andymatuschak

//
//  NSObject+BlockObservation.h
//  Version 1.0
//
//  Andy Matuschak
//  andy@andymatuschak.org
//  Public domain because I love you. Let me know how you use it.
//

#import <Cocoa/Cocoa.h>

mrflip

We couldn’t find that file to show.

clintongormley

# First, create the synonyms file /opt/elasticsearch/name_synonyms.txt
# with the contents:
#
# rob,bob => robert
#

## CREATE THE INDEX WITH ANALYZERS AND MAPPINGS

curl -XPUT 'http://127.0.0.1:9200/test/?pretty=1'  -d '
{

mdbecker

from multiprocessing import Pool as MPool
from time import sleep
import datetime
import multiprocessing
import random

def time_request():
    from gevent import monkey; monkey.patch_socket
    from jsonrequester import JsonRequester

alexmacedo

#! /usr/bin/env python

class Pipeline(object):
    def __init__(self):
        self.source = None
    
    def __iter__(self):
        return self.generator()
    
    def generator(self):

duydo

If you want, I can try and help with pointers as to how to improve the indexing speed you get. Its quite easy to really increase it by using some simple guidelines, for example:

- Use create in the index API (assuming you can).
- Relax the real time aspect from 1 second to something a bit higher (index.engine.robin.refresh_interval).
- Increase the indexing buffer size (indices.memory.index_buffer_size), it defaults to the value 10% which is 10% of the heap.
- Increase the number of dirty operations that trigger automatic flush (so the translog won't get really big, even though its FS based) by setting index.translog.flush_threshold (defaults to 5000).
- Increase the memory allocated to elasticsearch node. By default its 1g.
- Start with a lower replica count (even 0), and then once the bulk loading is done, increate it to the value you want it to be using the update_settings API. This will improve things as possibly less shards will be allocated to each machine.
- Increase the number of machines you have so

piscisaureus

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = git@github.com:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

clintongormley

Yesterday I upgraded our running elasticsearch cluster on a site which serves a few million search requests a day, with zero downtime. I've been asked to describe the process, hence this blogpost.

To make it more complicated, the cluster was running elasticsearch version 0.17.8 (released 6 Oct 2011) and I upgraded it to the latest 0.19.10. There have been 21 releases between those two versions, with a lot of functional changes, so I needed to be ready to roll back if necessary.

Our setup:

• elasticsearch

We run elasticsearch on two biggish boxes: 16 cores plus 32GB of RAM. All indices have 1 replica, so all data is stored on both boxes (about 45GB of data). The primary data for our main indices is also stored in our database. We have a few other indices whose data is stored only in elasticsearch, but are updated once daily only. Finally, we store our sessions in elasticsearch, but active sessions are cached in memcached.

reyjrar

##################################################################
# /etc/elasticsearch/elasticsearch.yml
#
# Base configuration for a write heavy cluster
#

# Cluster / Node Basics
cluster.name: logng

# Node can have abritrary attributes we can use for routing

inklesspen

(This gist is pretty old; I've written up my current approach to the Pyramid integration on this blog post, but that blog post doesn't go into the transactional management, so you may still find this useful.)

Fast and flexible unit tests with live Postgres databases and fixtures

I've created a Pyramid scaffold which integrates Alembic, a migration tool, with the standard SQLAlchemy scaffold. (It also configures the Mako template system, because I prefer Mako.)

I am also using PostgreSQL for my database. PostgreSQL supports nested transactions. This means I can setup the tables at the beginning of the test session, then start a transaction before each test happens and roll it back after the test; in turn, this means my tests operate in the same environment I expect to use in production, but they are also fast.

I based my approach on [sontek's blog post](http://sontek.net/blog/