Josie

josie

These are mistakes that I’ve made writing Go. Although these might not cause any sort of error but they can potentially affect the software. 1. Inside Loop There are several ways to make a mess inside a loop which you need to be aware of. 1.1 Using reference to loop iterator variable Due to efficiency, the loop iterator variable is a single variable that takes different values in each loop iteration. It might lead to unwitting behavior. in := [] { , , } out []* _, v := in {
	out = (out, &v)
}

fmt.Println( , *out[ ], *out[ ], *out[ ])
fmt.Println( , out[ ], out[ ], out[ ]) int 1 2 3 var int for range append "Values:" 0 1 2 "Addresses:" 0 1 2 The result will be: Values: 3 3 3
Addresses: 0xc000014188 0xc000014188 0xc000014188 As you can see all elements in slice are 3. It is actually easy to explain why this did happen: in every iteration we append the address of to the slice. As mentioned before is a single variable which takes new values in each iteration. So as you can see in second line of the output, addresses are the same and all of them are pointing to the same value. out v out v The simple fix is to copy the loop iterator variable into a new variable: in := [] { , , } out []* _, v := in {
	v := v
	out = (out, &v)
}

fmt.Println( , *out[ ], *out[ ], *out[ ])
fmt.Println( , out[ ], out[ ], out[ ]) int 1 2 3 var int for range append "Values:" 0 1 2 "Addresses:" 0 1 2 The new output: Values: 1 2 3
Addresses: 0xc0000b6010 0xc0000b6018 0xc0000b6020 The same issue can be found in the loop iteration variable being used in a Goroutine. list := [] { , , } _, v := list { {
		fmt.Printf( , v)
	}()
} int 1 2 3 for range go func () "%d " Output will be: 3 3 3 It can be fixed using the very same solution mentioned above. Note that without running the function with Goroutine, the code runs as expected. 1.2 Calling WaitGroup.Wait in loop This mistake can be made using a shared variable of type , as shown in the below code the at line 7 can only be unblocked, when at line 5 is invoked times, since it is used as parameter to call at line2. However, the is called inside the loop, so that it blocks Goroutine creation at line 4 in the next iterations. The simple solution is to move the invocation of out from the loop. WaitGroup Wait() Done() len(tasks) Add() Wait() Wait() wg sync.WaitGroup
wg.Add( (tasks)) _, t := tasks { { group.Done()
	}(t) }

