Optimizing Networking

The simplicity of Cloud Functions lets you quickly develop code and run it in a serverless environment. At moderate scale, the cost of running functions is low, and optimizing your code might not seem like a high priority. As your deployment scales up, however, optimizing your code becomes increasingly important.

This document describes how to optimize networking for your functions. Some of the benefits of optimizing networking are as follows:

  • Reduce CPU time spent in establishing new connections at each function call.
  • Reduce the likelihood of running out of connection or DNS quotas.

Maintaining Persistent Connections

This section gives examples of how to maintain persistent connections in a function. Failure to do so can result in quickly exhausting connection quotas.

The following scenarios are covered in this section:

  • HTTP/S
  • Google APIs

HTTP/S Requests

The optimized code snippet below shows how to maintain persistent connections instead of creating a new connection upon every function invocation:

Node.js

const http = require('http');
const agent = new http.Agent({keepAlive: true});

/**
 * HTTP Cloud Function that caches an HTTP agent to pool HTTP connections.
 *
 * @param {Object} req Cloud Function request context.
 * @param {Object} res Cloud Function response context.
 */
exports.connectionPooling = (req, res) => {
  req = http.request({
    host: '',
    port: 80,
    path: '',
    method: 'GET',
    agent: agent
  }, resInner => {
    let rawData = '';
    resInner.setEncoding('utf8');
    resInner.on('data', chunk => { rawData += chunk; });
    resInner.on('end', () => {
      res.status(200).send(`Data: ${rawData}`);
    });
  });
  req.on('error', e => {
    res.status(500).send(`Error: ${e.message}`);
  });
  req.end();
};

Python

import requests

# Create a global HTTP session (which provides connection pooling)
session = requests.Session()


def connection_pooling(request):
    """
    HTTP Cloud Function that uses a connection pool to make HTTP requests.
    Args:
        request (flask.Request): The request object.
    Returns:
        The response text, or any set of values that can be turned into a
        Response object using `make_response`
        <http://flask.pocoo.org/docs/0.12/api/#flask.Flask.make_response>.
    """

    # The URL to send the request to
    url = 'http://example.com'

    # Process the request
    response = session.get(url)
    response.raise_for_status()
    return 'Success!'

Accessing Google APIs

The example below uses Cloud Pub/Sub, but this approach also works for other client libraries—for example, Cloud Natural Language or Cloud Spanner. Note that performance improvements may depend on the current implementation of particular client libraries.

Creating a PubSub client object results in one connection and two DNS queries per invocation. To avoid unnecessary connections and DNS queries, create the PubSub client object in global scope as shown in the sample below:

Node.js

const Pubsub = require('@google-cloud/pubsub');
const pubsub = Pubsub();

/**
 * HTTP Cloud Function that uses a cached client library instance to
 * reduce the number of connections required per function invocation.
 *
 * @param {Object} req Cloud Function request context.
 * @param {Object} req.body Cloud Function request context body.
 * @param {String} req.body.topic The Cloud Pub/Sub topic to publish to.
 * @param {Object} res Cloud Function response context.
 */
exports.gcpApiCall = (req, res) => {
  const topic = pubsub.topic(req.body.topic);

  topic.publish('Test message', err => {
    if (err) {
      res.status(500).send(`Error publishing the message: ${err}`);
    } else {
      res.status(200).send('1 message published');
    }
  });
};

Python

import os
from google.cloud import pubsub_v1

# Create a global Pub/Sub client to avoid unneeded network activity
pubsub = pubsub_v1.PublisherClient()


def gcp_api_call(request):
    """
    HTTP Cloud Function that uses a cached client library instance to
    reduce the number of connections required per function invocation.
    Args:
        request (flask.Request): The request object.
    Returns:
        The response text, or any set of values that can be turned into a
        Response object using `make_response`
        <http://flask.pocoo.org/docs/0.12/api/#flask.Flask.make_response>.
    """

    project = os.getenv('GCP_PROJECT')
    request_json = request.get_json()

    topic_name = request_json['topic']
    topic_path = pubsub.topic_path(project, topic_name)

    # Process the request
    data = 'Test message'.encode('utf-8')
    pubsub.publish(topic_path, data=data)

    return '1 message published'

Load-testing Your Function

To measure how many connections your function performs on average, simply deploy it as a HTTP function and use a performance-testing framework to invoke it at certain QPS. One possible choice is Artillery, which you can invoke with a single line:

$ artillery quick -d 300 -r 30 <URL>

This command fetches the given URL at 30 QPS for 300 seconds.

After performing the test, check the usage of your connection quota on the Cloud Functions API quota page in Cloud Console. If the usage is consistently around 30 (or its multiple), you are establishing one (or several) connections in every invocation. After you optimize your code, you should see a few (10-30) connections occur only at the beginning of the test.

You can also compare the CPU cost before and after the optimization on the CPU quota plot on the same page.

Was this page helpful? Let us know how we did:

Send feedback about...

Cloud Functions Documentation