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Changed schema in Youtube dataset #48306

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merged 3 commits into from
Apr 1, 2023
Merged

Changed schema in Youtube dataset #48306

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8c65cc7
Update youtube-dislikes.md
rfraposa Mar 31, 2023
abe7846
Update youtube-dislikes.md
rfraposa Mar 31, 2023
3b6c599
Update youtube-dislikes.md
rfraposa Mar 31, 2023
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161 changes: 146 additions & 15 deletions docs/en/getting-started/example-datasets/youtube-dislikes.md
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,8 @@ CREATE TABLE youtube
(
`id` String,
`fetch_date` DateTime,
`upload_date` String,
`upload_date_str` String,
`upload_date` Date,
`title` String,
`uploader_id` String,
`uploader` String,
Expand All @@ -87,7 +88,7 @@ CREATE TABLE youtube
`video_badges` String
)
ENGINE = MergeTree
ORDER BY (upload_date, uploader);
ORDER BY (uploader, upload_date);
```

3. The following command streams the records from the S3 files into the `youtube` table.
Expand All @@ -101,8 +102,9 @@ INSERT INTO youtube
SETTINGS input_format_null_as_default = 1
SELECT
id,
parseDateTimeBestEffortUS(toString(fetch_date)) AS fetch_date,
upload_date,
parseDateTimeBestEffortUSOrZero(toString(fetch_date)) AS fetch_date,
upload_date AS upload_date_str,
toDate(parseDateTimeBestEffortUSOrZero(upload_date::String)) AS upload_date,
ifNull(title, '') AS title,
uploader_id,
ifNull(uploader, '') AS uploader,
Expand All @@ -121,13 +123,26 @@ SELECT
ifNull(uploader_badges, '') AS uploader_badges,
ifNull(video_badges, '') AS video_badges
FROM s3Cluster(
'default',
'https://clickhouse-public-datasets.s3.amazonaws.com/youtube/original/files/*.zst',
'JSONLines'
);
'default',
'https://clickhouse-public-datasets.s3.amazonaws.com/youtube/original/files/*.zst',
'JSONLines'
)
SETTINGS
max_download_threads = 24,
max_insert_threads = 64,
max_insert_block_size = 100000000,
min_insert_block_size_rows = 100000000,
min_insert_block_size_bytes = 500000000;
```

4. Open a new tab in the SQL Console of ClickHouse Cloud (or a new `clickhouse-client` window) and watch the count increase. It will take a while to insert 4.56B rows, depending on your server resources. (Withtout any tweaking of settings, it takes about 4.5 hours.)
Some comments about our `INSERT` command:

- The `parseDateTimeBestEffortUSOrZero` function is handy when the incoming date fields may not be in the proper format. If `fetch_date` does not get parsed properly, it will be set to `0`
- The `upload_date` column contains valid dates, but it also contains strings like "4 hours ago" - which is certainly not a valid date. We decided to store the original value in `upload_date_str` and attempt to parse it with `toDate(parseDateTimeBestEffortUSOrZero(upload_date::String))`. If the parsing fails we just get `0`
- We used `ifNull` to avoid getting `NULL` values in our table. If an incoming value is `NULL`, the `ifNull` function is setting the value to an empty string
- It takes a long time to download the data, so we added a `SETTINGS` clause to spread out the work over more threads while making sure the block sizes stayed fairly large

4. Open a new tab in the SQL Console of ClickHouse Cloud (or a new `clickhouse-client` window) and watch the count increase. It will take a while to insert 4.56B rows, depending on your server resources. (Without any tweaking of settings, it takes about 4.5 hours.)

```sql
SELECT formatReadableQuantity(count())
Expand Down Expand Up @@ -200,7 +215,7 @@ FROM youtube
WHERE (title ILIKE '%ClickHouse%') OR (description ILIKE '%ClickHouse%')
ORDER BY
like_count DESC,
view_count DESC
view_count DESC;
```

This query has to process every row, and also parse through two columns of strings. Even then, we get decent performance at 4.15M rows/second:
Expand All @@ -224,7 +239,6 @@ The results look like:

When commenting is disabled, are people more likely to like or dislike to express their feelings about a video?


```sql
SELECT
concat('< ', formatReadableQuantity(view_range)) AS views,
Expand Down Expand Up @@ -276,6 +290,127 @@ ORDER BY

Enabling comments seems to be correlated with a higher rate of engagement.


### How does the number of videos change over time - notable events?

```sql
SELECT
toStartOfMonth(toDateTime(upload_date)) AS month,
uniq(uploader_id) AS uploaders,
count() as num_videos,
sum(view_count) as view_count
FROM youtube
GROUP BY month
ORDER BY month ASC;
```