group.Wait() var len for range go func (t *task) defer // group.Wait() 1.3 Using defer in loop does not execute until the returns. You shouldn’t use in a loop unless you’re sure what you are doing. defer function defer mutex sync.Mutex Person {
	Age }
persons := ([]Person, ) _, p := persons {
	mutex.Lock() p.Age = mutex.Unlock()
} var type struct int make 10 for range // defer mutex.Unlock() 13 In the above example, if you use line 8 instead of line 10, next iteration can not hold mutex lock because the lock is already in use and the loop blocks forever. If you really need to use defer inside loop you might wanna entrust another function to do the work. mutex sync.Mutex Person {
	Age }
persons := ([]Person, ) _, p := persons { {
		mutex.Lock() mutex.Unlock()
		p.Age = }()
} var type struct int make 10 for range func () defer 13 But, sometimes using in loop may become handy. So you really need to know what you are doing. defer Go suffers no fools 2. Sending into an unguaranteed channel You can send values into channels from one Goroutine and receive those values into another Goroutine. By default, sends and receives block until the other side is ready. This allows Goroutines to synchronize without explicit locks or condition variables. { ch := ( obj, ) {
		obj := do()
		ch <- result
	} () { result = <- ch : result <- time.After(timeout): }
} func doReq (timeout time.Duration) obj // ch :=make(chan obj) make chan 1 go func () select case return case return nil Let’s check the above code. The function creates a child Goroutine at line 4 to handle a request which is a common practice in Go server programs. doReq The child Goroutine executes function and sends the result back to the parent through channel ch at line 6. The child will block at line 6 until the parent receives the result from ch at line 9. Meanwhile, the parent will block at until either when the child sends the result to (line 9) or when a timeout happens (line 11). do select ch If timeout happens earlier, the parent will return from func at line 12, and no one else can receive the result from any more, this results in the child being blocked forever. The fix is to change from an unbuffered channel to a buffered one, so that the child Goroutine can always send the result even when the parent has exit. doReq ch ch Another fix can be using a statement with empty default case at line 6 so if no Goroutine receiving the , default will happen. Although this solution might not work always. select ch ... { ch <- result: :
}
... select case default 3. Not using interfaces Interfaces can make code more flexible. It is a way to bring polymorphism in code. Interface allows you to ask for a set of behaviors instead of a particular type. Not using interfaces might not cause any error but it can lead to a less simple, less flexible, and less extensible code. Among many interfaces, and might be the most beloved ones. io.Reader io.Writer Reader {
    Read(p [] ) (n , err error)
} Writer {
    Write(p [] ) (n , err error)
} type interface byte int type interface byte int These interfaces can be very powerful. Let’s assume you are going to write an object into a file, so you defined a method: Save func (o *obj) Save (file os.File) error What if you need to write into a next day? you don’t want to define a new method. Do you? So use . http.ResponseWriter io.Writer func (o *obj) Save (w io.Writer) error Also, an important note you should know is that always ask for behaviors you are going to use. In the above example, asking for an can work as well but it’s not a best practice when the only method you are going to use is . The bigger the interface, the weaker the abstraction. io.ReadWriteCloser Write So most of the time you better stay with behaviors instead of concrete type. 4. Bad ordered struct This mistake can not cause any error as well but it can cause more memory usage. BadOrderedPerson {
	Veteran Name Age } OrderedPerson {
	Name Age Veteran } type struct bool // 1 byte string // 16 byte int32 // 4 byte type struct string int32 bool It seems like both types have the same size of 21 bytes, but the result shows something totally different . Compiling code using , the type allocates 32 bytes while type does 24 bytes. Why? Well, the reason is . In 64 bit architecture memory allocates consecutive packet of 8 bytes. Padding need to be added can be calculate by: GOARCH=amd64 BadOrderedPerson OrderedPerson Data structure alignment padding = (align - (offset mod align)) mod align
aligned = offset + padding
        = offset + ((align - (offset mod align)) mod align) BadOrderedPerson {
	Veteran _       [ ] Name Age _ {} } OrderedPerson {
	Name Age Veteran _ {} 
} type struct bool // 1 byte 7 byte // 7 byte: padding for alignment string // 16 byte int32 // 4 byte struct // to prevent unkeyed literals // zero sized values, like struct{} and [0]byte occurring at // the end of a structure are assumed to have a size of one byte. // so padding also will be addedd here as well. type struct string int32 bool struct It can lead to a performance issue when you have a big frequently used type. But don’t worry, you don’t have to take care of all your structs manually. Using you can easily check your code for this issue. maligned 5. Not using race detector in test Data races cause mysterious failures, often long after the code has been deployed to production. Because of that those are among the most common and hardest to debug types of bugs in concurrent systems. To help distinguish those kind of bugs, Go 1.1 introduced a built-in data race detector. It can be used simply adding flag. -race $ go -race pkg $ go run -race pkg.go $ go build -race $ go install -race pkg test # to test the package # to run the source file # to build the package # to install the package When race detector enabled, compiler will record when and how the memory was accessed within the code, while the watches for unsynchronized accesses to shared variables. runtime When a data race has been found, race detector prints a report which contains stack traces for conflicting accesses. Here is an example: WARNING: DATA RACE
Read by goroutine 185:
  net.(*pollServer).AddFD()
      src/net/fd_unix.go:89 +0x398
  net.(*pollServer).WaitWrite()
      src/net/fd_unix.go:247 +0x45
  net.(*netFD).Write()
      src/net/fd_unix.go:540 +0x4d4
  net.(*conn).Write()
      src/net/net.go:129 +0x101
  net.func·060()
      src/net/timeout_test.go:603 +0xaf

Previous write by goroutine 184:
  net.setWriteDeadline()
      src/net/sockopt_posix.go:135 +0xdf
  net.setDeadline()
      src/net/sockopt_posix.go:144 +0x9c
  net.(*conn).SetDeadline()
      src/net/net.go:161 +0xe3
  net.func·061()
      src/net/timeout_test.go:616 +0x3ed

