| # zonecfg -z <uuid> | |
| zonecfg:uuid> add attr | |
| zonecfg:uuid:attr> set name=resolvers | |
| zonecfg:uuid:attr> set type=string | |
| zonecfg:uuid:attr> set value=8.8.8.8,8.8.4.4 | |
| zonecfg:uuid:attr> end | |
| zonecfg:uuid> verify | |
| zonecfg:uuid> commit | |
| zonecfg:uuid> exit | |
| # vmadm reboot <uuid> |
| {-# LANGUAGE BangPatterns #-} | |
| module LazyLength ( | |
| LazyLength(), | |
| fromLazyLength, | |
| toLazyLength, | |
| lazyLength, | |
| -- QuickCheck properties | |
| prop_invariant, | |
| prop_invertible, | |
| prop_addition, |
| # Licensed under CC BY 3.0 http://creativecommons.org/licenses/by/3.0/ | |
| # Derived works must attribute https://gist.github.com/4492300 at the beginning, and the date. | |
| ########################################################## | |
| Installing and Configuring SmartOS on Hetzner (with a /29) | |
| ########################################################## | |
| # if you find this gist useful, please star it | |
| # thanks to: jamesog, linuxprofessor, ryancnelson for help with routing |
#Anonymous records. A solution to the problems of record-system.
Please, beware that the proposal that follows has been implemented as a library.
The current record system is notorious for three major flaws:
-
It does not solve the namespacing problem. I.e., you cannot have two records sharing field names in a single module. E.g., the following won't compile:
data A = A { field :: String }
| # assuming you have Joyent Compute Service available | |
| sdc-createmachine --dataset 17c98640-1fdb-11e3-bf51-3708ce78e75a --package g3-standard-1-smartos --name alain-demo-$(uuid) | json -a id |
| #!/bin/sh | |
| inPreprocessorMode () { | |
| hasE=0 | |
| hasU=0 | |
| hasT=0 | |
| for arg in "$@" | |
| do | |
| if [ 'x-E' = "x$arg" ]; then hasE=1; fi | |
| if [ 'x-undef' = "x$arg" ]; then hasU=1; fi |
Slightly disorganized but reasonably complete notes on the algorithms, strategies and optimizations of the Akka Cluster implementation. Could use a lot more links and context etc., but was just written for my own understanding. Might be expanded later.
Links to papers and talks that have inspired the implementation can be found on the 10 last pages of this presentation.
This is the Gossip state representation:
- 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.
- Models and Issues in Data Stream Systems
- 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
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t