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@ocadaruma

SQL再入門

Last updated at Posted at 2016-12-09
1 / 27

@Opt Technologies 社内勉強会 2016/12/09

こちらは、社内勉強会で使った資料となります。

近況

  • BigQueryすごい

皆さんSQL好きですか？

よくある声

  • DBMSによって方言があってつらい
  • コード書かせろ

でも時代はSQL

  • BigQuery / Redshift等、SQLをクエリ言語とする分散処理システムの台頭
  • Sparkも2.0でSQL2003をサポートするなど、SQLの機能が強化
  • 逃げられない

今日やること

  1. JOIN再入門
  2. Window Functions
  3. Common Table Expressions
  4. Example
  • Sessionization

実行計画についてはふれません

環境

  • Postgresql 9.6.1

1. JOIN再入門

create table account(
  id integer not null,
  name varchar(100) not null,
  email varchar(100) not null
);

create table post(
  account_id integer not null,
  message text
);

insert into account values(0, 'john', 'john@example.com');
insert into account values(1, 'clifford', 'clifford@example.com');

insert into post values(0, 'hello');
insert into post values(0, 'hi');

insert into post values(2, 'no');

CROSS

行の組み合わせ全部

select * from account cross join post;

 id |   name   |        email         | account_id | message
----+----------+----------------------+------------+---------
  0 | john     | john@example.com     |          0 | hello
  1 | clifford | clifford@example.com |          0 | hello
  0 | john     | john@example.com     |          0 | hi
  1 | clifford | clifford@example.com |          0 | hi
  0 | john     | john@example.com     |          2 | no
  1 | clifford | clifford@example.com |          2 | no
(6 rows)

ちなみに、INNER/OUTERは、CROSS JOIN(直積)の部分集合かどうか、から来ているそうだ。

INNER

CROSS JOINの結果を、ON句でfilterするイメージ

select * from account a inner join post p on a.id = p.account_id;

 id | name |      email       | account_id | message
----+------+------------------+------------+---------
  0 | john | john@example.com |          0 | hello
  0 | john | john@example.com |          0 | hi
(2 rows)

LEFT OUTER

inner joinの結果に加え、左側のテーブルのうち、on条件を満たさないものも残る

select * from account a left join post p on a.id = p.account_id;

 id |   name   |        email         | account_id | message
----+----------+----------------------+------------+---------
  0 | john     | john@example.com     |          0 | hello
  0 | john     | john@example.com     |          0 | hi
  1 | clifford | clifford@example.com |            |
(3 rows)

RIGHT OUTER

inner joinの結果に加え、右側のテーブルのうち、on条件を満たさないものも残る

select * from account a right join post p on a.id = p.account_id;

 id | name |      email       | account_id | message
----+------+------------------+------------+---------
  0 | john | john@example.com |          0 | hello
  0 | john | john@example.com |          0 | hi
    |      |                  |          2 | no
(3 rows)

FULL OUTER

inner joinの結果に加え、右側 & 左側のテーブルのうちon条件を満たさないものも残る

select * from account a full join post p on a.id = p.account_id;

 id |   name   |        email         | account_id | message
----+----------+----------------------+------------+---------
  0 | john     | john@example.com     |          0 | hello
  0 | john     | john@example.com     |          0 | hi
    |          |                      |          2 | no
  1 | clifford | clifford@example.com |            |
(4 rows)

2. Window Functions

  • 集約しないGROUP BYみたいなノリ
    • GROUP BYと違って結果の行数は変わらない
  • SELECT句に書く
  • いろんな関数があって便利

先のpostテーブルには今こんな行が入っている

select * from post;

 account_id | message
------------+---------
          0 | hello
          0 | hi
          2 | no
(3 rows)

account_idごとのpost数 (GROUP BY)

select account_id, count(*) from post group by account_id;

 account_id | count
------------+-------
          0 |     2
          2 |     1
(2 rows)

messageも含めたaccount_idごとのpost数（Window関数）

select account_id, message, count(*) over (partition by account_id) from post;

 account_id | message | count
------------+---------+-------
          0 | hello   |     2
          0 | hi      |     2
          2 | no      |     1
(3 rows)

Windowには名前をつけられる

いくつかの列で同じWindowを使うとき便利

select account_id, message, count(*) over w
from post window w as (partition by account_id);

3. Common Table Expressions

  • 共通テーブル表式
  • 一時テーブル的なもの
  • サブクエリと似てるとも言える
with account_with_post(id, name, email, message) as
(
  select u.id, u.name, u.email, p.message
  from account u left join post p on account.id = post.account_id
)
select * from account_with_post;