Goroutine 185 (running) created at:
  net.func·061()
      src/net/timeout_test.go:609 +0x288

Goroutine 184 (running) created at:
  net.TestProlongTimeout()
      src/net/timeout_test.go:618 +0x298
  testing.tRunner()
      src/testing/testing.go:301 +0xe8 Last words The only real mistake is one from which we learn nothing. Also published behind a paywall on: https://medium.com/swlh/5-mistakes-ive-made-in-go-75fb64b943b8

Don't Make These 5 Golang Mistakes

Go is a programming language often used for applications in which performance matters. Optimizing your code based on assumptions is not a best practice of course. You need to have insights about your code performance and bottlenecks to be able to optimize it efficiently. What is a profiler? Profiler is a dynamic performance analysis tool that provides critical execution insights in various dimensions which enable resolving performance issues, locating memory leaks, thread contention and more. Go comes with built-in support for profiling! What kinds of profiles can I get? Go has several built in profiles: stack traces of all current Goroutines Goroutine: stack traces of CPU returned by runtime CPU: a sampling of memory allocations of live objects Heap: a sampling of all past memory allocations Allocation: stack traces that led to the creation of new OS threads Thread: stack traces that led to blocking on synchronization primitives Block: stack traces of holders of contended mutexes Mutex: How to get a profile? There are a few ways to create a profile. 1. Using “go test” to generate profile Support for profiling the standard testing package. As an example, the following command runs all benchmarks and writes CPU and memory profile to and : built into cpu.prof mem.prof go -cpuprofile cpu.prof -memprofile mem.prof -bench . test 2. Download live profile using HTTP If you need to have a live profile in a long-running service, package is your ultimate solution. Solely import the package in your code and you are good to go: net/http/pprof _ import "net/http/pprof" The initialization of this package is the reason you need to import it solely. {
	http.HandleFunc( , Index)
	http.HandleFunc( , Cmdline)
	http.HandleFunc( , Profile)
	http.HandleFunc( , Symbol)
	http.HandleFunc( , Trace)
} func init () "/debug/pprof/" "/debug/pprof/cmdline" "/debug/pprof/profile" "/debug/pprof/symbol" "/debug/pprof/trace" As you can see the package initialization registers handlers for the given URL in . These handlers write the profile into a which you can download. Now the only thing you need to do is running a HTTP server with a handler: DefaultServeMux http.ResponseWriter nil { {
		http.ListenAndServe( , )
	}()
} func init () go func () ":1234" nil Note that if you already run your http server with , you don’t need to run another server and profiles are available on your server. DefaultServeMux Now, you can download live profile data from the URL. /debug/pprof/ 3. Profiling in code Using , You can also profile directly within the code. For example you can start a CPU profile using and then stop it by . Now you have the CPU profile written to the given . runtime/pprof pprof.StartCPUProfile(io.Writer) pprof.StopCPUProfile() io.Writer But wait, there is an easier option. has developed a package named which can make profiling easy as pie. First you have to