- 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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### MATPLOTLIBRC FORMAT | |
# This is a sample matplotlib configuration file - you can find a copy | |
# of it on your system in | |
# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it | |
# there, please note that it will be overridden in your next install. | |
# If you want to keep a permanent local copy that will not be | |
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux | |
# like systems) and C:\Documents and Settings\yourname\.matplotlib | |
# (win32 systems). |
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# template tag: {% url django.contrib.auth.views.password_reset_confirm uidb36=uidb36 token=token %} | |
from django.utils.http import int_to_base36 | |
from django.contrib.auth.tokens import default_token_generator | |
from django.contrib.auth.models import User | |
user = User.objects.get(pk=1) | |
context = { | |
'uidb36': int_to_base36(user.pk), | |
'token' = default_token_generator.make_token(user), |
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function ltrim(s) { sub(/^[ \t\r\n]+/, "", s); return s } | |
function rtrim(s) { sub(/[ \t\r\n]+$/, "", s); return s } | |
function trim(s) { return rtrim(ltrim(s)); } | |
BEGIN { | |
# whatever | |
} | |
{ | |
# whatever | |
} | |
END { |
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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 |
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-- show running queries (pre 9.2) | |
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query | |
FROM pg_stat_activity | |
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' | |
ORDER BY query_start desc; | |
-- show running queries (9.2) | |
SELECT pid, age(clock_timestamp(), query_start), usename, query | |
FROM pg_stat_activity | |
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' |
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Testing fnv.New64() with size 235886 | |
m = 3391472, n = 235886, k = 10, s = 339148, p = 0.500000, e = 0.001000 | |
Total items: 235886 | |
Bits in partition 0: 170044 (50.1%) | |
Bits in partition 1: 170171 (50.2%) | |
Bits in partition 2: 169914 (50.1%) | |
Bits in partition 3: 170022 (50.1%) | |
Bits in partition 4: 170144 (50.2%) | |
Bits in partition 5: 169979 (50.1%) | |
Bits in partition 6: 170080 (50.1%) |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
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[diff] | |
tool = Kaleidoscope | |
[difftool] | |
prompt = false | |
[difftool "Kaleidoscope"] | |
cmd = ksdiff --partial-changeset --relative-path \"$MERGED\" -- \"$LOCAL\" \"$REMOTE\" | |
[merge] | |
tool = Kaleidoscope | |
keepBackup = false | |
[mergetool] |
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#!/usr/bin/env sh | |
# serve.sh | |
# Modular version: https://gist.github.com/alganet/a22a1373dcee7c175d1e | |
# Expansion on zsh | |
command -v setopt 2>&1 >/dev/null && setopt SH_WORD_SPLIT | |
# POSIX on bash | |
export POSIXLY_CORRECT=1 | |
# Lists files and folders as HTML |
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