{"slug": "concurrent-resource-scheduler-v1-2-3-sharded-priority-heaps-under-concurrent", "title": "Concurrent Resource Scheduler v1.2.3: Sharded Priority Heaps Under Concurrent Load", "summary": "Concurrent Resource Scheduler (CRS) v1.2.3, a domain-agnostic Go library for managing reusable resources under high concurrency, has been released. The library partitions the resource pool into independently synchronized shards, each with its own priority heap and mutex, to reduce lock contention and avoid O(N) scans. This architecture aims to handle thousands of concurrent goroutines efficiently.", "body_md": "GitHub:[https://github.com/phero20/concurrent-resource-scheduler]\n\nGo documentation:[https://pkg.go.dev/github.com/phero20/concurrent-resource-scheduler]\n\nA resource scheduler sounds simple until many goroutines start competing for a small number of resources.\n\nAt that point, the problem stops being:\n\n\"How do I pick the next item?\"\n\nand becomes:\n\n\"How do I pick the right item while thousands of goroutines are acquiring, releasing, updating, and observing resources concurrently?\"\n\nThat is the problem I built **Concurrent Resource Scheduler (CRS)** to solve.\n\nCRS is a domain-agnostic Go library for managing reusable resources with:\n\nThe current release is **v1.2.3**.\n\nThis post is a release-focused technical deep dive into the architecture, the reasoning behind it, and the actual measurements from the current release.\n\nImagine a service with a pool of reusable resources.\n\nThose resources could be:\n\nNow imagine:\n\n```\n             10,000 concurrent requests\n                       │\n                       ▼\n              ┌─────────────────┐\n              │    Scheduler    │\n              └────────┬────────┘\n                       │\n          ┌────────────┼────────────┐\n          ▼            ▼            ▼\n       Resource A   Resource B   Resource C\n```\n\nThe scheduler needs to answer several questions at once:\n\nA simple slice and mutex can answer some of these questions.\n\nThe difficult part is doing all of them **concurrently and predictably**.\n\nThe first design most people naturally reach for is something like:\n\n```\ntype Scheduler struct {\n    mu        sync.Mutex\n    resources []*Resource\n}\n```\n\nThen acquisition becomes:\n\n```\nfunc (s *Scheduler) Acquire() (*Resource, error) {\n    s.mu.Lock()\n    defer s.mu.Unlock()\n\n    // Scan resources.\n    // Find the best available resource.\n    // Mark it acquired.\n    // Return it.\n\n    return resource, nil\n}\n```\n\nFor a small pool, this is completely reasonable.\n\nThe problem appears when concurrency and resource count grow.\n\nSuppose:\n\n```\nResources:           10,000\nConcurrent goroutines: 10,000\n```\n\nEvery operation now enters the same synchronization boundary:\n\n```\n                 GLOBAL MUTEX\n                      │\n       ┌──────────────┼──────────────┐\n       ▼              ▼              ▼\n   goroutine       goroutine      goroutine\n       │              │              │\n       └──────────────┼──────────────┘\n                      ▼\n                    WAIT\n```\n\nEven if the actual operation only concerns a small part of the resource pool, the lock protects everything.\n\nThere is another problem.\n\nIf resources are stored in a slice and the scheduler has to find the best candidate, acquisition can require a linear scan:\n\n```\nO(N)\n```\n\nNow the same global lock is protecting an operation whose amount of work grows with the number of resources.\n\nThat is the combination I wanted to avoid.\n\nThe core idea behind CRS is:\n\nPartition the active resource pool into independently synchronized shards.\n\nInstead of:\n\n```\n                    ONE LOCK\n                       │\n                 ONE BIG POOL\n```\n\nCRS uses:\n\n```\n                         Scheduler\n                             │\n              ┌──────────────┼──────────────┐\n              ▼              ▼              ▼\n           Shard 0        Shard 1        Shard N\n              │              │              │\n            Heap           Heap           Heap\n              │              │              │\n            Mutex          Mutex          Mutex\n```\n\nEach shard owns a priority heap.