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 기능 을 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 만들고, 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 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=${MDB_RESOURCE_NAME}"
2
MongoDB 도구 포드를 배포합니다.
mongodb-tools-pod 이라는 이름의 Pod를 배포하여 Kubernetes 클러스터 내에서 mongosh 및 mongorestore 와 같은 MongoDB 클라이언트 도구를 실행 . 클러스터 내부에서 파드를 실행하면 데이터베이스 외부에 노출할 필요 없이 mongodb에 대한 연결이 간소화됩니다.
다음 명령은 MongoDB Community 서버 이미지를 실행하는 mongodb-tools-pod 이라는 포드를 배포합니다. 포드는 sleep infinity 명령으로 계속 실행 되어 kubectl exec 를 사용하여 MongoDB 클라이언트 도구를 실행 수 있습니다.
1 kubectl apply -n "${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. 및 쿼리를 $searchMeta $search $vectorSearch 실행 하려면 MongoDB Search 인덱스 생성하고 쿼리를 실행 하려면 Vector Search 인덱스 생성해야 합니다.
MongoDB Search는 동적 및 정적 필드 매핑, 다양한 분석기(표준, 언어별, 사용자 지정)는 물론 동의어 및 패싯 검색 과 같은 기능을 지원 유연한 인덱스 정의를 제공합니다. 자세한 학습 은 인덱스 정의를 참조하세요.
벡터 검색은 ANN 및 ENN 검색 알고리즘 사용하여 시맨틱 유사성을 기반으로 데이터의 벡터 임베딩 쿼리를 지원하는 간단한 인덱스 정의를 제공합니다. 자세한 학습 은 벡터 검색 인덱스 정의를 참조하세요.
터미널에서 다음 명령을 복사하여 붙여넣은 후 실행 MongoDB Search 또는 Vector Search 인덱스 생성합니다.
이 명령은
kubectl exec를 사용하여mongodb-tools-pod에서mongosh를 실행 .sample_mflixdb.<collection>.createSearchIndex()메서드에 연결하여 인덱스 생성합니다.다음 명령은
movies컬렉션 에 동적 매핑을 사용하여 이름이default인 MongoDB Search 인덱스 생성합니다. 동적 매핑은 동적으로 인덱싱할 수 있는 모든 필드 유형을 자동으로 인덱싱합니다.1 kubectl exec --context "${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 컬렉션 에서 차원 수가 인 필드 에
vector_indexplot_embedding_voyage_3_large2048이름이 인 벡터 검색 인덱스 생성합니다.embedded_movies1 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 "${MDB_CONNECTION_STRING}" \ 3 --eval "use sample_mflix" \ 4 --eval 'db.runCommand({"listSearchIndexes": "embedded_movies"});'
5
인덱스 사용하여 데이터를 쿼리합니다.
인덱스 생성이 성공적인 후에는 인덱스 사용하여 MongoDB Search 또는 Vector Search 쿼리를 실행 수 있습니다. MongoDB Search와 Vector Search를 결합할 수도 있습니다. MongoDB Search 쿼리 실행 에 대해 자세히 학습 쿼리 정의를 참조하세요. 벡터 검색 쿼리에 대해 자세히 학습 벡터 검색 쿼리 실행을 참조하세요.
다음 스크립트 집계 파이프라인 쿼리 기준을 정의하고 kubectl exec 명령을 사용하여 mongosh 를 로드하여 쿼리 실행합니다.
다음 쿼리 plot 필드 에서 baseball이라는 텀 포함된 영화 줄거리를 검색합니다. comedy 및 romance 장르의 영화는 제외됩니다.
1 mdb_script=$(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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