Set<E> inEdges();

  Set<E> outEdges();

  /**
   * Returns the set of edges connecting the origin node to {@code node}. For networks without
   * parallel edges, this set cannot be of size greater than one.
   */
  Set<E> edgesConnecting(N node);

  /**
   * Returns the node that is adjacent to the origin node along {@code edge}.
   *
   * <p>In the directed case, {@code edge} is assumed to be an outgoing edge.
   */

[kubernetes-node-linux-arm64.tar.gz](https://dl.k8s.io/v1.9.10/kubernetes-node-linux-arm64.tar.gz) | `8b6d61316e99c17d51ffd966c26d2034960947968a5a6840e547a1966a087ad7`
[kubernetes-node-linux-ppc64le.tar.gz](https://dl.k8s.io/v1.9.10/kubernetes-node-linux-ppc64le.tar.gz) | `471d3a21f9bd8e8a7f03f8d225c4a72d7305f888102556e0ba29e5ec86dc763e`

      return true;
    }
    if (!(obj instanceof Graph)) {
      return false;
    }
    Graph<?> other = (Graph<?>) obj;

    return isDirected() == other.isDirected()
        && nodes().equals(other.nodes())
        && edges().equals(other.edges());
  }

  @Override
  public final int hashCode() {
    return edges().hashCode();
  }

  /** Returns a string representation of this graph. */

	// can easily eat into the internode bandwidth. This function would be mostly
	// TX saturating, however there are situations where a RX might also saturate.
	// To avoid these problems we must split the work at scale. With 1000 node
	// setup becoming a reality we must try to shard the work properly such as
	// pick 10 nodes that precisely can send those 100 requests the first node

[kubernetes-node-linux-arm64.tar.gz](https://dl.k8s.io/v1.7.16/kubernetes-node-linux-arm64.tar.gz) | `87e768d59fd71a3a558ea5775bedec1f74f983af9a568ccec52f9394431db547`
[kubernetes-node-linux-ppc64le.tar.gz](https://dl.k8s.io/v1.7.16/kubernetes-node-linux-ppc64le.tar.gz) | `cece0dddbe6cac79ccc5c490d3132a85f72a4da6daf3c88202ad864dff5c98aa`

[kubernetes-node-linux-arm64.tar.gz](https://dl.k8s.io/v1.8.15/kubernetes-node-linux-arm64.tar.gz) | `3eb94233791c50c57a56979af256f4d947963bf81039b1919e06895755aee57a`
[kubernetes-node-linux-ppc64le.tar.gz](https://dl.k8s.io/v1.8.15/kubernetes-node-linux-ppc64le.tar.gz) | `9d7ebb3dd33183f6c63e6dee24ec43cefddf702f41159d34b3fa9de73191cd23`

}

// HasLogListeners returns true if console log listeners are registered
// for this node or peers
func (sys *HTTPConsoleLoggerSys) HasLogListeners() bool {
	return sys != nil && sys.pubsub.Subscribers() > 0
}

// Subscribe starts console logging for this node.
func (sys *HTTPConsoleLoggerSys) Subscribe(subCh chan log.Info, doneCh <-chan struct{}, node string, last int, logKind madmin.LogMask, filter func(entry log.Info) bool) error {

				node.Pools = append(node.Pools, ep.PoolIdx)
			}
			nodesMap[ep.Host] = node
		}
	}
	nodes = make([]Node, 0, len(nodesMap))
	for _, v := range nodesMap {
		nodes = append(nodes, v)
	}
	sort.Slice(nodes, func(i, j int) bool {
		return nodes[i].Host < nodes[j].Host
	})
	return
}

// GetPoolIdx return pool index
func (l EndpointServerPools) GetPoolIdx(pool string) int {

Esse tipo de assincronicidade é o que fez NodeJS popular (embora NodeJS não seja paralelo) e que essa seja a força do Go como uma linguagem de programa.

E esse é o mesmo nível de performance que você tem com o **FastAPI**.

      "name": "DS_PROMETHEUS",
      "label": "Prometheus",
      "description": "",
      "type": "datasource",
      "pluginId": "prometheus",
      "pluginName": "Prometheus"
    }
  ],
  "description": "MinIO Nodes Grafana Dashboard - https://min.io/",
  "editable": true,
  "fiscalYearStartMonth": 0,
  "gnetId": 15306,
  "graphTooltip": 0,
  "id": 267,
  "links": [
    {
      "icon": "external link",