\n\nEach heap has its own synchronization boundary.\n\nThat gives us a much more useful concurrency model:\n\n```\ngoroutine A ──► shard 0 ──► heap 0\ngoroutine B ──► shard 1 ──► heap 1\ngoroutine C ──► shard 2 ──► heap 2\n```\n\nThe goal is not to claim that sharding magically eliminates contention.\n\nIt doesn't.\n\nThe goal is to **reduce the amount of unrelated work competing for the same lock**.\n\nThat distinction matters.\n\nSharding solves one problem:\n\nHow do we reduce contention?\n\nWe still need to solve another:\n\nHow do we efficiently maintain resource priority?\n\nA slice makes this straightforward but potentially expensive:\n\n```\nresources:\n[ A, B, C, D, E, F, G, ... ]\n\nFind minimum priority:\nscan everything\n```\n\nA priority heap gives us an ordered structure where the highest-priority candidate can be accessed from the heap root.\n\nConceptually:\n\n```\n             best resource\n                  │\n                  ▼\n               [ 10 ]\n              /      \\\n           [ 20 ]   [ 30 ]\n           /   \\\n        [40]   [50]\n```\n\nSo CRS combines the two ideas:\n\n```\n                 RESOURCE POOL\n                       │\n             ┌─────────┴─────────┐\n             ▼                   ▼\n          Sharding            Priority\n             │                   │\n             ▼                   ▼\n        Lower lock           Heap ordering\n         contention          / fast candidate\n```\n\nThis is the central design tradeoff of the project.\n\nOne thing I learned while building CRS is that the heap is actually only one part of the problem.\n\nThe scheduler has several independent concerns:\n\n```\n                         CRS\n                          │\n       ┌──────────────────┼──────────────────┐\n       │                  │                  │\n       ▼                  ▼                  ▼\n   Acquisition          State             Lookup\n    Strategy          Lifecycle             Map\n       │                  │                  │\n       ▼                  ▼                  ▼\n    Adaptive          Active/Inactive       O(1)\n    Weighted          Acquire/Release       lookup\n    Round Robin       Include/Exclude\n    Affinity\n```\n\nThen there are extensions:\n\n```\n                Event Dispatcher\n                       │\n             ┌─────────┴─────────┐\n             ▼                   ▼\n          Cooldown           Telemetry\n                                 │\n                                 ▼\n                             Prometheus\n```\n\nKeeping these responsibilities separated makes the implementation easier to reason about and lets the core remain domain-agnostic.\n\nPriority ordering tells us:\n\n\"Which resource should be considered first?\"\n\nBut APIs also need direct resource lookup.\n\nFor example:\n\n```\nresource, err := sched.Get(\"worker-42\")\n```\n\nWe don't want to search every heap for that.\n\nCRS therefore maintains a lookup structure alongside the active heap shards.\n\nConceptually:\n\n```\n                resource ID\n                     │\n                     ▼\n              ┌─────────────┐\n              │ Lookup Map  │\n              └──────┬──────┘\n                     │\n                     ▼\n                  node\n                     │\n             ┌───────┴───────┐\n             ▼               ▼\n          resource          shard\n```\n\nThis gives the scheduler a useful separation:\n\nThese are different access patterns, so they shouldn't be forced into the same data structure.\n\nAnother design decision was not to hard-code one definition of \"best resource.\"\n\nDifferent systems want different behavior.\n\nFor example:\n\nChoose resources based on observed scheduling state.\n\nSome resources should receive more traffic than others.