get the package using following command (or using Go modules): Dave Cheney profile go get github.com/pkg/profile Then you can use it like this: { profile.Start(profile.ProfilePath( )).Stop() } func main () defer "." // do something Above code will write CPU profile into in working directory. The default has been set to CPU profiling but you can pass options to to be able to get other profiles as well. cpu.pprof profile.Start() profile.Start(profile.CPUProfile).Stop() profile.Start(profile.GoroutineProfile).Stop() profile.Start(profile.BlockProfile).Stop() profile.Start(profile.ThreadcreationProfile).Stop() profile.Start(profile.MemProfileHeap).Stop() profile.Start(profile.MemProfileAllocs).Stop() profile.Start(profile.MutexProfile).Stop() // CPUProfile enables cpu profiling. Note: Default is CPU defer // GoroutineProfile enables goroutine profiling. // It returns all Goroutines alive when defer occurs. defer // BlockProfile enables block (contention) profiling. defer // ThreadcreationProfile enables thread creation profiling. defer // MemProfileHeap changes which type of memory profiling to // profile the heap. defer // MemProfileAllocs changes which type of memory to profile // allocations. defer // MutexProfile enables mutex profiling. defer How to use a profile? So we have collected the profile data, what now? is a tool for visualization and analysis of profiling data. It reads a collection of profiling samples in format and generates reports to visualize and help analyze the data. It can generate both text and graphical reports. pprof profile.proto All of the ways that mentioned above are using under the hood and this package writes runtime profiling data in the format expected by the pprof visualization tool. runtime/pprof Write A Simple Code To Profile For a better understanding, I’ve written the below code to profile. It is a simple HTTP server which handles requests on URL. Every request comes in, the function will declare a and create a child Goroutine at line 16. The child will decode the given JSON log within the and if it was successful it does a time consuming (~400 millisecond) write and then sends to . Meanwhile the function blocks on select statement (line 27) until either when the child sends the result to (line 24) or when a timeout happens (line 30). /log logHandler chan int Request.Body http.StatusOK ch logHandler ch ( _ ) {
	http.HandleFunc( , logHandler)
	http.ListenAndServe( , )
} {
	ch := ( ) {
		obj := ( [ ] ) err := json.NewDecoder(r.Body).Decode(&obj); err != {
			ch <- http.StatusBadRequest } time.Sleep(time.Duration(rand.Intn( )) * time.Millisecond)
		ch <- http.StatusOK
	}() { status := <-ch:
		w.WriteHeader(status) <-time.After( * time.Millisecond):
		w.WriteHeader(http.StatusRequestTimeout)
	}
} import "encoding/json" "math/rand" "net/http" "net/http/pprof" "time" func main () "/log" ":8080" nil func logHandler (w http.ResponseWriter, r *http.Request) make chan int go func () make map string float64 if nil return // simulation of a time consuming process like writing logs into db 400 select case case 200 I’ve compiled and run the code and have sent some loads to it using . Here is the output: hey Summary:
  Total:        0.8292 secs
  Slowest:      0.2248 secs
  Fastest:      0.0039 secs
  Average:      0.1574 secs
  Requests/sec: 241.1882Latency 

