Reference Table 생성 - Reference Tables

POSThttps://api.datadoghq.com/api/v2/reference-tables/tableshttps://api.us3.datadoghq.com/api/v2/reference-tables/tableshttps://api.us5.datadoghq.com/api/v2/reference-tables/tableshttps://api.datadoghq.eu/api/v2/reference-tables/tableshttps://api.ap1.datadoghq.com/api/v2/reference-tables/tableshttps://api.ap2.datadoghq.com/api/v2/reference-tables/tableshttps://api.uk1.datadoghq.com/api/v2/reference-tables/tableshttps://api.ddog-gov.com/api/v2/reference-tables/tableshttps://api.us2.ddog-gov.com/api/v2/reference-tables/tables

개요

Reference Table을 생성합니다. 데이터는 다음과 같이 두 가지 방법으로 제공할 수 있습니다.

POST /api/v2/reference-tables/upload를 호출하여 업로드 ID를 얻습니다. 그런 다음 요청 본문에서 CSV 데이터(파일 자체가 아님)를 각 URL에 청크 단위로 PUT합니다. 마지막으로, file_metadata의 upload_id를 사용하여 POST 엔드포인트를 호출합니다.
file_metadata에 클라우드 스토리지의 CSV 파일을 가리키는 access_details를 제공합니다.

요청

Body Data (required)

항목

유형

설명

data

object

로컬 파일 또는 클라우드 스토리지에서 새 Reference Table을 생성하기 위한 요청 본문입니다.

attributes

object

Attributes that define the reference table's configuration and properties.

description

string

Optional text describing the purpose or contents of this reference table.

file_metadata

<oneOf>

Metadata specifying where and how to access the reference table's data file.

schema [required]

object

Schema defining the structure and columns of the reference table.

source [required]

enum

The source type for creating reference table data. Only these source types can be created through this API.

Allowed enum values: LOCAL_FILE, S3, GCS, AZURE

table_name [required]

string

Name to identify this reference table.

tags

[string]

Tags for organizing and filtering reference tables.

type [required]

enum

Reference table resource type.

Default: reference_table

Allowed enum values: reference_table

{
  "data": {
    "attributes": {
      "description": "string",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "123456789000",
            "aws_bucket_name": "example-data-bucket",
            "file_path": "reference-tables/users.csv"
          },
          "azure_detail": {
            "azure_client_id": "aaaaaaaa-1111-2222-3333-bbbbbbbbbbbb",
            "azure_container_name": "reference-data",
            "azure_storage_account_name": "examplestorageaccount",
            "azure_tenant_id": "cccccccc-4444-5555-6666-dddddddddddd",
            "file_path": "tables/users.csv"
          },
          "gcp_detail": {
            "file_path": "data/reference_tables/users.csv",
            "gcp_bucket_name": "example-data-bucket",
            "gcp_project_id": "example-gcp-project-12345",
            "gcp_service_account_email": "example-service@example-gcp-project-12345.iam.gserviceaccount.com"
          }
        },
        "sync_enabled": false
      },
      "schema": {
        "fields": [
          {
            "name": "field_1",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "field_1"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "table_1",
      "tags": [
        "tag_1",
        "tag_2"
      ]
    },
    "type": "reference_table"
  }
}

응답

Created

A reference table resource containing its full configuration and state.

항목

유형

설명

data

object

The data object containing the reference table configuration and state.

attributes

object

Attributes that define the reference table's configuration and properties.

created_by

string

UUID of the user who created the reference table.

description

string

Optional text describing the purpose or contents of this reference table.

file_metadata

object

Metadata specifying where and how to access the reference table's data file.

For cloud storage tables (S3/GCS/Azure):

sync_enabled and access_details will always be present
error fields (error_message, error_row_count, error_type) are present only when errors occur

For local file tables:

error fields (error_message, error_row_count) are present only when errors occur
sync_enabled, access_details are never present

last_updated_by

string

UUID of the user who last updated the reference table.

row_count

int64

The number of successfully processed rows in the reference table.

schema

object

Schema defining the structure and columns of the reference table.

source

enum

The source type for reference table data. Includes all possible source types that can appear in responses.

Allowed enum values: LOCAL_FILE, S3, GCS, AZURE, SERVICENOW, SALESFORCE, DATABRICKS, SNOWFLAKE

status

string

The processing status of the table.

table_name

string

Unique name to identify this reference table. Used in enrichment processors and API calls.

tags

[string]

Tags for organizing and filtering reference tables.

updated_at

string

When the reference table was last updated, in ISO 8601 format.

id

string

Unique identifier for the reference table.

type [required]

enum

Reference table resource type.