\n\nDistribute acquisitions sequentially.\n\nPrefer a resource or shard associated with some identifier.\n\nThat means the scheduler's core doesn't need to know why an application considers one resource better than another.\n\nThe application provides the policy.\n\nThe scheduler provides the concurrency-safe machinery around that policy.\n\nThis distinction is especially important.\n\nA resource might support:\n\n```\nShared\n```\n\nwhere multiple callers can use it simultaneously.\n\nOr:\n\n```\nExclusive\n```\n\nwhere only one caller can own it at a time.\n\nThese are not just two API names.\n\nThey produce different synchronization behavior.\n\nFor example, under shared acquisition, the scheduler can inspect the best candidate without removing it from the heap.\n\nConceptually:\n\n```\nShared:\n\nheap\n  │\n  ▼\npeek candidate\n  │\n  ▼\nuse resource\n```\n\nWhereas exclusive acquisition may need to remove the candidate from the active scheduling position:\n\n```\nExclusive:\n\nheap\n  │\n  ▼\npop candidate\n  │\n  ▼\nexclusive owner\n```\n\nThis distinction is also reflected in the documentation and complexity model.\n\nResources don't simply exist or disappear.\n\nThey move through states.\n\nA simplified lifecycle looks like:\n\n```\n              ┌──────────────┐\n              │    ACTIVE    │\n              └──────┬───────┘\n                     │\n            acquire / exclude\n                     │\n                     ▼\n              ┌──────────────┐\n              │   INACTIVE   │\n              └──────┬───────┘\n                     │\n               include / release\n                     │\n                     ▼\n              ┌──────────────┐\n              │    ACTIVE    │\n              └──────────────┘\n```\n\nThere are also terminal operations such as removal and shutdown.\n\nThe important part is that the scheduler must maintain consistent state while these operations happen concurrently.\n\nThat's why lifecycle management is part of the scheduler design rather than an afterthought.\n\nCooldown is a good example of why the architecture is modular.\n\nSuppose a resource fails or needs temporary exclusion:\n\n```\nResource\n   │\n   ▼\nCooldown\n   │\n   ├── removed from active scheduling\n   │\n   └── restored after duration\n```\n\nThe core scheduler shouldn't need to understand every possible reason a resource temporarily leaves the pool.\n\nThe cooldown extension coordinates with the scheduler's lifecycle controller.\n\nThis was also one of the areas tightened in v1.2.3: the cooldown documentation and example now use the correct `LifecycleController`\n\nwrapper pattern instead of trying to initialize the cooldown manager before the scheduler exists.\n\nThat ordering matters because the scheduler is the object that ultimately owns the resource lifecycle.\n\nv1.2.3 is not a giant feature release.\n\nIt is a **correctness, consistency, and release-hardening release**.\n\nThe main fixes include:\n\nThe cooldown extension's initialization example was corrected to use the proper lifecycle-controller wrapper pattern.\n\nThis avoids the circular initialization problem of trying to construct a component that needs the scheduler before the scheduler has been created.\n\nThe cooldown example was adjusted so it does not immediately race an asynchronous exclusion event.\n\nThe example now demonstrates the intended lifecycle deterministically.\n\nThe scheduler benchmark file was moved to the black-box package boundary:\n\n```\npackage scheduler_test\n```\n\nbecause it only relies on exported APIs.\n\nThat makes the benchmark follow the same testing convention as the rest of the package.\n\nREADME and API documentation were updated to match the implementation, including error documentation and complexity behavior.\n\nThe optional Prometheus module is also aligned to:\n\n```\nv1.2.3\n```\n\nwhile remaining a separate nested Go module.\n\nArchitecture diagrams are useful.\n\nNumbers are better.\n\nI ran the current benchmark suite with:\n\n```\ngo test -run=\"^$\" -bench=\".\" -benchmem ./...