daohualiuxiang
/
AspNetCore.Docs
mirror of https://github.com/dotnet/AspNetCore.Docs.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
AspNetCore.Docs/aspnetcore/grpc/performance.md

18 KiB
Raw Blame History

title author description monikerRange ms.author ms.date uid
Performance best practices with gRPC jamesnk Learn the best practices for building high-performance gRPC services. >= aspnetcore-3.0 jamesnk 04/11/2023 grpc/performance

Performance best practices with gRPC

By James Newton-King

gRPC is designed for high-performance services. This document explains how to get the best performance possible from gRPC.

Reuse gRPC channels

A gRPC channel should be reused when making gRPC calls. Reusing a channel allows calls to be multiplexed through an existing HTTP/2 connection.

If a new channel is created for each gRPC call then the amount of time it takes to complete can increase significantly. Each call will require multiple network round-trips between the client and the server to create a new HTTP/2 connection:

  1. Opening a socket
  2. Establishing TCP connection
  3. Negotiating TLS
  4. Starting HTTP/2 connection
  5. Making the gRPC call

Channels are safe to share and reuse between gRPC calls:

  • gRPC clients are created with channels. gRPC clients are lightweight objects and don't need to be cached or reused.
  • Multiple gRPC clients can be created from a channel, including different types of clients.
  • A channel and clients created from the channel can safely be used by multiple threads.
  • Clients created from the channel can make multiple simultaneous calls.

gRPC client factory offers a centralized way to configure channels. It automatically reuses underlying channels. For more information, see xref:grpc/clientfactory.

Connection concurrency

HTTP/2 connections typically have a limit on the number of maximum concurrent streams (active HTTP requests) on a connection at one time. By default, most servers set this limit to 100 concurrent streams.

A gRPC channel uses a single HTTP/2 connection, and concurrent calls are multiplexed on that connection. When the number of active calls reaches the connection stream limit, additional calls are queued in the client. Queued calls wait for active calls to complete before they are sent. Applications with high load, or long running streaming gRPC calls, could see performance issues caused by calls queuing because of this limit.

:::moniker range=">= aspnetcore-5.0"

.NET 5 introduces the SocketsHttpHandler.EnableMultipleHttp2Connections property. When set to true, additional HTTP/2 connections are created by a channel when the concurrent stream limit is reached. When a GrpcChannel is created its internal SocketsHttpHandler is automatically configured to create additional HTTP/2 connections. If an app configures its own handler, consider setting EnableMultipleHttp2Connections to true:

var channel = GrpcChannel.ForAddress("https://localhost", new GrpcChannelOptions
{
    HttpHandler = new SocketsHttpHandler
    {
        EnableMultipleHttp2Connections = true,

        // ...configure other handler settings
    }
});

:::moniker-end

There are a couple of workarounds for .NET Core 3.1 apps:

  • Create separate gRPC channels for areas of the app with high load. For example, the Logger gRPC service might have a high load. Use a separate channel to create the LoggerClient in the app.
  • Use a pool of gRPC channels, for example, create a list of gRPC channels. Random is used to pick a channel from the list each time a gRPC channel is needed. Using Random randomly distributes calls over multiple connections.

[!IMPORTANT] Increasing the maximum concurrent stream limit on the server is another way to solve this problem. In Kestrel this is configured with xref:Microsoft.AspNetCore.Server.Kestrel.Core.Http2Limits.MaxStreamsPerConnection.

Increasing the maximum concurrent stream limit is not recommended. Too many streams on a single HTTP/2 connection introduces new performance issues:

  • Thread contention between streams trying to write to the connection.
  • Connection packet loss causes all calls to be blocked at the TCP layer.

ServerGarbageCollection in client apps

The .NET garbage collector has two modes: workstation garbage collection (GC) and server garbage collection. Each is each tuned for different workloads. ASP.NET Core apps use server GC by default.

Highly concurrent apps generally perform better with server GC. If a gRPC client app is sending and receiving a high number of gRPC calls at the same time, then there may be a performance benefit in updating the app to use server GC.

To enable server GC, set <ServerGarbageCollection> in the app's project file:

<PropertyGroup>
  <ServerGarbageCollection>true</ServerGarbageCollection>
</PropertyGroup>

For more information about garbage collection, see Workstation and server garbage collection.

[!NOTE] ASP.NET Core apps use server GC by default. Enabling <ServerGarbageCollection> is only useful in non-server gRPC client apps, for example in a gRPC client console app.

Load balancing

Some load balancers don't work effectively with gRPC. L4 (transport) load balancers operate at a connection level, by distributing TCP connections across endpoints. This approach works well for loading balancing API calls made with HTTP/1.1. Concurrent calls made with HTTP/1.1 are sent on different connections, allowing calls to be load balanced across endpoints.