distribution:
  10% 0.0409 secs
  25% 0.1143 secs
  50% 0.2007 secs
  75% 0.2013 secs
  90% 0.2093 secs
  95% 0.2187 secs
  99% 0.2245 secsStatus code 

distribution:
  [200] 87 responses
  [408] 113 responses in in in in in in in pprof Basics After writing, running, and sending load to our service, it is time to run the pprof tool on the profile of the service to see which functions were the most consuming ones during the load. Install pprof Tool You can use command or install to use it directly. go tool pprof pprof go get -u github.com/google/pprof Run pprof On Interactive Mode Now let’s run pprof on allocated objects using the following command: pprof -alloc_objects http://:8080/debug/pprof/allocs This will open a simple interactive shell that takes pprof commands to generate reports. Now enter the command , the output will be the top memory consumers. top Dropped Nodes As you can see in the output, 11 nodes has been dropped (line 3). A node is an object entry and dropping a node can help to reduce the noise, but sometimes it may hide the root cause of the issue. Adding the flag or assigning it directly inside the attractive shell will include all data of the profile in the result. -nodefraction=0 Difference Between Flat And Cumulative In the above output, you can see two columns, and . Flat means , while cum (cumulative) means For better understanding, let’s assume the following function, the flat time of function is 4s and the cum is 11s. flat cum only by this function by this function and functions called down the stack. A {
   B() DO STH DIRECTLY C() } func A () // takes 1s // takes 4s // takes 6s Difference Between Allocations And Heap Profile An important note to consider is that the difference between the allocations and heap profile is the way the pprof tool reads them at start time. Allocations profile will start pprof in a mode which displays the total number of allocated objects since the program began which as well, but heap profile will start in a mode that displays number of live allocated objects which . includes garbage collected ones does not include garbage collected bytes That means in the pprof tool you can change the mode regardless of the profile type (heap or allocations). It can be done using the following values. You can either set them as a pprof flag or assign them to inside the pprof interactive shell: sample_index amount of memory allocated and not released yet (heap) inuse_space : amount of objects allocated and not released yet (heap) inuse_objects : total amount of memory allocated (regardless of released) alloc_space : total amount of objects allocated (regardless of released) alloc_objects : Get An SVG Graph From The Profile You can generate a graph of your profile in an SVG format, and open it with a web browser. The following command will request for a 5s CPU profile and will launch a browser with an SVG file. pprof -web http://:8080/debug/pprof/profile?seconds=5 Run pprof Via A Web Interface One of the useful pprof features is the web interface. If the flag is specified, pprof starts a web server at the specified host:por that provides an interactive web-based interface to pprof. -http pprof -http :8080 http://:8080/debug/pprof/goroutine The preceding command should automatically open your web browser at the right page; if not, you can manually visit the specified port in your web browser. Monitoring Goroutines From Command Line As you can see in the Goroutine profile, 115 Goroutines are alive which is not normal for idle service. It should be a Goroutine leak. So let’s check Goroutines status using the tool. grmon is a command line monitoring for Goroutines. Let’s install it and run it on host that is serving the profile data: grmon go get -u github.com/bcicen/grmon
grmon -host :8080 The output is: It seems the function blocks on a action. After reviewing the code I found the bug. It has occurred due to sending into an unguaranteed channel. I’ve fixed that by turn the to a buffered one channel (line 15). It was the very same mistake that I have mentioned . logHandler chan send ch this post How does a profiler work? Although stack traces generated by profilers are often intelligible, it is sometimes necessary to understand how profilers work under the hood to get even more details from the generated profiles. CPU Profiler The Go CPU profiler uses a signal to record code execution statistics. Once the signal got registered, it will deliver every specified time interval. This timer unlike typical timers will increment when the CPU is executing the process. The signal will interrupt code execution and make it possible to see which code was interrupted. SIGPROF When the function is called, the signal handler will be registered to call every 10 milliseconds interval by default (100 Hz). On Unix, it uses a syscall to set the signal timer. pprof.StartCPUProfile SIGPROF setitimer(2) On every invocation signal, the handler will trace back the execution by unwinding it from the current value. This will generate a stack trace and increment its hit count. The whole process will result in a list of stack traces grouped by the number of times each stack trace is seen. PC After the profiler is stopped, the stack traces will be converted to pprof-supported stack frames with source file names, line numbers and function names. Memory Profiler The memory profiler samples allocations. It will show function calls allocations. Recording all allocation and unwinding thestack trace would be expensive, therefore a sampling technique is used. heap The sampling process relies on a pseudo random number generator based on to sample only a fraction of allocations. The generated numbers define the distance between samples in terms of allocated memory size. This means that only allocations that cross the next random sampling point will be sampled. exponential distribution The sampling rate defines the mean of exponential distribution . The default value is 512 KB which is specified by the . That means to tune the size of the fraction to be sampled, you have to change value. Setting the value to 1, will include every allocated block in the profile. Obviously this will slow down your application. runtime.MemProfileRate runtime.MemProfileRate Stack traces with corresponding sampled allocations information is available any time, since the memory allocation profiler is always active. Setting the to 0, will turn off the memory sampling entirely. runtime.MemProfileRate Once the stack traces got ready, those will be converted to pprof-supported stack frames. Also published here .