Default: reference_table

Allowed enum values: reference_table

{
  "data": {
    "attributes": {
      "created_by": "00000000-0000-0000-0000-000000000000",
      "description": "example description",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "123456789000",
            "aws_bucket_name": "my-bucket",
            "file_path": "path/to/file.csv"
          }
        },
        "sync_enabled": true
      },
      "last_updated_by": "00000000-0000-0000-0000-000000000000",
      "row_count": 5,
      "schema": {
        "fields": [
          {
            "name": "id",
            "type": "INT32"
          },
          {
            "name": "name",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "id"
        ]
      },
      "source": "S3",
      "status": "DONE",
      "table_name": "test_reference_table",
      "tags": [
        "tag1",
        "tag2"
      ],
      "updated_at": "2000-01-01T01:00:00+00:00"
    },
    "id": "00000000-0000-0000-0000-000000000000",
    "type": "reference_table"
  }
}

Bad Request

API error response.

항목

유형

설명

errors [required]

[string]

A list of errors.

{
  "errors": [
    "Bad Request"
  ]
}

Forbidden

API error response.

항목

유형

설명

errors [required]

[string]

A list of errors.

{
  "errors": [
    "Bad Request"
  ]
}

Too many requests

API error response.

항목

유형

설명

errors [required]

[string]

A list of errors.

{
  "errors": [
    "Bad Request"
  ]
}

코드 예시

Reference Table 생성

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="us3.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="us3.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="us3.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="us5.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="us5.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="us5.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="datadoghq.eu"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="datadoghq.eu"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="datadoghq.eu"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="ap1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="ap1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="ap1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="ap2.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="ap2.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="ap2.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="uk1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="uk1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="uk1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from cloud storage (S3)

# Set your Datadog site and credentials
export DD_SITE="us2.ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## default

# Set your Datadog site and credentials
export DD_SITE="us2.ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Customer reference data synced from S3",
      "file_metadata": {
        "access_details": {
          "aws_detail": {
            "aws_account_id": "924305315327",
            "aws_bucket_name": "my-data-bucket",
            "file_path": "customers.csv"
          }
        },
        "sync_enabled": true
      },
      "schema": {
        "fields": [
          {
            "name": "customer_id",
            "type": "STRING"
          },
          {
            "name": "customer_name",
            "type": "STRING"
          },
          {
            "name": "email",
            "type": "STRING"
          }
        ],
        "primary_keys": [
          "customer_id"
        ]
      },
      "source": "S3",
      "table_name": "customer_reference_data",
      "tags": [
        "team:data-platform"
      ]
    },
    "type": "reference_table"
  }
}
EOF

## Create table from local file upload

# Set your Datadog site and credentials
export DD_SITE="us2.ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"

curl -X POST "https://api.${DD_SITE}/api/v2/reference-tables/tables" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
  "data": {
    "attributes": {
      "description": "Product catalog uploaded via local file",
      "file_metadata": {
        "upload_id": "00000000-0000-0000-0000-000000000000"
      },
      "schema": {
        "fields": [
          {
            "name": "product_id",
            "type": "STRING"
          },
          {
            "name": "product_name",
            "type": "STRING"
          },
          {
            "name": "price",
            "type": "DOUBLE"
          }
        ],
        "primary_keys": [
          "product_id"
        ]
      },
      "source": "LOCAL_FILE",
      "table_name": "product_catalog",
      "tags": [
        "team:ecommerce"
      ]
    },
    "type": "reference_table"
  }
}
EOF

Reference Table 생성

// Create reference table returns "Created" response

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"os"

