byronellis commented on code in PR #1: URL: https://github.com/apache/beam-swift/pull/1#discussion_r1362982936
########## Sources/ApacheBeam/Runtime/Bundle/BundleProcessor.swift: ########## @@ -0,0 +1,138 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * License); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import Logging + +import Foundation + +struct BundleProcessor { + let log: Logging.Logger + + struct Step { + let transformId: String + let fn: SerializableFn + let inputs: [AnyPCollectionStream] + let outputs: [AnyPCollectionStream] + let payload: Data + } + + let steps: [Step] + + init(id: String, + descriptor: Org_Apache_Beam_Model_FnExecution_V1_ProcessBundleDescriptor, + collections: [String: AnyPCollection], + fns: [String: SerializableFn]) throws + { + log = Logging.Logger(label: "BundleProcessor(\(id) \(descriptor.id))") + + var temp: [Step] = [] + var coders = BundleCoderContainer(bundle: descriptor) + + var streams: [String: AnyPCollectionStream] = [:] + // First make streams for everything in this bundle (maybe I could use the pcollection array for this?) + for (_, transform) in descriptor.transforms { + for id in transform.inputs.values { + if streams[id] == nil { + streams[id] = collections[id]!.anyStream + } + } + for id in transform.outputs.values { + if streams[id] == nil { + streams[id] = collections[id]!.anyStream + } + } + } + + for (transformId, transform) in descriptor.transforms { + let urn = transform.spec.urn + // Map the input and output streams in the correct order + let inputs = transform.inputs.sorted().map { streams[$0.1]! } + let outputs = transform.outputs.sorted().map { streams[$0.1]! } + + if urn == "beam:runner:source:v1" { + let remotePort = try RemoteGrpcPort(serializedData: transform.spec.payload) + let coder = try Coder.of(name: remotePort.coderID, in: coders) + log.info("Source '\(transformId)','\(transform.uniqueName)' \(remotePort) \(coder)") + try temp.append(Step( + transformId: transform.uniqueName == "" ? transformId : transform.uniqueName, + fn: Source(client: .client(for: ApiServiceDescriptor(proto: remotePort.apiServiceDescriptor), worker: id), coder: coder), + inputs: inputs, + outputs: outputs, + payload: Data() + )) + } else if urn == "beam:runner:sink:v1" { + let remotePort = try RemoteGrpcPort(serializedData: transform.spec.payload) + let coder = try Coder.of(name: remotePort.coderID, in: coders) + log.info("Sink '\(transformId)','\(transform.uniqueName)' \(remotePort) \(coder)") + try temp.append(Step( + transformId: transform.uniqueName == "" ? transformId : transform.uniqueName, + fn: Sink(client: .client(for: ApiServiceDescriptor(proto: remotePort.apiServiceDescriptor), worker: id), coder: coder), + inputs: inputs, + outputs: outputs, + payload: Data() + )) + + } else if urn == "beam:transform:pardo:v1" { + let pardoPayload = try Org_Apache_Beam_Model_Pipeline_V1_ParDoPayload(serializedData: transform.spec.payload) + if let fn = fns[transform.uniqueName] { + temp.append(Step(transformId: transform.uniqueName, + fn: fn, + inputs: inputs, + outputs: outputs, + payload: pardoPayload.doFn.payload)) + } else { + log.warning("Unable to map \(transform.uniqueName) to a known SerializableFn. Will be skipped during processing.") + } + } else { + log.warning("Unable to map \(urn). Will be skipped during processing.") + } + } + steps = temp + } + + public func process(instruction: String, responder: AsyncStream<Org_Apache_Beam_Model_FnExecution_V1_InstructionResponse>.Continuation) async { + _ = await withThrowingTaskGroup(of: (String, String).self) { group in + log.info("Starting bundle processing for \(instruction)") + var count: Int = 0 + do { + for step in steps { Review Comment: Yes, the interface is "async throws" which allows for the propagation of exceptions and stack traces properly -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
