From fa1a52bb120faa0d2e6e8f9eda107f0fd6d65594 Mon Sep 17 00:00:00 2001 From: Seoneui Date: Fri, 2 Aug 2024 19:06:12 +0900 Subject: [PATCH 1/4] [week21] DynamoDB --- AWS/DynamoDB.md | 218 +++++++++++++++++++++++++ images/Pasted image 20240802165312.png | Bin 0 -> 461196 bytes images/Pasted image 20240802165639.png | Bin 0 -> 104618 bytes images/Pasted image 20240802181148.png | Bin 0 -> 118466 bytes images/Pasted image 20240802181440.png | Bin 0 -> 406442 bytes images/Pasted image 20240802182313.png | Bin 0 -> 267811 bytes images/Pasted image 20240802182716.png | Bin 0 -> 575807 bytes images/Pasted image 20240802183000.png | Bin 0 -> 207567 bytes images/Pasted image 20240802183719.png | Bin 0 -> 518073 bytes images/Pasted image 20240802184234.png | Bin 0 -> 910893 bytes images/Pasted image 20240802184433.png | Bin 0 -> 841772 bytes images/Pasted image 20240802184705.png | Bin 0 -> 582216 bytes images/Pasted image 20240802184840.png | Bin 0 -> 718821 bytes images/Pasted image 20240802185338.png | Bin 0 -> 447681 bytes 14 files changed, 218 insertions(+) create mode 100644 AWS/DynamoDB.md create mode 100644 images/Pasted image 20240802165312.png create mode 100644 images/Pasted image 20240802165639.png create mode 100644 images/Pasted image 20240802181148.png create mode 100644 images/Pasted image 20240802181440.png create mode 100644 images/Pasted image 20240802182313.png create mode 100644 images/Pasted image 20240802182716.png create mode 100644 images/Pasted image 20240802183000.png create mode 100644 images/Pasted image 20240802183719.png create mode 100644 images/Pasted image 20240802184234.png create mode 100644 images/Pasted image 20240802184433.png create mode 100644 images/Pasted image 20240802184705.png create mode 100644 images/Pasted image 20240802184840.png create mode 100644 images/Pasted image 20240802185338.png diff --git a/AWS/DynamoDB.md b/AWS/DynamoDB.md new file mode 100644 index 0000000..08c3db9 --- /dev/null +++ b/AWS/DynamoDB.md @@ -0,0 +1,218 @@ +--- + +--- +#AWS #DynamoDB + +### DynamoDB란? + +DynamoDB는 NoSQL(Not Only SQL) 데이터베이스 + +--- + +### SQL? NoSQL? + +#### SQL + +SQL을 사용하면 RDBMS에서 데이터를 저장, 수정, 삭제 및 검색 할 수 있음 + +- **RDB(Relational Database)** 관계형 데이타 모델에 기초를 