```response
┌──────month─┬─uploaders─┬─num_videos─┬───view_count─┐
│ 2005-04-01 │ 5 │ 6 │ 213597737 │
│ 2005-05-01 │ 6 │ 9 │ 2944005 │
│ 2005-06-01 │ 165 │ 351 │ 18624981 │
│ 2005-07-01 │ 395 │ 1168 │ 94164872 │
│ 2005-08-01 │ 1171 │ 3128 │ 124540774 │
│ 2005-09-01 │ 2418 │ 5206 │ 475536249 │
│ 2005-10-01 │ 6750 │ 13747 │ 737593613 │
│ 2005-11-01 │ 13706 │ 28078 │ 1896116976 │
│ 2005-12-01 │ 24756 │ 49885 │ 2478418930 │
│ 2006-01-01 │ 49992 │ 100447 │ 4532656581 │
│ 2006-02-01 │ 67882 │ 138485 │ 5677516317 │
│ 2006-03-01 │ 103358 │ 212237 │ 8430301366 │
│ 2006-04-01 │ 114615 │ 234174 │ 9980760440 │
│ 2006-05-01 │ 152682 │ 332076 │ 14129117212 │
│ 2006-06-01 │ 193962 │ 429538 │ 17014143263 │
│ 2006-07-01 │ 234401 │ 530311 │ 18721143410 │
│ 2006-08-01 │ 281280 │ 614128 │ 20473502342 │
│ 2006-09-01 │ 312434 │ 679906 │ 23158422265 │
│ 2006-10-01 │ 404873 │ 897590 │ 27357846117 │
```

A spike of uploaders [around covid is noticeable](https://www.theverge.com/2020/3/27/21197642/youtube-with-me-style-videos-views-coronavirus-cook-workout-study-home-beauty).


### More subtitiles over time and when

With advances in speech recognition, it’s easier than ever to create subtitles for video with youtube adding auto-captioning in late 2009 - was the jump then?

```sql
SELECT
toStartOfMonth(upload_date) AS month,
countIf(has_subtitles) / count() AS percent_subtitles,
percent_subtitles - any(percent_subtitles) OVER (
ORDER BY month ASC ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING
) AS previous
FROM youtube
GROUP BY month
ORDER BY month ASC;
```

```response
┌──────month─┬───percent_subtitles─┬────────────────previous─┐
│ 2015-01-01 │ 0.2652653881082824 │ 0.2652653881082824 │
│ 2015-02-01 │ 0.3147556050309162 │ 0.049490216922633834 │
│ 2015-03-01 │ 0.32460464492371877 │ 0.009849039892802558 │
│ 2015-04-01 │ 0.33471963051468445 │ 0.010114985590965686 │
│ 2015-05-01 │ 0.3168087575501062 │ -0.017910872964578273 │
│ 2015-06-01 │ 0.3162609788438222 │ -0.0005477787062839745 │
│ 2015-07-01 │ 0.31828767677518033 │ 0.0020266979313581235 │
│ 2015-08-01 │ 0.3045551564286859 │ -0.013732520346494415 │
│ 2015-09-01 │ 0.311221133995152 │ 0.006665977566466086 │
│ 2015-10-01 │ 0.30574870926812175 │ -0.005472424727030245 │
│ 2015-11-01 │ 0.31125409712077234 │ 0.0055053878526505895 │
│ 2015-12-01 │ 0.3190967954651779 │ 0.007842698344405541 │
│ 2016-01-01 │ 0.32636021432496176 │ 0.007263418859783877 │

```

The data results show a spike in 2009. Apparently at that, time YouTube was removing their community captions feature, which allowed you to upload captions for other people's video.
This prompted a very successful campaign to have creators add captions to their videos for hard of hearing and deaf viewers.


### Top uploaders over time

```sql
WITH uploaders AS
(
SELECT uploader
FROM youtube
GROUP BY uploader
ORDER BY sum(view_count) DESC
LIMIT 10
)
SELECT
month,
uploader,
sum(view_count) AS total_views,
avg(dislike_count / like_count) AS like_to_dislike_ratio
FROM youtube
WHERE uploader IN (uploaders)
GROUP BY
toStartOfMonth(upload_date) AS month,
uploader
ORDER BY
month ASC,
total_views DESC;
```

```response
┌──────month─┬─uploader───────────────────┬─total_views─┬─like_to_dislike_ratio─┐
│ 1970-01-01 │ T-Series │ 10957099 │ 0.022784656361208206 │
│ 1970-01-01 │ Ryan's World │ 0 │ 0.003035559410234172 │
│ 1970-01-01 │ SET India │ 0 │ nan │
│ 2006-09-01 │ Cocomelon - Nursery Rhymes │ 256406497 │ 0.7005566715978622 │
│ 2007-06-01 │ Cocomelon - Nursery Rhymes │ 33641320 │ 0.7088650914344298 │
│ 2008-02-01 │ WWE │ 43733469 │ 0.07198856488734842 │
│ 2008-03-01 │ WWE │ 16514541 │ 0.1230603715431997 │
│ 2008-04-01 │ WWE │ 5907295 │ 0.2089399470159618 │
│ 2008-05-01 │ WWE │ 7779627 │ 0.09101676560436774 │
│ 2008-06-01 │ WWE │ 7018780 │ 0.0974184753155297 │
│ 2008-07-01 │ WWE │ 4686447 │ 0.1263845422065158 │
│ 2008-08-01 │ WWE │ 4514312 │ 0.08384574274791441 │
│ 2008-09-01 │ WWE │ 3717092 │ 0.07872802579349912 │
```

### How do like ratio changes as views go up?

```sql
Expand Down Expand Up @@ -322,8 +457,6 @@ ORDER BY
│ < 10.00 billion │ false │ 1.77 │
│ < 10.00 billion │ true │ 19.5 │
└───────────────────┴─────────────────────┴────────────┘

20 rows in set. Elapsed: 63.664 sec. Processed 4.56 billion rows, 113.93 GB (71.59 million rows/s., 1.79 GB/s.)
```

### How are views distributed?
Expand Down Expand Up @@ -359,6 +492,4 @@ ARRAY JOIN
│ 20th │ 16 │
│ 10th │ 6 │
└────────────┴─────────┘

12 rows in set. Elapsed: 1.864 sec. Processed 4.56 billion rows, 36.46 GB (2.45 billion rows/s., 19.56 GB/s.)
```