Envoy DFP: Getting Per-Host Metrics For Dynamic Forward Proxy

by Alex Johnson 62 views

Introduction

In today's dynamic and complex microservices architectures, efficient traffic management and observability are paramount. Envoy Proxy, a high-performance proxy designed for modern application architectures, offers a powerful Dynamic Forward Proxy (DFP) feature that enables on-demand DNS resolution for a large number of potential upstream services. This is particularly useful when dealing with thousands of services, where some might receive very little or no traffic. However, a common challenge arises when trying to monitor these services at a granular level. This article delves into the problem of obtaining per-host metrics when using Envoy's DFP and explores potential solutions and considerations.

The Challenge of Per-Host Metrics with Dynamic Forward Proxy

The user's scenario involves a setup with a high number of potential upstreams (approximately 10,000), where DNS resolution is intended to happen on demand. This approach is beneficial because it avoids the overhead of resolving all hostnames upfront, especially when many services might not be actively used. When DFP is employed, Envoy aggregates metrics under the clusters scope, typically using the IP address and port of the upstream service. An example of such aggregated metrics is shown below:

dynamic_forward_proxy_cluster::23.215.0.138:443::cx_active::0
dynamic_forward_proxy_cluster::23.215.0.138:443::cx_connect_fail::3
dynamic_forward_proxy_cluster::23.215.0.138:443::cx_total::18
dynamic_forward_proxy_cluster::23.215.0.138:443::rq_active::0
dynamic_forward_proxy_cluster::23.215.0.138:443::rq_error::3
dynamic_forward_proxy_cluster::23.215.0.138:443::rq_success::0
dynamic_forward_proxy_cluster::23.215.0.138:443::rq_timeout::0
dynamic_forward_proxy_cluster::23.215.0.138:443::rq_total::0

While these metrics provide valuable insights, the requirement is to obtain metrics at the hostname level rather than the IP address level. This distinction is crucial for several reasons:

  • Service Identification: Hostnames are typically more human-readable and directly tied to service names, making it easier to identify and monitor specific services.
  • Dynamic IP Addresses: In dynamic environments, IP addresses can change frequently, making it challenging to correlate IP-based metrics with specific services over time.
  • Virtual Hosting: Multiple hostnames can resolve to the same IP address, and hostname-based metrics are essential for distinguishing traffic and performance for each virtual host.

Understanding Envoy's Dynamic Forward Proxy

Before diving into potential solutions, let's briefly recap how Envoy's Dynamic Forward Proxy (DFP) works. DFP is a powerful feature in Envoy that allows the proxy to dynamically resolve the IP addresses of upstream services based on the host header of incoming requests. This is particularly useful in environments with a large number of services or services that scale frequently. Here’s a breakdown of the key components and how they interact:

Key Components of DFP

  1. DNS Cache:

    • Envoy maintains an internal DNS cache that stores the mappings between hostnames and IP addresses. This cache is used to reduce the latency and overhead of DNS resolution. When a new request arrives, Envoy first checks its DNS cache to see if the hostname has already been resolved.

  2. DNS Resolution:

    • If the hostname is not in the cache or the cached entry has expired, Envoy performs a DNS query to resolve the hostname. Envoy supports various DNS resolution strategies, including using the system’s resolver, an internal DNS server, or an external DNS service.

  3. Cluster Creation:

    • For each unique hostname, Envoy dynamically creates a cluster. A cluster in Envoy represents a group of identical upstream services. In the context of DFP, each cluster corresponds to a specific hostname.

  4. Routing:

    • Envoy uses the host header of the incoming request to determine which cluster to route the traffic to. This ensures that requests are sent to the correct upstream service.

How DFP Works

  1. Request Arrival:

    • A new request arrives at the Envoy proxy.

  2. Hostname Extraction:

    • Envoy extracts the hostname from the request’s host header.

  3. DNS Cache Lookup:

    • Envoy checks its internal DNS cache for the hostname.

  4. DNS Resolution (if needed):

    • If the hostname is not in the cache or the cached entry has expired, Envoy resolves the hostname using its configured DNS resolution mechanism.

  5. Cluster Selection:

    • Envoy uses the resolved IP address and port to select the appropriate cluster. If a cluster does not exist for the resolved address, Envoy dynamically creates one.

  6. Traffic Forwarding:

    • Envoy forwards the request to one of the healthy endpoints within the selected cluster.