Go: The Complete Guide to Profiling Your Code

Go: la guía completa para perfilar su código

119 Stories To Learn About Oct_network

239 Stories To Learn About Web3 Writing Contest

Reflecting on 2023, it’s a little surreal that amidst the ultra-high-speed blockchain transactions and coffee machines more advanced than last decade’s supercomputers, the FinTech world still retains a nostalgic hangover from centuries past: gender inequality. Time-Travelling with Claudia: A Nobel Notion Meet This researcher, with her distinguished Nobel Economics prize, delved into the depths of history. She uncovered that although our technology has advanced leaps and bounds, our strides in gender parity seem comparatively measured. Two centuries of data suggest a progression from confining women to domestic roles to under-compensating them in the professional arena. Goldin’s historical perspective sheds light on this inequality, highlighting it not merely as an injustice but also an inefficiency, especially when considering that the academic prowess of women often surpasses that of men. Claudia Goldin. “A few years ago, I took a class on the most influential modern economists. It was at an Ivy League institution in the US, and dozens of old, white men and their theories made the syllabus. Claudia Goldin was the only woman. On the slide accompanying the lecture in which she was featured, her name was misspelt”, . Cox is a journalist and broadcaster specialising in business, finance, and gender equality. writes Josie Cox in her article for The Guardian FinTech’s Gender Conundrum: An Industry’s Midlife Crisis If FinTech were a person, it’d be that flashy guy with the latest tech gadget, claiming to be ahead of the curve yet still holding some, let’s say, ‘traditional’ beliefs about gender roles. A grim 22% pay gap paints the stark reality, and it’s not for lack of talent. Studies scream it loud and clear: the sector is underutilising half of its potential brainpower. A recent study called , undertaken by EY and Innovate Finance, gathered perspectives from 120 contenders from the Innovate Finance Women in FinTech Powerlist. The findings underscored issues like the fact that 27% of the participants felt underappreciated, and a quarter pointed out ambiguous career progression routes. “Transforming UK FinTech: Overcoming Barriers to Female Advancement” In a , the FinTech firm Payrow highlighted, “The gender pay gap in the UK tech, finance, and FinTech sectors is notably higher than the national average. Recognising these challenges, Payrow reports that 30% of its team comprises women. The firm emphasises that a diverse team, in terms of age and gender, results in varied perspectives, enhances problem-solving, and strengthens team cohesion”. press release on www.insidermedia.com Tackling the Goliath: Solutions or Well-Dressed Platitudes? Championing gender diversity is more than a PR exercise. From setting hard targets on gender diversity to overhauling recruitment practices, companies need a game plan. The suggestion of a ‘women-only shortlist’, while commendable, can only be a starting point. Beyond hiring, the workplace culture must evolve. As businesses and as societies, we need to ensure that the FinTech environment is conducive to women’s growth and leadership. Promoting gender diversity isn’t just about numbers; it’s about meaningful . Establishing transparent gender diversity goals, especially for high-ranking roles, is a primary step. Such goals shouldn’t remain on paper, and regular monitoring ensures accountability. Mentorship and sponsorship programmes can act as valuable conduits to guide female professionals, helping them steer through career paths and leap over hurdles. Recruitment practices, too, need introspection. Ensuring they are devoid of biases and actively drawing a diversified talent pool is essential. Furthermore, in the dynamic world of FinTech, flexibility is paramount. Offering adaptable working arrangements can serve as a magnet for retaining female talent. Finally, it all boils down to culture. A truly inclusive organisational environment, where every employee feels cherished and backed, paves the way for nurturing gender diversity and fostering female leadership. integration at every level The Road Ahead: Robots, AI, and... Equal Pay? The realm of FinTech isn’t static. As we move forward, the skills required to excel in the industry are evolving. While STEM remains paramount, with the rise of AI, perhaps softer skills might finally get their day in the sun. Interpersonal communication, emotional intelligence, and an understanding of diverse clientele could become the new gold standards. Maybe, in this changing landscape, the gender scales will find a natural balance. Or, perhaps we’ll be having the same conversation in another two centuries, just with more sophisticated tech lingo. The Future is... Complicated For all our advancements, the road to gender equality in FinTech is riddled with potholes. While the optimist in me wants to believe that change is around the corner, the realist knows it’s going to be a tough ride. But, as Goldin has shown us, understanding our past can provide a roadmap for the future. And if we can manage to not lose it in the vast cloud space of blockchain transactions and AI algorithms, perhaps, just perhaps, the future of FinTech will be as bright and balanced as we hope.

