[db tech showcase Tokyo 2018]　#dbts2018 #C37 『進化を続ける Amazon Redshift のパフォーマンスチューニングテクニックと最新アップデート』 アマゾン ウェブ サービス ジャパン 株式会社 - 技術統括本部 レディネスソリューション本部 ソリューションアーキテクト　大薗 純平 氏

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Columnar
MPP
OLAP

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SELECT COUNT(*)
FROM S3.EXT_TABLE
GROUP BY …

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• Core Core Core Core Core Core

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ANALYZE COMPRESSION < >;
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• (1MB)
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SELECT name
FROM table
WHERE id = 512;

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SELECT count(*) FROM deep_dive WHERE dt = '06-09-2018';
MIN: 01-JUNE-2018
MAX: 06-JUNE-2018
MIN: 07-JUNE-2018
MAX: 12-JUNE-2018
MIN: 13-JUNE-2018
MAX: 21-JUNE-2018
MIN: 21-JUNE-2018
MAX: 30-JUNE-2018
date
MIN: 01-JUNE-2018
MAX: 20-JUNE-2018
MIN: 08-JUNE-2018
MAX: 30-JUNE-2018
MIN: 12-JUNE-2018
MAX: 20-JUNE-2018
MIN: 02-JUNE-2018
MAX: 25-JUNE-2018

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KEY
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
keyA
keyB
keyC
keyD
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
EVEN
ALL
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
CREATE TABLE deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE (KEY|ALL|EVEM);

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SELECT diststyle, skew_rows FROM svv_table_info WHERE "table" = 'deep_dive';
diststyle | skew_rows
-----------+-----------
KEY(aid) | 1.07 ß 1

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CPU CPU CPU CPU CPU CPU

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BI /
Amazon
Redshift

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Performance improvement of popular memory-intensive workloads

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0
500
1000
1500
2000
2500
3000
3500
4000
March June July August
ElapsedTime(seconds)
Query speeds are nearly 2x faster

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COPY table FROM ‘s3 prefix’ FORMAT AS ORC | PARQUET ;

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• M L L h ge
D
[ QDa] !
L T .5 53 4
.5 5 132
W iD L T
W iD

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select
clicks / impressions as probability,
round(100 * probability, 1) as percentage
from
raw_data;

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{ Id: 1,
Name: {Given:"John", Family:"Smith"},
Phones: ["123-457789"],
Orders: [ {Shipdate: ”Jul 12,2018 11:59:59",
Price: 100.50}
{Shipdate: ”Jul 13,2018 09:10:00",
Price: 99.12} ]
}
{ Id: 2,
Name: {Given:"Jenny", Family:"Doe"},
Phones: ["858-8675309", "415-9876543"],
Orders: [ ]
}
{ Id: 3,
Name: {Given:"Andy", Family:"Jones"},
Phones: [ ]
Orders: [ {Shipdate: ”Jul 12,2018 08:02:15",
Price: 13.50} ]
}
create external table
datalake.nested_customers_orc(
id int,
name struct<given:varchar(20),
family:varchar(20)>,
phones array<varchar(20)>,
orders array<struct
<shipdate:timestamp,
price:double precision>>
)
STORED AS ORC
LOCATION 's3://mybucket/nested_orc/'
;

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id | given | family | shipdate | price
----+-------+--------+---------------------+-------
2 | Jenny | Doe | |
3 | Andy | Jones | 2018-07-12 08:02:15 | 13.5
1 | John | Smith | 2018-07-13 09:10:00 | 99.12
1 | John | Smith | 2018-07-12 08:02:15 | 100.5
(4 rows)
CREATE TABLE nested_customers_rs AS
SELECT
c.id, c.name.given, c.name.family, o.shipdate, o.price
FROM
datalake.nested_customers_orc c
LEFT JOIN
c.orders o
ON true;
SELECT * FROM nested_customers_rs;

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