Because L4 load balancers operate at a connection level, they don't work well with gRPC. gRPC uses HTTP/2, which multiplexes multiple calls on a single TCP connection. All gRPC calls over that connection go to one endpoint.

There are two options to effectively load balance gRPC:

  • Client-side load balancing
  • L7 (application) proxy load balancing

[!NOTE] Only gRPC calls can be load balanced between endpoints. Once a streaming gRPC call is established, all messages sent over the stream go to one endpoint.

Client-side load balancing

With client-side load balancing, the client knows about endpoints. For each gRPC call, it selects a different endpoint to send the call to. Client-side load balancing is a good choice when latency is important. There's no proxy between the client and the service, so the call is sent to the service directly. The downside to client-side load balancing is that each client must keep track of the available endpoints that it should use.

Lookaside client load balancing is a technique where load balancing state is stored in a central location. Clients periodically query the central location for information to use when making load balancing decisions.

For more information, see xref:grpc/loadbalancing.

Proxy load balancing

An L7 (application) proxy works at a higher level than an L4 (transport) proxy. L7 proxies understand HTTP/2. The proxy receives gRPC calls multiplexed on one HTTP/2 connection and distributes them across multiple backend endpoints. Using a proxy is simpler than client-side load balancing, but adds extra latency to gRPC calls.

There are many L7 proxies available. Some options are:

:::moniker range=">= aspnetcore-5.0"

Inter-process communication

gRPC calls between a client and service are usually sent over TCP sockets. TCP is great for communicating across a network, but inter-process communication (IPC) is more efficient when the client and service are on the same machine.

Consider using a transport like Unix domain sockets or named pipes for gRPC calls between processes on the same machine. For more information, see xref:grpc/interprocess.

Keep alive pings

Keep alive pings can be used to keep HTTP/2 connections alive during periods of inactivity. Having an existing HTTP/2 connection ready when an app resumes activity allows for the initial gRPC calls to be made quickly, without a delay caused by the connection being reestablished.

Keep alive pings are configured on xref:System.Net.Http.SocketsHttpHandler:

var handler = new SocketsHttpHandler
{
    PooledConnectionIdleTimeout = Timeout.InfiniteTimeSpan,
    KeepAlivePingDelay = TimeSpan.FromSeconds(60),
    KeepAlivePingTimeout = TimeSpan.FromSeconds(30),
    EnableMultipleHttp2Connections = true
};

var channel = GrpcChannel.ForAddress("https://localhost:5001", new GrpcChannelOptions
{
    HttpHandler = handler
});

The preceding code configures a channel that sends a keep alive ping to the server every 60 seconds during periods of inactivity. The ping ensures the server and any proxies in use won't close the connection because of inactivity.

:::moniker-end

Flow control

HTTP/2 flow control is a feature that prevents apps from being overwhelmed with data. When using flow control:

  • Each HTTP/2 connection and request has an available buffer window. The buffer window is how much data the app can receive at once.
  • Flow control activates if the buffer window is filled up. When activated, the sending app pauses sending more data.
  • Once the receiving app has processed data, then space in the buffer window is available. The sending app resumes sending data.

Flow control can have a negative impact on performance when receiving large messages. If the buffer window is smaller than incoming message payloads or there's latency between the client and server, then data can be sent in start/stop bursts.

Flow control performance issues can be fixed by increasing buffer window size. In Kestrel, this is configured with xref:Microsoft.AspNetCore.Server.Kestrel.Core.Http2Limits.InitialConnectionWindowSize and xref:Microsoft.AspNetCore.Server.Kestrel.Core.Http2Limits.InitialStreamWindowSize at app startup:

builder.WebHost.ConfigureKestrel(options =>
{
    var http2 = options.Limits.Http2;
    http2.InitialConnectionWindowSize = 1024 * 1024 * 2; // 2 MB
    http2.InitialStreamWindowSize = 1024 * 1024; // 1 MB
});

Recommendations:

  • If a gRPC service often receives messages larger than 96 KB, Kestrel's default stream window size, then consider increasing the connection and stream window size.
  • The connection window size should always be equal to or greater than the stream window size. A stream is part of the connection, and the sender is limited by both.

For more information about how flow control works, see HTTP/2 Flow Control (blog post).

[!IMPORTANT] Increasing Kestrel's window size allows Kestrel to buffer more data on behalf of the app, which possibly increases memory usage. Avoid configuring an unnecessarily large window size.

Streaming

gRPC bidirectional streaming can be used to replace unary gRPC calls in high-performance scenarios. Once a bidirectional stream has started, streaming messages back and forth is faster than sending messages with multiple unary gRPC calls. Streamed messages are sent as data on an existing HTTP/2 request and eliminates the overhead of creating a new HTTP/2 request for each unary call.

