jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

richhickey

(defmacro test->
  "Takes an expression and a set of test/form pairs. Threads expr (via ->)
  through each form for which the corresponding test expression (not threaded) is true."
  [expr
  & clauses]
  (assert (even? (count clauses)))
  (let [g (gensym)
        pstep (fn [[test step]] `(if ~test (-> ~g ~step) ~g))]
    `(let [~g ~expr
           ~@(interleave (repeat g) (map pstep (partition 2 clauses)))]

ndarville

Business Models

Advertising

Models	Examples
Display ads	Yahoo!
Search ads	Google

aaronfeng

Application specific host grouping in Riemann-dash

It is generally desirable to group all the hosts for a specific service into a single dashboard view. For example, all the web servers are in single view while all the database servers are in another view.

This is usually not an issue when you are sending custom metrics using Riemann client. However, there are cases where you are using something that you do not control how the metrics are being sent. i.e., Riemann-tools.

Since Riemann-tools scripts are application agnostic, in order for the dashboard view to group hosts, we must inject some application specific information into the tags field. Tags is a collection of arbitrary strings. In the case of Riemann-tools scripts you can pass in arbitrary strings on the command line.

riemann-health --host 127.0.0.1 --tag "prod" --tag "webserver"

w01fe

;; Copyright Jason Wolfe and Prismatic, 2013.
;; Licensed under the EPL, same license as Clojure

(use 'plumbing.core)
(require '[clojure.java.shell :as shell]
         '[clojure.string :as str])

(import '[java.util HashSet] '[java.io File])

(defn double-quote [s] (str "\"" s "\""))

tavisrudd

Remapped on kinesis itself
--------------------------------------------------------------------------------
     caps lock             -> backspace (freq use)
     backspace             -> right gui/windows (freq use)
     left alt              -> return/enter (very infreq use)

     right ctrl            -> return/enter (freq use)
     right gui/windows     -> left gui
     enter                 -> right gui  (infreq use)

alexmic

import os
import pytest

from alembic.command import upgrade
from alembic.config import Config

from project.factory import create_app
from project.database import db as _db

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

mookerji

(ns org.flausenhaus.rocksdb
  "Idiomatic Clojure bindings for the C++ RocksDB library, an embedded
   persistent key-value store for flash storage and server workloads based on
   LevelDB. More details about RocksDB are given at
   https://github.com/facebook/rocksdb/wiki/Rocksdb-Architecture-Guide.
   The implementation here borrows heavily from Factual's clj-leveldb.

   This namespace provides a few things:
   0. Protocols/type definitions: byte serialization (IByteSerializable),
   closeable sequences (CloseableSeq), and mutable

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)