\n```\n\nThe measurements below were collected locally on:\n\n```\nOS:   Windows\nArch: amd64\nCPU:  AMD Ryzen 5 6600H with Radeon Graphics\n```\n\nThese are **local measurements**, not universal performance guarantees.\n\nThe current release produced the following verified measurements:\n\n| Strategy | Select/GetShard Cost |\n|---|---|\n`ConsistentHashRing.GetShard` |\n7.2 ns |\n`WeightedStrategy.Select` |\n19.1 ns |\n`AdaptiveStrategy.Select` |\n27.0 ns |\n\nThe strategy selection costs are all in the low-nanosecond range on this machine. The scheduler benchmarks below separately report allocation counts for the larger operations.\n\nThe scheduler benchmarks are more representative of actual scheduler operations.\n\n| Operation | HeapCount=1 | HeapCount=8 | HeapCount=32 | Allocs/op |\n|---|---|---|---|---|\n`Add` |\n591.9 ns | 836.2 ns | 718.5 ns | 3 |\n`Update` |\n280.6 ns | 228.9 ns | 200.4 ns | 1 |\n`BatchAdd` (1,000 resources) |\n308.1 µs | 351.3 µs | 331.1 µs | ~1,100 |\n`Acquire` (Shared, Sequential) |\n12.36 ns | 11.52 ns | 11.23 ns | 0 |\n`Acquire` (Shared, Parallel) |\n63.14 ns | 16.80 ns | 17.99 ns | 0 |\n`Acquire` + `Release` (Exclusive) |\n241.2 ns | 249.2 ns | 208.8 ns | 0 |\n\nThere is also a parallel acquisition benchmark:\n\n| Benchmark | Heap count | ns/op | B/op | allocs/op |\n|---|---|---|---|---|\n| AcquireSharedParallel | 1 | 63.14 | 0 | 0 |\n| AcquireSharedParallel | 8 | 16.80 | 0 | 0 |\n| AcquireSharedParallel | 32 | 17.99 | 0 | 0 |\n\nThe parallel benchmark is particularly useful when thinking about the sharding architecture.\n\nOn this machine and workload, moving from a single heap to multiple heaps reduced the measured `AcquireSharedParallel`\n\ntime substantially:\n\n```\nHeapCount=1   63.14 ns/op\nHeapCount=8   16.80 ns/op\nHeapCount=32  17.99 ns/op\n```\n\nThat is **one workload on one machine**, not a universal scaling law.\n\nBut it is exactly the kind of behavior the architecture is intended to make measurable.\n\nIt would be easy to benchmark only:\n\n```\nHeapCount = 1\n```\n\nand stop there.\n\nThat wouldn't tell us much about the reason for sharding.\n\nInstead, the scheduler benchmarks compare:\n\n```\nHeapCount = 1\nHeapCount = 8\nHeapCount = 32\n```\n\nThis gives us a way to observe how the synchronization structure behaves as the number of shards changes.\n\nThe expected tradeoff is not:\n\n\"More shards are always faster.\"\n\nIt is:\n\n\"The right number of shards depends on workload, resource count, contention, and scheduling strategy.\"\n\nMore shards also mean more structures to manage.\n\nSo sharding is a tuning dimension, not a magic constant.\n\nThe benchmark suite also measures batch insertion:\n\n| Benchmark | Heap count | ns/op | B/op | allocs/op |\n|---|---|---|---|---|\n| BatchAdd | 1 | 308.1 µs | 283,715 | 1,042 |\n| BatchAdd | 8 | 351.3 µs | 283,530 | 1,095 |\n| BatchAdd | 32 | 331.1 µs | 282,314 | 1,223 |\n\nThis is important because it prevents cherry-picking only the fastest numbers.\n\n`AcquireShared`\n\nis extremely cheap in the measured workload.\n\n`BatchAdd`\n\nis much more expensive.\n\nThat's expected.\n\nAdding a large batch involves substantially more work than peeking at an already-populated scheduling structure.\n\nA useful benchmark suite should expose those differences instead of presenting one \"magic\" performance number.\n\nA concurrency library can produce beautiful microbenchmarks and still be broken.\n\nThat's why v1.2.3 was validated with multiple layers.\n\n```\ngo test ./...\n```\n\nResult:\n\n```\nPASS\n```\n\nAll tested packages completed successfully.\n\n```\ngo test -race ./...\n```\n\nResult:\n\n```\nPASS\n```\n\nNo data races were reported.\n\n```\ngo vet ./...\n```\n\nResult:\n\n```\nPASS\ngofmt -l .\n```\n\nResult:\n\n```\n(no output)\n```\n\nThe working tree is therefore clean according to `gofmt`\n\n.\n\nFor a concurrency-heavy library, this command is not optional validation theater:\n\n```\ngo test -race ./...\n```\n\nA normal test can pass while a race exists.