Example service:

public override async Task SayHello(IAsyncStreamReader<HelloRequest> requestStream,
    IServerStreamWriter<HelloReply> responseStream, ServerCallContext context)
{
    await foreach (var request in requestStream.ReadAllAsync())
    {
        var helloReply = new HelloReply { Message = "Hello " + request.Name };

        await responseStream.WriteAsync(helloReply);
    }
}

Example client:

var client = new Greet.GreeterClient(channel);
using var call = client.SayHello();

Console.WriteLine("Type a name then press enter.");
while (true)
{
    var text = Console.ReadLine();

    // Send and receive messages over the stream
    await call.RequestStream.WriteAsync(new HelloRequest { Name = text });
    await call.ResponseStream.MoveNext();

    Console.WriteLine($"Greeting: {call.ResponseStream.Current.Message}");
}

Replacing unary calls with bidirectional streaming for performance reasons is an advanced technique and is not appropriate in many situations.

Using streaming calls is a good choice when:

  1. High throughput or low latency is required.
  2. gRPC and HTTP/2 are identified as a performance bottleneck.
  3. A worker in the client is sending or receiving regular messages with a gRPC service.

Be aware of the additional complexity and limitations of using streaming calls instead of unary:

  1. A stream can be interrupted by a service or connection error. Logic is required to restart stream if there is an error.
  2. RequestStream.WriteAsync is not safe for multi-threading. Only one message can be written to a stream at a time. Sending messages from multiple threads over a single stream requires a producer/consumer queue like xref:System.Threading.Channels.Channel%601 to marshall messages.
  3. A gRPC streaming method is limited to receiving one type of message and sending one type of message. For example, rpc StreamingCall(stream RequestMessage) returns (stream ResponseMessage) receives RequestMessage and sends ResponseMessage. Protobuf's support for unknown or conditional messages using Any and oneof can work around this limitation.

Binary payloads

Binary payloads are supported in Protobuf with the bytes scalar value type. A generated property in C# uses ByteString as the property type.

syntax = "proto3";

message PayloadResponse {
    bytes data = 1;
}

Protobuf is a binary format that efficiently serializes large binary payloads with minimal overhead. Text based formats like JSON require encoding bytes to base64 and add 33% to the message size.

When working with large ByteString payloads there are some best practices to avoid unnecessary copies and allocations that are discussed below.

Send binary payloads

ByteString instances are normally created using ByteString.CopyFrom(byte[] data). This method allocates a new ByteString and a new byte[]. Data is copied into the new byte array.

Additional allocations and copies can be avoided by using UnsafeByteOperations.UnsafeWrap(ReadOnlyMemory<byte> bytes) to create ByteString instances.

var data = await File.ReadAllBytesAsync(path);

var payload = new PayloadResponse();
payload.Data = UnsafeByteOperations.UnsafeWrap(data);

Bytes are not copied with UnsafeByteOperations.UnsafeWrap so they must not be modified while the ByteString is in use.

UnsafeByteOperations.UnsafeWrap requires Google.Protobuf version 3.15.0 or later.

Read binary payloads

Data can be efficiently read from ByteString instances by using ByteString.Memory and ByteString.Span properties.

var byteString = UnsafeByteOperations.UnsafeWrap(new byte[] { 0, 1, 2 });
var data = byteString.Span;

for (var i = 0; i < data.Length; i++)
{
    Console.WriteLine(data[i]);
}

These properties allow code to read data directly from a ByteString without allocations or copies.

Most .NET APIs have ReadOnlyMemory<byte> and byte[] overloads, so ByteString.Memory is the recommended way to use the underlying data. However, there are circumstances where an app might need to get the data as a byte array. If a byte array is required then the xref:System.Runtime.InteropServices.MemoryMarshal.TryGetArray%2A?displayProperty=nameWithType method can be used to get an array from a ByteString without allocating a new copy of the data.

var byteString = GetByteString();

ByteArrayContent content;
if (MemoryMarshal.TryGetArray(byteString.Memory, out var segment))
{
    // Success. Use the ByteString's underlying array.
    content = new ByteArrayContent(segment.Array, segment.Offset, segment.Count);
}
else
{
    // TryGetArray didn't succeed. Fall back to creating a copy of the data with ToByteArray.
    content = new ByteArrayContent(byteString.ToByteArray());
}

var httpRequest = new HttpRequestMessage();
httpRequest.Content = content;

The preceding code:

gRPC services and large binary payloads

gRPC and Protobuf can send and receive large binary payloads. Although binary Protobuf is more efficient than text-based JSON at serializing binary payloads, there are still important performance characteristics to keep in mind when working with large binary payloads.

gRPC is a message-based RPC framework, which means:

  • The entire message is loaded into memory before gRPC can send it.
  • When the message is received, the entire message is deserialized into memory.

Binary payloads are allocated as a byte array. For example, a 10 MB binary payload allocates a 10 MB byte array. Messages with large binary payloads can allocate byte arrays on the large object heap. Large allocations impact server performance and scalability.

Advice for creating high-performance applications with large binary payloads: