---
# System prepended metadata

title: go notes

---




To optimize a financial exchange protocol implemented in Go and reduce latency, you can consider the following steps:

1. Use a fast and reliable network: The first step in optimizing a financial exchange protocol is to ensure that you are using a fast and reliable network. This can be achieved by using a low-latency network, such as a dedicated fiber optic line, and by minimizing network hops.
2. Use a lightweight serialization format: The choice of serialization format can have a significant impact on the performance of a financial exchange protocol. Using a lightweight serialization format, such as Protocol Buffers or MessagePack, can significantly reduce latency.
3. Minimize memory allocations: Memory allocations can be a significant source of latency in Go programs. To minimize memory allocations, you can use object pools and reuse objects where possible.
4. Use non-blocking I/O: Non-blocking I/O can help to maximize the utilization of CPU resources and reduce latency. Go provides built-in support for non-blocking I/O, which can be used to implement an efficient financial exchange protocol.
5. Use a connection pool: Creating new connections can be a time-consuming process, especially in high-throughput environments. Using a connection pool can help to minimize the overhead associated with creating new connections.
6. Implement flow control: To prevent overload, it is important to implement flow control in a financial exchange protocol. This can be achieved by setting limits on the number of requests or messages that can be sent or received within a given time period.
7. Use a distributed architecture: To scale a financial exchange protocol, it is important to use a distributed architecture. This can be achieved by using a load balancer to distribute requests across multiple servers, or by implementing a distributed messaging system.

[GCP instances](https://cloud.google.com/compute/docs/memory-optimized-machines)

profiling tools:

[pprof](https://github.com/google/pprof)
[benchstat](https://pkg.go.dev/golang.org/x/perf/cmd/benchstat)
[profile](https://github.com/pkg/profile)
[gops](https://github.com/google/gops)
[tracing with Istio](https://github.com/GoogleCloudPlatform/istio-samples)

8. Use a high-performance data store: The choice of data store can have a significant impact on the performance of a financial exchange protocol. Using a high-performance data store, such as a memory-mapped database or an in-memory key-value store, can help to reduce latency and improve throughput.
9. Use asynchronous programming: Asynchronous programming can help to improve the performance of a financial exchange protocol by allowing multiple tasks to be processed simultaneously. Go provides built-in support for asynchronous programming using goroutines and channels, which can be used to implement an efficient financial exchange protocol.
10. Optimize for CPU cache: Accessing data from main memory can be slow, especially if the data is not in the CPU cache. To improve performance, you can optimize your financial exchange protocol to take advantage of CPU cache by minimizing memory allocations and optimizing data access patterns.
11. Use compression: Compression can be used to reduce the size of data transmitted over the network, which can help to improve throughput and reduce latency. Go provides built-in support for compression using the gzip and zlib packages, which can be used to compress data before it is transmitted over the network.
12. Implement rate limiting: To prevent overload, it is important to implement rate limiting in a financial exchange protocol. This can be achieved by setting limits on the number of requests or messages that can be sent or received within a given time period. Go provides built-in support for rate limiting using the time and sync packages, which can be used to implement an efficient rate limiting system.

some examples of how to implement these optimizations in a financial exchange protocol implemented in Go:

- To use a lightweight serialization format, you can use the Protocol Buffers library. This library provides a fast and efficient way to serialize and deserialize structured data.

[protobuf](https://github.com/golang/protobuf)
- To minimize memory allocations, you can use object pools. An object pool is a collection of pre-allocated objects that can be reused instead of creating new objects each time they are needed.
- To use non-blocking I/O, you can use the net package in Go. This package provides built-in support for non-blocking I/O using the net.Conn interface.
- To implement flow control, you can use the rate limiter from the sync package in Go. This can be used to limit the rate of requests or messages sent or received over the network.
- To use a distributed architecture, you can use a load balancer such as Nginx or HAProxy. These load balancers can be used to distribute requests across multiple servers, which can help to improve the scalability of a financial exchange protocol.