Hitchhiking Through FinTech: Still Waiting at the Gender Equality Stop

279 Stories To Learn About Non Fungible Tokens

THE DREAM-CHILD

GLEN "NOTES" AND OTHER MATTERS

Anne's House of Dreams by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . IN THE GARRET OF GREEN GABLES CHAPTER I. IN THE GARRET OF GREEN GABLES "Thanks be, I'm done with geometry, learning or teaching it," said Anne Shirley, a trifle vindictively, as she thumped a somewhat battered volume of Euclid into a big chest of books, banged the lid in triumph, and sat down upon it, looking at Diana Wright across the Green Gables garret, with gray eyes that were like a morning sky. The garret was a shadowy, suggestive, delightful place, as all garrets should be. Through the open window, by which Anne sat, blew the sweet, scented, sun-warm air of the August afternoon; outside, poplar boughs rustled and tossed in the wind; beyond them were the woods, where Lover's Lane wound its enchanted path, and the old apple orchard which still bore its rosy harvests munificently. And, over all, was a great mountain range of snowy clouds in the blue southern sky. Through the other window was glimpsed a distant, white-capped, blue sea—the beautiful St. Lawrence Gulf, on which floats, like a jewel, Abegweit, whose softer, sweeter Indian name has long been forsaken for the more prosaic one of Prince Edward Island. Diana Wright, three years older than when we last saw her, had grown somewhat matronly in the intervening time. But her eyes were as black and brilliant, her cheeks as rosy, and her dimples as enchanting, as in the long-ago days when she and Anne Shirley had vowed eternal friendship in the garden at Orchard Slope. In her arms she held a small, sleeping, black-curled creature, who for two happy years had been known to the world of Avonlea as "Small Anne Cordelia." Avonlea folks knew why Diana had called her Anne, of course, but Avonlea folks were puzzled by the Cordelia. There had never been a Cordelia in the Wright or Barry connections. Mrs. Harmon Andrews said she supposed Diana had found the name in some trashy novel, and wondered that Fred hadn't more sense than to allow it. But Diana and Anne smiled at each other. They knew how Small Anne Cordelia had come by her name. "You always hated geometry," said Diana with a retrospective smile. "I should think you'd be real glad to be through with teaching, anyhow." "Oh, I've always liked teaching, apart from geometry. These past three years in Summerside have been very pleasant ones. Mrs. Harmon Andrews told me when I came home that I wouldn't likely find married life as much better than teaching as I expected. Evidently Mrs. Harmon is of Hamlet's opinion that it may be better to bear the ills that we have than fly to others that we know not of." Anne's laugh, as blithe and irresistible as of yore, with an added note of sweetness and maturity, rang through the garret. Marilla in the kitchen below, compounding blue plum preserve, heard it and smiled; then sighed to think how seldom that dear laugh would echo through Green Gables in the years to come. Nothing in her life had ever given Marilla so much happiness as the knowledge that Anne was going to marry Gilbert Blythe; but every joy must bring with it its little shadow of sorrow. During the three Summerside years Anne had been home often for vacations and weekends; but, after this, a bi-annual visit would be as much as could be hoped for. "You needn't let what Mrs. Harmon says worry you," said Diana, with the calm assurance of the four-years matron. "Married life has its ups and downs, of course. You mustn't expect that everything will always go smoothly. But I can assure you, Anne, that it's a happy life, when you're married to the right man." Anne smothered a smile. Diana's airs of vast experience always amused her a little. "I daresay I'll be putting them on too, when I've been married four years," she thought. "Surely my sense of humor will preserve me from it, though." "Is it settled yet where you are going to live?" asked Diana, cuddling Small Anne Cordelia with the inimitable gesture of motherhood which always sent through Anne's heart, filled with sweet, unuttered dreams and hopes, a thrill that was half pure pleasure and half a strange, ethereal pain. "Yes. That was what I wanted to tell you when I 'phoned to you to come down today. By the way, I can't realize that we really have telephones in Avonlea now. It sounds so preposterously up-to-date and modernish for this darling, leisurely old place." "We can thank the A. V. I. S. for them," said Diana. "We should never have got the line if they hadn't taken the matter up and carried it through. There was enough cold water thrown to discourage any society. But they stuck to it, nevertheless. You did a splendid thing for Avonlea when you founded that society, Anne. What fun we did have at our meetings! Will you ever forget the blue hall and Judson Parker's scheme for painting medicine advertisements on his fence?" "I don't know that I'm wholly grateful to the A. V. I. S. in the matter of the telephone," said Anne. "Oh, I know it's most convenient—even more so than our old device of signalling to each other by flashes of candlelight! And, as Mrs. Rachel says, 'Avonlea must keep up with the procession, that's what.' But somehow I feel as if I didn't want Avonlea spoiled by what