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API Analytics
with Redis
and
Google Bigquery
javier ramirez
@supercoco9
REST API
+
AngularJS web as
an API client
javier ramirez

@supercoco9

https://teowaki.com
obvious solution:
use a ready-made service
as 3scale or apigee

javier ramirez

@supercoco9

https://teowaki.com
1. non intrusive metrics
2. keep the history
3. avoid vendor lock-in
4. interactive queries
5. cheap
6. extra ball: real time
javier ramirez

@supercoco9

https://teowaki.com
javier ramirez

@supercoco9

https://teowaki.com
data that’s an order of
magnitude greater than
data you’re accustomed
to
Doug Laney
VP Research, Business Analytics and Performance Management at Gartner
javier ramirez

@supercoco9

https://teowaki.com
data that exceeds the
processing capacity of
conventional database
systems. The data is too big,
moves too fast, or doesn’t fit
the structures of your
database architectures.
Ed Dumbill
program chair for the O’Reilly Strata Conference
javier ramirez

@supercoco9

https://teowaki.com
bigdata is doing a
fullscan to 330MM rows,
matching them against a
regexp, and getting the
result (223MM rows) in
just 5 seconds
Javier Ramirez
impresionable teowaki founder
javier ramirez

@supercoco9

https://teowaki.com
javier ramirez

@supercoco9

https://teowaki.com
Intel(R) Xeon(R) CPU E5520 @ 2.27GHz (with pipelining)
$ ./redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -P 16 -q

SET: 552,028 requests per second
GET: 707,463 requests per second
LPUSH: 767,459 requests per second
LPOP: 770,119 requests per second
Intel(R) Xeon(R) CPU E5520 @ 2.27GHz (without pipelining)
$ ./redis-benchmark -r 1000000 -n 2000000 -t get,set,lpush,lpop -q

SET: 122,556 requests per second
GET: 123,601 requests per second
LPUSH: 136,752 requests per second
LPOP: 132,424 requests per second
javier ramirez

@supercoco9

https://teowaki.com
open source, BSD licensed, advanced
key-value store. It is often referred to as a
data structure server since keys can contain
strings, hashes, lists, sets and sorted sets.
http://redis.io
started in 2009 by Salvatore Sanfilippo @antirez
100 contributors at

https://github.com/antirez/redis
javier ramirez

@supercoco9

https://teowaki.com
what is it used for

javier ramirez

@supercoco9

https://teowaki.com
twitter
Every time line (800 tweets
per user) is stored in redis
5000 writes per second avg
300K reads per second
javier ramirez

@supercoco9

https://teowaki.com
nginx + lua + redis

apache + mruby + redis

javier ramirez

@supercoco9

https://teowaki.com
javier ramirez

@supercoco9

https://teowaki.com
Redis keeps

everything in
memory
all the time
javier ramirez

@supercoco9

https://teowaki.com
javier ramirez

@supercoco9

https://teowaki.com
easy:
store GZIPPED files into
S3/Glacier
* we are moving to google cloud now

javier ramirez

@supercoco9

https://teowaki.com
javier ramirez

@supercoco9

https://teowaki.com
Hadoop (map/reduce)

http://hadoop.apache.org/
started in 2005 by Doug Cutting and Mike Cafarella
javier ramirez

@supercoco9

https://teowaki.com
cassandra
http://cassandra.apache.org/
released in 2008 by facebook.
javier ramirez

@supercoco9

https://teowaki.com
other big data solutions:

hadoop+voldemort+kafka
http://engineering.linkedin.com/projects

hbase
http://hbase.apache.org/
javier ramirez

@supercoco9

https://teowaki.com
Amazon Redshift

http://aws.amazon.com/redshift/

javier ramirez

@supercoco9

https://teowaki.com
Our choice:

google bigquery
Data analysis as a service
http://developers.google.com/bigquery
javier ramirez

@supercoco9

https://teowaki.com
Based on Dremel
Specifically designed for
interactive queries over
petabytes of real-time
data
javier ramirez

@supercoco9

https://teowaki.com
Columnar storage
Easy to compress
Convenient for querying long
series over a single column

javier ramirez

@supercoco9

https://teowaki.com
loading data
You can feed flat CSV
files or nested JSON
objects

javier ramirez

@supercoco9

https://teowaki.com
bq cli
bq load --nosynchronous_mode
--encoding UTF-8
--field_delimiter 'tab'
--max_bad_records 100
--source_format CSV
api.stats
20131014T11-42-05Z.gz

javier ramirez

@supercoco9

https://teowaki.com
web console screenshot

javier ramirez

@supercoco9

https://teowaki.com
almost SQL
select
from
join
where
group by
having
order
limit

javier ramirez

@supercoco9

avg
count
max
min
sum
+
*
/
%
https://teowaki.com

&
|
^
<<
>>
~

=
!=
<>
>
<
>= <=
IN
IS NULL
BETWEEN

AND
OR
NOT
Functions overview
current_date
current_time
now
datediff
day
day_of_week
day_of_year
hour
minute
quarter
year...
javier ramirez

@supercoco9

abs
acos
atan
ceil
floor
degrees
log
log2
log10
PI
SQRT...

https://teowaki.com

concat
contains
left
length
lower
upper
lpad
rpad
right
substr
analytics specific
extensions
within
flatten
nest

stddev

variance

top
first
last
nth

var_pop
var_samp
covar_pop
covar_samp
quantiles

javier ramirez

@supercoco9

https://teowaki.com
window functions

javier ramirez

@supercoco9

https://teowaki.com
correlations

javier ramirez

@supercoco9

https://teowaki.com
Things you always wanted to
try but were too scare to
select count(*) from
publicdata:samples.wikipedia

where REGEXP_MATCH(title, "[0-9]*")
AND wp_namespace = 0;

223,163,387
Query complete (5.6s elapsed, 9.13 GB processed, Cost: 32¢)

javier ramirez

@supercoco9

https://teowaki.com
SELECT repository_name, repository_language,
repository_description, COUNT(repository_name) as cnt,
repository_url
FROM github.timeline
WHERE type="WatchEvent"
AND PARSE_UTC_USEC(created_at) >=
PARSE_UTC_USEC("#{yesterday} 20:00:00")
AND repository_url IN (
SELECT repository_url
FROM github.timeline
WHERE type="CreateEvent"
AND PARSE_UTC_USEC(repository_created_at) >=
PARSE_UTC_USEC('#{yesterday} 20:00:00')
AND repository_fork = "false"
AND payload_ref_type = "repository"
GROUP BY repository_url
)
GROUP BY repository_name, repository_language,
repository_description, repository_url
HAVING cnt >= 5
ORDER BY cnt DESC
LIMIT 25
country segmented traffic

javier ramirez

@supercoco9

https://teowaki.com
our most active user

javier ramirez

@supercoco9

https://teowaki.com
10 request we should be caching

javier ramirez

@supercoco9

https://teowaki.com
5 most created resources

javier ramirez

@supercoco9

http://teowaki.com
redis pricing
2* machines (master/slave) at digital
ocean
$10 monthly
* we were already using these
instances for a lot of redis use cases
javier ramirez

@supercoco9

https://teowaki.com
s3 pricing
$0.095 per GB
a gzipped 1.6 MB file stores 300K
rows
$0.0001541698 / monthly
javier ramirez

@supercoco9

https://teowaki.com
glacier pricing
$0.01 per GB
$0.000016 / monthly

javier ramirez

@supercoco9

https://teowaki.com
bigquery pricing
$80 per stored TB
300000 rows => $0.007629392 / month

$35 per processed TB
1 full scan = 84 MB
1 count = 0 MB
1 full scan over 1 column = 5.4 MB
10 GB => $0.35 / month
javier ramirez

@supercoco9

https://teowaki.com
redis
s3 storage
s3 transfer
glacier transfer
glacier storage
bigquery storage
bigquery queries

$10.0000000000
$00.0001541698
$00.0050000000
$00.0500000000
$00.0000160000
$00.0076293920
$00.3500000000

$10.41 / month
for our first 330000 rows
javier ramirez

@supercoco9

https://teowaki.com
1. non intrusive metrics
2. keep the history
3. avoid vendor lock-in
4. interactive queries
5. cheap
6. extra ball: real time
javier ramirez

@supercoco9

https://teowaki.com
Find related links at
https://teowaki.com/teams/javier-community/link-categories/bigquery-talk

Cheers!
Javier Ramírez
@supercoco9

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