aws

# WARNING: DO NOT EDIT, AUTO-GENERATED CODE!
# See https://github.com/aws-beam/aws-codegen for more details.

defmodule AWS.Datapipeline do
  @moduledoc """
  AWS Data Pipeline configures and manages a data-driven workflow called a
  pipeline.

  AWS Data Pipeline handles the details of scheduling and ensuring that data
  dependencies are met so that your application can focus on processing the data.

  AWS Data Pipeline provides a JAR implementation of a task runner called AWS Data
  Pipeline Task Runner. AWS Data Pipeline Task Runner provides logic for common
  data management scenarios, such as performing database queries and running data
  analysis using Amazon Elastic MapReduce (Amazon EMR). You can use AWS Data
  Pipeline Task Runner as your task runner, or you can write your own task runner
  to provide custom data management.

  AWS Data Pipeline implements two main sets of functionality. Use the first set
  to create a pipeline and define data sources, schedules, dependencies, and the
  transforms to be performed on the data. Use the second set in your task runner
  application to receive the next task ready for processing. The logic for
  performing the task, such as querying the data, running data analysis, or
  converting the data from one format to another, is contained within the task
  runner. The task runner performs the task assigned to it by the web service,
  reporting progress to the web service as it does so. When the task is done, the
  task runner reports the final success or failure of the task to the web service.
  """

  alias AWS.Client
  alias AWS.Request

  def metadata do
    %AWS.ServiceMetadata{
      abbreviation: nil,
      api_version: "2012-10-29",
      content_type: "application/x-amz-json-1.1",
      credential_scope: nil,
      endpoint_prefix: "datapipeline",
      global?: false,
      protocol: "json",
      service_id: nil,
      signature_version: "v4",
      signing_name: "datapipeline",
      target_prefix: "DataPipeline"
    }
  end

  @doc """
  Validates the specified pipeline and starts processing pipeline tasks.

  If the pipeline does not pass validation, activation fails.

  If you need to pause the pipeline to investigate an issue with a component, such
  as a data source or script, call `DeactivatePipeline`.

  To activate a finished pipeline, modify the end date for the pipeline and then
  activate it.
  """
  def activate_pipeline(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "ActivatePipeline", input, options)
  end

  @doc """
  Adds or modifies tags for the specified pipeline.
  """
  def add_tags(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "AddTags", input, options)
  end

  @doc """
  Creates a new, empty pipeline.

  Use `PutPipelineDefinition` to populate the pipeline.
  """
  def create_pipeline(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "CreatePipeline", input, options)
  end

  @doc """
  Deactivates the specified running pipeline.

  The pipeline is set to the `DEACTIVATING` state until the deactivation process
  completes.

  To resume a deactivated pipeline, use `ActivatePipeline`. By default, the
  pipeline resumes from the last completed execution. Optionally, you can specify
  the date and time to resume the pipeline.
  """
  def deactivate_pipeline(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "DeactivatePipeline", input, options)
  end

  @doc """
  Deletes a pipeline, its pipeline definition, and its run history.

  AWS Data Pipeline attempts to cancel instances associated with the pipeline that
  are currently being processed by task runners.

  Deleting a pipeline cannot be undone. You cannot query or restore a deleted
  pipeline. To temporarily pause a pipeline instead of deleting it, call
  `SetStatus` with the status set to `PAUSE` on individual components. Components
  that are paused by `SetStatus` can be resumed.
  """
  def delete_pipeline(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "DeletePipeline", input, options)
  end

  @doc """
  Gets the object definitions for a set of objects associated with the pipeline.

  Object definitions are composed of a set of fields that define the properties of
  the object.
  """
  def describe_objects(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "DescribeObjects", input, options)
  end

  @doc """
  Retrieves metadata about one or more pipelines.

  The information retrieved includes the name of the pipeline, the pipeline
  identifier, its current state, and the user account that owns the pipeline.
  Using account credentials, you can retrieve metadata about pipelines that you or
  your IAM users have created. If you are using an IAM user account, you can
  retrieve metadata about only those pipelines for which you have read
  permissions.

  To retrieve the full pipeline definition instead of metadata about the pipeline,
  call `GetPipelineDefinition`.
  """
  def describe_pipelines(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "DescribePipelines", input, options)
  end

  @doc """
  Task runners call `EvaluateExpression` to evaluate a string in the context of
  the specified object.

  For example, a task runner can evaluate SQL queries stored in Amazon S3.
  """
  def evaluate_expression(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "EvaluateExpression", input, options)
  end

  @doc """
  Gets the definition of the specified pipeline.

