-gheorghe

#!/usr/bin/env bash

###############FUNCTIONS############

function prepare {
    #optimize the index
    echo -n "Optimizing index $INDEX_NAME..."
    curl -XPOST "$ADDRESS/$INDEX_NAME/_optimize" 2>/dev/null| grep 'failed":0' >/dev/null
    if [ $? -eq 0 ]; then
        echo "done"