\n\nThe race detector instruments memory accesses and can expose unsafe concurrent access that ordinary functional assertions don't catch.\n\nIt does not prove that a concurrent system is mathematically bug-free.\n\nBut it is one of the most important tools available for catching a class of real concurrency bugs.\n\nFor v1.2.3, the race-enabled test suite completed without reported races.\n\nMicrobenchmarks answer:\n\n\"How quickly does this small operation execute?\"\n\nA load test asks a different question:\n\n\"What happens when the whole scheduler is placed inside a realistic concurrent workload?\"\n\nFor CRS, the dedicated load-test harness models things such as:\n\nThe distinction is important.\n\nA benchmark like:\n\n```\n25 ns/op\n```\n\ndoes not mean a real request takes 25 ns.\n\nA real request also includes:\n\n```\nscheduler\n   +\napplication work\n   +\nnetwork\n   +\nbackend\n   +\nserialization\n   +\nother system costs\n```\n\nSo I treat microbenchmarks and load tests as different measurements.\n\nOne of the release validation workloads uses:\n\n```\n10,000 concurrent workers\n```\n\nThis is useful because it puts the scheduler under a very different kind of pressure from a small benchmark.\n\nThe important question isn't:\n\n\"Can Go start 10,000 goroutines?\"\n\nOf course it can.\n\nThe question is:\n\n\"Can the resource-management layer maintain correct lifecycle and acquisition behavior while thousands of workers continuously compete for a small pool?\"\n\nThat is where the sharded architecture, lookup synchronization, acquisition strategies, and lifecycle rules interact.\n\nFor the load-test results themselves, I treat the numbers as workload-specific measurements rather than claiming they represent every deployment.\n\nThe load-test harness also separates scheduler-side failures from simulated backend failures, because those are operationally different failure modes.\n\nThis distinction is easy to lose in a load test.\n\nImagine:\n\n```\n10,000 workers\n       │\n       ▼\n    Scheduler\n       │\n       ▼\n    Resource\n       │\n       ▼\n Backend request\n       │\n       ▼\n    FAILURE\n```\n\nThe backend failed.\n\nThat doesn't mean:\n\n```\nscheduler failed\n```\n\nSimilarly:\n\n```\nscheduler acquire failure\n```\n\nis not the same thing as:\n\n```\nbackend failure\n```\n\nA serious load test should keep these categories separate.\n\nThat's why the harness tracks acquisition behavior, backend behavior, release behavior, and timeout/cancellation behavior independently.\n\nConsider four backend resources:\n\n```\nbackend-1\nbackend-2\nbackend-3\nbackend-4\n```\n\nand:\n\n```\n10,000 concurrent workers\n```\n\nYou cannot turn four resources into 10,000 concurrent backend operations just because there are 10,000 goroutines.\n\nIf the acquisition policy is exclusive, the resource pool remains the bottleneck:\n\n```\n10,000 workers\n      │\n      ▼\n ┌───────────┐\n │ Scheduler │\n └─────┬─────┘\n       │\n       ▼\n  4 resources\n```\n\nThat is not a failure.\n\nThat's exactly what resource scheduling is supposed to enforce.\n\nThe scheduler doesn't know whether a resource is:\n\n```\ntype APIKey struct {\n    ID string\n}\n```\n\nor:\n\n```\ntype GPUWorker struct {\n    ID string\n}\n```\n\nor:\n\n```\ntype DatabaseReplica struct {\n    ID string\n}\n```\n\nThe application provides the resource type and the functions needed to identify and compare it.\n\nFor example:\n\n```\ncompare := func(a, b *Worker) int {\n    if a.Priority < b.Priority {\n        return -1\n    }\n\n    if a.Priority > b.Priority {\n        return 1\n    }\n\n    return 0\n}\n\nkeyFunc := func(w *Worker) string {\n    return w.ID\n}\n```\n\nThe scheduler doesn't need to understand what `Priority`\n\nmeans.\n\nIt only needs a consistent comparison function.\n\nThat's what makes the library reusable across domains.