  You can call `GetPipelineDefinition` to retrieve the pipeline definition that
  you provided using `PutPipelineDefinition`.
  """
  def get_pipeline_definition(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "GetPipelineDefinition", input, options)
  end

  @doc """
  Lists the pipeline identifiers for all active pipelines that you have permission
  to access.
  """
  def list_pipelines(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "ListPipelines", input, options)
  end

  @doc """
  Task runners call `PollForTask` to receive a task to perform from AWS Data
  Pipeline.

  The task runner specifies which tasks it can perform by setting a value for the
  `workerGroup` parameter. The task returned can come from any of the pipelines
  that match the `workerGroup` value passed in by the task runner and that was
  launched using the IAM user credentials specified by the task runner.

  If tasks are ready in the work queue, `PollForTask` returns a response
  immediately. If no tasks are available in the queue, `PollForTask` uses
  long-polling and holds on to a poll connection for up to a 90 seconds, during
  which time the first newly scheduled task is handed to the task runner. To
  accomodate this, set the socket timeout in your task runner to 90 seconds. The
  task runner should not call `PollForTask` again on the same `workerGroup` until
  it receives a response, and this can take up to 90 seconds.
  """
  def poll_for_task(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "PollForTask", input, options)
  end

  @doc """
  Adds tasks, schedules, and preconditions to the specified pipeline.

  You can use `PutPipelineDefinition` to populate a new pipeline.

  `PutPipelineDefinition` also validates the configuration as it adds it to the
  pipeline. Changes to the pipeline are saved unless one of the following three
  validation errors exists in the pipeline.

    1. An object is missing a name or identifier field.
    2. A string or reference field is empty.
    3. The number of objects in the pipeline exceeds the maximum allowed
  objects.
    4. The pipeline is in a FINISHED state.

  Pipeline object definitions are passed to the `PutPipelineDefinition` action and
  returned by the `GetPipelineDefinition` action.
  """
  def put_pipeline_definition(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "PutPipelineDefinition", input, options)
  end

  @doc """
  Queries the specified pipeline for the names of objects that match the specified
  set of conditions.
  """
  def query_objects(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "QueryObjects", input, options)
  end

  @doc """
  Removes existing tags from the specified pipeline.
  """
  def remove_tags(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "RemoveTags", input, options)
  end

  @doc """
  Task runners call `ReportTaskProgress` when assigned a task to acknowledge that
  it has the task.

  If the web service does not receive this acknowledgement within 2 minutes, it
  assigns the task in a subsequent `PollForTask` call. After this initial
  acknowledgement, the task runner only needs to report progress every 15 minutes
  to maintain its ownership of the task. You can change this reporting time from
  15 minutes by specifying a `reportProgressTimeout` field in your pipeline.

  If a task runner does not report its status after 5 minutes, AWS Data Pipeline
  assumes that the task runner is unable to process the task and reassigns the
  task in a subsequent response to `PollForTask`. Task runners should call
  `ReportTaskProgress` every 60 seconds.
  """
  def report_task_progress(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "ReportTaskProgress", input, options)
  end

  @doc """
  Task runners call `ReportTaskRunnerHeartbeat` every 15 minutes to indicate that
  they are operational.

  If the AWS Data Pipeline Task Runner is launched on a resource managed by AWS
  Data Pipeline, the web service can use this call to detect when the task runner
  application has failed and restart a new instance.
  """
  def report_task_runner_heartbeat(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "ReportTaskRunnerHeartbeat", input, options)
  end

  @doc """
  Requests that the status of the specified physical or logical pipeline objects
  be updated in the specified pipeline.

  This update might not occur immediately, but is eventually consistent. The
  status that can be set depends on the type of object (for example, DataNode or
  Activity). You cannot perform this operation on `FINISHED` pipelines and
  attempting to do so returns `InvalidRequestException`.
  """
  def set_status(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "SetStatus", input, options)
  end

  @doc """
  Task runners call `SetTaskStatus` to notify AWS Data Pipeline that a task is
  completed and provide information about the final status.

  A task runner makes this call regardless of whether the task was sucessful. A
  task runner does not need to call `SetTaskStatus` for tasks that are canceled by
  the web service during a call to `ReportTaskProgress`.
  """
  def set_task_status(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "SetTaskStatus", input, options)
  end

  @doc """
  Validates the specified pipeline definition to ensure that it is well formed and
  can be run without error.
  """
  def validate_pipeline_definition(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "ValidatePipelineDefinition", input, options)
  end
end