最強にスケーラブルなカラムナーDBよ、 Hadoopとのタッグで

ビッグデータの地平を目指せ！

- Actian Matrix -

平間大輔 株式会社インサイトテクノロジー

Actian Analytics Platformの中核

Actian Analytics Platform™

Analyze

Act

Connect

Actian Analytics Accelerators

Actian DataFlow™

Actian Matrix™

Actian Vector™

Enterprise

Applications Data Warehouse

Social

Internet of Things

SaaS

WWW Machine Data

Mobile World-Class Risk

Management

Competitive Advantage

Customer Delight

Disruptive New Business Models

NoSQL Traditional

Actian Analytics Platform™

Accelerate Hadoop

Accelerate Analytics

Accelerate BI

Matrixとは?

Amazon RedshiftはMatrixを採用

“Actian has an industry leading solution and is rethinking database cloud – we’re excited to back such a strong team.”

- Jeff Blackburn, SVP of Business Development for Amazon

Amazon Redshift is the fastest growing service in their portfolio Selected after deep evaluation against all competitors based on price-performance value proposition of Actian’s platform Amazon Redshift service has over 1000 new customers since launch in Feb ’13 Actian complements Redshift with on-premise, high-scale analytics suite and support

Actian Analytics Platform Underpins AMAZON REDSHIFT Created New Cloud Service driving $50M+ revenue annually.

本当にスケールするの？

CentOS 6.4 64bit

Intel Xeon L5640

2.26GHz (2 cores only)

8GB

SATA SSD * 2

(RAID0 全ノード共有)

Actian Matrix 5.1

Virtual

Gigabit Ethernet

x N = ?

スケールアウト！

2nodes 4nodes 8nodes 16nodes

TPC-H 30GB

どうだ！

4nodes 8nodes 16nodes

TPC-H 100GB

スケールアウトの秘密を暴く Leader Node

Parser

Queries & Joins Non-Query Commands

Execution Planner & Optimizer

Code Generation

Queue & Workload Manager

Scheduler

Communication Layer

listener ODBC / JDBC / PSQL

データ処理の基本は「Slice」

Leader Node

Compute Node 1

Slice 1

1, 10, 20, 30, 40, 50, 60, 70….

5, 14, 24, 34, 44, 54, 64, 74….

Slice 2

2, 11, 21, 31, 41, 51, 61, 71….

6, 15, 25, 35, 45, 55, 65, 75….

Slice 3

3, 12, 22, 32, 42, 52, 62, 72....

7, 16, 26, 36, 46, 56,

66, 76…

Slice 4

4, 13, 33, 43, 53, 63,

73….

8, 17, 27, 37, 47, 57,

67, 77…

Compute Node 2

Slice 5

5, 14, 24, 34, 44, 54, 64, 74….

1, 10, 20, 30, 40, 50, 60, 70….

Slice 6

6, 15, 25, 35, 45, 55, 65, 75….

2, 11, 21, 31, 41, 51, 61, 71….

Slice 7

7, 16, 26, 36, 46, 56, 66, 76….

3, 12, 22, 32, 42, 52, 62, 72....

Slice 8

8, 17, 27, 37, 47, 57,

67, 77…

4, 13, 33, 43, 53, 63,

73….

Stream, Segment, Step

集計処理は２段階

Supplier

Nation AcctBal

S0---------

12448

S1---------

11778

S0---------

$5$10$12$15$30

S1---------

$10$11$15$5$8

S0Hash aggregate table

1 – $52 – $104 – $278 - $30

S1Hash aggregate table

1 – $217 – $208 - $8

S0-----------

11122

S1---------

44

S0---------

$5$21$15$10$7

S1---------

$27$1054

Aggr

Dist

Aggr

Supplier

Nation AcctBal

S2---------

1779

10

S3---------

24469

S2---------

$15$1

$12$10$3

S3---------

$7$1050

$4$10$2

S2Hash aggregate table

1 – $157 – $139 – $1010 - $3

S3Hash aggregate table

2 – $74 – $1054

6 – $109 = $2

S2-----------

677

S3---------

8899

10

S2---------

$10$20$13

S3---------

$30$8

$10$2$3

Node 2Node 1

... ...

