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z<^Oos_Fs5F677G#WdEZt*--RX+m9yhphc)6 + ![](img/map-leak-1.png) +

Figure 1: A hash table example with a focus on bucket 0.
+ + +Each bucket is a fixed-size array of eight elements. In the case of an insertion into a bucket that is already full (a bucket overflow), Go creates another bucket of eight elements and links the previous one to it. Figure 2 shows an example: + +
+ ![](img/map-leak-2.png) +
Figure 2: In case of a bucket overflow, Go allocates a new bucket and links the previous bucket to it.
+
+ + +Under the hood, a Go map is a pointer to a runtime.hmap struct. This struct contains multiple fields, including a B field, giving the number of buckets in the map: + +```go +type hmap struct { + B uint8 // log_2 of # of buckets + // (can hold up to loadFactor * 2^B items) + // ... +} +``` + +After adding 1 million elements, the value of B equals 18, which means 2¹⁸ = 262,144 buckets. When we remove 1 million elements, what’s the value of B? Still 18. Hence, the map still contains the same number of buckets. + +The reason is that the number of buckets in a map cannot shrink. Therefore, removing elements from a map doesn’t impact the number of existing buckets; it just zeroes the slots in the buckets. A map can only grow and have more buckets; it never shrinks. + +In the previous example, we went from 461 MB to 293 MB because the elements were collected, but running the GC didn’t impact the map itself. Even the number of extra buckets (the buckets created because of overflows) remains the same. + +Let’s take a step back and discuss when the fact that a map cannot shrink can be a problem. Imagine building a cache using a `map[int][128]byte`. This map holds per customer ID (the `int`), a sequence of 128 bytes. Now, suppose we want to save the last 1,000 customers. The map size will remain constant, so we shouldn’t worry about the fact that a map cannot shrink. + +However, let’s say we want to store one hour of data. Meanwhile, our company has decided to have a big promotion for Black Friday: in one hour, we may have millions of customers connected to our system. But a few days after Black Friday, our map will contain the same number of buckets as during the peak time. This explains why we can experience high memory consumption that doesn’t significantly decrease in such a scenario. + +What are the solutions if we don’t want to manually restart our service to clean the amount of memory consumed by the map? One solution could be to re-create a copy of the current map at a regular pace. For example, every hour, we can build a new map, copy all the elements, and release the previous one. The main drawback of this option is that following the copy and until the next garbage collection, we may consume twice the current memory for a short period. + +Another solution would be to change the map type to store an array pointer: `map[int]*[128]byte`. It doesn’t solve the fact that we will have a significant number of buckets; however, each bucket entry will reserve the size of a pointer for the value instead of 128 bytes (8 bytes on 64-bit systems and 4 bytes on 32-bit systems). + +Coming back to the original scenario, let’s compare the memory consumption for each map type following each step. The following table shows the comparison. + +| Step | `map[int][128]byte` | `map[int]*[128]byte` | +|---|---|---| +| Allocate an empty map | 0 MB | 0 MB | +| Add 1 million elements | 461 MB | 182 MB | +| Remove all the elements and run a GC | 293 MB | 38 MB | + +???+ note + + If a key or a value is over 128 bytes, Go won’t store it directly in the map bucket. Instead, Go stores a pointer to reference the key or the value. + +As we have seen, adding n elements to a map and then deleting all the elements means keeping the same number of buckets in memory. So, we must remember that because a Go map can only grow in size, so does its memory consumption. There is no automated strategy to shrink it. If this leads to high memory consumption, we can try different options such as forcing Go to re-create the map or using pointers to check if it can be optimized. diff --git a/docs/89-benchmarks.md b/docs/89-benchmarks.md new file mode 100644 index 0000000..db5d6f7 --- /dev/null +++ b/docs/89-benchmarks.md @@ -0,0 +1,367 @@ +--- +title: Writing inaccurate benchmarks (#89) +--- + +# Writing inaccurate benchmarks + +In general, we should never guess about performance. When writing optimizations, so many factors may come into play that even if we have a strong opinion about the results, it’s rarely a bad idea to test them. However, writing benchmarks isn’t straightforward. It can be pretty simple to write inaccurate benchmarks and make wrong assumptions based on them. The goal of this post is to examine four common and concrete traps leading to inaccuracy: + +* Not resetting or pausing the timer +* Making wrong assumptions about micro-benchmarks +* Not being careful about compiler optimizations +* Being fooled by the observer effect + +## General concepts + +Before discussing these traps, let’s briefly review how benchmarks work in Go. The skeleton of a benchmark is as follows: + +```go +func BenchmarkFoo(b *testing.B) { + for i := 0; i < b.N; i++ { + foo() + } +} +``` + +The function name starts with the `Benchmark` prefix. The function under test (foo) is called within the `for` loop. `b.N` represents a variable number of iterations. When running a benchmark, Go tries to make it match the requested benchmark time. The benchmark time is set by default to 1 second and can be changed with the `-benchtime` flag. `b.N` starts at 1; if the benchmark completes in under 1 second, `b.N` is increased, and the benchmark runs again until `b.N` roughly matches benchtime: + +``` +$ go test -bench=. +cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz +BenchmarkFoo-4 73 16511228 ns/op +``` + +Here, the benchmark took about 1 second, and `foo` was executed 73 times, for an average execution time of 16,511,228 nanoseconds. We can change the benchmark time using `-benchtime`: + +``` +$ go test -bench=. -benchtime=2s +BenchmarkFoo-4 150 15832169 ns/op +``` + +`foo` was executed roughly twice more than during the previous benchmark. + +Next, let’s look at some common traps. + +## Not resetting or pausing the timer + +In some cases, we need to perform operations before the benchmark loop. These operations may take quite a while (for example, generating a large slice of data) and may significantly impact the benchmark results: + +```go +func BenchmarkFoo(b *testing.B) { + expensiveSetup() + for i := 0; i < b.N; i++ { + functionUnderTest() + } +} +``` + +In this case, we can use the `ResetTimer` method before entering the loop: + +```go +func BenchmarkFoo(b *testing.B) { + expensiveSetup() + b.ResetTimer() // Reset the benchmark timer + for i := 0; i < b.N; i++ { + functionUnderTest() + } +} +``` + +Calling `ResetTimer` zeroes the elapsed benchmark time and memory allocation counters since the beginning of the test. This way, an expensive setup can be discarded from the test results. + +What if we have to perform an expensive setup not just once but within each loop iteration? + +```go +func BenchmarkFoo(b *testing.B) { + for i := 0; i < b.N; i++ { + expensiveSetup() + functionUnderTest() + } +} +``` + +We can’t reset the timer, because that would be executed during each loop iteration. But we can stop and resume the benchmark timer, surrounding the call to `expensiveSetup`: + +```go +func BenchmarkFoo(b *testing.B) { + for i := 0; i < b.N; i++ { + b.StopTimer() // Pause the benchmark timer + expensiveSetup() + b.StartTimer() // Resume the benchmark timer + functionUnderTest() + } +} +``` + +Here, we pause the benchmark timer to perform the expensive setup and then resume the timer. + +???+ note + + There’s one catch to remember about this approach: if the function under test is too fast to execute compared to the setup function, the benchmark may take too long to complete. The reason is that it would take much longer than 1 second to reach `benchtime`. Calculating the benchmark time is based solely on the execution time of `functionUnderTest`. So, if we wait a significant time in each loop iteration, the benchmark will be much slower than 1 second. If we want to keep the benchmark, one possible mitigation is to decrease `benchtime`. + +We must be sure to use the timer methods to preserve the accuracy of a benchmark. + +## Making wrong assumptions about micro-benchmarks + +A micro-benchmark measures a tiny computation unit, and it can be extremely easy to make wrong assumptions about it. Let’s say, for example, that we aren’t sure whether to use `atomic.StoreInt32` or `atomic.StoreInt64` (assuming that the values we handle will always fit in 32 bits). We want to write a benchmark to compare both functions: + +```go +func BenchmarkAtomicStoreInt32(b *testing.B) { + var v int32 + for i := 0; i < b.N; i++ { + atomic.StoreInt32(&v, 1) + } +} + +func BenchmarkAtomicStoreInt64(b *testing.B) { + var v int64 + for i := 0; i < b.N; i++ { + atomic.StoreInt64(&v, 1) + } +} +``` + +If we run this benchmark, here’s some example output: + +``` +cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz +BenchmarkAtomicStoreInt32 +BenchmarkAtomicStoreInt32-4 197107742 5.682 ns/op +BenchmarkAtomicStoreInt64 +BenchmarkAtomicStoreInt64-4 213917528 5.134 ns/op +``` + +We could easily take this benchmark for granted and decide to use `atomic.StoreInt64` because it appears to be faster. Now, for the sake of doing a fair benchmark, we reverse the order and test `atomic.StoreInt64` first, followed by `atomic.StoreInt32`. Here is some example output: + +``` +BenchmarkAtomicStoreInt64 +BenchmarkAtomicStoreInt64-4 224900722 5.434 ns/op +BenchmarkAtomicStoreInt32 +BenchmarkAtomicStoreInt32-4 230253900 5.159 ns/op +``` + +This time, `atomic.StoreInt32` has better results. What happened? + +In the case of micro-benchmarks, many factors can impact the results, such as machine activity while running the benchmarks, power management, thermal scaling, and better cache alignment of a sequence of instructions. We must remember that many factors, even outside the scope of our Go project, can impact the results. + +???+ note + + We should make sure the machine executing the benchmark is idle. However, external processes may run in the background, which may affect benchmark results. For that reason, tools such as `perflock` can limit how much CPU a benchmark can consume. For example, we can run a benchmark with 70% of the total available CPU, giving 30% to the OS and other processes and reducing the impact of the machine activity factor on the results. + +One option is to increase the benchmark time using the `-benchtime` option. Similar to the law of large numbers in probability theory, if we run a benchmark a large number of times, it should tend to approach its expected value (assuming we omit the benefits of instructions caching and similar mechanics). + +Another option is to use external tools on top of the classic benchmark tooling. For instance, the `benchstat` tool, which is part of the `golang.org/x` repository, allows us to compute and compare statistics about benchmark executions. + +Let’s run the benchmark 10 times using the `-count` option and pipe the output to a specific file: + +``` +$ go test -bench=. -count=10 | tee stats.txt +cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz +BenchmarkAtomicStoreInt32-4 234935682 5.124 ns/op +BenchmarkAtomicStoreInt32-4 235307204 5.112 ns/op +// ... +BenchmarkAtomicStoreInt64-4 235548591 5.107 ns/op +BenchmarkAtomicStoreInt64-4 235210292 5.090 ns/op +// ... +``` + +We can then run `benchstat` on this file: + +``` +$ benchstat stats.txt +name time/op +AtomicStoreInt32-4 5.10ns ± 1% +AtomicStoreInt64-4 5.10ns ± 1% +``` + +The results are the same: both functions take on average 5.10 nanoseconds to complete. We also see the percent variation between the executions of a given benchmark: ± 1%. This metric tells us that both benchmarks are stable, giving us more confidence in the computed average results. Therefore, instead of concluding that `atomic.StoreInt32` is faster or slower, we can conclude that its execution time is similar to that of `atomic.StoreInt64` for the usage we tested (in a specific Go version on a particular machine). + +In general, we should be cautious about micro-benchmarks. Many factors can significantly impact the results and potentially lead to wrong assumptions. Increasing the benchmark time or repeating the benchmark executions and computing stats with tools such as `benchstat` can be an efficient way to limit external factors and get more accurate results, leading to better conclusions. + +Let’s also highlight that we should be careful about using the results of a micro-benchmark executed on a given