둔 데이터 베이스 + 관계형 데이타 모델이란 데이타를 구성하는데 필요한 방법 중 하나로 모든 데이타를 2차원의 테이블 형태로 표현 +- **RDBMS**(Relational DataBase Management System)란 관계형 데이터베이스 관리 시스템으로, 모든 데이터를 2차원 테이블 형태로 표현하는 데이터베이스 + +SQL은 RDBMS에서 사용되는 언어로 국제표준화기구에서 표준 SQL을 정하지만 사용하는 DBMS 제품마다 기능에 차이가 있기 때문에, 제품마다 사용되는 언어의 이름이 조금씩 다름 +(MySQL: SQL,SQL 서버: T-SQL, Oracle: PL/SQL) + +#### NoSQL + +관계형 DB의 반대, **스키마도 없고, 관계도 없음** + +NoSQL은 관계형 데이터베이스처럼 행과 열이 존재하는 테이블 형태가 아니기에 데이터 모델을 어떻게 설계하느냐에 따라 데이터베이스의 포멧이 달라지는데 이는 크게 세 가지로 나눌 수있음 + +- 문서 데이터 베이스 + - 주로 JSON 형태의 문서를 보관. 문서는 필드와 값으로 구성되어 있으며 문자열, 숫자, 배열 등 다양한 데이터 타입을 허용 +- Key-Value 데이터 베이스 + - 문서 데이터베이스와 비슷하나 구조가 훨씬 간단하며, 마찬가지로 Key-Value 형태의 JSON 데이터를 보관 +- 그래프 데이터 베이스 + - 노드와 엣지를 사용하여 데이터를 보관 + - ![[Pasted image 20240802165312.png|300]] + +#### NoSQL 도입 + +NoSQL에 탄생하기 전까지, 데이터베이스 시스템은 RDBMS(관계형 데이터베이스) 세상이었음 +스토리지 비용이 비쌌던 시절, 정규화를 통해 중복 데이터를 최대한 줄이는 것이 중요했고, 관계형 데이터베이스는 그것을 수행하기에 최적의 도구였음 + +즉, **관계형 데이터베이스의 목적은 효율적인 스토리지의 사용**임 + +그러나, 최근들어 스토리지의 비용이 점차 저렴해지고, 대규모의 연산을 빠르게 처리해내는게 중요해짐에 따라, 데이터가 중복되도 좋으니, 데이터베이스도 빠르게 처리하는 것이 중요한 시대가 되었음 + +**스토리지를 희생하더라도, 성능을 최대한 끌어올리자, 그래서 NoSQL이 나온 것** + +--- + +### DynamoDB 개념 + +그래서 DynamoDB는 어떻게 생겼을까? + +![[Pasted image 20240802165639.png|500]] + +아이템 : {’ customer_id ’ : 644, ’age ’ : 26, ’visit ’ : True} -> 행 +특징 : {’age’ : [26, 31]} -> 열 + +테이블을 JSON 형태로 표현하면 다음과 같음 +![[Pasted image 20240802181148.png]] + +--- + +### DynamoDB 장점 + +- NoSQL 데이터베이스는 쿼리 속도(특히 읽기 속도) 가 매우 삐름 + - NoSQL은 테이블 비정규화 과정을 기본적으로 거치는데, 테이블 비정규 화는 쓰기 퍼포먼스를 낮추는 대신 읽기 퍼포먼스를 극대화하는 것이 핵심 + - 비정규화된 테이블은 이 미 중복 데이터를 포함하고 있기 때문에 다른 태이블과 합치는 과정(join)이 필요 없음 + ![[Pasted image 20240802181440.png|500]] + +- 오토스케일링 가능 + - 데이터베이스를 만들 때 크기가 정해지는데 오토스케일링 기능이 활성화된다면 들어오는 데이터 크기에 따라 태이블 크기가 자동으 로 변경됨 + - EC2 인스턴스와 마찬가지로 DynamoDB 역시 데이터의 처리량에 따라 성능을 늘렸다 줄였다 함 + - 이를 통해 불필요한 자원을 줄여 비용을 대폭 절약할 수 있음 + +- NoSQL이기 때문에 테이블 생성 시 스키마를 정의할 필요가 없음 + - 스키마에는 데이터 베이스의 전반적인 구조 및 데이터 개체, 속성등의 정보 포함 + - 하나 혹은 여러 개의 테이블이 존재할 수 있으며 다른 스키마와 어떤 관계에 놓여있는지에 대한 무결성 제약 조건을 정의할 수 있음 + - 테이블 생성 시 기본키를 제외한 어떤 것도 미리 정의되지 않기에 개제와 속성을 미리 알 필요가 없음. JSON 형태의 데 이터가 들어오면 그대로 스키마와 테이블이 생성되며 데이터 타입이 자동으로 정의됨 + +- DynamoDB는 IoT나 모바일, 웹 애플리케이션 데이터를 처리할 때 사용하면 좋음 + - 실시간으로 들어오는 데이터양을 미리 알 수 없기 때문에 DynamoDB의 오토스케일링 기능을 함께 사용하면 좋음 + +- DynamoDB는 SSD스토리지를 사용하기 때문에 데이터를 읽고 쓰는 데 속도가 빠름 + +--- + +### DynamoDB 기본키 + +#### 파티션키 + +테이블에 있는 데이터를 파티션으로 나누고 분리시키는 데 사용되는 키 +실제로 데이터가 들어가는 장소(장소란 DynamoDB 안에서 해시 함수를 실행시켜 반환되는 주솟값)를 정해줌 + +![[Pasted image 20240802182313.png]] +고려사항 +- 주로 높은 카디널리티(중복된 데이커가 많이 않은 것)가 파티션 키로 사용됨 + - ex. 