ruby
We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 62 columns, instead of 13 in line 1.
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time,_card_id,_user_id,analytics_version,assigned_to_user_id,attributes,average_rating,card_author_full_name,card_author_id,card_bookmark_id,card_duration,card_id,card_level,card_rating,card_resource_url,card_state,card_subtype,card_title,card_type,changed_column,channel_id,channel_name,comment_id,comment_message,comment_type,ecl_id,ecl_source_name,event,event_recorder,is_admin_request,is_card_promoted,is_channel_curated,is_channel_featured,is_channel_public,is_correct,is_live_stream,is_public,is_system_generated,is_user_generated,journey_id,journey_name,new_val,old_val,org_hostname,org_id,org_name,pathway_id,pathway_name,pin_id,platform,platform_version_number,poll_option_id,poll_option_label,quiz_option_id,quiz_option_label,readable_card_type,shared_to_user_id,team_id,user_agent,user_full_name,user_handle,user_id | |
2019-04-19T01:44:05.408710236Z,3554232,115112,2.0.0,4,SOME USER,112319,0,3554232,https://www.youtube.com/watch?v=5FUdrBq-WFo,published,video,Selenium Tutorial For Beginners | What Is Selenium? | Se |
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1970-01-01 - card_added_to_channel | |
2015-12-03 - channel_followed | |
2015-12-03 - user_followed | |
2016-01-06 - user_unfollowed | |
2016-04-06 - group_user_added | |
2016-09-02 - card_assigned | |
2016-12-06 - channel_created | |
2016-12-23 - channel_unfollowed | |
2017-01-03 - group_created | |
2017-01-04 - card_added_to_pathway |
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package main | |
import "fmt" | |
type myType struct { | |
fn func(int) int | |
} | |
func main () { | |
inst := myType{fn: func(i int) int { return i }} |
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( | |
SELECT | |
SUM(smartbites_created_count) AS smartbites_created, | |
SUM(smartbites_consumed_count) AS smartbites_consumed, | |
AVG(user_metrics_aggregations.time_spent_minutes) AS average_session, | |
COUNT(DISTINCT(teams_users.user_id)) AS total_users, | |
teams.id AS team_id, | |
COUNT(DISTINCT(teams_users.user_id)) AS active_users, | |
COUNT(DISTINCT(teams_users.user_id)) AS new_users | |
FROM user_metrics_aggregations |
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SELECT | |
'' AS smartbites_created, | |
'' AS smartbites_consumed, | |
'' AS average_session, | |
'' AS total_users, | |
teams.id AS team_id, | |
COUNT(DISTINCT(teams_users.user_id)) AS active_users | |
FROM user_metrics_aggregations | |
INNER JOIN users ON users.id = user_metrics_aggregations.user_id | |
INNER JOIN teams_users ON teams_users.user_id = users.id |
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[ | |
{ | |
"tags": { | |
"org_id": <org id>, | |
"channel_id": <channel id>, | |
"top_cards": [<top cards records>...], | |
"top_contributors": [<top contributors records>...], | |
"top_scoring_users: [<top scoring user records>...], | |
}, | |
"values": [<channel aggregation records>...] |
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class A | |
def a(arg) | |
b(c(arg)) | |
end | |
def b(arg) | |
do_expensive_thing(arg) | |
end | |
def c(arg) |
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using Fluent; | |
using System.Collections; | |
using System.Collections.Generic; | |
using UnityEngine; | |
using System.Linq; | |
public class RobotDialog : FluentScript | |
{ | |
public override FluentNode Create() |
This is the continuous query we run to get daily aggregations of user_scores. There is a unique record produced per user per day.
SELECT
MEAN(total_user_score) AS total_user_score,
MEAN(smartbites_commented_count) AS smartbites_commented_count,
MEAN(smartbites_completed_count) AS smartbites_completed_count,
MEAN(smartbites_created_count) AS smartbites_created_count,
MEAN(smartbites_consumed_count) AS smartbites_consumed_count,
MEAN(smartbites_liked_count) AS smartbites_liked_count,
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