[GLB](https://cloud.google.com/load-balancing/docs/https)



Samples of code implementation just as ref. 

1. Using Protocol Buffers for lightweight serialization:

```go
// Define a Protocol Buffers message
syntax = "proto3";
message Trade {
  string symbol = 1;
  int32 quantity = 2;
  double price = 3;
}

// Serialize a Trade message using Protocol Buffers
trade := &Trade{Symbol: "AAPL", Quantity: 100, Price: 135.50}
data, err := proto.Marshal(trade)
if err != nil {
  log.Fatalf("Failed to serialize trade: %v", err)
}

// Deserialize a Trade message using Protocol Buffers
trade = &Trade{}
err = proto.Unmarshal(data, trade)
if err != nil {
  log.Fatalf("Failed to deserialize trade: %v", err)
}
```

2. Using object pools to minimize memory allocations:

```go
// Define an object pool for Trade messages
type TradePool struct {
  pool sync.Pool
}

func NewTradePool() *TradePool {
  pool := sync.Pool{
    New: func() interface{} {
      return &Trade{}
    },
  }
  return &TradePool{pool: pool}
}

func (p *TradePool) Get() *Trade {
  return p.pool.Get().(*Trade)
}

func (p *TradePool) Put(trade *Trade) {
  p.pool.Put(trade)
}

// Use the TradePool to minimize memory allocations
pool := NewTradePool()
for i := 0; i < 1000; i++ {
  trade := pool.Get()
  trade.Symbol = "AAPL"
  trade.Quantity = 100
  trade.Price = 135.50
  // Process the trade message
  pool.Put(trade)
}
```

3. Using non-blocking I/O to improve performance:

```go
// Use non-blocking I/O to process requests
conn, err := net.Dial("tcp", "localhost:8080")
if err != nil {
  log.Fatalf("Failed to connect: %v", err)
}

data := []byte("Hello, World!")
buf := make([]byte, 1024)
_, err = conn.Write(data)
if err != nil {
  log.Fatalf("Failed to send data: %v", err)
}

for {
  n, err := conn.Read(buf)
  if err != nil {
    if err == io.EOF {
      break
    }
    log.Fatalf("Failed to read data: %v", err)
  }
  log.Printf("Received %d bytes: %s", n, string(buf[:n]))
}
```

4. Using rate limiting to implement flow control:

```go
// Use rate limiting to limit the rate of requests
limiter := rate.NewLimiter(rate.Every(time.Second), 10)
for i := 0; i < 100; i++ {
  if err := limiter.Wait(context.Background()); err != nil {
    log.Fatalf("Rate limit exceeded: %v", err)
  }
  // Process the request
}
```

5. Using a load balancer to distribute requests:

```go
// Use a load balancer to distribute requests across multiple servers
backend1 := "http://localhost:8080"
backend2 := "http://localhost:8081"
loadBalancer := &httputil.ReverseProxy{
  Director: func(req *http.Request) {
    req.URL.Scheme = "http"
    switch rand.Intn(2) {
    case 0:
      req.URL.Host = backend1
    case 1:
      req.URL.Host = backend2
    }
  },
}

http.ListenAndServe(":8000", loadBalancer)
```

#### You:
continue

#### ChatGPT:
Sure, here are some more examples of how to optimize a financial exchange protocol implemented in Go:

6. Using a high-performance data store:

```go
// Use an in-memory key-value store to store trades
type TradeStore struct {
  store map[string]*Trade
  mu sync.RWMutex
}

func NewTradeStore() *TradeStore {
  return &TradeStore{
    store: make(map[string]*Trade),
  }
}

func (s *TradeStore) Get(symbol string) (*Trade, error) {
  s.mu.RLock()
  defer s.mu.RUnlock()
  if trade, ok := s.store[symbol]; ok {
    return trade, nil
  }
  return nil, fmt.Errorf("Trade not found: %s", symbol)
}

func (s *TradeStore) Put(symbol string, trade *Trade) {
  s.mu.Lock()
  defer s.mu.Unlock()
  s.store[symbol] = trade
}

// Use the TradeStore to store trades
store := NewTradeStore()
trade := &Trade{Symbol: "AAPL", Quantity: 100, Price: 135.50}
store.Put("AAPL", trade)
```