再帰CTE

例

create table linked_list(
  id integer not null,
  next_id integer,
  value varchar(100)
);

-- 'H' -> 'e' -> 'l' -> 'l' -> 'o'
insert into linked_list values(0, 2, 'H');
insert into linked_list values(2, 4, 'e');
insert into linked_list values(4, 6, 'l');
insert into linked_list values(6, 8, 'l');
insert into linked_list values(8, null, 'o');

-- 's' -> 'q' -> 'l'
insert into linked_list values(1, 3, 's');
insert into linked_list values(3, 5, 'q');
insert into linked_list values(5, null, 'l');

構文

  • 以下の2つをUNION ALLしたものを、CTEの中身に書く
    1. スタートを表すselect（再帰を含まない）
    2. 再帰を表すselect
  • 1.から初めて再帰的に2.が行を返す
  • 2.が行を返さなくなったら終了
with recursive traverse(last_id, current_id, path) as
(
  select id, id, value from linked_list where next_id is null
  union all
  select t.last_id, l.id, (l.value || '->' || t.path)::varchar(100)
  from traverse t inner join linked_list l on l.next_id = t.current_id
)
select * from traverse;

 last_id | current_id |     path
---------+------------+---------------
       8 |          8 | o
       5 |          5 | l
       8 |          6 | l->o
       5 |          3 | q->l
       8 |          4 | l->l->o
       5 |          1 | s->q->l
       8 |          2 | e->l->l->o
       8 |          0 | H->e->l->l->o
(8 rows)

last_idごとにまとめれば、linked_listに含まれる連結リスト全体がわかる。

with recursive traverse(last_id, current_id, path) as
(
  select id, id, value from linked_list where next_id is null
  union all
  select t.last_id, l.id, (l.value || '->' || t.path)::varchar(100)
  from traverse t inner join linked_list l on l.next_id = t.current_id
)
select path from
  (select
    path,
    row_number() over (partition by last_id order by length(path) desc) as rn
  from traverse) l
where l.rn = 1;

         path
-----------------------
 s->q->l
 H->e->l->l->o
(2 rows)

4. Example

Sessionization

アクセス解析では、広告クリックや検索エンジンからのサイト流入 ~> サイト内を回遊 ~> 離脱の流れをセッションという単位で呼び、セッション単位で集計することがよくある。

ここでは、サイトのPVログがpage_viewテーブル、サイトをLPとした広告のクリックログがclickテーブルに入っているとき、これらをセッションに分割してsessionテーブルに格納することを考える。

なお、このSessionizationの例はこちらの記事をほぼそのまま参考にさせてもらってます。。

create table page_view(
  uid varchar(100) not null, -- ブラウザに割り振られるcookie idを表す
  epochmillis bigint not null,
  referer_domain varchar(1024)
);

create table click(
  uid varchar(100) not null,
  epochmillis bigint not null
);

create table session(
  uid varchar(100) not null,
  epochmillis bigint not null,
  page_views integer not null,
  start_by_click boolean not null
);

ここでのセッションの定義

  • セッションとは、page_viewとclickの集まりで、以下を満たすもの
    • clickはセッションの始まりである(サイトへの流入と同義なため)
    • 前回のpage_view or clickから30分時間が空いたら、セッションが切れる(離脱)
    • page_viewのreferer_domainがexample.com(アクセス解析対象サイトのドメインと思ってください)でない場合、そのpage_viewからセッションが始まる(外部サイトからの流入を表すため)

page_view, clickログは以下のようなものが入ってるとする。

-- session 1
insert into click values('a', 0);
insert into page_view values('a', 1, 'example.com');
insert into page_view values('a', 2, 'example.com');

-- session 2

-- 30分経った
insert into page_view values('a', 1800003, 'example.com');
insert into page_view values('a', 1800004, 'example.com');

-- session 3

-- 外部ドメインからの流入
insert into page_view values('a', 1800005, 'google.com');
insert into page_view values('a', 1800006, 'example.com');
insert into page_view values('a', 1800007, 'example.com');

完成系

-- 1. clickとpage_viewをまとめる
with event(uid, epochmillis, referer_domain, event_type) as
(
  select uid, epochmillis, referer_domain, 'page_view' from page_view
  union all
  select uid, epochmillis, null, 'click' from click

-- 2. 各行に、uid単位の時系列で一つ前の行の情報をくっつける
), event_with_previous(uid, epochmillis, referer_domain, event_type, prev_epochmillis) as
(select
  uid,
  epochmillis,
  referer_domain,
  event_type,
  lag(epochmillis) over (partition by uid order by epochmillis rows between 1 preceding and current row)
from event