machine if another system ends up running the application. The production system may act quite differently from the one on which we ran the micro-benchmark. + +## Not being careful about compiler optimizations + +Another common mistake related to writing benchmarks is being fooled by compiler optimizations, which can also lead to wrong benchmark assumptions. In this section, we look at Go issue 14813 (https://github.com/golang/go/issues/14813, also discussed by Go project member Dave Cheney) with a population count function (a function that counts the number of bits set to 1): + +```go +const m1 = 0x5555555555555555 +const m2 = 0x3333333333333333 +const m4 = 0x0f0f0f0f0f0f0f0f +const h01 = 0x0101010101010101 + +func popcnt(x uint64) uint64 { + x -= (x >> 1) & m1 + x = (x & m2) + ((x >> 2) & m2) + x = (x + (x >> 4)) & m4 + return (x * h01) >> 56 +} +``` + +This function takes and returns a `uint64`. To benchmark this function, we can write the following: + +```go +func BenchmarkPopcnt1(b *testing.B) { + for i := 0; i < b.N; i++ { + popcnt(uint64(i)) + } +} +``` + +However, if we execute this benchmark, we get a surprisingly low result: + +``` +cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz +BenchmarkPopcnt1-4 1000000000 0.2858 ns/op +``` + +A duration of 0.28 nanoseconds is roughly one clock cycle, so this number is unreasonably low. The problem is that the developer wasn’t careful enough about compiler optimizations. In this case, the function under test is simple enough to be a candidate for inlining: an optimization that replaces a function call with the body of the called function and lets us prevent a function call, which has a small footprint. Once the function is inlined, the compiler notices that the call has no side effects and replaces it with the following benchmark: + +```go +func BenchmarkPopcnt1(b *testing.B) { + for i := 0; i < b.N; i++ { + // Empty + } +} +``` + +The benchmark is now empty — which is why we got a result close to one clock cycle. To prevent this from happening, a best practice is to follow this pattern: + +1. During each loop iteration, assign the result to a local variable (local in the context of the benchmark function). +2. Assign the latest result to a global variable. + +In our case, we write the following benchmark: + +```go +var global uint64 // Define a global variable + +func BenchmarkPopcnt2(b *testing.B) { + var v uint64 // Define a local variable + for i := 0; i < b.N; i++ { + v = popcnt(uint64(i)) // Assign the result to the local variable + } + global = v // Assign the result to the global variable +} +``` + +`global` is a global variable, whereas v is a local variable whose scope is the benchmark function. During each loop iteration, we assign the result of `popcnt` to the local variable. Then we assign the latest result to the global variable. + +???+ note + + Why not assign the result of the popcnt call directly to global to simplify the test? Writing to a global variable is slower than writing to a local variable (these concepts are discussed in 100 Go Mistakes, mistake #95: “[Not understanding stack vs. heap](https://100go.co#not-understanding-stack-vs-heap-95)”). Therefore, we should write each result to a local variable to limit the footprint during each loop iteration. + +If we run these two benchmarks, we now get a significant difference in the results: + +``` +cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz +BenchmarkPopcnt1-4 1000000000 0.2858 ns/op +BenchmarkPopcnt2-4 606402058 1.993 ns/op +``` + +`BenchmarkPopcnt2` is the accurate version of the benchmark. It guarantees that we avoid the inlining optimizations, which can artificially lower the execution time or even remove the call to the function under test. Relying on the results of `BenchmarkPopcnt1` could have led to wrong assumptions. + +Let’s remember the pattern to avoid compiler optimizations fooling benchmark results: assign the result of the function under test to a local variable, and then assign the latest result to a global variable. This best practice also prevents us from making incorrect assumptions. + +## Being fooled by the observer effect + +In physics, the observer effect is the disturbance of an observed system by the act of observation. This effect can also be seen in benchmarks and can lead to wrong assumptions about results. Let’s look at a concrete example and then try to mitigate it. + +We want to implement a function receiving a matrix of `int64` elements. This matrix has a fixed number of 512 columns, and we want to compute the total sum of the first eight columns, as shown in figure 1. + +
+ ![](img/matrix.png) +
Figure 1: Computing the sum of the first eight columns.
+
+ +For the sake of optimizations, we also want to determine whether varying the number of columns has an impact, so we also implement a second function with 513 columns. The implementation is the following: + +```go +func calculateSum512(s [][512]int64) int64 { + var sum int64 + for i := 0; i < len(s); i++ { // Iterate over each row + for j := 0; j < 8; j++ { // Iterate over the first eight columns + sum += s[i][j] // Increment sum + } + } + return sum +} + +func calculateSum513(s [][513]int64) int64 { + // Same implementation as calculateSum512 +} +``` + +We iterate over each row and then over the first eight columns, and we increment a sum variable that we return. The implementation in `calculateSum513` remains the same. + +We want to benchmark these functions to decide which one is the most performant given a fixed number of rows: + +```go +const rows = 1000 + +var res int64 + +func BenchmarkCalculateSum512(b *testing.B) { + var sum int64 + s := createMatrix512(rows) // Create a matrix of 512 columns + b.ResetTimer() + for i := 0; i < b.N; i++ { + sum = calculateSum512(s) // Create a matrix of 512 columns + } + res = sum +} + +func BenchmarkCalculateSum513(b *testing.B) { + var sum int64 + s := createMatrix513(rows) // Create a matrix of 513 columns + b.ResetTimer() + for i := 0; i < b.N; i++ { + sum = calculateSum513(s) // Calculate the sum + } + res = sum +} +``` + +We want to create the matrix only once, to limit the footprint on the results. Therefore, we call `createMatrix512` and `createMatrix513` outside of the loop. We may expect the results to be similar as again we only want to iterate on the first eight columns, but this isn’t the case (on my machine): + +``` +cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz +BenchmarkCalculateSum512-4 81854 15073 ns/op +BenchmarkCalculateSum513-4 161479 7358 ns/op +``` + +The second benchmark with 513 columns is about 50% faster. Again, because we iterate only over the first eight columns, this result is quite surprising. + +To understand this difference, we need to understand the basics of CPU caches. In a nutshell, a CPU is composed of different caches (usually L1, L2, and L3). These caches reduce the average cost of accessing data from the main memory. In some conditions, the CPU can fetch data from the main memory and copy it to L1. In this case, the CPU tries to fetch into L1 the matrix’s subset that `calculateSum` is interested in (the first eight columns of each row). However, the matrix fits in memory in one case (513 columns) but not in the other case (512 columns). + +???+ note + + This isn’t in the scope of this post to explain why, but we look at this problem in 100 Go Mistakes, mistake #91: “[Not understanding CPU caches.](https://100go.co#not-understanding-cpu-caches-91)” + +Coming back to the benchmark, the main issue is that we keep reusing the same matrix in both cases. Because the function is repeated thousands of times, we don’t measure the function’s execution when it receives a plain new matrix. Instead, we measure a function that gets a matrix that already has a subset of the cells present in the cache. Therefore, because `calculateSum513` leads to fewer cache misses, it has a better execution time. + +This is an example of the observer effect. Because we keep observing a repeatedly called CPU-bound function, CPU caching may come into play and significantly affect the results. In this example, to prevent this effect, we should create a matrix during each test instead of reusing one: + +```go +func BenchmarkCalculateSum512(b *testing.B) { + var sum int64 + for i := 0; i < b.N; i++ { + b.StopTimer() + s := createMatrix512(rows) // Create a new matrix during each loop iteration + b.StartTimer() + sum = calculateSum512(s) + } + res = sum +} +``` + +A new matrix is now created during each loop iteration. If we run the benchmark again (and adjust `benchtime` — otherwise, it takes too long to execute), the results are closer to each other: + +``` +cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz +BenchmarkCalculateSum512-4 1116 33547 ns/op +BenchmarkCalculateSum513-4 998 35507 ns/op +``` + +Instead of making the incorrect assumption that calculateSum513 is faster, we see that both benchmarks lead to similar results when receiving a new matrix. + +As we have seen in this post, because we were reusing the same matrix, CPU caches significantly impacted the results. To prevent this, we had to create a new matrix during each loop iteration. In general, we should remember that observing a function under test may lead to significant differences in results, especially in the context of micro-benchmarks of CPU-bound functions where low-level optimizations matter. Forcing a benchmark to re-create data during each iteration can be a good way to prevent this effect. \ No newline at end of file diff --git a/docs/98-profiling-execution-tracing.md b/docs/98-profiling-execution-tracing.md index f42661c..197b755 100644 --- a/docs/98-profiling-execution-tracing.md +++ b/docs/98-profiling-execution-tracing.md @@ -67,11 +67,17 @@ $ go tool pprof -http=:8080 This command opens a web UI showing the call graph. The next figure shows an example taken from an application. The larger the arrow, the more it was a hot path. We can then navigate into this graph and get execution insights. -![](img/screen-pprof-cpu.png) +
+ ![](img/screen-pprof-cpu.png) +
Figure 1: The call graph of an application during 30 seconds.
+
For example, the graph in the next figure tells us that during 30 seconds, 0.06 seconds were spent in the `decode` method (`*FetchResponse` receiver). Of these 0.06 seconds, 0.02 were spent in `RecordBatch.decode` and 0.01 in `makemap` (creating a map). -![](img/screen-pprof-sarama.png) +
+ ![](img/screen-pprof-sarama.png) +
Figure 2: Example call graph.
+
We can also access this kind of information from the web UI with different representations. For example, the Top view sorts the functions per execution time, and Flame Graph visualizes the execution time hierarchy. The UI can even display the expensive parts of the source code line by line. @@ -99,7 +105,10 @@ If we reach /debug/pprof/heap/, we get raw data that can be hard to read. Howeve The next figure shows an example of a heap graph. Calling the `MetadataResponse.decode` method leads to allocating 1536 KB of heap data (which represents 6.32% of the total heap). However, 0 out of these 1536 KB were allocated by this function directly, so we need to inspect the second call. The `TopicMetadata.decode` method allocated 512 KB out of the 1536 KB; the rest — 1024 KB — were allocated in another method. -![](img/screen-pprof-heap.png) +
+ ![](img/screen-pprof-heap.png) +
Figure 3: A heap graph.
+
This is how we can navigate the call chain to understand what part of an application is responsible for most of the heap allocations. We can also look at different sample types: @@ -132,7 +141,10 @@ $ go tool pprof -http=:8080 -diff_base The next figure shows the kind of data we can access. For example, the amount of heap memory held by the newTopicProducer method (top left) has decreased (–513 KB). In contrast, the amount held by updateMetadata (bottom right) has increased (+512 KB). Slow increases are normal. The second heap profile may have been calculated in the middle of a service call, for example. We can repeat this process or wait longer; the important part is to track steady increases in allocations of a specific object. -![](img/screen-pprof-heap-diff.png) +
+ ![](img/screen-pprof-heap-diff.png) +
Figure 4: The differences between the two heap profiles.
+
???+ note @@ -142,7 +154,10 @@ The next figure shows the kind of data we can access. For example, the amount of The `goroutine` profile reports the stack trace of all the current goroutines in an application. We can download a file using /debug/pprof/goroutine/?debug=0 and use go tool again. The next figure shows the kind of information we can get. -![](img/screen-pprof-goroutines.png) +
+ ![](img/screen-pprof-goroutines.png) +
Figure 5: Goroutine graph.
+