7. Using compression to reduce the size of data transmitted over the network:

```go
// Use gzip compression to compress data
data := []byte("Hello, World!")
var buf bytes.Buffer
gz := gzip.NewWriter(&buf)
if _, err := gz.Write(data); err != nil {
  log.Fatalf("Failed to compress data: %v", err)
}
if err := gz.Close(); err != nil {
  log.Fatalf("Failed to close gzip writer: %v", err)
}
compressedData := buf.Bytes()

// Use gzip decompression to decompress data
gz, err := gzip.NewReader(bytes.NewReader(compressedData))
if err != nil {
  log.Fatalf("Failed to create gzip reader: %v", err)
}
var decompressedData bytes.Buffer
if _, err := io.Copy(&decompressedData, gz); err != nil {
  log.Fatalf("Failed to decompress data: %v", err)
}
if err := gz.Close(); err != nil {
  log.Fatalf("Failed to close gzip reader: %v", err)
}
data = decompressedData.Bytes()
```


##### DPDK Implementaion

To use DPDK with Go, you can use the Go binding for DPDK called `gdpdk`. `gdpdk` provides Go bindings for the DPDK libraries, allowing you to write high-performance networking applications in Go. You can use `gdpdk` to offload packet processing tasks to DPDK and achieve higher throughput and lower latency.

Here are some steps to use DPDK to optimize a Go implementation of the QuickFIX financial messaging protocol:

1. Install DPDK: To use DPDK, you need to install it on your system. You can download the DPDK source code from the DPDK website and follow the instructions in the DPDK Getting Started Guide to install it.
2. Configure DPDK: After installing DPDK, you need to configure it to work with your network interface. You can use the `dpdk-devbind.py` script provided with DPDK to bind your network interface to DPDK.
3. Use `gdpdk`: After installing and configuring DPDK, you can use `gdpdk` to write high-performance networking applications in Go. `gdpdk` provides Go bindings for the DPDK libraries, allowing you to offload packet processing tasks to DPDK and achieve higher throughput and lower latency.
4. Integrate QuickFIX with `gdpdk`: To integrate QuickFIX with `gdpdk`, you need to modify the QuickFIX code to use `gdpdk` for network I/O. You can use the `net.PacketConn` interface provided by Go to interact with `gdpdk`. By using `gdpdk` for network I/O, you can offload packet processing tasks to DPDK and achieve higher throughput and lower latency.

Here's an example of how to use `gdpdk` to implement a simple TCP echo server:

```go
package main

import (
	"github.com/yerden/go-dpdk"
	"net"
	"os"
	"syscall"
)

const (
	RTE_LOGTYPE_ECHO = dpdk.RTE_LOGTYPE_USER1
)

func main() {
	// Initialize DPDK
	dpdkArgs := []string{
		"-l", "1-3",
		"--no-pci",
		"--master-lcore=1",
		"--file-prefix=echo",
		"--log-level=user1",
	}
	dpdk.Initialize(dpdkArgs)

	// Create a TCP listener
	listener, err := net.Listen("tcp", ":5000")
	if err != nil {
		panic(err)
	}
	defer listener.Close()

	// Wait for incoming connections
	for {
		conn, err := listener.Accept()
		if err != nil {
			panic(err)
		}

		// Handle the connection in a new goroutine
		go func() {
			// Convert the net.Conn to a syscall.Conn
			sysConn, err := dpdk.MakeSyscallConn(conn)
			if err != nil {
				panic(err)
			}

			// Use the syscall.Conn with gdpdk
			socket, err := dpdk.Socket(dpdk.SOCKET_ID_ANY)
			if err != nil {
				panic(err)
			}
			packetConn, err := dpdk.NewPacketConn(socket, sysConn)
			if err != nil {
				panic(err)
			}

			// Echo the data back to the client
			buf := make
```



```go
			for {
				// Read data from the client
				data := make([]byte, 1024)
				n, err := packetConn.ReadFrom(data)
				if err != nil {
					break
				}

				// Echo the data back to the client
				_, err = packetConn.WriteTo(data[:n], nil)
				if err != nil {
					break
				}
			}

			// Close the connection
			packetConn.Close()
		}()
	}
}
```