Benefits of DFP

  • Scalability:

    • DFP allows Envoy to handle a large number of upstream services without the need to pre-configure each service.

  • Efficiency:

    • By resolving hostnames on demand, DFP avoids the overhead of resolving all hostnames upfront.

  • Flexibility:

    • DFP supports various DNS resolution strategies, making it adaptable to different environments.

  • Dynamic Environments:

    • DFP is well-suited for dynamic environments where services are frequently added, removed, or scaled.

Potential Solutions for Obtaining Per-Host Metrics

Addressing the need for per-host metrics with DFP requires a multifaceted approach. Several strategies can be employed, each with its trade-offs. Here are some potential solutions:

1. Custom Metrics with Envoy Filters

One of the most flexible approaches is to use Envoy filters to create custom metrics. Envoy filters allow you to intercept and modify requests and responses, as well as collect custom metrics based on specific criteria. In this case, you can use a filter to extract the hostname from the request headers and create metrics tagged with the hostname.

  • How it Works:

    • Write an Envoy filter (either in Lua or C++) that intercepts incoming requests.
    • Extract the hostname from the Host header.
    • Use Envoy's stats API to increment counters or record metrics with the hostname as a tag.

  • Example (Lua Filter):

    local function main(request_handle)
  local host = request_handle:headers():get("host")
  if host then
    local stats = request_handle:streamInfo():filterState()
    local counter = stats:counter("per_host."..host..".requests_total")
    counter:inc(1)
  end
end

return main

  • Pros:

    • Highly flexible and customizable.
    • Allows for fine-grained control over which metrics are collected.

  • Cons:

    • Requires writing and deploying custom filters, which can add complexity.
    • Performance overhead of running filters on every request.

2. Service Mesh Integration

If you are operating within a service mesh environment (such as Istio), you might be able to leverage the mesh's capabilities to obtain per-host metrics. Service meshes often provide built-in mechanisms for collecting and aggregating metrics at the service level.

  • How it Works:

    • Configure your service mesh to collect metrics based on service identities (which typically correspond to hostnames).
    • Use the service mesh's monitoring tools to query and visualize the metrics.

  • Pros:

    • Leverages existing infrastructure and tooling.
    • Provides a consistent and centralized approach to metrics collection.

  • Cons:

    • Requires adopting a service mesh, which can be a significant undertaking.
    • Might not provide the same level of customization as custom filters.

3. External Monitoring Systems with Custom Exporters

Another approach is to use external monitoring systems like Prometheus or Grafana, combined with custom exporters that can translate Envoy's metrics into a format suitable for these systems. You can create a custom exporter that scrapes Envoy's metrics endpoint and transforms the data to include hostname-based tags.

  • How it Works:

    • Create a custom exporter that scrapes Envoy's metrics endpoint.
    • Parse the metrics and extract relevant information, such as the upstream IP address and request counts.
    • Perform a reverse DNS lookup to map IP addresses to hostnames.
    • Export the metrics to your monitoring system with hostname-based tags.

  • Pros:

    • Integrates with existing monitoring infrastructure.
    • Provides a flexible way to transform and enrich metrics.

  • Cons:

    • Requires writing and deploying a custom exporter.
    • Reverse DNS lookups can add latency and complexity.

4. Envoy's Access Logs with Log Processing

Envoy's access logs provide detailed information about each request, including the requested hostname. You can process these logs to extract and aggregate metrics at the hostname level.

  • How it Works:

    • Configure Envoy to generate access logs in a structured format (e.g., JSON).
    • Use a log processing tool (e.g., Fluentd, Logstash) to parse the logs.
    • Extract the hostname and other relevant information from the logs.
    • Aggregate the data and export it to your monitoring system.

  • Pros:

    • Leverages existing logging infrastructure.
    • Provides a comprehensive view of traffic patterns.

  • Cons:

    • Log processing can be resource-intensive.
    • Metrics are derived from logs, which can introduce latency.

Implementing Custom Metrics with Envoy Filters: A Detailed Example

To illustrate how to obtain per-host metrics, let's delve deeper into implementing custom metrics using Envoy filters. This approach offers the most flexibility and control over the metrics collected. We'll use a Lua filter as an example, but the same principles apply to C++ filters.

