Kubernetes 연산자 사용한 MongoDB Search 및 MongoDB Vector Search 배포 지원은 미리보기 기능 으로 제공됩니다. 기능 및 해당 설명서는 미리 보기 기간에 언제든지 변경될 수 있습니다. 자세히 학습 미리보기 기능을 참조하세요.
MongoDBSearch 리소스 성공적으로 설치한 경우, MongoDB Search 및 Vector Search 인덱스를 mdb-user (으)로 생성하고 인덱스를 사용하여 MongoDB Search 및 Vector Search 쿼리를 실행 수 있습니다. 이 페이지에서는 샘플 데이터를 로드하고, 데이터에 대한 MongoDB Search 및 Vector Search 인덱스를 생성하고, 데이터에 대해 MongoDB Search 및 Vector Search 쿼리를 실행 방법을 설명합니다.
전제 조건
MongoDB Search 및 Vector Search를 사용하려면 다음이 필요합니다.
MongoDB및MongoDBSearch의 성공적인 배포서버 .자세한 내용은 다음을 참조하세요.
MongoDB Search 및 Vector Search를 생성하고 MongoDB Search 및 Vector Search 쿼리를 실행 수 있는 권한이 있는 사용자입니다.
자세한 학습 은 MongoDB 검색 및 벡터 검색을 위한 Cloud Manager 또는 MongoDB Ops Manager 사용자 구성을 참조하세요.
Kubernetes 명령줄 도구인
kubectl이(가) 클러스터 와 통신하도록 구성되었습니다.이 튜토리얼의 명령을 실행 하려면 Bash v5.1 이상을 사용해야 합니다.
절차
1
환경 변수를 설정합니다.
이 절차의 후속 단계에서 사용할 환경 변수를 설정하다 하려면 다음을 복사하고 환경 변수의 값을 설정하다 다음 환경 변수를 로드합니다.
1 # The env vars here are all commented out because this snippets module 2 # is reusable across different MongoDB deployments and all the necessary variables 3 # should be already defined there. 4 5 # set it to the context name of the k8s cluster 6 #export K8S_CTX="<local cluster context>" 7 # the following namespace will be used to deploy mongodb tools pod 8 #export MDB_NS="mongodb" 9 10 # regular user performing restore and search queries on sample mflix database 11 # the user should be able to restore database using mongorestore 12 #export MDB_USER_PASSWORD="mdb-user-password-CHANGE-ME" 13 14 # name of MongoDB or MongoDBCommunity resource in case it's deployed in the same cluster 15 # user only for the connection string in MDB_CONNECTION_STRING env var below 16 #export MDB_RESOURCE_NAME="mdbc-rs" 17 18 # default connection string if MongoDB database is deployed using the operator 19 #export MDB_CONNECTION_STRING="mongodb://mdb-user:${MDB_USER_PASSWORD}@${MDB_RESOURCE_NAME}-0.${MDB_RESOURCE_NAME}-svc.${MDB_NS}.svc.cluster.local:27017/?replicaSet=${MDB_RESOURCE_NAME}"
2
MongoDB 도구 포드를 배포합니다.
mongodb-tools-pod 이라는 이름의 Pod를 배포하여 Kubernetes 클러스터 내에서 mongosh 및 mongorestore 와 같은 MongoDB 클라이언트 도구를 실행 . 클러스터 내부에서 파드를 실행하면 데이터베이스 외부에 노출할 필요 없이 MongoDB 에 대한 연결이 간소화됩니다.
다음 명령은 MongoDB Community 서버 이미지를 실행하는 mongodb-tools-pod 이라는 포드를 배포합니다. 포드는 sleep infinity 명령으로 계속 실행 되어 kubectl exec 를 사용하여 MongoDB 클라이언트 도구를 실행 수 있습니다.
1 kubectl apply -n "${MDB_NS}" --context "${K8S_CTX}" -f - <<EOF 2 apiVersion: v1 3 kind: Pod 4 metadata: 5 name: mongodb-tools-pod 6 labels: 7 app: mongodb-tools 8 spec: 9 containers: 10 - name: mongodb-tools 11 image: mongodb/mongodb-community-server:${MDB_VERSION}-ubi8 12 command: ["/bin/bash", "-c"] 13 args: ["sleep infinity"] 14 restartPolicy: Never 15 EOF 16 17 echo "Waiting for the mongodb-tools to be ready..." 18 kubectl --context "${K8S_CTX}" -n "${MDB_NS}" wait \ 19 --for=condition=Ready pod/mongodb-tools-pod --timeout=60s
3
샘플 데이터를 가져옵니다.
다음 컬렉션이 포함된 sample_mflix 데이터베이스 가져옵니다.
