gcp_cloud_storage

Sends message parts as objects to a Google Cloud Storage bucket. Each object is uploaded with the path specified with the path field.

Introduced in version 3.43.0.

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: ""
    max_in_flight: 64
    batching:
      count: 0
      byte_size: 0
      period: ""
      check: ""

# Advanced 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: ""
    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 xref:guides:cloud/gcp.adoc[].

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" Requires version 3.53.0 or newer

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`

An optional field to set Google Service Account Credentials json.

This field supports interpolation functions.

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