Taming the events: How we regained petabyte-scale Hive query power

In the beginning, four long years ago, simply having every data set in Hadoop was good enough. We were taking baby steps into the big data world and had a limited user base migrating from other sytems into the big data clusters, enabling them to execute heavy queries on large data sets in a timely manner. As the number of users and size of the data sets grew in size, we were challenged by new performance problems.

booking.development
Jul 18, 2016 · 10 min read
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Cool and simple ideas

What our users were looking for

user ID
user name
job name
number of maps and reducers
resources used in CPU and MB-seconds
the query (in the case of Hive jobs)
(...)

We’d give them everything they wanted

A handful of simple tricks

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Was it worth it?

CREATE TEMPORARY FUNCTION event_parser AS 'com.booking.hive.udtf.EventParser';

create table mydb.sample_raw AS
SELECT event.epoch,
response.body_parameters,
response.experiment,
response.language
FROM default.raw_events as event
LATERAL VIEW event_parser( event.json,
'http.body_parameters',
'action.experiment',
'action.language',
'action.name'
) response
AS
body_parameters,
experiment,
language,
action_name
WHERE
yyyy_mm_dd = '2016-04-18'
AND hh = 21
AND is_frontend = 1
AND action_name = 'explorer';
create table mydb.sample_flat AS
SELECT epoch,
`http/body_parameters`,
`action/experiment`,
`action/language`
FROM default.events_flat_web
WHERE
yyyy_mm_dd = '2016-04-18'
AND hh = 21
AND is_frontend = 1
AND `action/name` = 'explorer';
1 days 1 hours 25 minutes 10 seconds 20 msec
0 days 2 hours 41 minutes 12 seconds 420 msec

We may want to keep it



Booking.com Development

Software development at Booking.com

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