Scan

S2Hash aggregate table

6 – $107 – $33

S3Hash aggregate table

8 – $389 – $1210 – $3

S0Hash aggregate table

1 – $412 – $17

S1Hash aggregate table

4 – $1081

select nation, sum(acctbal) from supplier group by nation;

create table supplier ( suppkey int4 not null distkey, name char(25) not null, address varchar(40) not null, nation int4 not null, phone char(15) not null, acctbal numeric(12,2) not null, comment varchar(101) not null ) sortkey (suppkey);

Queryの実行例 • TPC-H Q16

select p_brand, p_type, p_size, count(distinct ps_suppkey) as supplier_cnt from partsupp, part where p_partkey = ps_partkey and p_brand <> 'Brand#15' and p_type not like 'STANDARD POLISHED%' and p_size in (3, 8, 49, 19, 29, 9, 47, 32) and ps_suppkey not in ( select s_suppkey from supplier where s_comment like '%Customer%Complaints%' ) group by p_brand, p_type, p_size order by supplier_cnt desc, p_brand, p_type, p_size;

Queryの実行例 stm | seg | step | maxtime | avgtime | rows | bytes | label | slices

-----+-----+------+-----------------+-----------------+----------+------------+----------------------+--------

0 | 0 | 0 | 00:00:00.082427 | 00:00:00.047169 | 479 | 37414 | scan tblP =108506 | 32

0 | 0 | 2 | 00:00:00.082427 | 00:00:00.047169 | 479 | 1916 | bcast | 32

0 | 1 | 0 | 00:00:00.099844 | 00:00:00.094777 | 7664 | 30656 | scan fabric=67 | 32

0 | 1 | 2 | 00:00:00.099844 | 00:00:00.094777 | 7664 | 122624 | hash tblT =240 | 32

1 | 2 | 0 | 00:00:00.002559 | 00:00:00.001517 | 0 | 0 | scan tblP =108490 | 32

1 | 2 | 1 | 00:00:00.002559 | 00:00:00.001517 | 0 | 0 | sort tblT =242 | 32

2 | 3 | 0 | 00:00:00.002244 | 00:00:00.001048 | 0 | 0 | scan tblP =108514 | 32

2 | 3 | 1 | 00:00:00.002244 | 00:00:00.001048 | 0 | 0 | sort tblT =244 | 32

3 | 4 | 0 | 00:00:02.231598 | 00:00:01.725522 | 79999318 | 1279989088 | scan tblP =108514 | 32

3 | 4 | 1 | 00:00:02.231598 | 00:00:01.725522 | 0 | 0 | merge | 32

3 | 4 | 5 | 00:00:02.231598 | 00:00:01.725522 | 11885548 | 0 | mjoin tbl =239 | 32

3 | 4 | 8 | 00:00:02.231598 | 00:00:01.725522 | 11879878 | 0 | hjoin tblT =240 | 32

3 | 4 | 11 | 00:00:02.231598 | 00:00:01.725522 | 11879783 | 712462256 | aggr tblT =257 | 32

3 | 4 | 12 | 00:00:02.231598 | 00:00:01.725522 | 11879783 | 565281908 | dist | 32

3 | 5 | 0 | 00:00:02.426613 | 00:00:02.371618 | 11879783 | 569904860 | scan fabric=68 | 32

3 | 5 | 1 | 00:00:02.426613 | 00:00:02.371618 | 11877404 | 712319756 | aggr tblT =260 | 32

4 | 6 | 0 | 00:00:00.192183 | 00:00:00.133277 | 11877404 | 712319756 | scan tblT =260 | 32

4 | 6 | 5 | 00:00:00.192183 | 00:00:00.133277 | 890878 | 56991616 | aggr tblT =265 | 32

4 | 6 | 6 | 00:00:00.192183 | 00:00:00.133277 | 890878 | 45434532 | dist | 32

4 | 7 | 0 | 00:00:00.301407 | 00:00:00.292919 | 890878 | 49864592 | scan fabric=69 | 32

4 | 7 | 1 | 00:00:00.301407 | 00:00:00.292919 | 27840 | 1780992 | aggr tblT =268 | 32

5 | 8 | 0 | 00:00:00.001789 | 00:00:00.001143 | 27840 | 1780992 | scan tblT =268 | 32

