milimetric

from datetime import datetime


def diff_datewise(left, right, left_format=None, right_format=None):
    """
    Parameters
        left            : a list of datetime strings or objects
        right           : a list of datetime strings or objects
        left_format     : None if left contains datetimes, or strptime format
        right_format    : None if right contains datetimes, or strptime format

milimetric

 select p1.page_title     as child_title
       ,p1.page_id        as child_id
       ,p2.page_title     as parent_title
       ,p2.page_id        as parent_id
   from categorylinks cl
            inner join
        page p1           on p1.page_id = cl.cl_from
            inner join
        page p2           on p2.page_title = cl.cl_to
                         and p2.page_id <> cl.cl_from

milimetric

*swp

milimetric

daemonize yes
pidfile /var/run/redis.pid
port 6379
timeout 0
loglevel debug
logfile /var/log/redis/redis-server.log
databases 16
save 900 1
save 300 10
save 60 20

milimetric

#!/usr/local/bin/python
# calculates per country weekly percentiles
#
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.dates as md
import csv
import datetime
from scipy import stats

milimetric

begin;

/* insert a RunReport for each AggregateReport, easiest way is to delete existing ones first */
 delete from report where name like '<RunReport("%';

 insert into report (name, show_in_ui, queue_result_key, result_key, created, user_id, status, parameters)
 select 'RunReport Will Rename Below' as name,
        0 as show_in_ui,
        a.queue_result_key,
        a.result_key,

milimetric

# 20 days in minutes / 10 minutes = 2880 chunks
# 1 day in minutes / 10 minutes = 144 chunks

use log;

    set @start = '20151014050000';

 select seq.tenMinuteChunk
   from (select left(date_format(date_add(@start, interval seq*10 minute), '%Y%m%d%H%i'), 11) as tenMinuteChunk
           from seq_0_to_144

milimetric

druid.host=localhost
druid.service=broker
druid.port=8000

druid.zk.service.host=localhost

# Change these to make Druid faster
druid.processing.buffer.sizeBytes=100000000
druid.processing.numThreads=1

milimetric

 select date, project, variant, page_title,
        rank() over (PARTITION by date, project, variant ORDER BY tmp.pageviews desc) as rank
    (select concat(year, lpad(month, 2, '0'), lpad(day, 2, '0')) as date,
            project,
            variant,
            page_title,
            sum(view_count) as pageviews
       from wmf.pageview_hourly
      where year = 2015
        and month = 7

milimetric

ADD JAR /srv/deployment/analytics/refinery/artifacts/refinery-hive.jar;
CREATE TEMPORARY FUNCTION client_ip as 'org.wikimedia.analytics.refinery.hive.ClientIpUDF';
CREATE TEMPORARY FUNCTION geocoded_data as 'org.wikimedia.analytics.refinery.hive.GeocodedDataUDF';
CREATE TEMPORARY FUNCTION is_pageview as 'org.wikimedia.analytics.refinery.hive.IsPageviewUDF';
CREATE TEMPORARY FUNCTION get_access_method as 'org.wikimedia.analytics.refinery.hive.GetAccessMethodUDF';
CREATE TEMPORARY FUNCTION classify_referer AS 'org.wikimedia.analytics.refinery.hive.RefererClassifierUDF';
CREATE TEMPORARY FUNCTION is_wikimedia_bot as 'org.wikimedia.analytics.refinery.hive.IsWikimediaBotUDF';
CREATE TEMPORARY FUNCTION ua_parser as 'org.wikimedia.analytics.refinery.hive.UAParserUDF';
CREATE TEMPORARY FUNCTION is_spider as 'org.wikimedia.analytics.refinery.hive.IsSpiderUDF';