- 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
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type re = C of char | Nil | Seq of re * re | Bot | Alt of re * re | Star of re | |
let rec null = function | |
| C _ | Bot -> false | |
| Nil | Star _ -> true | |
| Alt(r1, r2) -> null r1 || null r2 | |
| Seq(r1, r2) -> null r1 && null r2 | |
module R = Set.Make(struct type t = re let compare = compare end) | |
let rmap f rs = R.fold (fun r -> R.add (f r)) rs R.empty |
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private static final Calendar UTC = Calendar.getInstance(TimeZone.getTimeZone("UTC")); | |
private Calendar utc() { | |
return (Calendar) UTC.clone(); | |
} | |
//unchanged | |
public LocalDateTime getValue(ResultSet rs, int index) throws SQLException { | |
Timestamp ts = rs.getTimestamp(index, utc()); |
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## Day 1 - Track 1 ## | |
00:10:00 Pat Helland: Keystone - Between a ROC and a SOFT place | |
01:12:18 Lindsey Kuper: LVars: Lattice-based Data Structures for Deterministic Parallelism | |
02:11:54 Eric Redmond: Yokozuna! | |
04:10:50 Justin Shoffstall & Charlie Voiselle: The Seven-Layer Burrito; Troubleshooting a Distributed Database in Production | |
05:11:00 Peter Bailis: Bad as I wanna be - Coordination and Consistency in Distributed Databases | |
06:13:24 Joseph Blomstedt: Bringing Consistency to Riak (Part 2) | |
07:16:22 Lightning talks (_nb_: you **must** see @tsantero!) |
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<!DOCTYPE HTML> | |
<head> | |
<script type="text/javascript" src="http://d3js.org/d3.v3.min.js"></script> | |
</head> | |
<body> | |
<canvas width=1000 height=600></canvas> | |
<script type="text/javascript"> | |
var canvas = d3.select("canvas").node(); | |
var xgrid = 10, | |
ygrid = 10, |
This is not intended to be comprehensive or authoritative, just free online resources I've found valuable while learning more about Erlang.
- 0xAX's list of Erlang bookmarks
- Federico Carrone, Erlang Spawned Shelter
- Ivan Uemlianin's list of resources on various BEAM languages
- David Robakowski's curated list of awesome Erlang libraries, resources and shiny things
- Julius Beckmann's curated list of amazingly awesome Elixir and Erlang libraries, resources and shiny things
- Forecasting for Cloud computing on-demand resources based on pattern matching
- Auto-scaling Techniques for Elastic Applications in Cloud Environments
- Auto-Scaling Model for Cloud Computing System
- Rebalancing in a Multi-Cloud Environment
- Infrastructure Outsourcing in Multi-Cloud Environment
- Workload Classification for Efficient Auto-Scaling of Cloud Resources
- Dynamically Scaling Applications in the Cloud
- [Optimal Autoscaling in the IaaS Cloud](http://www.cse.yorku.ca/~hamoun/p
This script can be used to feed collectd with cpu and memory usage statistics for running docker containers using the collectd exec
plugin.
This script will report the used and cached memory as well as the user and system cpu usage by inspecting the appropriate cgroup stat file for each running container.
This script is intented to be executed by collectd on a host with running docker containers. To use, simply configure the exec
plugin in collectd to execute the collectd-docker.sh
script. You may need to adjust the script to match your particulars, such as the mount location for cgroup.
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import org.apache.commons.math3.linear._ | |
import com.twitter.algebird.Operators._ | |
import com.twitter.scalding._ | |
import cascading.pipe.Pipe | |
import cascading.pipe.joiner.InnerJoin | |
import cascading.tuple.Fields | |
object SVD extends Serializable { |