mongo_vector_crud_dataAPI1.yaml

min_version: "5.17.3"

# Example command line
# java -jar ~/nb5.jar -v mongo_vector_crud_dataAPI docscount=10000 threads=100 uri=https://us-west-2.aws.data.mongodb-api.com/app/data-fvelc/endpoint/data/v1 key=1PwepOs5phnQaQW4OcC3dtzTWxKKdhXSwY5J8CbGsVUEZBrnakzUsoyniYUF4Qez
# java -jar ~/nb5.jar -v mongo_vector_crud_dataAPI docscount=10000 threads=100 uri=https://us-west-2.aws.data.mongodb-api.com/app/data-sosch/endpoint/data/v1 key=wB7HHKnNVYIKbKnVNaNtIzGUsmQqtGcPJZh8Zc9Mksi9arlWJvVdUhY15ElMebLs
# java -jar ~/nb5.jar -v mongo_vector_crud_dataAPI docscount=10000 threads=100 --report-prompush-to http://127.0.0.1:8428/api/v1/import/prometheus/metrics/job/nosqlbench/instance/zen_brahmagupta uri=https://us-west-2.aws.data.mongodb-api.com/app/data-sosch/endpoint/data/v1 key=wB7HHKnNVYIKbKnVNaNtIzGUsmQqtGcPJZh8Zc9Mksi9arlWJvVdUhY15ElMebLs
# java -jar ~/nb5.jar -v mongo_vector_crud_dataAPI docscount=10000 threads=100 --report-prompush-to http://127.0.0.1:8428/api/v1/import/prometheus/metrics/job/nosqlbench/instance/zen_brahmagupta uri=https://us-west-2.aws.data.mongodb-api.com/app/data-fvelc/endpoint/data/v1 key=1PwepOs5phnQaQW4OcC3dtzTWxKKdhXSwY5J8CbGsVUEZBrnakzUsoyniYUF4Qez

#  "https://us-west-2.aws.data.mongodb-api.com/app/data-fvelc/endpoint/data/v1"
#  "1PwepOs5phnQaQW4OcC3dtzTWxKKdhXSwY5J8CbGsVUEZBrnakzUsoyniYUF4Qez"
#
#  "https://us-west-2.aws.data.mongodb-api.com/app/data-sosch/endpoint/data/v1"
#  "wB7HHKnNVYIKbKnVNaNtIzGUsmQqtGcPJZh8Zc9Mksi9arlWJvVdUhY15ElMebLs"


description: >2
  This workload emulates vector CRUD operations for MongoDB Data API
  It requires a data set file (default vector-dataset.txt), where contains vectors of size 1536
  1536 is a standard vector size that openAI embedding generates, using this size for benchmark


scenarios:
  default:
#    schema:   run driver=http tags==block:schema threads==1 cycles==UNDEF
    write:    run driver=http tags==name:"write.*" cycles===TEMPLATE(write-cycles,TEMPLATE(docscount,500)) threads=auto errors=timer,warn
#    read:     run driver=http tags==name:"read.*" cycles===TEMPLATE(read-cycles,TEMPLATE(docscount,500)) threads=auto errors=timer,warn
    update:   run driver=http tags==name:"update.*" cycles===TEMPLATE(update-cycles,TEMPLATE(docscount,500)) threads=auto errors=timer,warn
#    delete:   run driver=http tags==name:"delete.*" cycles===TEMPLATE(delete-cycles,TEMPLATE(docscount,500)) threads=auto errors=timer,warn

bindings:

  seq_key: Mod(<<docscount:500>>); ToString() -> String
  random_key: Uniform(0,<<docscount:500>>); ToString() -> String
  vector_json: HashedLineToString('<<dataset:vector-dataset.txt>>');

blocks:
  write:
    ops:
      write-insert-one-vector:
        method: POST
        uri: <<uri:xxx>>/action/insertOne
        Content-Type: "application/json"
        Api-key: <<key:xxx>>
        ok-body: '.*\"insertedId\":.*'
        body: >2
          {
            "dataSource": "Cluster0",
            "database": "testdb1",
            "collection": "testcollection",
            "document": {
              "_id" :         "{seq_key}",
              "vector_c" :      {vector_json}
            }
          }
  update:
    ops:
      find-one-update-vector:
        method: POST
        uri: <<uri:xxx>>/action/aggregate
        Content-Type: "application/json"
        Api-key: <<key:xxx>>
        ok-body: '.*\"documents\":.*'
        body: >2
          {
            "dataSource": "Cluster0",
            "database": "testdb1",
            "collection": "testcollection",
            "pipeline": [
              {
                "$search": {
                  "index": "vectorTestIndex",
                  "knnBeta": {
                    "vector": {vector_json},
                    "path": "vector_c",
                    "k": 1
                  }
                }
              },
              { "$set": {
                "status": "active"
              }
              }
            ]
          }