아이디필드가 주로 사용됨 + +#### 복합키 + +여러 개의 키를 합쳐놨다는 뜻이며 위에서 살펴본 파티션키와 정렬키(*정렬키는 파티션키로 데이터가 다른 장소로 분리된 다음 그 안에서 정렬키에 의해 데이터가 정렬될 때 사용되는 기준점*)를 합쳐 놓은 것 + +![[Pasted image 20240802182716.png|500]] + + +**DynamoDB 기본키는 파티션키 혹은 복합키 둘중 하나를 반드시 선택**해야하며 **정렬키만 기본키로 지정할 수 없음** +정렬키는 시간별로 데이터를 정렬하여 원하는 시간대에 포함되어 있는 데이터만 쿼리해서 가져올 때 시용되며 따라서 시간 정보가 들어있는 열이 주로 선택됨 + +--- + +### DynamoDB 데이터 접근 관리 + +DynamoDB에 있는 테이블과 데이터는 AWS 사용자 모두에게 공개될 수 있지만 특정 부서와 유저에게만 필요한 테이블을 쿼리할 수 있는 권한을 부여할 수도 있음 + +사용자에게 테이블 생성과 접근 권한을 부여할 수 있으며, 특정 테이블과 특정 데이터에만 접근을 가능하게 해주는 IAM 역할도 존재함 + +ex. 아래는 전 세계 게이머 정보가 들어가있는 테이블이고 쿼리를 통해 현재 본인 기록을 확인하고 싶을때 +![[Pasted image 20240802183000.png|400]] + +--- + +### 좋은 DB 디자인을 위한 인덱스 + +앞써 설명한 파티션키와 복합키를 올바르게 골랐다면 아무리 방대한 양의 데이터가 있어도 쿼리 속도는 빠르게 할 수 잇음 + +**그러나 여기서 쿼리 성능을 더 끌어올릴 수 있는 방법이 있는데, DynamoDB에는 인텍스(index)라는 개념이 존재** +인텍스란 데이터 쿼리 시 테이블 전체를 스캔하는 것이 아니라 선택한 특정 열을 기준으로 쿼리가 진행함 + +#### 로컬 보조 인덱스(LSI) + +LSI는 DynamoDB 테이블 생성 시에만 정의가능하고 테이블을 만든 후 LSI는 변경 또는 삭제가 불기능함 + +파티션키를 사용해야 하지만 정렬키는 다른 것을 사용할 수 있음 +즉, 똑같은 파티션키를 사용하더라도 정렬키가 다르기 때문에 전혀 다른 뷰를 생성할 수 있음 +파티션키와 정렬키를 같이 사용하기 때문에 LSI는 앞서 배운 복합키를 사용한다고 말할 수 있음 + +##### 뷰? + +테이블은 데이터가 들어가기 위해 만들어지는 컨테이너이며 실제로 데이터가 존재하는 곳 +뷰는 테이블과 마찬가지로 행과 열이 있으며 테이블과 구조가 똑같으나 뷰는 테이블에서 파생된 것이며 데이터가 들어있지 않음 +그래서 일반적으로 뷰에서 데이터 수정을 할 수 없음 + +테이블에서 데이터가 변경되면 뷰는 이를 인식하고 즉시 반영함 +인텍스를 정의한다는 의미는 테이블에서 어떤 변화를 주는 것이 아니라 기존 테이블에서 새로운 뷰를 만드는 것임 + +![[Pasted image 20240802183719.png|500]] + +#### 글로벌 보조 인덱스(GSI) + +LSI와 달리 GSI는 테이블 생성 후에도 추가, 변경, 삭제가 가능함. 뿐만 아니라 파티션키와 정렬키를 원래 테이블과 다르게 정의할 수 있음. 심지어 정렬키는 필수가 아닌 선택사항 +**따라서 완전히 다른 뷰를 만들 수 있음** + +![[Pasted image 20240802184234.png|500]] + +#### 그렇다면 두 인덱스는 언제 사용? + +정렬키를 사용해 정렬된 데이터에서 하나의 파티션키가 들어있는 테이블을 주로 쿼리하는 상황이 발생할 경우 LSI를 권장 +GSI에서 정렬키는 선택사항이기 때문에 정렬키의 중요성에 따라 LSI와 GSI의 선택이 갈릴 수 있음 + +--- + +### 데이터를 가져오는 방법(쿼리 vs 스캔) + +#### 쿼리 + +쿼리는 테이블을 만들 때 정의한 기본키를 가지고 데이터를 가져오는 방법 + +![[Pasted image 20240802184433.png|500]] + +만약 쿼리를 하고 반환되는 데이터 양이 너무 많다면 정렬키를 제공하고 추가로 필터링하여 원하는 데이터를 가져올 수 있음 +거래 아이디 AAA로 가장 최근에 진행된 거래가 무엇인지 알고 싶다면 거래 시간을 정렬키로 설정하여 쿼리를 수행하면 됨 + +기본적으로 쿼리는 모든 열을 가져오는데 ‘ProjectionExpression’이라는 파라미터를 추가로 제공하여 우리가 원하는 열만 가져올 수도 있음 + +#### 스캔 + +기본키를 사용하지 않고 테이블 안에 있는 모든 데이터를 불러옴 +모든 데이터를 불러온 다음 필터를 따로 추가하여 원하는 데이터만 볼 수 있는 기능을 제공함. 