Step-by-Step Guide

  1. Write the Lua Filter:

    Create a Lua script that extracts the hostname from the request headers and increments a counter with the hostname as a tag. Here's an example:

    local function main(request_handle)
  local host = request_handle:headers():get("host")
  if host then
    local stats = request_handle:streamInfo():filterState()
    local counter = stats:counter("per_host."..host..".requests_total")
    counter:inc(1)
  end
end

return main

    This script defines a function main that takes a request_handle as input. It extracts the Host header from the request, and if the header is present, it retrieves the filter state and increments a counter named per_host.<hostname>.requests_total. The filter state is used to store custom data associated with the stream.

  2. Configure Envoy to Use the Filter:

    To use the filter, you need to configure Envoy to load and execute it. This involves modifying your Envoy configuration file to include a lua filter in the HTTP filter chain.

    static_resources:
  listeners:
  - address:
      socket_address:
        address: 0.0.0.0
        port_value: 8080
    filter_chains:
    - filters:
      - name: envoy.filters.network.http_connection_manager
        typed_config:
          "@type": type.googleapis.com/envoy.extensions.filters.network.http_connection_manager.v3.HttpConnectionManager
          stat_prefix: ingress_http
          codec_type: AUTO
          route_config:
            name: local_route
            virtual_hosts:
            - name: local_service
              domains:
              - "*"
              routes:
              - match:
                  prefix: "/"
                route:
                  cluster: service_cluster
          http_filters:
          - name: envoy.filters.http.lua
            typed_config:
              "@type": type.googleapis.com/envoy.extensions.filters.http.lua.v3.Lua
              inline_code: |
                local function main(request_handle)
                  local host = request_handle:headers():get("host")
                  if host then
                    local stats = request_handle:streamInfo():filterState()
                    local counter = stats:counter("per_host." .. host .. ".requests_total")
                    counter:inc(1)
                  end
                end

                return main
          - name: envoy.filters.http.router
            typed_config:
              "@type": type.googleapis.com/envoy.extensions.filters.http.router.v3.Router
  clusters:
  - name: service_cluster
    connect_timeout: 0.25s
    type: STRICT_DNS
    lb_policy: ROUND_ROBIN
    load_assignment:
      cluster_name: service_cluster
      endpoints:
      - lb_endpoints:
        - endpoint:
            address:
              socket_address:
                address: 127.0.0.1
                port_value: 8000

    In this configuration, the envoy.filters.http.lua filter is added to the HTTP filter chain. The inline_code option is used to specify the Lua script directly in the configuration file. Alternatively, you can use the source_code option to load the script from a file.

  3. Deploy Envoy and Test the Filter:

    Deploy Envoy with the updated configuration. Send traffic through Envoy and verify that the custom metrics are being generated. You can access Envoy's metrics endpoint (typically /stats) to view the metrics.

  4. Monitor the Metrics:

    Configure your monitoring system (e.g., Prometheus) to scrape Envoy's metrics endpoint and visualize the per-host metrics. You can create dashboards and alerts based on these metrics to monitor the performance and health of your services.

Considerations for Implementing Custom Filters

  • Performance Overhead:

    • Running filters on every request can introduce performance overhead. Optimize your filters to minimize their impact on latency and throughput.

  • Filter Complexity:

    • Complex filters can be challenging to write and maintain. Keep your filters as simple and focused as possible.

  • Filter Testing:

    • Thoroughly test your filters to ensure they are working correctly and not introducing any unexpected issues.

Conclusion

Obtaining per-host metrics in Envoy's Dynamic Forward Proxy requires careful consideration and the implementation of appropriate strategies. While Envoy's default metrics aggregation focuses on IP addresses, the need for hostname-based metrics is crucial for service identification, dynamic environments, and virtual hosting scenarios. The solutions discussed in this article, including custom metrics with Envoy filters, service mesh integration, external monitoring systems with custom exporters, and Envoy's access logs with log processing, offer various paths to achieve this goal.

Implementing custom metrics with Envoy filters provides the most flexibility and control, allowing you to define precisely which metrics to collect and how to tag them. However, it also requires writing and deploying custom code, which can add complexity. Other approaches, such as leveraging service mesh capabilities or using external monitoring systems, can provide a more integrated and streamlined solution, but might not offer the same level of customization.

Ultimately, the best approach depends on your specific requirements, infrastructure, and expertise. By understanding the challenges and potential solutions, you can effectively monitor your services in dynamic environments and ensure the health and performance of your applications.

For further reading on Envoy Proxy and its capabilities, you can visit the official Envoy documentation: Envoy Proxy Official Website.