Docs Cloud Redpanda Connect Components Outputs gcp_cloud_storage gcp_cloud_storage Type: OutputCacheInput Available in: Cloud, Self-Managed Sends message parts as objects to a Google Cloud Storage bucket. Each object is uploaded with the path specified with the path field. Common Advanced # Common config fields, showing default values output: label: "" gcp_cloud_storage: bucket: "" # No default (required) path: ${!count("files")}-${!timestamp_unix_nano()}.txt content_type: application/octet-stream collision_mode: overwrite timeout: 3s credentials_json: "" # No default (optional) max_in_flight: 64 batching: count: 0 byte_size: 0 period: "" check: "" # All config fields, showing default values output: label: "" gcp_cloud_storage: bucket: "" # No default (required) path: ${!count("files")}-${!timestamp_unix_nano()}.txt content_type: application/octet-stream content_encoding: "" collision_mode: overwrite chunk_size: 16777216 timeout: 3s credentials_json: "" # No default (optional) max_in_flight: 64 batching: count: 0 byte_size: 0 period: "" check: "" processors: [] # No default (optional) In order to have a different path for each object you should use function interpolations described in Bloblang queries, which are calculated per message of a batch. Metadata Metadata fields on messages will be sent as headers, in order to mutate these values (or remove them) check out the metadata docs. Credentials By default Redpanda Connect will use a shared credentials file when connecting to GCP services. You can find out more in Google Cloud Platform. Batching It’s common to want to upload messages to Google Cloud Storage as batched archives, the easiest way to do this is to batch your messages at the output level and join the batch of messages with an archive and/or compress processor. For example, if we wished to upload messages as a .tar.gz archive of documents we could achieve that with the following config: output: gcp_cloud_storage: bucket: TODO path: ${!count("files")}-${!timestamp_unix_nano()}.tar.gz batching: count: 100 period: 10s processors: - archive: format: tar - compress: algorithm: gzip Alternatively, if we wished to upload JSON documents as a single large document containing an array of objects we can do that with: output: gcp_cloud_storage: bucket: TODO path: ${!count("files")}-${!timestamp_unix_nano()}.json batching: count: 100 processors: - archive: format: json_array Performance This output benefits from sending multiple messages in flight in parallel for improved performance. You can tune the max number of in flight messages (or message batches) with the field max_in_flight. This output benefits from sending messages as a batch for improved performance. Batches can be formed at both the input and output level. You can find out more in this doc. Fields bucket The bucket to upload messages to. Type: string path The path of each message to upload. This field supports interpolation functions. Type: string Default: "${!count(\"files\")}-${!timestamp_unix_nano()}.txt" # Examples path: ${!count("files")}-${!timestamp_unix_nano()}.txt path: ${!meta("kafka_key")}.json path: ${!json("doc.namespace")}/${!json("doc.id")}.json content_type The content type to set for each object. This field supports interpolation functions. Type: string Default: "application/octet-stream" content_encoding An optional content encoding to set for each object. This field supports interpolation functions. Type: string Default: "" collision_mode Determines how file path collisions should be dealt with. Type: string Default: "overwrite" Option Summary append Append the message bytes to the original file. error-if-exists Return an error, this is the equivalent of a nack. ignore Do not modify the original file, the new data will be dropped. overwrite Replace the existing file with the new one. chunk_size An optional chunk size which controls the maximum number of bytes of the object that the Writer will attempt to send to the server in a single request. If ChunkSize is set to zero, chunking will be disabled. Type: int Default: 16777216 timeout The maximum period to wait on an upload before abandoning it and reattempting. Type: string Default: "3s" # Examples timeout: 1s timeout: 500ms credentials_json Optional field to set Google Service Account Credentials JSON. This field contains sensitive information. Review your cluster security before adding it to your configuration. Type: string Default: "" max_in_flight The maximum number of message batches to have in flight at a given time. Increase this to improve throughput. Type: int Default: 64 batching Allows you to configure a batching policy. Type: object # Examples batching: byte_size: 5000 count: 0 period: 1s batching: count: 10 period: 1s batching: check: this.contains("END BATCH") count: 0 period: 1m batching.count A number of messages at which the batch should be flushed. If 0 disables count based batching. Type: int Default: 0 batching.byte_size An amount of bytes at which the batch should be flushed. If 0 disables size based batching. Type: int Default: 0 batching.period A period in which an incomplete batch should be flushed regardless of its size. Type: string Default: "" # Examples period: 1s period: 1m period: 500ms batching.check A Bloblang query that should return a boolean value indicating whether a message should end a batch. Type: string Default: "" # Examples check: this.type == "end_of_transaction" batching.processors A list of processors to apply to a batch as it is flushed. This allows you to aggregate and archive the batch however you see fit. Please note that all resulting messages are flushed as a single batch, therefore splitting the batch into smaller batches using these processors is a no-op. Type: array # Examples processors: - archive: format: concatenate processors: - archive: format: lines processors: - archive: format: json_array Back to top × Simple online edits For simple changes, such as fixing a typo, you can edit the content directly on GitHub. Edit on GitHub Or, open an issue to let us know about something that you want us to change. Open an issue Contribution guide For extensive content updates, or if you prefer to work locally, read our contribution guide . Was this helpful? thumb_up thumb_down group Ask in the community mail Share your feedback group_add Make a contribution gcp_bigquery gcp_pubsub