	"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
	"github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
)

func main() {
	body := datadogV2.CreateTableRequest{
		Data: &datadogV2.CreateTableRequestData{
			Attributes: &datadogV2.CreateTableRequestDataAttributes{
				FileMetadata: &datadogV2.CreateTableRequestDataAttributesFileMetadata{
					CreateTableRequestDataAttributesFileMetadataCloudStorage: &datadogV2.CreateTableRequestDataAttributesFileMetadataCloudStorage{
						AccessDetails: datadogV2.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails{
							AwsDetail: &datadogV2.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail{
								AwsAccountId:  "123456789000",
								AwsBucketName: "example-data-bucket",
								FilePath:      "reference-tables/users.csv",
							},
							AzureDetail: &datadogV2.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail{
								AzureClientId:           "aaaaaaaa-1111-2222-3333-bbbbbbbbbbbb",
								AzureContainerName:      "reference-data",
								AzureStorageAccountName: "examplestorageaccount",
								AzureTenantId:           "cccccccc-4444-5555-6666-dddddddddddd",
								FilePath:                "tables/users.csv",
							},
							GcpDetail: &datadogV2.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail{
								FilePath:               "data/reference_tables/users.csv",
								GcpBucketName:          "example-data-bucket",
								GcpProjectId:           "example-gcp-project-12345",
								GcpServiceAccountEmail: "example-service@example-gcp-project-12345.iam.gserviceaccount.com",
							},
						},
						SyncEnabled: false,
					}},
				Schema: datadogV2.CreateTableRequestDataAttributesSchema{
					Fields: []datadogV2.CreateTableRequestDataAttributesSchemaFieldsItems{
						{
							Name: "field_1",
							Type: datadogV2.REFERENCETABLESCHEMAFIELDTYPE_STRING,
						},
					},
					PrimaryKeys: []string{
						"field_1",
					},
				},
				Source:    datadogV2.REFERENCETABLECREATESOURCETYPE_LOCAL_FILE,
				TableName: "table_1",
				Tags: []string{
					"tag_1",
					"tag_2",
				},
			},
			Type: datadogV2.CREATETABLEREQUESTDATATYPE_REFERENCE_TABLE,
		},
	}
	ctx := datadog.NewDefaultContext(context.Background())
	ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
	configuration := datadog.NewConfiguration()
	apiClient := datadog.NewAPIClient(configuration)
	api := datadogV2.NewReferenceTablesApi(apiClient)
	resp, r, err := api.CreateReferenceTable(ctx, body)

	if err != nil {
		fmt.Fprintf(os.Stderr, "Error when calling `ReferenceTablesApi.CreateReferenceTable`: %v\n", err)
		fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
	}

	responseContent, _ := json.MarshalIndent(resp, "", "  ")
	fmt.Fprintf(os.Stdout, "Response from `ReferenceTablesApi.CreateReferenceTable`:\n%s\n", responseContent)
}

Instructions

First install the library and its dependencies and then save the example to main.go and run following commands:

DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"

Reference Table 생성

// Create reference table returns "Created" response

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.ReferenceTablesApi;
import com.datadog.api.client.v2.model.CreateTableRequest;
import com.datadog.api.client.v2.model.CreateTableRequestData;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributes;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesFileMetadata;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesFileMetadataCloudStorage;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesSchema;
import com.datadog.api.client.v2.model.CreateTableRequestDataAttributesSchemaFieldsItems;
import com.datadog.api.client.v2.model.CreateTableRequestDataType;
import com.datadog.api.client.v2.model.ReferenceTableCreateSourceType;
import com.datadog.api.client.v2.model.ReferenceTableSchemaFieldType;
import com.datadog.api.client.v2.model.TableResultV2;
import java.util.Arrays;
import java.util.Collections;

public class Example {
  public static void main(String[] args) {
    ApiClient defaultClient = ApiClient.getDefaultApiClient();
    defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
    ReferenceTablesApi apiInstance = new ReferenceTablesApi(defaultClient);

    CreateTableRequest body =
        new CreateTableRequest()
            .data(
                new CreateTableRequestData()
                    .attributes(
                        new CreateTableRequestDataAttributes()
                            .fileMetadata(
                                new CreateTableRequestDataAttributesFileMetadata(
                                    new CreateTableRequestDataAttributesFileMetadataCloudStorage()
                                        .accessDetails(
                                            new CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails()
                                                .awsDetail(
                                                    new CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail()
                                                        .awsAccountId("123456789000")
                                                        .awsBucketName("example-data-bucket")
                                                        .filePath("reference-tables/users.csv"))
                                                .azureDetail(
                                                    new CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail()
                                                        .azureClientId(
                                                            "aaaaaaaa-1111-2222-3333-bbbbbbbbbbbb")
                                                        .azureContainerName("reference-data")
                                                        .azureStorageAccountName(
                                                            "examplestorageaccount")
                                                        .azureTenantId(
                                                            "cccccccc-4444-5555-6666-dddddddddddd")
                                                        .filePath("tables/users.csv"))
                                                .gcpDetail(
                                                    new CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail()
                                                        .filePath("data/reference_tables/users.csv")
                                                        .gcpBucketName("example-data-bucket")
                                                        .gcpProjectId("example-gcp-project-12345")
                                                        .gcpServiceAccountEmail(
                                                            "example-service@example-gcp-project-12345.iam.gserviceaccount.com")))
                                        .syncEnabled(false)))
                            .schema(
                                new CreateTableRequestDataAttributesSchema()
                                    .fields(
                                        Collections.singletonList(
                                            new CreateTableRequestDataAttributesSchemaFieldsItems()
                                                .name("field_1")
                                                .type(ReferenceTableSchemaFieldType.STRING)))
                                    .primaryKeys(Collections.singletonList("field_1")))
                            .source(ReferenceTableCreateSourceType.LOCAL_FILE)
                            .tableName("table_1")
                            .tags(Arrays.asList("tag_1", "tag_2")))
                    .type(CreateTableRequestDataType.REFERENCE_TABLE));