We can see the current state of the application and how many goroutines were created per function. In this case, `withRecover` has created 296 ongoing goroutines (63%), and 29 were related to a call to `responseFeeder`. @@ -222,19 +237,31 @@ $ go tool trace trace.out The web browser opens, and we can click View Trace to see all the traces during a specific timeframe, as shown in the next figure. This figure represents about 150 ms. We can see multiple helpful metrics, such as the goroutine count and the heap size. The heap size grows steadily until a GC is triggered. We can also observe the activity of the Go application per CPU core. The timeframe starts with user-level code; then a “stop the world” is executed, which occupies the four CPU cores for approximately 40 ms. -![](img/tracing.png) +
+ ![](img/tracing.png) +
Figure 6: Showing goroutine activity and runtime events such as a GC phase.
+
Regarding concurrency, we can see that this version uses all the available CPU cores on the machine. However, the next figure zooms in on a portion of 1 ms. Each bar corresponds to a single goroutine execution. Having too many small bars doesn’t look right: it means execution that is poorly parallelized. -![](img/screen-mergesort1.png) +
+ ![](img/screen-mergesort1.png) +
Figure 7: Too many small bars mean poorly parallelized execution.
+
The next figure zooms even closer to see how these goroutines are orchestrated. Roughly 50% of the CPU time isn’t spent executing application code. The white spaces represent the time the Go runtime takes to spin up and orchestrate new goroutines. -![](img/screen-mergesort11.png) +
+ ![](img/screen-mergesort11.png) +
Figure 8: About 50% of CPU time is spent handling goroutine switches.
+
Let’s compare this with the second parallel implementation, which was about an order of magnitude faster. The next figure again zooms to a 1 ms timeframe. -![](img/screen-mergesort2.png) +
+ ![](img/screen-mergesort2.png) +
Figure 9: The number of white spaces has been significantly reduced, proving that the CPU is more fully occupied.
+
Each goroutine takes more time to execute, and the number of white spaces has been significantly reduced. Hence, the CPU is much more occupied executing application code than it was in the first version. Each millisecond of CPU time is spent more efficiently, explaining the benchmark differences. @@ -261,7 +288,12 @@ fibStore.End() Using `go tool`, we can get more precise information about how these two tasks perform. In the previous trace UI, we can see the boundaries for each task per goroutine. In User-Defined Tasks, we can follow the duration distribution: -![](img/screen-tracing-user-level.png) +
+ ![](img/screen-tracing-user-level.png) +
Figure 10: Distribution of user-level tasks.
+
+ + We see that in most cases, the `fibonacci` task is executed in less than 15 microseconds, whereas the `store` task takes less than 6309 nanoseconds. diff --git a/docs/chapter-1.md b/docs/chapter-1.md index c601548..f7b8547 100644 --- a/docs/chapter-1.md +++ b/docs/chapter-1.md @@ -63,7 +63,7 @@ This book aims to help accelerate our journey toward proficiency by delving into Why should we read a book about common Go mistakes? Why not deepen our knowledge with an ordinary book that would dig into different topics? -In a 2011 article, neuroscientists proved that the best time for brain growth is when we’re facing mistakes. Haven’t we all experienced the process of learning from a mistake and recalling that occasion after months or even years, when some context related to it? As presented in another article, by Janet Metcalfe, this happens because mistakes have a facilitative effect. The main idea is that we can remember not only the error but also the context surrounding the mistake. This is one of the reasons why learning from mistakes is so efficient. +In a 2011 article, neuroscientists proved that the best time for brain growth is when we’re facing mistakes. [^1] Haven’t we all experienced the process of learning from a mistake and recalling that occasion after months or even years, when some context related to it? As presented in another article, by Janet Metcalfe, this happens because mistakes have a facilitative effect. [^2] The main idea is that we can remember not only the error but also the context surrounding the mistake. This is one of the reasons why learning from mistakes is so efficient. To strengthen this facilitative effect, this book accompanies each mistake as much as possible with real-world examples. This book isn’t only about theory; it also helps us get better at avoiding mistakes and making more well-informed, conscious decisions because we now understand the rationale behind them. @@ -85,7 +85,7 @@ We introduce each mistake category next. ### Bugs -The first type of mistake and probably the most obvious is software bugs. In 2020, a study conducted by Synopsys estimated the cost of software bugs in the U.S. alone to be over $2 trillion. +The first type of mistake and probably the most obvious is software bugs. In 2020, a study conducted by Synopsys estimated the cost of software bugs in the U.S. alone to be over $2 trillion. [^3] Furthermore, bugs can also lead to tragic impacts. We can, for example, mention cases such as Therac-25, a radiation therapy machine produced by Atomic Energy of Canada Limited (AECL). Because of a race condition, the machine gave its patients radiation doses that were hundreds of times greater than expected, leading to the death of three patients. Hence, software bugs aren’t only about money. As developers, we should remember how impactful our jobs are. @@ -129,4 +129,8 @@ In this book, we will cover many cases and concrete examples that will help us t * Go is a modern programming language that enables developer productivity, which is crucial for most companies today. * Go is simple to learn but not easy to master. This is why we need to deepen our knowledge to make the most effective use of the language. -* Learning via mistakes and concrete examples is a powerful way to be proficient in a language. This book will accelerate our path to proficiency by exploring 100 common mistakes. \ No newline at end of file +* Learning via mistakes and concrete examples is a powerful way to be proficient in a language. This book will accelerate our path to proficiency by exploring 100 common mistakes. + +[^1]: J. S. Moser, H. S. Schroder, et al., “Mind Your Errors: Evidence for a Neural Mechanism Linking Growth Mindset to Adaptive Posterror Adjustments,” Psychological Science, vol. 22, no. 12, pp. 1484–1489, Dec. 2011. +[^2]: J. Metcalfe, “Learning from Errors,” Annual Review of Psychology, vol. 68, pp. 465–489, Jan. 2017. +[^3]: Synopsys, “The Cost of Poor Software Quality in the US: A 2020 Report.” 2020. [https://news.synopsys.com/2021-01-06-Synopsys-Sponsored-CISQ-Research-Estimates-Cost-of-Poor-Software-Quality-in-the-US-2-08-Trillion-in-2020](https://news.synopsys.com/2021-01-06-Synopsys-Sponsored-CISQ-Research-Estimates-Cost-of-Poor-Software-Quality-in-the-US-2-08-Trillion-in-2020). diff --git a/docs/img/map-leak-1.png b/docs/img/map-leak-1.png new file mode 100644 index 0000000000000000000000000000000000000000..7c478152110b7085b8168ca68569c780fba30123 GIT binary patch literal 6719 zcmbVQ2{@E{+n*s6DP?a+Qpy%%8~eWRTgVzR5(YD4jC~1{UCP!(5+YebGWIQ7_B9OB zOm-t=E6X>|Ip2BDdEe`Ozw3LS>$$Ic`Tu_Rb3gy>dj3zek%1;X?Il_O06?#;rDg&E z0I_7joTem4j7`q-lhr8~WqoA;;9Vl!fdduUzv852q7MN0^8x^&5dgqXawv2W0Pqn3 z0G96p05a(S0IS#Y8e=&CfFjmN&rJR3=;-+P_~*}`ySuvwzYcbGcJ}x8_xAP<4-fV9 z^vLw^@bHzD6$%Q9vuDpHB_#<83N|z}Kp+rSR@VOhekUiVoSYm31A~c)2}46eb8~ZL zW#!!5-1zu-0Re%&zCH&B2S!FlEiJ8xh=|qIRVWl{YHDg`X6Ekh?(OXj0)ft)IkUdL z&c?<@O-+qNB57!7E?v4bHa1pPR(ARF<>268AP{)__H8s8-PP3<6cjW%IvN%hMny$c 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zYk~szgdTHr;U>N!k4bKnmQ&LV3M*)qddC<=fudh_i(w*$%@Q-XC^cZ(&ULKq%(ogj sGRzJN&n7d2i&`0gQu(q$VweK{KRpg$l!M*)r2qf`07*qoM6N<$f@K|lc>n+a literal 0 HcmV?d00001 diff --git a/docs/index.md b/docs/index.md index c7aad01..4955e29 100644 --- a/docs/index.md +++ b/docs/index.md @@ -169,8 +169,6 @@ The `any` type can be helpful if there is a genuine need for accepting or return Read the full section [here](9-generics.md). - - [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/02-code-project-organization/9-generics/main.go) ### Not being aware of the possible problems with type embedding (#10) @@ -479,7 +477,7 @@ When using slicing, we must remember that we can face a situation leading to uni [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/03-data-types/25-slice-append/main.go) -### Slice and memory leaks (#26) +### Slices and memory leaks (#26) ???+ info "TL;DR" @@ -509,13 +507,13 @@ If we know up front the number of elements a map will contain, we should create [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/03-data-types/27-map-init/main_test.go) -### [Map and memory leaks](https://teivah.medium.com/maps-and-memory-leaks-in-go-a85ebe6e7e69) (#28) +### Maps and memory leaks (#28) ???+ info "TL;DR" A map can always grow in memory, but it never shrinks. Hence, if it leads to some memory issues, you can try different options, such as forcing Go to recreate the map or using pointers. - +Read the full section [here](28-maps-memory-leaks.md). [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/03-data-types/28-map-memory-leak/main.go) @@ -1570,33 +1568,21 @@ You should have a good reason to specify a channel size other than one for buffe [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/11-testing/88-utility-package/iotest/main_test.go) -### [Writing inaccurate benchmarks](https://teivah.medium.com/how-to-write-accurate-benchmarks-in-go-4266d7dd1a95) (#89) +### Writing inaccurate benchmarks (#89) -* Not resetting or pausing the timer +???+ info "TL;DR" - Use time methods to preserve the accuracy of a benchmark. + Regarding benchmarks: - [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/11-testing/89-benchmark/timer/main_test.go) + * Use time methods to preserve the accuracy of a benchmark. + * Increasing benchtime or using tools such as benchstat can be helpful when dealing with micro-benchmarks. + * Be careful with the results of a micro-benchmark if the system that ends up running the application is different from the one running the micro-bench- mark. + * Make sure the function under test leads to a side effect, to prevent compiler optimizations from fooling you about the benchmark results. + * To prevent the observer effect, force a benchmark to re-create the data used by a CPU-bound function. -* Making wrong assumptions about micro-benchmarks +Read the full section [here](89-benchmarks.md). - Increasing `benchtime` or using tools such as `benchstat` can be helpful when dealing with micro-benchmarks. - - Be careful with the results of a micro-benchmark if the system that ends up running the application is different from the one running the micro-benchmark. - - [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/11-testing/89-benchmark/wrong-assumptions/main_test.go) - -* Not being careful about compiler optimizations - - Make sure the function under test leads to a side effect, to prevent compiler optimizations from fooling you about the benchmark results. - - [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/11-testing/89-benchmark/compiler-optimizations/main_test.go) - -* Being fooled by the observer effect - - To prevent the observer effect, force a benchmark to re-create the data used by a CPU-bound function. - - [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/11-testing/89-benchmark/observer-effect/main_test.go) + [Source code](https://github.com/teivah/100-go-mistakes/tree/master/src/11-testing/89-benchmark/) ### Not exploring all the Go testing features (#90) diff --git a/mkdocs.yml b/mkdocs.yml index 48a9fde..a0f254f 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -15,6 +15,7 @@ theme: - search.highlight - search.share - search.suggest + - content.code.copy palette: # Palette toggle for light mode - scheme: default @@ -31,6 +32,7 @@ theme: repo_url: https://github.com/teivah/100-go-mistakes plugins: - search + - glightbox - social: cards_layout_options: logo: img/cover.png @@ -59,6 +61,8 @@ nav: - index.md - Full Sections: - 9-generics.md + - 28-maps-memory-leaks.md + - 89-benchmarks.md - 98-profiling-execution-tracing.md - zh.md - ❤️ Go Jobs: @@ -73,4 +77,8 @@ markdown_extensions: - pymdownx.inlinehilite - pymdownx.snippets - pymdownx.superfences + - tables + - attr_list + - md_in_html + - footnotes copyright: Copyright © 2022 - 2023 Teiva Harsanyi diff --git a/site/28-maps-memory-leaks/index.html b/site/28-maps-memory-leaks/index.html new file mode 100644 index 0000000..0240525 --- /dev/null +++ b/site/28-maps-memory-leaks/index.html @@ -0,0 +1,984 @@ + + + + + + + + + + + + + + + + + + + + + + + + 100 Go Mistakes + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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Maps and memory leaks