Mr. Harrison, when he wants to be witty, calls 'modern inconveniences.' I should like to have it kept always just as it was in the dear old years. That's foolish—and sentimental—and impossible. So I shall immediately become wise and practical and possible. The telephone, as Mr. Harrison concedes, is 'a buster of a good thing'—even if you do know that probably half a dozen interested people are listening along the line." "That's the worst of it," sighed Diana. "It's so annoying to hear the receivers going down whenever you ring anyone up. They say Mrs. Harmon Andrews insisted that their 'phone should be put in their kitchen just so that she could listen whenever it rang and keep an eye on the dinner at the same time. Today, when you called me, I distinctly heard that queer clock of the Pyes' striking. So no doubt Josie or Gertie was listening." "Oh, so that is why you said, 'You've got a new clock at Green Gables, haven't you?' I couldn't imagine what you meant. I heard a vicious click as soon as you had spoken. I suppose it was the Pye receiver being hung up with profane energy. Well, never mind the Pyes. As Mrs. Rachel says, 'Pyes they always were and Pyes they always will be, world without end, amen.' I want to talk of pleasanter things. It's all settled as to where my new home shall be." "Oh, Anne, where? I do hope it's near here." "No-o-o, that's the drawback. Gilbert is going to settle at Four Winds Harbor—sixty miles from here." "Sixty! It might as well be six hundred," sighed Diana. "I never can get further from home now than Charlottetown." "You'll have to come to Four Winds. It's the most beautiful harbor on the Island. There's a little village called Glen St. Mary at its head, and Dr. David Blythe has been practicing there for fifty years. He is Gilbert's great-uncle, you know. He is going to retire, and Gilbert is to take over his practice. Dr. Blythe is going to keep his house, though, so we shall have to find a habitation for ourselves. I don't know yet what it is, or where it will be in reality, but I have a little house o'dreams all furnished in my imagination—a tiny, delightful castle in Spain." "Where are you going for your wedding tour?" asked Diana. "Nowhere. Don't look horrified, Diana dearest. You suggest Mrs. Harmon Andrews. She, no doubt, will remark condescendingly that people who can't afford wedding 'towers' are real sensible not to take them; and then she'll remind me that Jane went to Europe for hers. I want to spend MY honeymoon at Four Winds in my own dear house of dreams." "And you've decided not to have any bridesmaid?" "There isn't any one to have. You and Phil and Priscilla and Jane all stole a march on me in the matter of marriage; and Stella is teaching in Vancouver. I have no other 'kindred soul' and I won't have a bridesmaid who isn't." "But you are going to wear a veil, aren't you?" asked Diana, anxiously. "Yes, indeedy. I shouldn't feel like a bride without one. I remember telling Matthew, that evening when he brought me to Green Gables, that I never expected to be a bride because I was so homely no one would ever want to marry me—unless some foreign missionary did. I had an idea then that foreign missionaries couldn't afford to be finicky in the matter of looks if they wanted a girl to risk her life among cannibals. You should have seen the foreign missionary Priscilla married. He was as handsome and inscrutable as those daydreams we once planned to marry ourselves, Diana; he was the best dressed man I ever met, and he raved over Priscilla's 'ethereal, golden beauty.' But of course there are no cannibals in Japan." "Your wedding dress is a dream, anyhow," sighed Diana rapturously. "You'll look like a perfect queen in it—you're so tall and slender. How DO you keep so slim, Anne? I'm fatter than ever—I'll soon have no waist at all." "Stoutness and slimness seem to be matters of predestination," said Anne. "At all events, Mrs. Harmon Andrews can't say to you what she said to me when I came home from Summerside, 'Well, Anne, you're just about as skinny as ever.' It sounds quite romantic to be 'slender,' but 'skinny' has a very different tang." "Mrs. Harmon has been talking about your trousseau. She admits it's as nice as Jane's, although she says Jane married a millionaire and you are only marrying a 'poor young doctor without a cent to his name.'" Anne laughed. "My dresses ARE nice. I love pretty things. I remember the first pretty dress I ever had—the brown gloria Matthew gave me for our school concert. Before that everything I had was so ugly. It seemed to me that I stepped into a new world that night." "That was the night Gilbert recited 'Bingen on the Rhine,' and looked at you when he said, 'There's another, NOT a sister.' And you were so furious because he put your pink tissue rose in his breast pocket! You didn't much imagine then that you would ever marry him." "Oh, well, that's another instance of predestination," laughed Anne, as they went down the garret stairs. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (1996). Anne's House of Dreams. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/544/pg544-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html. The lead image was generated using HackerNoon's Stable Disffusion Al Image Generator feature, via the prompt "ANNE'S HOUSE OF DREAMS".

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