    try {
      TableResultV2 result = apiInstance.createReferenceTable(body);
      System.out.println(result);
    } catch (ApiException e) {
      System.err.println("Exception when calling ReferenceTablesApi#createReferenceTable");
      System.err.println("Status code: " + e.getCode());
      System.err.println("Reason: " + e.getResponseBody());
      System.err.println("Response headers: " + e.getResponseHeaders());
      e.printStackTrace();
    }
  }
}

Instructions

First install the library and its dependencies and then save the example to Example.java and run following commands:

DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"

Reference Table 생성

"""
Create reference table returns "Created" response
"""

from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.reference_tables_api import ReferenceTablesApi
from datadog_api_client.v2.model.create_table_request import CreateTableRequest
from datadog_api_client.v2.model.create_table_request_data import CreateTableRequestData
from datadog_api_client.v2.model.create_table_request_data_attributes import CreateTableRequestDataAttributes
from datadog_api_client.v2.model.create_table_request_data_attributes_file_metadata_cloud_storage import (
    CreateTableRequestDataAttributesFileMetadataCloudStorage,
)
from datadog_api_client.v2.model.create_table_request_data_attributes_file_metadata_one_of_access_details import (
    CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails,
)
from datadog_api_client.v2.model.create_table_request_data_attributes_file_metadata_one_of_access_details_aws_detail import (
    CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail,
)
from datadog_api_client.v2.model.create_table_request_data_attributes_file_metadata_one_of_access_details_azure_detail import (
    CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail,
)
from datadog_api_client.v2.model.create_table_request_data_attributes_file_metadata_one_of_access_details_gcp_detail import (
    CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail,
)
from datadog_api_client.v2.model.create_table_request_data_attributes_schema import (
    CreateTableRequestDataAttributesSchema,
)
from datadog_api_client.v2.model.create_table_request_data_attributes_schema_fields_items import (
    CreateTableRequestDataAttributesSchemaFieldsItems,
)
from datadog_api_client.v2.model.create_table_request_data_type import CreateTableRequestDataType
from datadog_api_client.v2.model.reference_table_create_source_type import ReferenceTableCreateSourceType
from datadog_api_client.v2.model.reference_table_schema_field_type import ReferenceTableSchemaFieldType

body = CreateTableRequest(
    data=CreateTableRequestData(
        attributes=CreateTableRequestDataAttributes(
            file_metadata=CreateTableRequestDataAttributesFileMetadataCloudStorage(
                access_details=CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails(
                    aws_detail=CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail(
                        aws_account_id="123456789000",
                        aws_bucket_name="example-data-bucket",
                        file_path="reference-tables/users.csv",
                    ),
                    azure_detail=CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail(
                        azure_client_id="aaaaaaaa-1111-2222-3333-bbbbbbbbbbbb",
                        azure_container_name="reference-data",
                        azure_storage_account_name="examplestorageaccount",
                        azure_tenant_id="cccccccc-4444-5555-6666-dddddddddddd",
                        file_path="tables/users.csv",
                    ),
                    gcp_detail=CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail(
                        file_path="data/reference_tables/users.csv",
                        gcp_bucket_name="example-data-bucket",
                        gcp_project_id="example-gcp-project-12345",
                        gcp_service_account_email="example-service@example-gcp-project-12345.iam.gserviceaccount.com",
                    ),
                ),
                sync_enabled=False,
            ),
            schema=CreateTableRequestDataAttributesSchema(
                fields=[
                    CreateTableRequestDataAttributesSchemaFieldsItems(
                        name="field_1",
                        type=ReferenceTableSchemaFieldType.STRING,
                    ),
                ],
                primary_keys=[
                    "field_1",
                ],
            ),
            source=ReferenceTableCreateSourceType.LOCAL_FILE,
            table_name="table_1",
            tags=[
                "tag_1",
                "tag_2",
            ],
        ),
        type=CreateTableRequestDataType.REFERENCE_TABLE,
    ),
)

configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
with ApiClient(configuration) as api_client:
    api_instance = ReferenceTablesApi(api_client)
    response = api_instance.create_reference_table(body=body)

    print(response)

Instructions

First install the library and its dependencies and then save the example to example.py and run following commands:

DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"

Reference Table 생성

# Create reference table returns "Created" response

require "datadog_api_client"
DatadogAPIClient.configure do |config|
  config.access_token = ENV["DD_BEARER_TOKEN"]
end
api_instance = DatadogAPIClient::V2::ReferenceTablesAPI.new

body = DatadogAPIClient::V2::CreateTableRequest.new({
  data: DatadogAPIClient::V2::CreateTableRequestData.new({
    attributes: DatadogAPIClient::V2::CreateTableRequestDataAttributes.new({
      file_metadata: DatadogAPIClient::V2::CreateTableRequestDataAttributesFileMetadataCloudStorage.new({
        access_details: DatadogAPIClient::V2::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails.new({
          aws_detail: DatadogAPIClient::V2::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail.new({
            aws_account_id: "123456789000",
            aws_bucket_name: "example-data-bucket",
            file_path: "reference-tables/users.csv",
          }),
          azure_detail: DatadogAPIClient::V2::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail.new({
            azure_client_id: "aaaaaaaa-1111-2222-3333-bbbbbbbbbbbb",
            azure_container_name: "reference-data",
            azure_storage_account_name: "examplestorageaccount",
            azure_tenant_id: "cccccccc-4444-5555-6666-dddddddddddd",
            file_path: "tables/users.csv",
          }),
          gcp_detail: DatadogAPIClient::V2::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail.new({
            file_path: "data/reference_tables/users.csv",
            gcp_bucket_name: "example-data-bucket",
            gcp_project_id: "example-gcp-project-12345",
            gcp_service_account_email: "example-service@example-gcp-project-12345.iam.gserviceaccount.com",
          }),
        }),
        sync_enabled: false,
      }),
      schema: DatadogAPIClient::V2::CreateTableRequestDataAttributesSchema.new({
        fields: [
          DatadogAPIClient::V2::CreateTableRequestDataAttributesSchemaFieldsItems.new({
            name: "field_1",
            type: DatadogAPIClient::V2::ReferenceTableSchemaFieldType::STRING,
          }),
        ],
        primary_keys: [
          "field_1",
        ],
      }),
      source: DatadogAPIClient::V2::ReferenceTableCreateSourceType::LOCAL_FILE,
      table_name: "table_1",
      tags: [
        "tag_1",
        "tag_2",
      ],
    }),
    type: DatadogAPIClient::V2::CreateTableRequestDataType::REFERENCE_TABLE,
  }),
})
p api_instance.create_reference_table(body)

Instructions

First install the library and its dependencies and then save the example to example.rb and run following commands:

DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"

Reference Table 생성

// Create reference table returns "Created" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_reference_tables::ReferenceTablesAPI;
use datadog_api_client::datadogV2::model::CreateTableRequest;
use datadog_api_client::datadogV2::model::CreateTableRequestData;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributes;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesFileMetadata;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesFileMetadataCloudStorage;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesSchema;
use datadog_api_client::datadogV2::model::CreateTableRequestDataAttributesSchemaFieldsItems;
use datadog_api_client::datadogV2::model::CreateTableRequestDataType;
use datadog_api_client::datadogV2::model::ReferenceTableCreateSourceType;
use datadog_api_client::datadogV2::model::ReferenceTableSchemaFieldType;