스캔도 쿼리와 마찬가지로 Projection Expression 파라미터를 제공하기에 스캔을 통해 원하는 열만 볼 수 있음 + +![[Pasted image 20240802184705.png|500]] + +스캔을 하면 테이블에서 한 번에 모든 데이터를 가져오는 것이 아니라 첫 1MB에 해당하는 데이터를 반환함 +그다음 1MB에 해당하는 배치 데이터를 반환하는데 이것을 **순차적 스캔**이라고 함 + +작은 태이블을 스캔할 경우 첫 배치 데이터 크기가 1MB 미만이라면 한 번에 반환되지만 테이블의 크기가 100MB가 넘는다면 총 100번의 배치 데이터가 반환되며 그만큼 속도가 저하됨 + +![[Pasted image 20240802184840.png|500]] + +스캔의 성능을 향상시키기 위해 스캔을 실행할 때 수많은 스캔 일꾼을 여러 군데 분산시켜 병행하는 기능을 활용 +이를 **병렬 스캔**이라고 함 + +테이블 전체를 N등분한 후 스캔 일꾼을 N등분된 조각으로 보내 동시다발적으로 스캔을 진행함 + +#### 쿼리 VS 스캔 + +쿼리와 스캔은 쿼리는 초반에 테이블의 일부만 검색하여 데이터를 가져오는 반면, 스캔은 일단 테이블 전체를 한 번 살펴본 후 필터링 작업이 이루어진다는 차이점이 있음 + +따라서 방대한 양의 데이터가 있는 태이블을 시용할 시 쿼리가 스캔보다 훨씬 효율적임 + +시간이 지나면서 데이터가 테이블에 계속 쌓이고 테이블의 크기는 계속 늘어나는데 그럼 스캔을 사용할 필요가 전혀 없는 것일까? + +AWS에서는 가급적이면 쿼리를 사용하라고 권장하지만 참고용이 존재하며, 아이디 필드가 없고, 기본키가 정의되어 있지 않고, 테이블의 크기가 크지 않다면 스캔을 사용해도 무방함 + +-> 상품 고유코드를 담고 있는 테이블은 크기가 크지 않고 기본키도 없음. Lookup 테이블의 특징은 실시간으로 데이터가 자주 업데이트 되지 않고 데이터 중복이 없다는 점이 있음 + +![[Pasted image 20240802185338.png|300]] + + +--- +### 참고 + +- https://alphahackerhan.tistory.com/39 +- https://gyoogle.dev/blog/computer-science/data-base/SQL%20&%20NOSQL.html +- https://velog.io/@rik963/Database-RDBMS-%EA%B5%AC%EC%A1%B0%EC%99%80-%EC%84%A4%EA%B3%84 + diff --git a/images/Pasted image 20240802165312.png b/images/Pasted image 20240802165312.png new file mode 100644 index 0000000000000000000000000000000000000000..f41170767ea1537f854a349e0b6440a634ffba62 GIT binary patch literal 461196 zcmeFYgdHUf&!$5mz!K7qi z0|0PD&Bev#-;0Y=%iBXt%&m+80IAQ>YUt`py~N2{-y%iLfG9ux7g6vTfj|96#11jI z#hy@qe~SF3Gn|E~hFJM+OJGq5gF3L3)`_@tkpvs7uTH&$8KNi(^W1Ph^@MIU-zQ!T z|A3kvG$Q-VVunk756eT2q#YpWM!6d8rhhs9UdRW4H9$;<%Gu!4v_4zIXsG{*FaHJ|$PY zY*8y*>$iB)=g)FQwtwEsayqIcjm3P*fcWk0nZ{%ZXb(L2g ztsG-~?F|ck@uW3P+Z%i>xrtR={#Tw#L$nf$@W?m$dW*h}@XMNU?LG=!&jk$`x zcBAeMF=aZ_Gd-HY@|3Bhre(wbh}E%q<>!wPtU}||GZ=!aG9Aj?qyG+PCio@C*ySKi 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