Garbage collection finds objects that a program can still reach, then makes unreachable objects’ storage available for reuse. That does not necessarily return memory to the operating system or lower the process’s resident set size (RSS). In a concurrent collector, a write barrier helps preserve a correct view of the object graph while the program continues changing it.
What garbage collection does
A tracing collector starts from roots—references the runtime treats as entry points, such as references in globals and stacks—and follows pointers from them. Objects it can reach are live from the collector’s point of view. Objects it cannot reach are candidates for reclamation. In a mark-sweep design, the collector marks discovered objects and later sweeps through managed storage, making space occupied by unreachable objects available for future allocations. The Go guide describes this approach for the standard Go toolchain; other runtimes can use different collection algorithms. Go’s garbage collector guide
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“Reclaimed” does not necessarily mean that an object’s bytes disappear from the process immediately. It means the runtime can reuse the storage, subject to that runtime’s allocator and collection policy. Whether memory is later released to the operating system is a separate question.
Tri-color marking, step by step
Tri-color marking is a way to reason about tracing, not a promise that every runtime literally assigns each object one of three stored colors. The model describes what the collector knows about objects during a marking phase:
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- White: not yet discovered by the collector.
- Grey: discovered, but its outgoing pointers have not all been scanned.
- Black: scanned; the collector has processed its outgoing pointers.
- The collector begins with objects treated as white and shades roots grey.
- It selects a grey object and follows its pointers. Newly discovered white objects become grey.
- After scanning an object’s pointers, the collector treats it as black.
- When no grey work remains, white objects are unreachable in the collector’s view and can be considered for reclamation under the full collector algorithm.
The model’s important safety condition during concurrent marking is that a black object must not point to a white object without the collector learning about that white object. Otherwise, the collector could finish scanning the black object, miss a newly connected live object, and mistakenly treat it as unreachable. Go’s conceptual account explains this invariant and its role in concurrent collection. Go GC: Prioritizing low latency and simplicity
Why a concurrent collector needs a write barrier
While a concurrent collector marks objects, the application—often called the mutator—may continue changing pointers. For example, after the collector scans an object, the application might store a reference to a previously undiscovered object in it. Without a barrier or another synchronization strategy, the collector could miss that object even though the program now reaches it.
A write barrier is runtime work associated with pointer changes that helps the collector account for mutations. In the Go article’s explanation, the barrier shades a newly reachable white object grey so the collector will scan it. The article puts the invariant this way: “Maintaining this invariant is the job of the write barrier, which is a small function run by the mutator whenever a pointer in the heap is modified.” The Go article’s tri-color explanation
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That describes the concept, not a universal recipe. Barrier algorithms and combinations of barriers differ among collectors and versions. Go’s runtime source documents implementation details for its own runtime, which should not be generalized to other languages or collectors. Go runtime write-barrier source
Why RSS can stay high after a collection
Memory measurements answer different questions. A collection may lower the amount of live object data while leaving the process’s RSS largely unchanged. To interpret a memory graph, separate these layers:
| Concept or measure | What it tells you | What it does not establish by itself |
|---|---|---|
| Reachable or live objects | Which managed objects the collector can find from roots at the relevant point in a collection. | How many pages the operating system currently counts as resident. |
| Reclaimed managed storage | Storage the runtime can reuse for later allocations. | That the runtime has returned the storage to the operating system. |
| Reserved or committed runtime memory | Address space or pages managed for the heap and runtime structures, according to the runtime’s accounting. | That all of it is physically resident, or that it consists of live objects. |
| RSS | Resident memory counted for the process, which can include managed heap pages as well as stacks, native allocations, mappings and other resident memory. | How much of that memory is live managed data. |
One possible explanation for a high post-collection RSS is that the runtime keeps freed spans or pages available for future allocations, or releases memory only under particular conditions. Memory outside the managed heap—such as stacks, native allocations and mappings—can also contribute. These are diagnostic possibilities, not conclusions: the actual explanation depends on the runtime, operating system and measurements.
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For Go specifically, the living Go garbage collector guide warns that virtual-memory measures such as VSS can be poor indicators of process footprint because of large address-space reservations, and recommends RSS and similar measures for physical usage in its stated context. The guide describes the standard Go toolchain as of Go 1.19; its examples and advice should be read within that scope, not as guarantees about every Go implementation or all current runtime internals.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallHigh RSS alone does not prove a memory leak. A more direct warning sign is a live heap or retained object graph that keeps growing when measured at comparable points after completed collections. If live managed heap is stable while RSS remains high, retained allocator pages, non-heap memory or residency accounting may be involved; inspect measurements before drawing a conclusion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What different collector examples illustrate
Collector behavior is implementation-specific. These examples show why it matters to name the runtime and version rather than treating a technique or tuning control as universal.
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| Runtime or collector | Documented detail | Scope to keep in mind |
|---|---|---|
| Go standard toolchain | The Go guide describes tracing mark-sweep, root scanning, heap targets and a runtime memory limit. | The guide expressly describes the standard gc toolchain as of Go 1.19. Its Go-specific controls and accounting are not general rules for other runtimes. Go GC guide |
| Go concurrent-marking explanation | The Go engineering article presents a tri-color model and a barrier that shades newly reachable objects. | It is a conceptual account associated with the Go 1.5 collector, not a current benchmark or complete description of every present implementation detail. Go GC article |
| HotSpot G1 | Oracle’s Java 23 tuning guide says G1 uses Snapshot-At-The-Beginning (SATB) marking and may retain some additional memory compared with other marking approaches. | This is a documented G1 design caveat; it does not identify the cause of a particular process’s RSS. Oracle HotSpot Java 23 tuning guide |
How to investigate high memory use
Compare measurements taken under consistent conditions and interpret each according to the runtime’s definitions. A practical sequence is:
- Identify the environment. Record the language and runtime version, collector configuration, operating system, container memory limit, and whether native code or memory-mapped files are involved.
- Measure comparable moments. Where the runtime exposes them, record live heap or object-profile data, allocated and released heap, committed or runtime-managed memory, and OS- or container-reported RSS. Include whether a collection completed before the reading.
- Check whether live data is growing. Inspect a heap profile or retained-object graph across comparable post-collection points. A growing live set calls for finding retaining references and allocation sources, not just watching RSS.
- Read runtime-specific GC signals. Use the runtime’s logs and metrics to understand collection frequency, work, pauses and memory-release activity. For Go, the guide discusses heap profiles, runtime metrics, GC traces,
GOGCandGOMEMLIMIT; those names and semantics apply to Go, not automatically to other runtimes. Go guide and its diagnostic material - Account for memory outside live objects. If live heap is stable but RSS is high, examine runtime-retained pages, stacks, native allocations, mappings and platform accounting before deciding that collection failed.
- Tune only after establishing a cause. Change one runtime-specific control at a time, then compare memory alongside CPU use and latency. In Go,
GOGCcontrols a target trade-off and the runtime memory limit is soft; neither setting is a universal collector control. Go GC guide
What to compare when evaluating a memory graph
For a useful comparison across runs, collector configurations or runtimes, keep the context attached to every measurement:
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- Marking and barrier strategy, where documented.
- Pause time and collector CPU work.
- Live heap versus allocated or committed memory.
- RSS versus virtual size, using each metric’s documented definition.
- When unused pages may be returned to the operating system.
- Container and runtime memory limits.
A change in one metric does not imply the same change in another. For example, a smaller live heap after collection is evidence about reachable managed objects; it is not, on its own, evidence that RSS should fall by the same amount.
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