#[tokio::main]
async fn main() {
    let body =
        CreateTableRequest
        ::new().data(
            CreateTableRequestData::new(
                CreateTableRequestDataType::REFERENCE_TABLE,
            ).attributes(
                CreateTableRequestDataAttributes::new(
                    CreateTableRequestDataAttributesSchema::new(
                        vec![
                            CreateTableRequestDataAttributesSchemaFieldsItems::new(
                                "field_1".to_string(),
                                ReferenceTableSchemaFieldType::STRING,
                            )
                        ],
                        vec!["field_1".to_string()],
                    ),
                    ReferenceTableCreateSourceType::LOCAL_FILE,
                    "table_1".to_string(),
                )
                    .file_metadata(
                        CreateTableRequestDataAttributesFileMetadata
                        ::CreateTableRequestDataAttributesFileMetadataCloudStorage(
                            Box::new(
                                CreateTableRequestDataAttributesFileMetadataCloudStorage::new(
                                    CreateTableRequestDataAttributesFileMetadataOneOfAccessDetails::new()
                                        .aws_detail(
                                            CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAwsDetail
                                            ::new(
                                                "123456789000".to_string(),
                                                "example-data-bucket".to_string(),
                                                "reference-tables/users.csv".to_string(),
                                            ),
                                        )
                                        .azure_detail(
                                            CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsAzureDetail
                                            ::new(
                                                "aaaaaaaa-1111-2222-3333-bbbbbbbbbbbb".to_string(),
                                                "reference-data".to_string(),
                                                "examplestorageaccount".to_string(),
                                                "cccccccc-4444-5555-6666-dddddddddddd".to_string(),
                                                "tables/users.csv".to_string(),
                                            ),
                                        )
                                        .gcp_detail(
                                            CreateTableRequestDataAttributesFileMetadataOneOfAccessDetailsGcpDetail
                                            ::new(
                                                "data/reference_tables/users.csv".to_string(),
                                                "example-data-bucket".to_string(),
                                                "example-gcp-project-12345".to_string(),
                                                "example-service@example-gcp-project-12345.iam.gserviceaccount.com".to_string(),
                                            ),
                                        ),
                                    false,
                                ),
                            ),
                        ),
                    )
                    .tags(vec!["tag_1".to_string(), "tag_2".to_string()]),
            ),
        );
    let configuration = datadog::Configuration::new();
    let api = ReferenceTablesAPI::with_config(configuration);
    let resp = api.create_reference_table(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

Instructions

First install the library and its dependencies and then save the example to src/main.rs and run following commands:

DD_SITE="datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="us3.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="us5.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="datadoghq.eu" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="ap1.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="ap2.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="uk1.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

DD_SITE="us2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo run

Reference Table 생성

/**
 * Create reference table returns "Created" response
 */

import { client, v2 } from "@datadog/datadog-api-client";

const configuration = client.createConfiguration({
  authMethods: {
    AuthZ: {
      accessToken: process.env.DD_BEARER_TOKEN as string,
    },
  },
});
const apiInstance = new v2.ReferenceTablesApi(configuration);

const params: v2.ReferenceTablesApiCreateReferenceTableRequest = {
  body: {
    data: {
      attributes: {
        fileMetadata: {
          accessDetails: {
            awsDetail: {
              awsAccountId: "123456789000",
              awsBucketName: "example-data-bucket",
              filePath: "reference-tables/users.csv",
            },
            azureDetail: {
              azureClientId: "aaaaaaaa-1111-2222-3333-bbbbbbbbbbbb",
              azureContainerName: "reference-data",
              azureStorageAccountName: "examplestorageaccount",
              azureTenantId: "cccccccc-4444-5555-6666-dddddddddddd",
              filePath: "tables/users.csv",
            },
            gcpDetail: {
              filePath: "data/reference_tables/users.csv",
              gcpBucketName: "example-data-bucket",
              gcpProjectId: "example-gcp-project-12345",
              gcpServiceAccountEmail:
                "example-service@example-gcp-project-12345.iam.gserviceaccount.com",
            },
          },
          syncEnabled: false,
        },
        schema: {
          fields: [
            {
              name: "field_1",
              type: "STRING",
            },
          ],
          primaryKeys: ["field_1"],
        },
        source: "LOCAL_FILE",
        tableName: "table_1",
        tags: ["tag_1", "tag_2"],
      },
      type: "reference_table",
    },
  },
};

apiInstance
  .createReferenceTable(params)
  .then((data: v2.TableResultV2) => {
    console.log(
      "API called successfully. Returned data: " + JSON.stringify(data)
    );
  })
  .catch((error: any) => console.error(error));

Instructions

First install the library and its dependencies and then save the example to example.ts and run following commands:

DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"

DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"