+

When working with maps in Go, we need to understand some important characteristics of how a map grows and shrinks. Let’s delve into this to prevent issues that can cause memory leaks.

+

First, to view a concrete example of this problem, let’s design a scenario where we will work with the following map:

+
m := make(map[int][128]byte)
+
+

Each value of m is an array of 128 bytes. We will do the following:

+
    +
  1. Allocate an empty map.
    2. +
  2. Add 1 million elements.
    3. +
  3. Remove all the elements, and run a Garbage Collection (GC).
    4. +
+

After each step, we want to print the size of the heap (using a printAlloc utility function). This shows us how this example behaves memory-wise:

+
func main() {
+    n := 1_000_000
+    m := make(map[int][128]byte)
+    printAlloc()
+
+    for i := 0; i < n; i++ { // Adds 1 million elements
+        m[i] = [128]byte{}
+    }
+    printAlloc()
+
+    for i := 0; i < n; i++ { // Deletes 1 million elements
+        delete(m, i)
+    }
+
+    runtime.GC() // Triggers a manual GC
+    printAlloc()
+    runtime.KeepAlive(m) // Keeps a reference to m so that the map isn’t collected
+}
+
+func printAlloc() {
+    var m runtime.MemStats
+    runtime.ReadMemStats(&m)
+    fmt.Printf("%d KB\n", m.Alloc/1024)
+}
+
+

We allocate an empty map, add 1 million elements, remove 1 million elements, and then run a GC. We also make sure to keep a reference to the map using runtime.KeepAlive so that the map isn’t collected as well. Let’s run this example:

+
0 MB   <-- After m is allocated
+461 MB <-- After we add 1 million elements
+293 MB <-- After we remove 1 million elements
+
+

What can we observe? At first, the heap size is minimal. Then it grows significantly after having added 1 million elements to the map. But if we expected the heap size to decrease after removing all the elements, this isn’t how maps work in Go. In the end, even though the GC has collected all the elements, the heap size is still 293 MB. So the memory shrunk, but not as we might have expected. What’s the rationale? We need to delve into how a map works in Go.

+

A map provides an unordered collection of key-value pairs in which all the keys are distinct. In Go, a map is based on the hash table data structure: an array where each element is a pointer to a bucket of key-value pairs, as shown in figure 1.

+
+

+

+
Figure 1: A hash table example with a focus on bucket 0.
+
+

Each bucket is a fixed-size array of eight elements. In the case of an insertion into a bucket that is already full (a bucket overflow), Go creates another bucket of eight elements and links the previous one to it. Figure 2 shows an example:

+
+

+

+
Figure 2: In case of a bucket overflow, Go allocates a new bucket and links the previous bucket to it.
+
+

Under the hood, a Go map is a pointer to a runtime.hmap struct. This struct contains multiple fields, including a B field, giving the number of buckets in the map:

+
type hmap struct {
+    B uint8 // log_2 of # of buckets
+            // (can hold up to loadFactor * 2^B items)
+    // ...
+}
+
+

After adding 1 million elements, the value of B equals 18, which means 2¹⁸ = 262,144 buckets. When we remove 1 million elements, what’s the value of B? Still 18. Hence, the map still contains the same number of buckets.

+

The reason is that the number of buckets in a map cannot shrink. Therefore, removing elements from a map doesn’t impact the number of existing buckets; it just zeroes the slots in the buckets. A map can only grow and have more buckets; it never shrinks.

+

In the previous example, we went from 461 MB to 293 MB because the elements were collected, but running the GC didn’t impact the map itself. Even the number of extra buckets (the buckets created because of overflows) remains the same.

+

Let’s take a step back and discuss when the fact that a map cannot shrink can be a problem. Imagine building a cache using a map[int][128]byte. This map holds per customer ID (the int), a sequence of 128 bytes. Now, suppose we want to save the last 1,000 customers. The map size will remain constant, so we shouldn’t worry about the fact that a map cannot shrink.

+

However, let’s say we want to store one hour of data. Meanwhile, our company has decided to have a big promotion for Black Friday: in one hour, we may have millions of customers connected to our system. But a few days after Black Friday, our map will contain the same number of buckets as during the peak time. This explains why we can experience high memory consumption that doesn’t significantly decrease in such a scenario.

+

What are the solutions if we don’t want to manually restart our service to clean the amount of memory consumed by the map? One solution could be to re-create a copy of the current map at a regular pace. For example, every hour, we can build a new map, copy all the elements, and release the previous one. The main drawback of this option is that following the copy and until the next garbage collection, we may consume twice the current memory for a short period.

+

Another solution would be to change the map type to store an array pointer: map[int]*[128]byte. It doesn’t solve the fact that we will have a significant number of buckets; however, each bucket entry will reserve the size of a pointer for the value instead of 128 bytes (8 bytes on 64-bit systems and 4 bytes on 32-bit systems).

+

Coming back to the original scenario, let’s compare the memory consumption for each map type following each step. The following table shows the comparison.

+ + + + + + + + + + + + + + + + + + + + + + + + + +
Stepmap[int][128]bytemap[int]*[128]byte
Allocate an empty map0 MB0 MB
Add 1 million elements461 MB182 MB
Remove all the elements and run a GC293 MB38 MB
+
+Note +

If a key or a value is over 128 bytes, Go won’t store it directly in the map bucket. Instead, Go stores a pointer to reference the key or the value.

+
+

As we have seen, adding n elements to a map and then deleting all the elements means keeping the same number of buckets in memory. So, we must remember that because a Go map can only grow in size, so does its memory consumption. There is no automated strategy to shrink it. If this leads to high memory consumption, we can try different options such as forcing Go to re-create the map or using pointers to check if it can be optimized.

+ + + + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/site/404.html b/site/404.html index 6834d85..672e304 100644 --- a/site/404.html +++ b/site/404.html @@ -553,6 +553,46 @@ +
  • + + + + + Maps and memory leaks (#28) + + + + +
    • + + + + + + + + + +
  • + + + + + Writing inaccurate benchmarks (#89) + + + + +
    • + + + + + + + + +
  • @@ -755,7 +795,7 @@
    - + diff --git a/site/89-benchmarks/index.html b/site/89-benchmarks/index.html new file mode 100644 index 0000000..2a87f5f --- /dev/null +++ b/site/89-benchmarks/index.html @@ -0,0 +1,1257 @@ + + + + + + + + + + + + + + + + + + + + + + + + 100 Go Mistakes + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +     +
    + +
    + + + + + + +
    + + + + + + + +
    + +
    + + + + +
    +
    + + + +
    +
    +
    + + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + +

    Writing inaccurate benchmarks

    +

    In general, we should never guess about performance. When writing optimizations, so many factors may come into play that even if we have a strong opinion about the results, it’s rarely a bad idea to test them. However, writing benchmarks isn’t straightforward. It can be pretty simple to write inaccurate benchmarks and make wrong assumptions based on them. The goal of this post is to examine four common and concrete traps leading to inaccuracy:

    +
      +
    • Not resetting or pausing the timer
      • +
    • Making wrong assumptions about micro-benchmarks
      • +
    • Not being careful about compiler optimizations
      • +
    • Being fooled by the observer effect
      • +
    +

    General concepts

    +

    Before discussing these traps, let’s briefly review how benchmarks work in Go. The skeleton of a benchmark is as follows:

    +
    func BenchmarkFoo(b *testing.B) {
+    for i := 0; i < b.N; i++ {
+        foo()
+    }
+}
+
    +

    The function name starts with the Benchmark prefix. The function under test (foo) is called within the for loop. b.N represents a variable number of iterations. When running a benchmark, Go tries to make it match the requested benchmark time. The benchmark time is set by default to 1 second and can be changed with the -benchtime flag. b.N starts at 1; if the benchmark completes in under 1 second, b.N is increased, and the benchmark runs again until b.N roughly matches benchtime:

    +
    $ go test -bench=.
+cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
+BenchmarkFoo-4                73          16511228 ns/op
+
    +

    Here, the benchmark took about 1 second, and foo was executed 73 times, for an average execution time of 16,511,228 nanoseconds. We can change the benchmark time using -benchtime:

    +
    $ go test -bench=. -benchtime=2s
+BenchmarkFoo-4               150          15832169 ns/op
+
    +

    foo was executed roughly twice more than during the previous benchmark.

    +

    Next, let’s look at some common traps.