-- 3. 各行がsessionの開始かどうか判定し、それぞれに対しsession_idをふっていく
), event_with_session_id(uid, epochmillis, event_type, session_id) as
(select
  uid,
  epochmillis,
  event_type,
  sum(case
    when prev_epochmillis is null then 1
    when epochmillis - prev_epochmillis >= 30 * 60 * 1000 then 1
    when event_type = 'click' then 1
    when referer_domain <> 'example.com' then 1
    else 0
  end) over (partition by uid order by epochmillis) as session_id
from event_with_previous)

-- 4. 各セッションに含まれるpv数を集計し、セッションを出力
select uid, epochmillis, page_views, event_type = 'click' as start_by_click from
  (select
    uid,
    epochmillis,
    event_type,
    session_id,
    row_number() over (partition by uid, session_id order by epochmillis) as rn,
    sum(case when event_type = 'page_view' then 1 else 0 end) over (partition by uid, session_id) as page_views
  from event_with_session_id
) s where s.rn = 1;

 uid | epochmillis | page_views | start_by_click
-----+-------------+------------+----------------
 a   |           0 |          2 | t
 a   |     1800003 |          2 | f
 a   |     1800005 |          3 | f
(3 rows)

それぞれのパートを見ていく

1
select uid, epochmillis, referer_domain, 'page_view' from page_view
union all
select uid, epochmillis, null, 'click' from click;

 uid | epochmillis | referer_domain | ?column?
-----+-------------+----------------+-----------
 a   |           1 | example.com    | page_view
 a   |           2 | example.com    | page_view
 a   |     1800003 | example.com    | page_view
 a   |     1800004 | example.com    | page_view
 a   |     1800005 | google.com     | page_view
 a   |     1800006 | example.com    | page_view
 a   |     1800007 | example.com    | page_view
 a   |           0 |                | click
(8 rows)
2
-- 1. clickとpage_viewをまとめる
with event(uid, epochmillis, referer_domain, event_type) as
(
  select uid, epochmillis, referer_domain, 'page_view' from page_view
  union all
  select uid, epochmillis, null, 'click' from click
)
select
  uid,
  epochmillis,
  referer_domain,
  event_type,
  lag(epochmillis) over (partition by uid order by epochmillis rows between 1 preceding and current row)
from event;

 uid | epochmillis | referer_domain | event_type |   lag
-----+-------------+----------------+------------+---------
 a   |           0 |                | click      |
 a   |           1 | example.com    | page_view  |       0
 a   |           2 | example.com    | page_view  |       1
 a   |     1800003 | example.com    | page_view  |       2
 a   |     1800004 | example.com    | page_view  | 1800003
 a   |     1800005 | google.com     | page_view  | 1800004
 a   |     1800006 | example.com    | page_view  | 1800005
 a   |     1800007 | example.com    | page_view  | 1800006
(8 rows)
3
-- 1. clickとpage_viewをまとめる
with event(uid, epochmillis, referer_domain, event_type) as
(
  select uid, epochmillis, referer_domain, 'page_view' from page_view
  union all
  select uid, epochmillis, null, 'click' from click

-- 2. 各行に、uid単位の時系列で一つ前の行の情報をくっつける
), event_with_previous(uid, epochmillis, referer_domain, event_type, prev_epochmillis) as
(select
  uid,
  epochmillis,
  referer_domain,
  event_type,
  lag(epochmillis) over (partition by uid order by epochmillis rows between 1 preceding and current row)
from event
)
select
  uid,
  epochmillis,
  event_type,
  sum(case
    when prev_epochmillis is null then 1
    when epochmillis - prev_epochmillis >= 30 * 60 * 1000 then 1
    when event_type = 'click' then 1
    when referer_domain <> 'example.com' then 1
    else 0
  end) over (partition by uid order by epochmillis) as session_id
from event_with_previous;

 uid | epochmillis | event_type | session_id
-----+-------------+------------+------------
 a   |           0 | click      |          1
 a   |           1 | page_view  |          1
 a   |           2 | page_view  |          1
 a   |     1800003 | page_view  |          2
 a   |     1800004 | page_view  |          2
 a   |     1800005 | page_view  |          3
 a   |     1800006 | page_view  |          3
 a   |     1800007 | page_view  |          3
(8 rows)

まとめ

  • SQLで色々解ける
  • まずSQLでなんとかできないか考えよう

参考

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