컬렉션 이름 | 설명 |
|---|---|
| 특정 영화와 관련된 댓글을 포함합니다. |
| 다음 추가 필드와 함께
|
| 개봉 연도, 감독, 리뷰 등의 영화 정보가 포함되어 있습니다. |
| 메타데이터 필드. 사용자의 JSON Web Token을 포함합니다. |
| 영화관 위치를 포함합니다. |
| 사용자 정보를 포함합니다. |
다음 명령은 샘플 데이터 세트를 다운로드하고 mongorestore 을(를) 사용하여 MongoDB deployment 의 sample_mflix 데이터베이스 에 데이터를 로드하고, mdb-user(으)로 연결합니다.
1 kubectl exec -n "${MDB_NS}" --context "${K8S_CTX}" \ 2 mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF 3 echo "Downloading sample database archive..." 4 curl https://atlas-education.s3.amazonaws.com/sample_mflix.archive \ 5 -o /tmp/sample_mflix.archive 6 echo "Restoring sample database" 7 mongorestore \ 8 --archive=/tmp/sample_mflix.archive \ 9 --verbose=1 \ 10 --drop \ 11 --nsInclude 'sample_mflix.*' \ 12 --uri="${MDB_CONNECTION_STRING}" 13 EOF 14 )"
4
인덱스를 생성합니다.
MongoDB Search 또는 Vector Search 인덱스 만들 수 있습니다. $search 및 $searchMeta 쿼리를 실행 하려면 MongoDB Search 인덱스 생성하고 $vectorSearch 쿼리를 실행 하려면 Vector Search 인덱스 생성해야 합니다.
MongoDB Search는 동적 및 정적 필드 매핑, 다양한 분석기(표준, 언어별, 사용자 지정)는 물론 동의어 및 패싯 검색 과 같은 기능을 지원 유연한 인덱스 정의를 제공합니다. 자세한 학습 은 인덱스 정의를 참조하세요.
벡터 검색은 ANN 및 ENN 검색 알고리즘 사용하여 시맨틱 유사성을 기반으로 데이터의 벡터 임베딩 쿼리를 지원하는 간단한 인덱스 정의를 제공합니다. 자세한 학습은 벡터 검색 인덱스 정의를 참조하세요.
터미널에서 다음 명령을 복사하여 붙여넣은 후 실행 MongoDB Search 또는 Vector Search 인덱스 생성합니다.
이 명령은
kubectl exec를 사용하여mongodb-tools-pod에서mongosh를 실행 .sample_mflixdb.<collection>.createSearchIndex()메서드에 연결하여 인덱스 생성합니다.다음 명령은
movies컬렉션 에 동적 매핑을 사용하여 이름이default인 MongoDB Search 인덱스 생성합니다. 동적 매핑은 동적으로 인덱싱할 수 있는 모든 필드 유형을 자동으로 인덱싱합니다.1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet \ 3 "${MDB_CONNECTION_STRING}" \ 4 --eval "use sample_mflix" \ 5 --eval 'db.movies.createSearchIndex("default", { mappings: { dynamic: true } });' 다음 명령은 ANN 쿼리를 실행 위해
embedded_movies컬렉션 에서 차원 수가2048인plot_embedding_voyage_3_large필드 에 이름이vector_index인 벡터 검색 인덱스 생성합니다.1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet "${MDB_CONNECTION_STRING}" \ 3 --eval "use sample_mflix" \ 4 --eval 'db.embedded_movies.createSearchIndex("vector_index", "vectorSearch", 5 { "fields": [ { 6 "type": "vector", 7 "path": "plot_embedding_voyage_3_large", 8 "numDimensions": 2048, 9 "similarity": 10 "dotProduct", 11 "quantization": "scalar" 12 } ] });' 인덱스 생성이 완료될 때까지 기다립니다.
인덱스 생성은 비동기식 프로세스 입니다. 다음 스크립트 실행하여 인덱스 생성이 완료될 때까지 60 초 동안 기다립니다.
1 Currently it's not possible to check the status of search indexes, we need to just wait 2 echo "Sleeping to wait for search indexes to be created" 3 sleep 60 또는 다음 명령을 실행 컬렉션 의 인덱스 목록을 조회 .
1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet "${MDB_CONNECTION_STRING}" \ 3 --eval "use sample_mflix" \ 4 --eval 'db.runCommand({"listSearchIndexes": "movies"});' 1 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- \ 2 mongosh --quiet "${MDB_CONNECTION_STRING}" \ 3 --eval "use sample_mflix" \ 4 --eval 'db.runCommand({"listSearchIndexes": "embedded_movies"});'
5
인덱스 사용하여 데이터를 쿼리합니다.
인덱스 생성이 성공적인 후에는 인덱스 사용하여 MongoDB Search 또는 Vector Search 쿼리를 실행 수 있습니다. MongoDB Search와 Vector Search를 결합할 수도 있습니다. MongoDB Search 쿼리 실행에 대해 자세히 학습하려면 쿼리 정의를 참조하세요. 벡터 검색 쿼리에 대해 자세히 학습하려면 벡터 검색 쿼리 실행을 참조하세요.
