##Intro
Janus is a "basic distribution of vim plugins and tools intended to be run on top of the latest MacVIM snapshot." It is maintained by Yehuda Katz and Carl Lerche.
I recently whinged on Twitter that all I really want is vim with TextMate's Command-T go-to-file functionality and some efficient visual tab/buffer navigation. Turns out MacVim + Janus, with just a few tweaks, is all that and more.
Follow the installation instructions on the git page
| # Ubuntu upstart file at /etc/init/mongodb.conf | |
| limit nofile 32768 32768 | |
| kill timeout 300 # wait 300s between SIGTERM and SIGKILL. | |
| pre-start script | |
| mkdir -p /data/mongodb &> /dev/null | |
| mkdir -p /data/logs/mongo &> /dev/null | |
| chown mongodb:nogroup /data/mongodb &> /dev/null |
| #!/bin/bash | |
| # herein we backup our indexes! this script should run at like 6pm or something, after logstash | |
| # rotates to a new ES index and theres no new data coming in to the old one. we grab metadatas, | |
| # compress the data files, create a restore script, and push it all up to S3. | |
| TODAY=`date +"%Y.%m.%d"` | |
| INDEXNAME="logstash-$TODAY" # this had better match the index name in ES | |
| INDEXDIR="/usr/local/elasticsearch/data/logstash/nodes/0/indices/" | |
| BACKUPCMD="/usr/local/backupTools/s3cmd --config=/usr/local/backupTools/s3cfg put" | |
| BACKUPDIR="/mnt/es-backups/" | |
| YEARMONTH=`date +"%Y-%m"` |
| ### blogged @ http://dojo4.com/blog/easy-cheasy-realtime-log-tailing-in-a-rails-admin-view | |
| ### the su controller action | |
| def logs | |
| log = File.join(Rails.root, "log", "#{ Rails.env }.log") | |
| @lines = `tail -1024 #{ log }`.split(/\n/).reverse |
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs
| unicorn.rb | |
| ----------------------------------- | |
| application = "jarvis" | |
| remote_user = "vagrant" | |
| env = ENV["RAILS_ENV"] || "development" | |
| RAILS_ROOT = File.join("/home", remote_user, application) | |
| worker_processes 8 | |
| timeout 30 |
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.
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.
| object Main extends App { | |
| val printer = new Printer[String]() | |
| val break = true | |
| val text = "access granted" | |
| //cannot be accessed: | |
| //printer.printCodeName | |
| //printer.codeName = "Rejewski" | |
| //printer.printCodeName |
Consumer key: IQKbtAYlXLripLGPWd0HUA
Consumer secret: GgDYlkSvaPxGxC4X8liwpUoqKwwr3lCADbz8A7ADU
Consumer key: 3nVuSoBZnx6U4vzUxf5w
Consumer secret: Bcs59EFbbsdF6Sl9Ng71smgStWEGwXXKSjYvPVt7qys
Consumer key: CjulERsDeqhhjSme66ECg