5 | 8 | 3 | 00:00:00.001789 | 00:00:00.001143 | 27840 | 1558272 | sort tblT =238 | 32

6 | 9 | 0 | 00:00:00.000595 | 00:00:00.000336 | 27840 | 1558272 | scan tblT =238 | 32

6 | 9 | 1 | 00:00:00.000595 | 00:00:00.000336 | 27840 | 1419844 | return | 32

6 | 10 | 0 | 00:00:00.046609 | 00:00:00.046609 | 27840 | 0 | merge | 1

6 | 10 | 2 | 00:00:00.046609 | 00:00:00.046609 | 0 | 0 | return | 1

いぢめないで…

select l_partkey, l_extendedprice from lineitem order by 2, 1;

stm | seg | step | maxtime | avgtime | rows | bytes | label | slices

-----+-----+------+-----------------+-----------------+-----------+-------------+----------------------+--------

0 | 0 | 0 | 00:00:23.496729 | 00:00:19.806 | 600037902 | 12000758040 | scan tblP =108542 | 32

0 | 0 | 3 | 00:00:23.496729 | 00:00:19.806 | 600037902 | 9600606432 | sort tblT =238 | 32

1 | 1 | 0 | 00:08:29.514698 | 00:08:29.109248 | 600037902 | 9600606432 | scan tblT =238 | 32

1 | 1 | 1 | 00:08:29.514698 | 00:08:29.109248 | 600037902 | 9600474240 | return | 32

1 | 2 | 0 | 00:08:33.764077 | 00:08:33.764077 | 600037902 | 0 | merge | 1

1 | 2 | 2 | 00:08:33.764077 | 00:08:33.764077 | 0 | 0 | return | 1

データの持ち方も重要

分散キー (distkey)

catid

category

catgroup catname catdesc

distkey

Slice 0 Slice 1

catitem

catid

distkey itemid

ソートキー (sortkey)

Amazon Buffer Create Drive Enjoy

.

.

.

. Xbox You zone

Super Final high

VACUUM

カラムナーDBの十八番、圧縮 エンコード名 キーワード サポートされるデータ型 圧縮の特徴

Byte dictionary BYTEDICT VARCHARとBOOLEAN を除く全て

ブロックごとに最大255通りの値を辞書に持たせることで1カラム1バイトに圧縮。

Global dictionary

GLOBALDICT256, GLOBALDICT64K

BOOLEAN以外全て カラム全体で1つの辞書を持つ。256だと255通りの値を1バイトに、64Kだと65535通りの値を2バイトに圧縮。

Delta DELTA, DELTA32K

SMALLINT (DELTAのみ), INT, BIGINT, DATE, TIMESTAMP, DECIMAL

前の値との差分を格納。差分の値が元データ型のサイズよりも小さければ圧縮可能。

Run-length RUNLENGTH 全て ランレングス圧縮。同一のデータがいくつ連続するかを格納。連続した同一値には高い圧縮率。

LZ (Lempel-Ziv) DEFLATE BOOLEAN, REAL, DOUBLE PRECISION を除く全て

LZ法(ZIPなど一般的なファイル圧縮で使われているアルゴリズム)で圧縮。圧縮率は高いが圧縮・展開のCPU負荷も高い。

Mostly MOSTLY8 SMALLINT, INT, BIGINT, DECIMAL

カラム内の値が指定されたデータ型よりも小さいサイズで格納可能な場合、指定された(元のデータ型よりも小さい)サイズでデータを格納する。収まらない場合は元のサイズで格納。

MOSTLY16 INT, BIGINT, DECIMAL

MOSTLY32 BIGINT, DECIMAL

TEXT255, 32K TEXT255, TEXT32K

VARCHARのみ 辞書を使用した圧縮のVARCHAR版。245通りの単語まで辞書に登録可能。

上手に圧縮、サイズは1/2以下！ create table lineitem (

l_orderkey int8 not null encode delta sortkey distkey,

l_partkey int4 not null,

l_suppkey int4 not null encode mostly16,

l_linenumber int4 not null encode mostly8,

l_quantity numeric(19,2) not null encode bytedict,

l_extendedprice numeric(19,2) not null encode mostly32,

l_discount numeric(19,2) not null encode mostly8,

l_tax numeric(19,2) not null encode mostly8,

l_returnflag char(1) not null encode runlength,

l_linestatus char(1) not null encode runlength,

l_shipdate date not null encode delta,

l_commitdate date not null encode delta,

l_receiptdate date not null encode delta,

l_shipinstruct char(25) not null encode bytedict,

l_shipmode char(10) not null encode bytedict,

l_comment varchar(44) not null

);