\n\nThe basic flow remains intentionally small:\n\n```\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n\n    \"github.com/phero20/concurrent-resource-scheduler/config\"\n    \"github.com/phero20/concurrent-resource-scheduler/scheduler\"\n)\n\ntype Worker struct {\n    ID       string\n    Priority int\n}\n\nfunc main() {\n    compare := func(a, b *Worker) int {\n        if a.Priority < b.Priority {\n            return -1\n        }\n\n        if a.Priority > b.Priority {\n            return 1\n        }\n\n        return 0\n    }\n\n    keyFunc := func(w *Worker) string {\n        return w.ID\n    }\n\n    cfg := config.Config[*Worker, string]{\n        HeapCount: 8,\n        Comparator: compare,\n        KeyFunc:    keyFunc,\n    }\n\n    sched, err := scheduler.New(cfg)\n    if err != nil {\n        log.Fatal(err)\n    }\n\n    defer sched.Shutdown()\n\n    _ = sched.Add(&Worker{\n        ID:       \"worker-1\",\n        Priority: 10,\n    })\n\n    _ = sched.Add(&Worker{\n        ID:       \"worker-2\",\n        Priority: 20,\n    })\n\n    resource, err := sched.Acquire()\n    if err != nil {\n        log.Fatal(err)\n    }\n\n    fmt.Println(\"Acquired:\", resource.ID)\n}\n```\n\nThe important thing is how little domain logic is inside the scheduler.\n\nThe application defines:\n\n```\nResource type\nKey function\nComparator\nConfiguration\n```\n\nCRS manages the rest.\n\nThe release isn't about replacing the architecture.\n\nIt is about tightening the implementation and making the public surface accurately represent what the implementation already does.\n\nThe release includes:\n\nThe cooldown manager now has documentation and examples that correctly reflect its dependency on a lifecycle controller.\n\nThe example accounts for asynchronous event dispatch so the demonstration is deterministic instead of racing an event-driven state transition.\n\nThe scheduler benchmark uses:\n\n```\npackage scheduler_test\n```\n\nrather than relying on internal package access.\n\nThe README and API documentation were aligned with the actual implementation, including exported errors and shared/exclusive complexity behavior.\n\nThe optional Prometheus extension is tagged:\n\n```\nextensions/prometheus/v1.2.3\n```\n\nand references the corresponding core release.\n\nThis is probably the most important disclaimer in the entire post.\n\nI don't want to say:\n\n\"CRS is 10x faster than every mutex-based scheduler.\"\n\nI haven't proven that.\n\nI don't want to say:\n\n\"CRS handles 37,000 requests/sec in production.\"\n\nA workload-specific test is not a production guarantee.\n\nI don't want to say:\n\n\"32 shards is always optimal.\"\n\nIt isn't.\n\nThe benchmarks show what happened under a particular machine and workload.\n\nThe architecture explains **why those measurements are interesting**.\n\nThat's a much more useful claim.\n\nSharding is most interesting when:\n\n```\nmany concurrent operations\n          +\nmultiple independent resource groups\n          +\nshared scheduling state\n```\n\nIt can reduce contention by allowing unrelated operations to work against different synchronization boundaries.\n\nBut it introduces its own tradeoffs:\n\nSo the design isn't:\n\n\"Sharding is always better.\"\n\nIt's:\n\n\"Sharding is a useful way to control contention when the workload benefits from partitioning.\"\n\nThe design can be summarized as four decisions:\n\n```\n1. Heap\n   ↓\nefficient priority ordering\n\n2. Sharding\n   ↓\nreduce shared lock contention\n\n3. Lookup map\n   ↓\nfast direct resource access\n\n4. Pluggable strategies\n   ↓\nseparate scheduling policy from\nresource-management mechanics\n```\n\nThen the lifecycle/event system sits around those primitives:\n\n```\n                Scheduler Core\n                      │\n       ┌──────────────┼──────────────┐\n       ▼              ▼              ▼\n     Heap           Lookup         Policy\n       │              │              │\n       └──────────────┼──────────────┘\n                      │\n                      ▼\n                  Lifecycle\n                      │\n              ┌───────┴───────┐\n              ▼               ▼\n           Cooldown        Events\n                              │\n                              ▼\n                          Telemetry\n```\n\nThat separation is the part I'm most interested in continuing to improve.