    +

    Not resetting or pausing the timer

    +

    In some cases, we need to perform operations before the benchmark loop. These operations may take quite a while (for example, generating a large slice of data) and may significantly impact the benchmark results:

    +
    func BenchmarkFoo(b *testing.B) {
+    expensiveSetup()
+    for i := 0; i < b.N; i++ {
+        functionUnderTest()
+    }
+}
+
    +

    In this case, we can use the ResetTimer method before entering the loop:

    +
    func BenchmarkFoo(b *testing.B) {
+    expensiveSetup()
+    b.ResetTimer() // Reset the benchmark timer
+    for i := 0; i < b.N; i++ {
+        functionUnderTest()
+    }
+}
+
    +

    Calling ResetTimer zeroes the elapsed benchmark time and memory allocation counters since the beginning of the test. This way, an expensive setup can be discarded from the test results.

    +

    What if we have to perform an expensive setup not just once but within each loop iteration?

    +
    func BenchmarkFoo(b *testing.B) {
+    for i := 0; i < b.N; i++ {
+        expensiveSetup()
+        functionUnderTest()
+    }
+}
+
    +

    We can’t reset the timer, because that would be executed during each loop iteration. But we can stop and resume the benchmark timer, surrounding the call to expensiveSetup:

    +
    func BenchmarkFoo(b *testing.B) {
+    for i := 0; i < b.N; i++ {
+        b.StopTimer() // Pause the benchmark timer
+        expensiveSetup()
+        b.StartTimer() // Resume the benchmark timer
+        functionUnderTest()
+    }
+}
+
    +

    Here, we pause the benchmark timer to perform the expensive setup and then resume the timer.

    +
    +Note +

    There’s one catch to remember about this approach: if the function under test is too fast to execute compared to the setup function, the benchmark may take too long to complete. The reason is that it would take much longer than 1 second to reach benchtime. Calculating the benchmark time is based solely on the execution time of functionUnderTest. So, if we wait a significant time in each loop iteration, the benchmark will be much slower than 1 second. If we want to keep the benchmark, one possible mitigation is to decrease benchtime.

    +
    +

    We must be sure to use the timer methods to preserve the accuracy of a benchmark.

    +

    Making wrong assumptions about micro-benchmarks

    +

    A micro-benchmark measures a tiny computation unit, and it can be extremely easy to make wrong assumptions about it. Let’s say, for example, that we aren’t sure whether to use atomic.StoreInt32 or atomic.StoreInt64 (assuming that the values we handle will always fit in 32 bits). We want to write a benchmark to compare both functions:

    +
    func BenchmarkAtomicStoreInt32(b *testing.B) {
+    var v int32
+    for i := 0; i < b.N; i++ {
+        atomic.StoreInt32(&v, 1)
+    }
+}
+
+func BenchmarkAtomicStoreInt64(b *testing.B) {
+    var v int64
+    for i := 0; i < b.N; i++ {
+        atomic.StoreInt64(&v, 1)
+    }
+}
+
    +

    If we run this benchmark, here’s some example output:

    +
    cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
+BenchmarkAtomicStoreInt32
+BenchmarkAtomicStoreInt32-4    197107742           5.682 ns/op
+BenchmarkAtomicStoreInt64
+BenchmarkAtomicStoreInt64-4    213917528           5.134 ns/op
+
    +

    We could easily take this benchmark for granted and decide to use atomic.StoreInt64 because it appears to be faster. Now, for the sake of doing a fair benchmark, we reverse the order and test atomic.StoreInt64 first, followed by atomic.StoreInt32. Here is some example output:

    +
    BenchmarkAtomicStoreInt64
+BenchmarkAtomicStoreInt64-4    224900722           5.434 ns/op
+BenchmarkAtomicStoreInt32
+BenchmarkAtomicStoreInt32-4    230253900           5.159 ns/op
+
    +

    This time, atomic.StoreInt32 has better results. What happened?

    +

    In the case of micro-benchmarks, many factors can impact the results, such as machine activity while running the benchmarks, power management, thermal scaling, and better cache alignment of a sequence of instructions. We must remember that many factors, even outside the scope of our Go project, can impact the results.

    +
    +Note +

    We should make sure the machine executing the benchmark is idle. However, external processes may run in the background, which may affect benchmark results. For that reason, tools such as perflock can limit how much CPU a benchmark can consume. For example, we can run a benchmark with 70% of the total available CPU, giving 30% to the OS and other processes and reducing the impact of the machine activity factor on the results.

    +
    +

    One option is to increase the benchmark time using the -benchtime option. Similar to the law of large numbers in probability theory, if we run a benchmark a large number of times, it should tend to approach its expected value (assuming we omit the benefits of instructions caching and similar mechanics).

    +

    Another option is to use external tools on top of the classic benchmark tooling. For instance, the benchstat tool, which is part of the golang.org/x repository, allows us to compute and compare statistics about benchmark executions.

    +

    Let’s run the benchmark 10 times using the -count option and pipe the output to a specific file:

    +
    $ go test -bench=. -count=10 | tee stats.txt
+cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
+BenchmarkAtomicStoreInt32-4     234935682                5.124 ns/op
+BenchmarkAtomicStoreInt32-4     235307204                5.112 ns/op
+// ...
+BenchmarkAtomicStoreInt64-4     235548591                5.107 ns/op
+BenchmarkAtomicStoreInt64-4     235210292                5.090 ns/op
+// ...
+
    +

    We can then run benchstat on this file:

    +
    $ benchstat stats.txt
+name                time/op
+AtomicStoreInt32-4  5.10ns ± 1%
+AtomicStoreInt64-4  5.10ns ± 1%
+
    +

    The results are the same: both functions take on average 5.10 nanoseconds to complete. We also see the percent variation between the executions of a given benchmark: ± 1%. This metric tells us that both benchmarks are stable, giving us more confidence in the computed average results. Therefore, instead of concluding that atomic.StoreInt32 is faster or slower, we can conclude that its execution time is similar to that of atomic.StoreInt64 for the usage we tested (in a specific Go version on a particular machine).

    +

    In general, we should be cautious about micro-benchmarks. Many factors can significantly impact the results and potentially lead to wrong assumptions. Increasing the benchmark time or repeating the benchmark executions and computing stats with tools such as benchstat can be an efficient way to limit external factors and get more accurate results, leading to better conclusions.

    +

    Let’s also highlight that we should be careful about using the results of a micro-benchmark executed on a given machine if another system ends up running the application. The production system may act quite differently from the one on which we ran the micro-benchmark.

    +

    Not being careful about compiler optimizations

    +

    Another common mistake related to writing benchmarks is being fooled by compiler optimizations, which can also lead to wrong benchmark assumptions. In this section, we look at Go issue 14813 (https://github.com/golang/go/issues/14813, also discussed by Go project member Dave Cheney) with a population count function (a function that counts the number of bits set to 1):

    +
    const m1 = 0x5555555555555555
+const m2 = 0x3333333333333333
+const m4 = 0x0f0f0f0f0f0f0f0f
+const h01 = 0x0101010101010101
+
+func popcnt(x uint64) uint64 {
+    x -= (x >> 1) & m1
+    x = (x & m2) + ((x >> 2) & m2)
+    x = (x + (x >> 4)) & m4
+    return (x * h01) >> 56
+}
+
    +

    This function takes and returns a uint64. To benchmark this function, we can write the following:

    +
    func BenchmarkPopcnt1(b *testing.B) {
+    for i := 0; i < b.N; i++ {
+        popcnt(uint64(i))
+    }
+}
+
    +

    However, if we execute this benchmark, we get a surprisingly low result:

    +
    cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
+BenchmarkPopcnt1-4      1000000000               0.2858 ns/op
+
    +

    A duration of 0.28 nanoseconds is roughly one clock cycle, so this number is unreasonably low. The problem is that the developer wasn’t careful enough about compiler optimizations. In this case, the function under test is simple enough to be a candidate for inlining: an optimization that replaces a function call with the body of the called function and lets us prevent a function call, which has a small footprint. Once the function is inlined, the compiler notices that the call has no side effects and replaces it with the following benchmark:

    +
    func BenchmarkPopcnt1(b *testing.B) {
+    for i := 0; i < b.N; i++ {
+        // Empty
+    }
+}
+
    +

    The benchmark is now empty — which is why we got a result close to one clock cycle. To prevent this from happening, a best practice is to follow this pattern:

    +
      +
    1. During each loop iteration, assign the result to a local variable (local in the context of the benchmark function).
      2. +
    2. Assign the latest result to a global variable.
      3. +
    +

    In our case, we write the following benchmark:

    +
    var global uint64 // Define a global variable
+
+func BenchmarkPopcnt2(b *testing.B) {
+    var v uint64 // Define a local variable
+    for i := 0; i < b.N; i++ {
+        v = popcnt(uint64(i)) // Assign the result to the local variable
+    }
+    global = v // Assign the result to the global variable
+}
+
    +

    global is a global variable, whereas v is a local variable whose scope is the benchmark function. During each loop iteration, we assign the result of popcnt to the local variable. Then we assign the latest result to the global variable.

    +
    +Note +

    Why not assign the result of the popcnt call directly to global to simplify the test? Writing to a global variable is slower than writing to a local variable (these concepts are discussed in 100 Go Mistakes, mistake #95: “Not understanding stack vs. heap”). Therefore, we should write each result to a local variable to limit the footprint during each loop iteration.

    +
    +

    If we run these two benchmarks, we now get a significant difference in the results:

    +
    cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
+BenchmarkPopcnt1-4      1000000000               0.2858 ns/op
+BenchmarkPopcnt2-4      606402058                1.993 ns/op
+
    +

    BenchmarkPopcnt2 is the accurate version of the benchmark. It guarantees that we avoid the inlining optimizations, which can artificially lower the execution time or even remove the call to the function under test. Relying on the results of BenchmarkPopcnt1 could have led to wrong assumptions.

    +

    Let’s remember the pattern to avoid compiler optimizations fooling benchmark results: assign the result of the function under test to a local variable, and then assign the latest result to a global variable. This best practice also prevents us from making incorrect assumptions.

    +

    Being fooled by the observer effect

    +

    In physics, the observer effect is the disturbance of an observed system by the act of observation. This effect can also be seen in benchmarks and can lead to wrong assumptions about results. Let’s look at a concrete example and then try to mitigate it.

    +

    We want to implement a function receiving a matrix of int64 elements. This matrix has a fixed number of 512 columns, and we want to compute the total sum of the first eight columns, as shown in figure 1.

    +
    +

    +

    +
    Figure 1: Computing the sum of the first eight columns.
    +
    +

    For the sake of optimizations, we also want to determine whether varying the number of columns has an impact, so we also implement a second function with 513 columns. The implementation is the following:

    +
    func calculateSum512(s [][512]int64) int64 {
+    var sum int64
+    for i := 0; i < len(s); i++ { // Iterate over each row
+        for j := 0; j < 8; j++ { // Iterate over the first eight columns
+            sum += s[i][j] // Increment sum
+        }
+    }
+    return sum
+}
+
+func calculateSum513(s [][513]int64) int64 {
+    // Same implementation as calculateSum512
+}
+
    +

    We iterate over each row and then over the first eight columns, and we increment a sum variable that we return. The implementation in calculateSum513 remains the same.

