Original answer (2017)

UPD (2022)

For those who knows Russian, I made a presentation and wrote couple of articles on this topic:

Original answer (2017)

I was always confused about the growing residential memory of my Go applications, and finally I had to learn the profiling tools that are present in Go ecosystem. Runtime provides many metrics within a runtime.Memstats structure, but it may be hard to understand which of them can help to find out the reasons of memory growth, so some additional tools are needed.

Profiling environment

main

import(
    metrics "github.com/tevjef/go-runtime-metrics"
)
func main() {
    //...
    metrics.DefaultConfig.CollectionInterval = time.Second
    if err := metrics.RunCollector(metrics.DefaultConfig); err != nil {
        // handle error
    }
}

InfluxDBGrafanaDocker

docker run --name influxdb -d -p 8086:8086 influxdb
docker run -d -p 9090:3000/tcp --link influxdb --name=grafana grafana/grafana:4.1.0

GrafanaInfluxDBGrafana

Import dashboard #3242 from https://grafana.com (Grafana main page -> Top left corner -> Dashboard -> Import):

Influxdb

Memory consumption analysis

SysRSSHeapSysHeapIdleSysHeapReleased

HeapIdle minus HeapReleased estimates the amount of memory    
that could be returned to the OS, but is being retained by
the runtime so it can grow the heap without requesting more
memory from the OS.

For those who's trying to investigate the problem of memory consumption I would recommend to follow the described steps in order to exclude some trivial errors (like goroutine leak).

Freeing memory explicitly

debug.FreeOSMemory()

// in the top-level package
func init() {
   go func() {
       t := time.Tick(time.Second)
       for {
           <-t
           debug.FreeOSMemory()
       }
   }()
}

In fact, this approach saved about 35% of memory as compared with default conditions.