GCP Uploader
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Go to Apify Store
Pricing
Pay per usage
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5.0
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Cir◎cle
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5
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4 days ago
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GCP Uploader
Pricing
Pay per usage
Pricing
Pay per usage
Rating
5.0
(1)
Developer
Cir◎cle
Maintained by CommunityActor stats
7
Bookmarked
52
Total users
5
Monthly active users
4 days ago
Last modified
Categories
Share
You can access the GCP Uploader programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.
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If not provided, files will be uploaded to the root of the bucket. Example: 'my-folder/my-subfolder'" }, "datasetId": { "title": "Dataset ID", "type": "string", "description": "The ID of the dataset to be uploaded to the GCP bucket." }, "datasetFormat": { "title": "Dataset format", "enum": [ "json", "jsonl", "xml", "html", "csv", "xlsx", "rss" ], "type": "string", "description": "The format of the dataset to be uploaded. It's the same as the available dataset export formats." }, "fileName": { "title": "File name", "type": "string", "description": "File name to be uploaded to the bucket, otherwise the dataset ID will be used." }, "deleteDatasetAfterUpload": { "title": "Delete dataset after upload", "type": "boolean", "description": "If checked, the source dataset will be deleted from Apify once it has been successfully uploaded to the bucket. Nothing is deleted during a dry run.", "default": false }, "kvStoreId": { "title": "Key-value store ID", "type": "string", "description": "The ID of the key-value store to be uploaded to the GCP bucket." }, "kvStoreRegex": { "title": "Key-value store record regex", "type": "string", "description": "Only records matching this regex will be uploaded to the key-value store." }, "transformFileNameFunction": { "title": "Transform file name function", "type": "string", "description": "Function that creates the name of the uploaded file from the record key. It receives the record key as an argument and must return the new file name." }, "deleteRecordAfterUpload": { "title": "Delete record after upload", "type": "boolean", "description": "If checked, each key-value store record will be deleted from Apify once it has been successfully uploaded to the bucket. Nothing is deleted during a dry run.", "default": false }, "compression": { "title": "Compression", "enum": [ "none", "gzip", "zip" ], "type": "string", "description": "The compression method to be applied to the uploaded files." }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of files uploaded in parallel. Lower this if you hit rate limits.", "default": 23 }, "uploadDelayMs": { "title": "Delay before each upload (ms)", "minimum": 0, "type": "integer", "description": "Wait this many milliseconds before starting each file upload. Useful to avoid rate limiting when uploading many small files.", "default": 0 }, "uploadMetadata": { "title": "Upload metadata", "type": "boolean", "description": "If checked, metadata (currently only `contentType`) will be uploaded to the bucket along with the file.", "default": true }, "storeEntriesToDataset": { "title": "Store entries to dataset", "type": "boolean", "description": "If checked, uploaded file entries will be stored to the Apify dataset.", "default": true }, "dryRun": { "title": "Dry run", "type": "boolean", "description": "Test the GCP uploader without actually uploading anything", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 }, "PROXY_UNBLOCKER_UNITS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 }, "PROXY_UNBLOCKER_UNITS": { "type": "integer", "example": 0 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for GCP Uploader from the options below:
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