    +

    We want to benchmark these functions to decide which one is the most performant given a fixed number of rows:

    +
    const rows = 1000
+
+var res int64
+
+func BenchmarkCalculateSum512(b *testing.B) {
+    var sum int64
+    s := createMatrix512(rows) // Create a matrix of 512 columns
+    b.ResetTimer()
+    for i := 0; i < b.N; i++ {
+        sum = calculateSum512(s) // Create a matrix of 512 columns
+    }
+    res = sum
+}
+
+func BenchmarkCalculateSum513(b *testing.B) {
+    var sum int64
+    s := createMatrix513(rows) // Create a matrix of 513 columns
+    b.ResetTimer()
+    for i := 0; i < b.N; i++ {
+        sum = calculateSum513(s) // Calculate the sum
+    }
+    res = sum
+}
+
    +

    We want to create the matrix only once, to limit the footprint on the results. Therefore, we call createMatrix512 and createMatrix513 outside of the loop. We may expect the results to be similar as again we only want to iterate on the first eight columns, but this isn’t the case (on my machine):

    +
    cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
+BenchmarkCalculateSum512-4        81854             15073 ns/op
+BenchmarkCalculateSum513-4       161479              7358 ns/op
+
    +

    The second benchmark with 513 columns is about 50% faster. Again, because we iterate only over the first eight columns, this result is quite surprising.

    +

    To understand this difference, we need to understand the basics of CPU caches. In a nutshell, a CPU is composed of different caches (usually L1, L2, and L3). These caches reduce the average cost of accessing data from the main memory. In some conditions, the CPU can fetch data from the main memory and copy it to L1. In this case, the CPU tries to fetch into L1 the matrix’s subset that calculateSum is interested in (the first eight columns of each row). However, the matrix fits in memory in one case (513 columns) but not in the other case (512 columns).

    +
    +Note +

    This isn’t in the scope of this post to explain why, but we look at this problem in 100 Go Mistakes, mistake #91: “Not understanding CPU caches.”

    +
    +

    Coming back to the benchmark, the main issue is that we keep reusing the same matrix in both cases. Because the function is repeated thousands of times, we don’t measure the function’s execution when it receives a plain new matrix. Instead, we measure a function that gets a matrix that already has a subset of the cells present in the cache. Therefore, because calculateSum513 leads to fewer cache misses, it has a better execution time.

    +

    This is an example of the observer effect. Because we keep observing a repeatedly called CPU-bound function, CPU caching may come into play and significantly affect the results. In this example, to prevent this effect, we should create a matrix during each test instead of reusing one:

    +
    func BenchmarkCalculateSum512(b *testing.B) {
+    var sum int64
+    for i := 0; i < b.N; i++ {
+        b.StopTimer()
+        s := createMatrix512(rows) // Create a new matrix during each loop iteration
+        b.StartTimer()
+        sum = calculateSum512(s)
+    }
+    res = sum
+}
+
    +

    A new matrix is now created during each loop iteration. If we run the benchmark again (and adjust benchtime — otherwise, it takes too long to execute), the results are closer to each other:

    +
    cpu: Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
+BenchmarkCalculateSum512-4         1116             33547 ns/op
+BenchmarkCalculateSum513-4          998             35507 ns/op
+
    +

    Instead of making the incorrect assumption that calculateSum513 is faster, we see that both benchmarks lead to similar results when receiving a new matrix.

    +

    As we have seen in this post, because we were reusing the same matrix, CPU caches significantly impacted the results. To prevent this, we had to create a new matrix during each loop iteration. In general, we should remember that observing a function under test may lead to significant differences in results, especially in the context of micro-benchmarks of CPU-bound functions where low-level optimizations matter. Forcing a benchmark to re-create data during each iteration can be a good way to prevent this effect.

    + + + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/site/9-generics/index.html b/site/9-generics/index.html index 52c3f0b..527f5c2 100644 --- a/site/9-generics/index.html +++ b/site/9-generics/index.html @@ -14,7 +14,7 @@ - + @@ -89,7 +89,18 @@ - + @@ -657,6 +668,46 @@ +
  • + + + + + Maps and memory leaks (#28) + + + + +
    • + + + + + + + + + +
  • + + + + + Writing inaccurate benchmarks (#89) + + + + +
    • + + + + + + + + +
  • @@ -1062,11 +1113,11 @@
    - + - + \ No newline at end of file diff --git a/site/98-profiling-execution-tracing/index.html b/site/98-profiling-execution-tracing/index.html index 6109472..b7aa14c 100644 --- a/site/98-profiling-execution-tracing/index.html +++ b/site/98-profiling-execution-tracing/index.html @@ -11,7 +11,7 @@ - + @@ -89,7 +89,18 @@ - + @@ -595,6 +606,46 @@ + +
  • + + + + + Maps and memory leaks (#28) + + + + +
    • + + + + + + + + + +
  • + + + + + Writing inaccurate benchmarks (#89) + + + + +
    • + + + + + + + + @@ -961,9 +1012,17 @@ 
    $ go tool pprof -http=:8080 <file>

    This command opens a web UI showing the call graph. The next figure shows an example taken from an application. The larger the arrow, the more it was a hot path. We can then navigate into this graph and get execution insights.

    -

    +
    +

    +

    +
    Figure 1: The call graph of an application during 30 seconds.
    +

    For example, the graph in the next figure tells us that during 30 seconds, 0.06 seconds were spent in the decode method (*FetchResponse receiver). Of these 0.06 seconds, 0.02 were spent in RecordBatch.decode and 0.01 in makemap (creating a map).

    -

    +
    +

    +

    +
    Figure 2: Example call graph.
    +

    We can also access this kind of information from the web UI with different representations. For example, the Top view sorts the functions per execution time, and Flame Graph visualizes the execution time hierarchy. The UI can even display the expensive parts of the source code line by line.

    Note @@ -984,7 +1043,11 @@

    Heap profiling allows us to get statistics about the current heap usage. Like CPU profiling, heap profiling is sample-based. We can change this rate, but we shouldn’t be too granular because the more we decrease the rate, the more effort heap profiling will require to collect data. By default, samples are profiled at one allocation for every 512 KB of heap allocation.

    If we reach /debug/pprof/heap/, we get raw data that can be hard to read. However, we can download a heap profile using /debug/pprof/heap/?debug=0 and then open it with go tool (the same command as in the previous section) to navigate into the data using the web UI.

    The next figure shows an example of a heap graph. Calling the MetadataResponse.decode method leads to allocating 1536 KB of heap data (which represents 6.32% of the total heap). However, 0 out of these 1536 KB were allocated by this function directly, so we need to inspect the second call. The TopicMetadata.decode method allocated 512 KB out of the 1536 KB; the rest — 1024 KB — were allocated in another method.

    -

    +
    +

    +

    +
    Figure 3: A heap graph.
    +

    This is how we can navigate the call chain to understand what part of an application is responsible for most of the heap allocations. We can also look at different sample types:

    • alloc_objects— Total number of objects allocated
      • @@ -1012,14 +1075,22 @@ 
      $ go tool pprof -http=:8080 -diff_base <file2> <file1>

      The next figure shows the kind of data we can access. For example, the amount of heap memory held by the newTopicProducer method (top left) has decreased (–513 KB). In contrast, the amount held by updateMetadata (bottom right) has increased (+512 KB). Slow increases are normal. The second heap profile may have been calculated in the middle of a service call, for example. We can repeat this process or wait longer; the important part is to track steady increases in allocations of a specific object.

      -

      +
      +

      +

      +
      Figure 4: The differences between the two heap profiles.
      +
      Note

      Another type of profiling related to the heap is allocs, which reports allocations. Heap profiling shows the current state of the heap memory. To get insights about past memory allocations since the application started, we can use allocations profiling. As discussed, because stack allocations are cheap, they aren’t part of this profiling, which only focuses on the heap.

      Goroutine Profiling

      The goroutine profile reports the stack trace of all the current goroutines in an application. We can download a file using /debug/pprof/goroutine/?debug=0 and use go tool again. The next figure shows the kind of information we can get.

      -

      +
      +

      +

      +
      Figure 5: Goroutine graph.
      +

      We can see the current state of the application and how many goroutines were created per function. In this case, withRecover has created 296 ongoing goroutines (63%), and 29 were related to a call to responseFeeder.

      This kind of information is also beneficial if we suspect goroutine leaks. We can look at goroutine profiler data to know which part of a system is the suspect.

      Block Profiling

      @@ -1077,13 +1148,29 @@

      The web browser opens, and we can click View Trace to see all the traces during a specific timeframe, as shown in the next figure. This figure represents about 150 ms. We can see multiple helpful metrics, such as the goroutine count and the heap size. The heap size grows steadily until a GC is triggered. We can also observe the activity of the Go application per CPU core. The timeframe starts with user-level code; then a “stop the world” is executed, which occupies the four CPU cores for approximately 40 ms.

      -

      +
      +

      +

      +
      Figure 6: Showing goroutine activity and runtime events such as a GC phase.
      +

      Regarding concurrency, we can see that this version uses all the available CPU cores on the machine. However, the next figure zooms in on a portion of 1 ms. Each bar corresponds to a single goroutine execution. Having too many small bars doesn’t look right: it means execution that is poorly parallelized.

      -

      +
      +

      +

      +
      Figure 7: Too many small bars mean poorly parallelized execution.
      +

      The next figure zooms even closer to see how these goroutines are orchestrated. Roughly 50% of the CPU time isn’t spent executing application code. The white spaces represent the time the Go runtime takes to spin up and orchestrate new goroutines.

      -

      +
      +

      +

      +
      Figure 8: About 50% of CPU time is spent handling goroutine switches.
      +

      Let’s compare this with the second parallel implementation, which was about an order of magnitude faster. The next figure again zooms to a 1 ms timeframe.

      -

      +
      +

      +

      +
      Figure 9: The number of white spaces has been significantly reduced, proving that the CPU is more fully occupied.
      +

      Each goroutine takes more time to execute, and the number of white spaces has been significantly reduced. Hence, the CPU is much more occupied executing application code than it was in the first version. Each millisecond of CPU time is spent more efficiently, explaining the benchmark differences.

      Note that the granularity of the traces is per goroutine, not per function like CPU profiling. However, it’s possible to define user-level tasks to get insights per function or group of functions using the runtime/trace package.

      For example, imagine a function that computes a Fibonacci number and then writes it to a global variable using atomic. We can define two different tasks:

      @@ -1103,7 +1190,11 @@ world” is executed, which occupies the four CPU cores for approximately 40 ms. fibStore.End()

      Using go tool, we can get more precise information about how these two tasks perform. In the previous trace UI, we can see the boundaries for each task per goroutine. In User-Defined Tasks, we can follow the duration distribution:

      -

      +
      +

      +

      +
      Figure 10: Distribution of user-level tasks.
      +

      We see that in most cases, the fibonacci task is executed in less than 15 microseconds, whereas the store task takes less than 6309 nanoseconds.

      In the previous section, we discussed the kinds of information we can get from CPU profiling. What are the main differences compared to the data we can get from user-level traces?

        @@ -1190,11 +1281,11 @@ world” is executed, which occupies the four CPU cores for approximately 40 ms.
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_(X,{touchStart:function(t){if(r=!0,(c(t.targetTouches[0].target,"ginner-container")||u(t.targetTouches[0].target,".gslide-desc")||"a"==t.targetTouches[0].target.nodeName.toLowerCase())&&(r=!1),u(t.targetTouches[0].target,".gslide-inline")&&!c(t.targetTouches[0].target.parentNode,"gslide-inline")&&(r=!1),r){if(P=t.targetTouches[0],O.pageX=t.targetTouches[0].pageX,O.pageY=t.targetTouches[0].pageY,M=t.targetTouches[0].clientX,z=t.targetTouches[0].clientY,a=e.activeSlide,g=a.querySelector(".gslide-media"),n=a.querySelector(".gslide-inline"),f=null,c(g,"gslide-image")&&(f=g.querySelector("img")),(window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth)>769&&(g=a.querySelector(".ginner-container")),d(Y,"greset"),t.pageX>20&&t.pageXo){var a=O.pageX-P.pageX;if(Math.abs(a)<=13)return!1}p=!0;var h,d=s.targetTouches[0].clientX,c=s.targetTouches[0].clientY,u=M-d,m=z-c;if(Math.abs(u)>Math.abs(m)?(L=!1,I=!0):(I=!1,L=!0),t=P.pageX-O.pageX,E=100*t/l,i=P.pageY-O.pageY,A=100*i/o,L&&f&&(h=1-Math.abs(i)/o,Y.style.opacity=h,e.settings.touchFollowAxis&&(E=0)),I&&(h=1-Math.abs(t)/l,g.style.opacity=h,e.settings.touchFollowAxis&&(A=0)),!f)return v(g,"translate3d(".concat(E,"%, 0, 0)"));v(g,"translate3d(".concat(E,"%, ").concat(A,"%, 0)"))}},touchEnd:function(){if(r){if(p=!1,S||b)return C=w,void(k=T);var t=Math.abs(parseInt(A)),i=Math.abs(parseInt(E));if(!(t>29&&f))return t<29&&i<25?(h(Y,"greset"),Y.style.opacity=1,W(g)):void 0;e.close()}},multipointEnd:function(){setTimeout((function(){b=!1}),50)},multipointStart:function(){b=!0,m=x||1},pinch:function(e){if(!f||p)return!1;b=!0,f.scaleX=f.scaleY=m*e.zoom;var t=m*e.zoom;if(S=!0,t<=1)return S=!1,t=1,k=null,C=null,w=null,T=null,void f.setAttribute("style","");t>4.5&&(t=4.5),f.style.transform="scale3d(".concat(t,", ").concat(t,", 1)"),x=t},pressMove:function(e){if(S&&!b){var t=P.pageX-O.pageX,i=P.pageY-O.pageY;C&&(t+=C),k&&(i+=k),w=t,T=i;var n="translate3d(".concat(t,"px, ").concat(i,"px, 0)");x&&(n+=" scale3d(".concat(x,", ").concat(x,", 1)")),v(f,n)}},swipe:function(t){if(!S)if(b)b=!1;else{if("Left"==t.direction){if(e.index==e.elements.length-1)return W(g);e.nextSlide()}if("Right"==t.direction){if(0==e.index)return W(g);e.prevSlide()}}}});e.events.touch=q}var H=function(){function e(i,n){var s=this,l=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null;if(t(this,e),this.img=i,this.slide=n,this.onclose=l,this.img.setZoomEvents)return!1;this.active=!1,this.zoomedIn=!1,this.dragging=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.img.addEventListener("mousedown",(function(e){return s.dragStart(e)}),!1),this.img.addEventListener("mouseup",(function(e){return s.dragEnd(e)}),!1),this.img.addEventListener("mousemove",(function(e){return s.drag(e)}),!1),this.img.addEventListener("click",(function(e){return s.slide.classList.contains("dragging-nav")?(s.zoomOut(),!1):s.zoomedIn?void(s.zoomedIn&&!s.dragging&&s.zoomOut()):s.zoomIn()}),!1),this.img.setZoomEvents=!0}return n(e,[{key:"zoomIn",value:function(){var e=this.widowWidth();if(!(this.zoomedIn||e<=768)){var t=this.img;if(t.setAttribute("data-style",t.getAttribute("style")),t.style.maxWidth=t.naturalWidth+"px",t.style.maxHeight=t.naturalHeight+"px",t.naturalWidth>e){var i=e/2-t.naturalWidth/2;this.setTranslate(this.img.parentNode,i,0)}this.slide.classList.add("zoomed"),this.zoomedIn=!0}}},{key:"zoomOut",value:function(){this.img.parentNode.setAttribute("style",""),this.img.setAttribute("style",this.img.getAttribute("data-style")),this.slide.classList.remove("zoomed"),this.zoomedIn=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.onclose&&"function"==typeof this.onclose&&this.onclose()}},{key:"dragStart",value:function(e){e.preventDefault(),this.zoomedIn?("touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset),e.target===this.img&&(this.active=!0,this.img.classList.add("dragging"))):this.active=!1}},{key:"dragEnd",value:function(e){var t=this;e.preventDefault(),this.initialX=this.currentX,this.initialY=this.currentY,this.active=!1,setTimeout((function(){t.dragging=!1,t.img.isDragging=!1,t.img.classList.remove("dragging")}),100)}},{key:"drag",value:function(e){this.active&&(e.preventDefault(),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.img.isDragging=!0,this.dragging=!0,this.setTranslate(this.img,this.currentX,this.currentY))}},{key:"onMove",value:function(e){if(this.zoomedIn){var t=e.clientX-this.img.naturalWidth/2,i=e.clientY-this.img.naturalHeight/2;this.setTranslate(this.img,t,i)}}},{key:"setTranslate",value:function(e,t,i){e.style.transform="translate3d("+t+"px, "+i+"px, 0)"}},{key:"widowWidth",value:function(){return window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth}}]),e}(),V=function(){function e(){var i=this,n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};t(this,e);var s=n.dragEl,l=n.toleranceX,o=void 0===l?40:l,r=n.toleranceY,a=void 0===r?65:r,h=n.slide,d=void 0===h?null:h,c=n.instance,u=void 0===c?null:c;this.el=s,this.active=!1,this.dragging=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.direction=null,this.lastDirection=null,this.toleranceX=o,this.toleranceY=a,this.toleranceReached=!1,this.dragContainer=this.el,this.slide=d,this.instance=u,this.el.addEventListener("mousedown",(function(e){return i.dragStart(e)}),!1),this.el.addEventListener("mouseup",(function(e){return i.dragEnd(e)}),!1),this.el.addEventListener("mousemove",(function(e){return i.drag(e)}),!1)}return n(e,[{key:"dragStart",value:function(e){if(this.slide.classList.contains("zoomed"))this.active=!1;else{"touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset);var t=e.target.nodeName.toLowerCase();e.target.classList.contains("nodrag")||u(e.target,".nodrag")||-1!==["input","select","textarea","button","a"].indexOf(t)?this.active=!1:(e.preventDefault(),(e.target===this.el||"img"!==t&&u(e.target,".gslide-inline"))&&(this.active=!0,this.el.classList.add("dragging"),this.dragContainer=u(e.target,".ginner-container")))}}},{key:"dragEnd",value:function(e){var t=this;e&&e.preventDefault(),this.initialX=0,this.initialY=0,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.active=!1,this.doSlideChange&&(this.instance.preventOutsideClick=!0,"right"==this.doSlideChange&&this.instance.prevSlide(),"left"==this.doSlideChange&&this.instance.nextSlide()),this.doSlideClose&&this.instance.close(),this.toleranceReached||this.setTranslate(this.dragContainer,0,0,!0),setTimeout((function(){t.instance.preventOutsideClick=!1,t.toleranceReached=!1,t.lastDirection=null,t.dragging=!1,t.el.isDragging=!1,t.el.classList.remove("dragging"),t.slide.classList.remove("dragging-nav"),t.dragContainer.style.transform="",t.dragContainer.style.transition=""}),100)}},{key:"drag",value:function(e){if(this.active){e.preventDefault(),this.slide.classList.add("dragging-nav"),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.el.isDragging=!0,this.dragging=!0,this.doSlideChange=!1,this.doSlideClose=!1;var t=Math.abs(this.currentX),i=Math.abs(this.currentY);if(t>0&&t>=Math.abs(this.currentY)&&(!this.lastDirection||"x"==this.lastDirection)){this.yOffset=0,this.lastDirection="x",this.setTranslate(this.dragContainer,this.currentX,0);var n=this.shouldChange();if(!this.instance.settings.dragAutoSnap&&n&&(this.doSlideChange=n),this.instance.settings.dragAutoSnap&&n)return this.instance.preventOutsideClick=!0,this.toleranceReached=!0,this.active=!1,this.instance.preventOutsideClick=!0,this.dragEnd(null),"right"==n&&this.instance.prevSlide(),void("left"==n&&this.instance.nextSlide())}if(this.toleranceY>0&&i>0&&i>=t&&(!this.lastDirection||"y"==this.lastDirection)){this.xOffset=0,this.lastDirection="y",this.setTranslate(this.dragContainer,0,this.currentY);var s=this.shouldClose();return!this.instance.settings.dragAutoSnap&&s&&(this.doSlideClose=!0),void(this.instance.settings.dragAutoSnap&&s&&this.instance.close())}}}},{key:"shouldChange",value:function(){var e=!1;if(Math.abs(this.currentX)>=this.toleranceX){var t=this.currentX>0?"right":"left";("left"==t&&this.slide!==this.slide.parentNode.lastChild||"right"==t&&this.slide!==this.slide.parentNode.firstChild)&&(e=t)}return e}},{key:"shouldClose",value:function(){var e=!1;return Math.abs(this.currentY)>=this.toleranceY&&(e=!0),e}},{key:"setTranslate",value:function(e,t,i){var n=arguments.length>3&&void 0!==arguments[3]&&arguments[3];e.style.transition=n?"all .2s ease":"",e.style.transform="translate3d(".concat(t,"px, ").concat(i,"px, 0)")}}]),e}();function j(e,t,i,n){var s=e.querySelector(".gslide-media"),l=new Image,o="gSlideTitle_"+i,r="gSlideDesc_"+i;l.addEventListener("load",(function(){T(n)&&n()}),!1),l.src=t.href,""!=t.sizes&&""!=t.srcset&&(l.sizes=t.sizes,l.srcset=t.srcset),l.alt="",I(t.alt)||""===t.alt||(l.alt=t.alt),""!==t.title&&l.setAttribute("aria-labelledby",o),""!==t.description&&l.setAttribute("aria-describedby",r),t.hasOwnProperty("_hasCustomWidth")&&t._hasCustomWidth&&(l.style.width=t.width),t.hasOwnProperty("_hasCustomHeight")&&t._hasCustomHeight&&(l.style.height=t.height),s.insertBefore(l,s.firstChild)}function F(e,t,i,n){var s=this,l=e.querySelector(".ginner-container"),o="gvideo"+i,r=e.querySelector(".gslide-media"),a=this.getAllPlayers();h(l,"gvideo-container"),r.insertBefore(m('