\n\nBefore publishing this release, I wanted the project to pass more than just a version bump.\n\nThe current validation includes:\n\n```\ngo test ./...\ngo test -race ./...\ngo vet ./...\ngofmt -l .\n```\n\nThe benchmark suite was also run with:\n\n```\ngo test -run=\"^$\" -bench=\".\" -benchmem ./...\n```\n\nAnd the release artifacts are available through the Go module ecosystem:\n\n```\ngithub.com/phero20/concurrent-resource-scheduler@v1.2.3\n\ngithub.com/phero20/concurrent-resource-scheduler/extensions/prometheus@v1.2.3\n```\n\nThe core module targets Go 1.22+.\n\nThe optional Prometheus extension is a separate module targeting the newer Go toolchain used by that extension.\n\nv1.2.3 is a good point to stop and evaluate the architecture under more workloads.\n\nThe areas I'm particularly interested in are:\n\nThe goal isn't simply to produce a bigger benchmark number.\n\nThe goal is to understand **where the scheduler's architecture helps, where it doesn't, and what tradeoffs become visible at scale.**\n\nBuilding a concurrent scheduler taught me that the difficult part isn't implementing:\n\n```\nAcquire()\n```\n\nThe difficult part is everything around it.\n\nYou need to coordinate:\n\n```\n                 RESOURCE SCHEDULING\n\n                      Priority\n                         │\n                         ▼\nConcurrency ───────► Scheduler ◄────── Acquisition Policy\n                         │\n             ┌───────────┼───────────┐\n             ▼           ▼           ▼\n           Lookup     Lifecycle    Events\n             │           │           │\n             ▼           ▼           ▼\n          O(1) map    Active/     Telemetry\n                      Inactive        │\n                                     ▼\n                                 Prometheus\n```\n\nThat is what CRS is trying to provide.\n\nNot just a priority queue.\n\nNot just a resource pool.\n\nBut a reusable concurrency layer where:\n\ncan exist as separate pieces without forcing the application to rebuild the entire system.\n\n**v1.2.3 is the current release.**\n\nIf you're building an LLM gateway, proxy pool, database router, GPU worker pool, API-key manager, or another system where many concurrent requests compete for reusable resources, I'd genuinely like to hear how you're solving it.\n\n**Concurrent Resource Scheduler**\n\nGitHub: [https://github.com/phero20/concurrent-resource-scheduler](https://github.com/phero20/concurrent-resource-scheduler)\n\nGo documentation: [https://pkg.go.dev/github.com/phero20/concurrent-resource-scheduler](https://pkg.go.dev/github.com/phero20/concurrent-resource-scheduler)\n\nCurrent release:\n\n```\nv1.2.3\n```\n\nOptional Prometheus extension:\n\n```\nextensions/prometheus/v1.2.3\n```\n\nIf you find the project useful, a GitHub star is appreciated.\n\nIf you find a concurrency bug, even better: open an issue.\n\nThe most useful validation for a concurrency library isn't another diagram.\n\nIt's putting it under a workload the author didn't design for.", "url": "https://wpnews.pro/news/concurrent-resource-scheduler-v1-2-3-sharded-priority-heaps-under-concurrent", "canonical_source": "https://dev.to/phero20/concurrent-resource-scheduler-v123-sharded-priority-heaps-under-concurrent-load-bpk", "published_at": "2026-08-13 13:09:03+00:00", "updated_at": "2026-08-13 13:19:59.124066+00:00", "lang": "en", "topics": ["developer-tools"], "entities": ["Concurrent Resource Scheduler", "Go"], "alternates": {"html": "https://wpnews.pro/news/concurrent-resource-scheduler-v1-2-3-sharded-priority-heaps-under-concurrent", "markdown": "https://wpnews.pro/news/concurrent-resource-scheduler-v1-2-3-sharded-priority-heaps-under-concurrent.md", "text": "https://wpnews.pro/news/concurrent-resource-scheduler-v1-2-3-sharded-priority-heaps-under-concurrent.txt", "jsonld": "https://wpnews.pro/news/concurrent-resource-scheduler-v1-2-3-sharded-priority-heaps-under-concurrent.jsonld"}}