In this example, `gdpdk` is used to create a `PacketConn` from a TCP `net.Conn`. The `PacketConn` is used to read data from the client and echo it back to the client. By using `gdpdk`, the packet processing is offloaded to DPDK, which can result in much higher throughput and lower latency.

While this example is a simple TCP echo server, the same approach can be used to integrate QuickFIX with `gdpdk` and achieve higher performance for financial messaging applications. By offloading packet processing to DPDK, it is possible to reduce latency and improve the overall performance of a financial messaging application.



1. Use a high-performance VM instance: GCP provides a wide range of VM instances with different CPU and memory configurations. For a financial messaging application, it is important to use a VM instance with high CPU and memory performance to handle the high message throughput. You can use a VM instance with a high number of CPU cores and a high amount of memory to achieve the best performance.
2. Use a managed load balancer: GCP provides a managed load balancer that can be used to distribute the incoming messages across multiple VM instances. By using a managed load balancer, you can ensure that the message processing is evenly distributed across the VM instances, which can help to reduce latency and improve the overall performance of the financial messaging application.
3. Use a high-performance network: GCP provides a high-performance network that can be used to achieve low latency and high throughput for financial messaging applications. By using a high-performance network, you can ensure that the messages are transmitted quickly and efficiently between the VM instances, which can help to improve the performance of the financial messaging application.
4. Use SSDs for storage: GCP provides high-performance SSDs that can be used for storing the financial messaging data. By using SSDs, you can achieve high I/O performance and reduce the latency of the messaging application.
5. Use a distributed caching system: A distributed caching system, such as Redis or Memcached, can be used to cache the financial messaging data and reduce the I/O load on the VM instances. By using a distributed caching system, you can reduce the latency and improve the performance of the financial messaging application.
6. Use autoscaling: GCP provides autoscaling capabilities that can be used to automatically scale the number of VM instances based on the incoming message load. By using autoscaling, you can ensure that the financial messaging application can handle high message throughput and achieve high performance even during peak message periods.

Here is an example of how to optimize the QuickFIX financial messaging protocol over VMs on GCP:

1. Choose a high-performance VM instance: Select a VM instance with high CPU and memory performance, such as the n2-highcpu-16 instance type, which provides 16 vCPUs and 64 GB of memory.
2. Use a managed load balancer: Use the GCP load balancer to distribute the incoming messages across multiple VM instances. The load balancer can be configured to distribute the traffic evenly across the VM instances, which can help to reduce latency and improve the performance of the financial messaging application.
3. Use a high-performance network: Use the GCP high-performance network to achieve low latency and high throughput for the financial messaging application. The network can be configured to use low-latency routes between the VM instances, which can help to reduce the message transmission time.
4. Use SSDs for storage: Use the GCP SSDs for storing the financial messaging data. The SSDs provide high I/O performance, which can help to reduce the latency of the messaging application.
5. Use a distributed caching system: Use a distributed caching system, such as Redis or Memcached, to cache the financial messaging data and reduce the I/O load on the VM instances. By using a distributed caching system, you can reduce the latency and improve the performance of the financial messaging application.
6. Use autoscaling: Use the GCP autoscaling capabilities to automatically scale the number of VM instances based on the incoming message load. By using autoscaling, you can ensure that the financial messaging application can handle high message throughput and achieve high performance even during peak message periods.

