| select | |
| date_trunc('week', act_at) as act_at_week, | |
| sum(amount / 1e18) as volume | |
| from ( | |
| select | |
| e.block_signed_at as act_at, | |
| '0x' || encode(e.tx_hash, 'hex') as tx_hash, | |
| live.hex_to_int(encode(e.topics[4], 'hex')) as amount, | |
| 'DSR_DEPOSIT' as act | |
| from live.block_log_events e |
| "block_signed_at","address","balance" | |
| "2019-11-30 21:43:30+00","2d1d6d5deae5cc68d893a73781dd573fde302796",1227556400000000000 | |
| "2019-11-30 21:43:30+00","2b5634c42055806a59e9107ed44d43c426e58258",239060270600000000000 | |
| "2019-11-30 21:33:10+00","39755357759ce0d7f32dc8dc45414cca409ae24e",437140390807426204185 | |
| "2019-11-30 21:33:10+00","69076e44a9c70a67d5b79d95795aba299083c275",5878988991879486728884 | |
| "2019-11-30 21:33:10+00","e54365c85ca9b7e3ef68c5bed244dc10330bb264",0 | |
| "2019-11-30 21:32:32+00","ac8d6652067ca9b33bd15eeeeb01102d2d2cfc64",843100000000000000 | |
| "2019-11-30 21:29:27+00","2c4bd064b998838076fa341a83d007fc2fa50957",2964877658410763648961 | |
| "2019-11-30 21:29:27+00","9ba4ae48b8ca9e98e511b3371a4d454d1a3e1003",0 | |
| "2019-11-30 21:17:29+00","6cc5f688a315f3dc28a7781717a9a798a59fda7b",2633319886385353620901 |
| '''A python script to export the raw data from mailchimp lists and campaigns. | |
| The output of this script will be a CSV file with the open and click rates for each campaign | |
| and each list member (identified by an email.) You can use this data for further analysis | |
| as seen here: | |
| http://www.silota.com/docs/recipes/sql-email-customer-list-segmentation-lead-scoring.html | |
| Written by Ganesh, 2017. | |
| ''' |
| { | |
| "data": [ | |
| { | |
| "created_at": "2015-12-17T02:02:18.858Z", | |
| "created_by": { | |
| "id": 702942504735, | |
| "name": "Ganesh Swami" | |
| }, | |
| "id": 74602192326071, | |
| "text": "changed the due date to December 31", |
| """ | |
| Custom parser for nginx log suitable for use by Datadog 'dogstreams'. | |
| To use, add to datadog.conf as follows: | |
| dogstreams: [path to ngnix log (e.g: "/var/log/nginx/access.log"]:[path to this python script (e.g "/usr/share/datadog/agent/dogstream/nginx.py")]:[name of parsing method of this file ("parse")] | |
| so, an example line would be: | |
| dogstreams: /var/log/nginx/access.log:/usr/share/datadog/agent/dogstream/nginx.py:parse | |
| Log of nginx should be defined like that: | |
| log_format time_log '$time_local "$request" S=$status $bytes_sent T=$request_time R=$http_x_forwarded_for'; | |
| when starting dd-agent, you can find the collector.log and check if the dogstream initialized successfully | |
| """ |
| { | |
| "title": "Hackers", | |
| "release_year": 1995, | |
| "genre": ["Action", "Crime", "Drama"], | |
| "actors": ["Johnny Lee Miller", "Angelina Jolie"], | |
| "description": "High-school age computer expert Zero Cool and his hacker friends take on an evil corporation's computer virus with their hacking skills." | |
| } | |
| { | |
| "title": "Johnny Mnemonic", |
This tutorial is an Elasticsearch bootcamp. Elasticsearch is a fully-distributed and scalable search server based on Apache Lucene. Companies like foursquare, soundcloud, github and hundreds more use it to power search and analytics in their applications.
At the end of the day, you’ll: