Presto Meetup 2016

My Use case

Small Start

self introduction‐• @toyama0919• Analytics Infra.• Nearly working embulk…• Presto using one and a half years.

Our development situation• We commonly used sql.• Marketing occupation don't write sql.• I often write the complicated SQL, that is

100 lines..• We love OSS.• Not use Update, Insert, Delete by Presto.

Our Business situation• We manage and operate web site of

BtoB.• Our data lifecycle is long.• Business side not write sql.• watching re:dash and Adobe analytics.• Sales increase 15 straight year.

analyst want data quickly

Ruby Batch

CollectBatchVisualize Data Store

(Digdag)

Analytics Priolity1. Direct SQL

2. Presto

3. ETL

Cost is large differencefrom 1 to 3

Why use presto?• Cross server Join• Window function• UDF

Cross Server Join

Join• Cross server and cross database.• A single Presto query can combine data

from multiple sources.• We use multiple sources join query.• reduce ETL pain.

Collect data in one place?• Equal able to get data by one query.• I not want to have duplicate data.(master

data, user data)• Collect the data in one place, high

develop cost.

with mysql_user as ( select user_id, user_name from mysql.schema.users),redshift_user_log as ( select user_id, log_time from redshift.schema.pageview)select user_id, user_name, count(*)from mysql_userinner join redshift_user_log on mysql_user.user_id = redshift_user_log.user_idgroup by user_id, user_name

Parallel

UDF

Mysql not support mechanism

• window function• with query– not support Recursive.

• URL function• Array data type• cross join unnest

URL Functionselect url_encode('Presto最高 ');=> Presto%8d%c5%8d%82

select url_decode('Presto%8d%c5%8d%82');=> Presto最高

Regexp Functionselect regexp_extract_all('1a 2b 14m', '\d+')=> [1, 2, 14]

select regexp_extract('超低床型自動梱包機　 RQ-8LD', '([a-zA-z0-9\-]+)’)=> RQ-8LD

SQL no good at it• Normalization of the character string.• split csv string.• Morphological analysis.

Normalization

select normalize(upper('hoｇｅ '), NFKC)#=> HOGE

Array typeselect

split(keywords, ',') as keywordsFrom

mysql_keywords_table

keywords---------------------------- keyword1,keyword2,keyword3

keywords---------------------------- ['keyword1','keyword2','keyword3']

horizontal to verticalSELECT

keywordFROM

mysql_keywords_tableCROSS JOIN

UNNEST(split(keywords, ',')) AS t (keyword)

horizontal to vertical keywords---------------------------- keyword1,keyword2,keyword3 keyword4,keyword5 keyword6 keyword1

keyword---------------------------- keyword1 keyword2 keyword3 keyword4 keyword5 keyword6 keyword1

window function• We use window function for Mysql.

(Presto on mysql) • data source is Mysql, But Presto world

can use.• But can not use original function of

mysql.

Rank function on mysqlselect company_id, category_id, count(*), rank() over ( partition by company_id order by count(*) desc )from mysql.schema.mysql_tablegroup by company_id, category_id

other window function• last_value• first_value• dense_rank• percent_rank

Prestogres• PostgreSQL protocol gateway for Presto.• rewrite queries before sending Presto to

PostgreSQL.• have password-based authentication and

SSL.

Why Prestogres?• Other application connectivity.– pgAdmin, psql command.– re:dash connecte with PostgreSQL protocol to presto.– But can directly connect to presto.

• We connect to presto, need Presto client.– I not want use java client.

• Weak security. – certification is taken by prestogres

Prestogres Limitation• prepared statement.– not support Presto too.– so not work embulk-input-postgresql• Can’t fetch schema by sql.

• Temporary table• DROP TABLE

re:dash

• Visualization platform, write by python.• Supports many data sources.• Sharing query with member.• Scheduling query.(per day, per hour)• Very active contribution.

increased rapidly Presto query by re:dash

• Number of the presto queries increased than 10 times.

• That won't change with writing ETL on re:dash.

• Re:dash having a good reputation in internal.

Okay,analytics

problems all clear!

No.. Can’t escape from ETL

Embulk with Presto• use embulk-input-presto of own making.– Support json type.

• Create point in time data.• Create machine learning data.

Why Embulk?• Very active plugin ecosystem.• Complicated string analysis can not only

sql.• With digdag combination is very

powerful.• Want can do it shortest distance.• Fluentd overwork..

Operation

Install by RPM• Presto have RPM.– not distribution.– need source build..

• include init script.• But not support open-jdk..– Pull requesting..

AWS integration• We build Presto on ec2.• Not use EMR.• Worker is spot instance, multi instance

types.– prevent down all at once

networking• Presto cluster(coordinator and workers)

place in the same AZ.• If other AZ, very high traffic cost(and

money).– should not multi AZ.

Networking on AWS

Availability Zone Availability Zone

cordinator

worker

worker

worker

NotCluster

problem• Very huge repository.• SPOF cordinator.• run long range query, occur

OutOfMemory Error.

Very huge repository• monolithic application.– I want Separate repository.

• First build takes 30 minutes.• After the second time build takes 10 minutes.• All connector is main repository.– MongoDB、 Kafka、 cassandra..– will nearly support Elasticsearch

• Hard to do the contribution.

Big change for jdbc• support multi data type predicate

pushdown.• We used apply patch presto…• Let's try mysql people.

listened Presto impression• extended technology of Hadoop.

=>I don't know hadoop. Presto have many connector.• parallel processing looks difficult.

=>Presto not have storage, There is not so influence.・ I do not have so big data.

=>I don't so big player.

Summary• Presto is great software.• So not difficult.• Let's use it more.