다음 스크립트 집계 파이프라인 쿼리 기준을 정의하고 kubectl exec 명령을 사용하여 mongosh 를 로드하여 쿼리 실행합니다.
다음 쿼리 plot 필드 에서 baseball이라는 텀 포함된 영화 줄거리를 검색합니다. comedy 및 romance 장르의 영화는 제외됩니다.
1 mdb_script=$(cat <<'EOF' 2 use sample_mflix; 3 db.movies.aggregate([ 4 { 5 $search: { 6 "compound": { 7 "must": [ { 8 "text": { 9 "query": "baseball", 10 "path": "plot" 11 } 12 }], 13 "mustNot": [ { 14 "text": { 15 "query": ["Comedy", "Romance"], 16 "path": "genres" 17 } 18 } ] 19 }, 20 "sort": { 21 "released": -1 22 } 23 } 24 }, 25 { 26 $limit: 3 27 }, 28 { 29 $project: { 30 "_id": 0, 31 "title": 1, 32 "plot": 1, 33 "genres": 1, 34 "released": 1 35 } 36 } 37 ]); 38 EOF 39 ) 40 41 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" \ 42 mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF 43 echo '${mdb_script}' > /tmp/mdb_script.js 44 mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js 45 EOF 46 )"
1 mdbc-rs [primary] test> switched to db sample_mflix 2 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [ 3 { 4 plot: 'A sports agent stages an unconventional recruitment strategy to get talented Indian cricket players to play Major League Baseball.', 5 genres: [ 'Biography', 'Drama', 'Sport' ], 6 title: 'Million Dollar Arm', 7 released: ISODate('2014-05-16T00:00:00.000Z') 8 }, 9 { 10 plot: 'A Taiwanese high school baseball team travels to Japan in 1931 to compete in a national tournament.', 11 genres: [ 'Biography', 'Drama', 'History' ], 12 title: 'Kano', 13 released: ISODate('2014-02-27T00:00:00.000Z') 14 }, 15 { 16 plot: "12-year-old Josh is a mixed race boy and a promising baseball player. He is abused by his mother's boyfriend Byrd, and neglected by his mother Debbie. He forges his own path in life when ...", 17 genres: [ 'Drama' ], 18 title: 'Calloused Hands', 19 released: ISODate('2013-03-03T00:00:00.000Z') 20 } 21 ] 22 mdbc-rs [primary] sample_mflix>
다음 쿼리 Voyage AI의 voyage-3-large 모델을 사용하여 생성된 문자열 time travel에 대해 2048 차원 벡터 임베딩을 사용하여 plot_embedding_voyage_3_large 필드 검색합니다.
1 mdb_script=$(cat <<'EOF' 2 use sample_mflix; 3 db.embedded_movies.aggregate([ 4 { 5 "$vectorSearch": { 6 "index": "vector_index", 7 "path": "plot_embedding_voyage_3_large", 8 "queryVector": 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9 "numCandidates": 150, 10 "limit": 10, 11 "quantization": "scalar" 12 } 13 }, 14 { 15 "$project": { 16 "_id": 0, 17 "plot": 1, 18 "title": 1, 19 "score": { $meta: "vectorSearchScore" } 20 } 21 } 22 ]); 23 EOF 24 ) 25 26 kubectl exec --context "${K8S_CTX}" -n "${MDB_NS}" mongodb-tools-pod -- /bin/bash -eu -c "$(cat <<EOF 27 echo '${mdb_script}' > /tmp/mdb_script.js 28 mongosh --quiet "${MDB_CONNECTION_STRING}" < /tmp/mdb_script.js 29 EOF 30 )"
1 mdbc-rs [primary] test> switched to db sample_mflix 2 mdbc-rs [primary] sample_mflix> ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... [ 3 { 4 plot: 'At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.', 5 title: 'About Time', 6 score: 0.7704131603240967 7 }, 8 { 9 plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', 10 title: 'Retroactive', 11 score: 0.7597770690917969 12 }, 13 { 14 plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', 15 title: 'Timecop', 16 score: 0.7574796676635742 17 }, 18 { 19 plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', 20 title: 'A.P.E.X.', 21 score: 0.7573235034942627 22 }, 23 { 24 plot: 'After visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.', 25 title: 'Back to the Future Part II', 26 score: 0.751945972442627 27 }, 28 { 29 plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', 30 title: 'Thrill Seekers', 31 score: 0.7503504753112793 32 }, 33 { 34 plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', 35 title: 'The Time Machine', 36 score: 0.750007152557373 37 }, 38 { 39 plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', 40 title: 'Timerider: The Adventure of Lyle Swann', 41 score: 0.7499568462371826 42 }, 43 { 44 plot: 'A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.', 45 title: "The Time Traveler's Wife", 46 score: 0.7492842674255371 47 }, 48 { 49 plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', 50 title: 'The Final Countdown', 51 score: 0.7472751140594482 52 } 53 ] 54 mdbc-rs [primary] sample_mflix>
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