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Platform: macOS 12.4, MacBook Pro 2.3GHz Quad-Core i5, 16GB RAM I'm trying to read an OSLog concurrently because it is big and I don't need any of the data in order. Because the return from .getEntries is a sequence, the most efficient approach seems to be to iterate through. Iteration through the sequence from start to finish can take a long time so I thought I can split it up into days and concurrently process each day. I'm using a task group to do this and it works as long as the number of tasks is less than 8. When it works, I do get the result faster but not a lot faster. I guess there is a lot of overhead but actually it seems to be that my log file is dominated by the processing on 1 of the days. Ideally I want more concurrent tasks to break up the day into smaller blocks. But as soon as I try to create 8 or more tasks, I get a lockup with the following error posted to the console. enable_updates_common timed out waiting for updates to reenable Here are my tests. First - a pure iterative approach. No tasks. completion of this routine on my i5 quad core takes 229s    func scanLogarchiveIterative(url: URL) async {     do {       let timer = Date()               let logStore = try OSLogStore(url: url)       let last5days = logStore.position(timeIntervalSinceEnd: -3600*24*5)       let filteredEntries = try logStore.getEntries(at: last5days)               var processedEntries: [String] = []               for entry in filteredEntries {         processedEntries.append(entry.composedMessage)       }               print("Completed iterative scan in: ", timer.timeIntervalSinceNow)     } catch {             }   } Next is a concurrent approach using a TaskGroup which creates 5 child tasks. Completion takes 181s. Faster but the last day dominates so not a huge benefit as the most time is taken by a single task processing the last day.   func scanLogarchiveConcurrent(url: URL) async {     do {       let timer = Date()               var processedEntries: [String] = []               try await withThrowingTaskGroup(of: [String].self) { group in         let timestep = 3600*24         for logSectionStartPosition in stride(from: 0, to: -3600*24*5, by: -1*timestep) {           group.addTask {             let logStore = try OSLogStore(url: url)             let filteredEntries = try logStore.getEntries(at: logStore.position(timeIntervalSinceEnd: TimeInterval(logSectionStartPosition)))             var processedEntriesConcurrent: [String] = []             let endDate = logStore.position(timeIntervalSinceEnd: TimeInterval(logSectionStartPosition + timestep)).value(forKey: "date") as? Date             for entry in filteredEntries {               if entry.date > (endDate ?? Date()) {                 break               }               processedEntriesConcurrent.append(entry.composedMessage)             }             return processedEntriesConcurrent           }         }                   for try await processedEntriesConcurrent in group {           print("received task completion")           processedEntries.append(contentsOf: processedEntriesConcurrent)         }       }               print("Completed concurrent scan in: ", timer.timeIntervalSinceNow)             } catch {             }   } If I split this further to concurrently process half days, then the app locks up. The console periodically prints enable_updates_common timed out waiting for updates to reenable If I pause the debugger, it seems like there is a wait on a semaphore which must be internal to the concurrent framework?    func scanLogarchiveConcurrentManyTasks(url: URL) async {     do {       let timer = Date()               var processedEntries: [String] = []               try await withThrowingTaskGroup(of: [String].self) { group in         let timestep = 3600*12         for logSectionStartPosition in stride(from: 0, to: -3600*24*5, by: -1*timestep) {           group.addTask {             let logStore = try OSLogStore(url: url)             let filteredEntries = try logStore.getEntries(at: logStore.position(timeIntervalSinceEnd: TimeInterval(logSectionStartPosition)))             var processedEntriesConcurrent: [String] = []             let endDate = logStore.position(timeIntervalSinceEnd: TimeInterval(logSectionStartPosition + timestep)).value(forKey: "date") as? Date             for entry in filteredEntries {               if entry.date > (endDate ?? Date()) {                 break               }               processedEntriesConcurrent.append(entry.composedMessage)             }             return processedEntriesConcurrent           }         }                   for try await processedEntriesConcurrent in group {           print("received task completion")           processedEntries.append(contentsOf: processedEntriesConcurrent)         }       }               print("Completed concurrent scan in: ", timer.timeIntervalSinceNow)             } catch {             }   } I read that it may be possible to get more insight into concurrency issue by setting the following environment: LIBDISPATCH_COOPERATIVE_POOL_STRICT 1 This stops the lockup but it is because each task is run sequentially so there is no benefit from concurrency anymore. I cannot see where to go next apart from accept the linear processing time. It also feels like doing any concurrency (even if under 8 tasks) is risky as there is no documentation to suggest that is a limit. Could it be that concurrently processing the sequence from OSLog .getEntries is not suitable for concurrent access and shouldn't be done? Again, I don't see any documentation to suggest this is the case. Finally, the processing of each entry is so light that there is little benefit to offloading just the processing to other tasks. The time taken seems to be purely dominated by iterating the sequence. In reality I do use a predicate in .getEntries which helps a bit but its not enough and concurrency would still be valuable if I could process 1 hour blocks concurrently.