create table lineitem (

l_orderkey int8 not null sortkey distkey,

l_partkey int4 not null,

l_suppkey int4 not null,

l_linenumber int4 not null,

l_quantity numeric(19,2) not null,

l_extendedprice numeric(19,2) not null,

l_discount numeric(19,2) not null,

l_tax numeric(19,2) not null,

l_returnflag char(1) not null,

l_linestatus char(1) not null,

l_shipdate date not null,

l_commitdate date not null,

l_receiptdate date not null,

l_shipinstruct char(25) not null,

l_shipmode char(10) not null,

l_comment varchar(44) not null

);

行数： 600,037,902

40,204 MB 18,900 MB !

隠し味はUDF(ユーザー定義関数) • PL/pgSQLの例

CREATE OR REPLACE FUNCTION f_echo(_text varchar) RETURNS varchar AS $$ BEGIN return _text; END; $$ LANGUAGE plpgsql;

• C++の例 #include "padb_udf.hpp" PADB_UDF_VERSION(charcount); extern "C" { padb_udf::int_t charcount ( padb_udf::ScalarArg &aux, padb_udf::varchar_t *target, padb_udf::varchar_t *tst ) { padb_udf::int_t ret = 0; if ( tst->len != 1 ) { aux.throwError( __func__,"probe length must be = 1" ); } char ch = tst->str[0]; for ( padb_udf::len_t ix = 0; ix < target->len; ix++ ) { if ( target->str[ix] == ch ) { ret++; } } return aux.retIntVal( ret ); } }

CREATE OR REPLACE FUNCTION charcount (target_string varchar, search_character varchar) RETURNS int AS '/tmp/scalar_charcount.o' LANGUAGE C STABLE;

• ユーザーのビジネスロジック組み込み • サードパーティ製分析関数 • ODI(On-Demand Integration)の実現

ビッグなデータはHadoopから取り込め！

ビッグなデータはHadoopから取り込め！

DataNode

DataNode

DataNode

NameNode

Compute Node

Compute Node

Compute Node

LeaderNode

これでは遅い…

ビッグなデータはHadoopから取り込め！

DataNode

DataNode

DataNode

NameNode

Compute Node

Compute Node

Compute Node

LeaderNode

直接ロードしたい！ 直接読み込みたい！

Google Booksで現れる単語を見てみよう

まずはHadoopでデータ加工だ！ algorithm_PRON 1991 297 185 algorithm_PRON 1992 325 217 algorithm_PRON 1993 336 235 algorithm_PRON 1994 308 202 algorithm_PRON 1995 331 244 algorithm_PRON 1996 376 243 algorithm_PRON 1997 404 243 algorithm_PRON 1998 404 269 algorithm_PRON 1999 495 295 algorithm_PRON 2000 480 318

algorithm PRON 1991 297 185 algorithm PRON 1992 325 217 algorithm PRON 1993 336 235 algorithm PRON 1994 308 202 algorithm PRON 1995 331 244 algorithm PRON 1996 376 243 algorithm PRON 1997 404 243 algorithm PRON 1998 404 269 algorithm PRON 1999 495 295 algorithm PRON 2000 480 318

単語と品詞を分離したい

MapReduceだ！

ちょっとしたクエリはODIで

こんなクエリが書けちゃう

select word, sum(wordcount) from odi_hadoop_import(

with jobname('googlebooks_job')

masternode('hadoop01.com')

inputdir('/user/dhirama/googlebooksModifyDemo')

padb_schema('googlebooksWords')

delimiter('¥t')

)

where word like 'another%'

group by word;

ODIを実行だ！

がっつりロードするならDataFlow！

高速ロード、すぐに分析！

最強のタッグがBigDataの世界を変える！

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株式会社インサイトテクノロジーは本書の内容に関していかなる保証もしません。また、本書の内容に関連したいかなる損害についても責任を負いかねます。 本書で使用している製品やサービス名の名称は、各社の商標または登録商標です。