<p>
Go approaches its memory model in much the same way as the rest of the language,
aiming to keep the semantics simple, understandable, and useful.
This section gives a general overview of the approach and should suffice for most programmers.
The memory model is specified more formally in the next section.
</p>

<p>
A data race is defined as

net.core.busy_read=50
net.core.busy_poll=50
kernel.numa_balancing=1

# Do not use swap at all
vm.swappiness=0
vm.vfs_cache_pressure=50

# Start writeback at 3% memory
vm.dirty_background_ratio=3
# Force writeback at 10% memory
vm.dirty_ratio=10

# Quite a few memory map
# areas may be consumed
vm.max_map_count=524288

# Default is 500000 = 0.5ms
kernel.sched_migration_cost_ns=5000000

# stalled hdd io threads

        long memory1 = MemoryUtil.getUsedMemory();
        assertTrue(memory1 >= 0);

        // Create some objects to change memory usage
        @SuppressWarnings("unused")
        String[] largeArray = new String[1000];
        for (int i = 0; i < 1000; i++) {
            largeArray[i] = "test" + i;
        }

        long memory2 = MemoryUtil.getUsedMemory();

All Go pointers passed to C must point to pinned Go memory. Go pointers
passed as function arguments to C functions have the memory they point to
implicitly pinned for the duration of the call. Go memory reachable from
these function arguments must be pinned as long as the C code has access
to it. Whether Go memory is pinned is a dynamic property of that memory
region; it has nothing to do with the type of the pointer.

		case <-r.qTimer.C:
			r.qCache.update()
		}
	}
}

// Delete deletes in-memory replication statistics for a bucket.
func (r *ReplicationStats) Delete(bucket string) {
	if r == nil {
		return
	}

	r.Lock()
	defer r.Unlock()
	delete(r.Cache, bucket)
}

// UpdateReplicaStat updates in-memory replica statistics with new values.
func (r *ReplicationStats) UpdateReplicaStat(bucket string, n int64) {

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

 * backing store (e.g. file system, database, memory).
 * <p>
 * This is mainly used to parse files into objects such as Maven projects,
 * models, settings, or toolchains. The source implementation handles
 * all the details of accessing the underlying content while providing
 * a uniform API to consumers.
 * <p>
 * Sources can represent:
 * <ul>
 *   <li>Local filesystem files</li>
 *   <li>In-memory content</li>
 *   <li>Database entries</li>

        this.remainingBytes = remainingBytes;
    }

    /**
     * Add RDMA channel information for direct memory access
     *
     * @param remoteKey remote memory key
     * @param address remote memory address
     * @param length length of memory region
     */
    public void addRdmaChannelInfo(int remoteKey, long address, int length) {

	rootAccess                  bool
	syncEvents                  bool
	objectMaxVersions           int64
}

const (
	cgroupV1MemLimitFile = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
	cgroupV2MemLimitFile = "/sys/fs/cgroup/memory.max"
)

func cgroupMemLimit() (limit uint64) {
	buf, err := os.ReadFile(cgroupV2MemLimitFile)
	if err != nil {
		buf, err = os.ReadFile(cgroupV1MemLimitFile)
	}

		total:             "Total memory on the node",
		memUsed:           "Used memory on the node",
		memUsedPerc:       "Used memory percentage on the node",
		memFree:           "Free memory on the node",
		memShared:         "Shared memory on the node",
		memBuffers:        "Buffers memory on the node",
		memCache:          "Cache memory on the node",
		memAvailable:      "Available memory on the node",