        '),r.firstChild);var d=e.querySelector(".gvideo-wrapper");S(this.settings.plyr.css,"Plyr");var c=t.href,u=location.protocol.replace(":",""),g="",v="",f=!1;"file"==u&&(u="http"),r.style.maxWidth=t.width,S(this.settings.plyr.js,"Plyr",(function(){if(c.match(/vimeo\.com\/([0-9]*)/)){var l=/vimeo.*\/(\d+)/i.exec(c);g="vimeo",v=l[1]}if(c.match(/(youtube\.com|youtube-nocookie\.com)\/watch\?v=([a-zA-Z0-9\-_]+)/)||c.match(/youtu\.be\/([a-zA-Z0-9\-_]+)/)||c.match(/(youtube\.com|youtube-nocookie\.com)\/embed\/([a-zA-Z0-9\-_]+)/)){var r=function(e){var t="";t=void 0!==(e=e.replace(/(>|<)/gi,"").split(/(vi\/|v=|\/v\/|youtu\.be\/|\/embed\/)/))[2]?(t=e[2].split(/[^0-9a-z_\-]/i))[0]:e;return t}(c);g="youtube",v=r}if(null!==c.match(/\.(mp4|ogg|webm|mov)$/)){g="local";var u='")}var w=f||m('
        '));h(d,"".concat(g,"-video gvideo")),d.appendChild(w),d.setAttribute("data-id",o),d.setAttribute("data-index",i);var C=O(s.settings.plyr,"config")?s.settings.plyr.config:{},k=new Plyr("#"+o,C);k.on("ready",(function(e){var t=e.detail.plyr;a[o]=t,T(n)&&n()})),b((function(){return e.querySelector("iframe")&&"true"==e.querySelector("iframe").dataset.ready}),(function(){s.resize(e)})),k.on("enterfullscreen",R),k.on("exitfullscreen",R)}))}function R(e){var t=u(e.target,".gslide-media");"enterfullscreen"==e.type&&h(t,"fullscreen"),"exitfullscreen"==e.type&&d(t,"fullscreen")}function G(e,t,i,n){var s,l=this,o=e.querySelector(".gslide-media"),r=!(!O(t,"href")||!t.href)&&t.href.split("#").pop().trim(),d=!(!O(t,"content")||!t.content)&&t.content;if(d&&(C(d)&&(s=m('
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        "))),k(d))){"none"==d.style.display&&(d.style.display="block");var c=document.createElement("div");c.className="ginlined-content",c.appendChild(d),s=c}if(r){var u=document.getElementById(r);if(!u)return!1;var g=u.cloneNode(!0);g.style.height=t.height,g.style.maxWidth=t.width,h(g,"ginlined-content"),s=g}if(!s)return console.error("Unable to append inline slide content",t),!1;o.style.height=t.height,o.style.width=t.width,o.appendChild(s),this.events["inlineclose"+r]=a("click",{onElement:o.querySelectorAll(".gtrigger-close"),withCallback:function(e){e.preventDefault(),l.close()}}),T(n)&&n()}function Z(e,t,i,n){var s=e.querySelector(".gslide-media"),l=function(e){var t=e.url,i=e.allow,n=e.callback,s=e.appendTo,l=document.createElement("iframe");return l.className="vimeo-video 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t=e;if(null!==(e=e.toLowerCase()).match(/\.(jpeg|jpg|jpe|gif|png|apn|webp|avif|svg)/))return"image";if(e.match(/(youtube\.com|youtube-nocookie\.com)\/watch\?v=([a-zA-Z0-9\-_]+)/)||e.match(/youtu\.be\/([a-zA-Z0-9\-_]+)/)||e.match(/(youtube\.com|youtube-nocookie\.com)\/embed\/([a-zA-Z0-9\-_]+)/))return"video";if(e.match(/vimeo\.com\/([0-9]*)/))return"video";if(null!==e.match(/\.(mp4|ogg|webm|mov)/))return"video";if(null!==e.match(/\.(mp3|wav|wma|aac|ogg)/))return"audio";if(e.indexOf("#")>-1&&""!==t.split("#").pop().trim())return"inline";return e.indexOf("goajax=true")>-1?"ajax":"external"}},{key:"parseConfig",value:function(e,t){var i=this,n=l({descPosition:t.descPosition},this.defaults);if(L(e)&&!k(e)){O(e,"type")||(O(e,"content")&&e.content?e.type="inline":O(e,"href")&&(e.type=this.sourceType(e.href)));var s=l(n,e);return this.setSize(s,t),s}var r="",a=e.getAttribute("data-glightbox"),h=e.nodeName.toLowerCase();if("a"===h&&(r=e.href),"img"===h&&(r=e.src,n.alt=e.alt),n.href=r,o(n,(function(s,l){O(t,l)&&"width"!==l&&(n[l]=t[l]);var o=e.dataset[l];I(o)||(n[l]=i.sanitizeValue(o))})),n.content&&(n.type="inline"),!n.type&&r&&(n.type=this.sourceType(r)),I(a)){if(!n.title&&"a"==h){var d=e.title;I(d)||""===d||(n.title=d)}if(!n.title&&"img"==h){var c=e.alt;I(c)||""===c||(n.title=c)}}else{var u=[];o(n,(function(e,t){u.push(";\\s?"+t)})),u=u.join("\\s?:|"),""!==a.trim()&&o(n,(function(e,t){var s=a,l=new RegExp("s?"+t+"s?:s?(.*?)("+u+"s?:|$)"),o=s.match(l);if(o&&o.length&&o[1]){var r=o[1].trim().replace(/;\s*$/,"");n[t]=i.sanitizeValue(r)}}))}if(n.description&&"."===n.description.substring(0,1)){var g;try{g=document.querySelector(n.description).innerHTML}catch(e){if(!(e instanceof DOMException))throw e}g&&(n.description=g)}if(!n.description){var v=e.querySelector(".glightbox-desc");v&&(n.description=v.innerHTML)}return this.setSize(n,t,e),this.slideConfig=n,n}},{key:"setSize",value:function(e,t){var i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,n="video"==e.type?this.checkSize(t.videosWidth):this.checkSize(t.width),s=this.checkSize(t.height);return e.width=O(e,"width")&&""!==e.width?this.checkSize(e.width):n,e.height=O(e,"height")&&""!==e.height?this.checkSize(e.height):s,i&&"image"==e.type&&(e._hasCustomWidth=!!i.dataset.width,e._hasCustomHeight=!!i.dataset.height),e}},{key:"checkSize",value:function(e){return M(e)?"".concat(e,"px"):e}},{key:"sanitizeValue",value:function(e){return"true"!==e&&"false"!==e?e:"true"===e}}]),e}(),U=function(){function e(i,n,s){t(this,e),this.element=i,this.instance=n,this.index=s}return n(e,[{key:"setContent",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(c(t,"loaded"))return!1;var n=this.instance.settings,s=this.slideConfig,l=w();T(n.beforeSlideLoad)&&n.beforeSlideLoad({index:this.index,slide:t,player:!1});var o=s.type,r=s.descPosition,a=t.querySelector(".gslide-media"),d=t.querySelector(".gslide-title"),u=t.querySelector(".gslide-desc"),g=t.querySelector(".gdesc-inner"),v=i,f="gSlideTitle_"+this.index,p="gSlideDesc_"+this.index;if(T(n.afterSlideLoad)&&(v=function(){T(i)&&i(),n.afterSlideLoad({index:e.index,slide:t,player:e.instance.getSlidePlayerInstance(e.index)})}),""==s.title&&""==s.description?g&&g.parentNode.parentNode.removeChild(g.parentNode):(d&&""!==s.title?(d.id=f,d.innerHTML=s.title):d.parentNode.removeChild(d),u&&""!==s.description?(u.id=p,l&&n.moreLength>0?(s.smallDescription=this.slideShortDesc(s.description,n.moreLength,n.moreText),u.innerHTML=s.smallDescription,this.descriptionEvents(u,s)):u.innerHTML=s.description):u.parentNode.removeChild(u),h(a.parentNode,"desc-".concat(r)),h(g.parentNode,"description-".concat(r))),h(a,"gslide-".concat(o)),h(t,"loaded"),"video"!==o){if("external"!==o)return"inline"===o?(G.apply(this.instance,[t,s,this.index,v]),void(s.draggable&&new V({dragEl:t.querySelector(".gslide-inline"),toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:this.instance}))):void("image"!==o?T(v)&&v():j(t,s,this.index,(function(){var i=t.querySelector("img");s.draggable&&new V({dragEl:i,toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:e.instance}),s.zoomable&&i.naturalWidth>i.offsetWidth&&(h(i,"zoomable"),new H(i,t,(function(){e.instance.resize()}))),T(v)&&v()})));Z.apply(this,[t,s,this.index,v])}else F.apply(this.instance,[t,s,this.index,v])}},{key:"slideShortDesc",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:50,i=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=document.createElement("div");n.innerHTML=e;var s=n.innerText,l=i;if((e=s.trim()).length<=t)return e;var o=e.substr(0,t-1);return l?(n=null,o+'... '+i+""):o}},{key:"descriptionEvents",value:function(e,t){var i=this,n=e.querySelector(".desc-more");if(!n)return!1;a("click",{onElement:n,withCallback:function(e,n){e.preventDefault();var s=document.body,l=u(n,".gslide-desc");if(!l)return!1;l.innerHTML=t.description,h(s,"gdesc-open");var o=a("click",{onElement:[s,u(l,".gslide-description")],withCallback:function(e,n){"a"!==e.target.nodeName.toLowerCase()&&(d(s,"gdesc-open"),h(s,"gdesc-closed"),l.innerHTML=t.smallDescription,i.descriptionEvents(l,t),setTimeout((function(){d(s,"gdesc-closed")}),400),o.destroy())}})}})}},{key:"create",value:function(){return m(this.instance.settings.slideHTML)}},{key:"getConfig",value:function(){k(this.element)||this.element.hasOwnProperty("draggable")||(this.element.draggable=this.instance.settings.draggable);var e=new $(this.instance.settings.slideExtraAttributes);return this.slideConfig=e.parseConfig(this.element,this.instance.settings),this.slideConfig}}]),e}(),J=w(),K=null!==w()||void 0!==document.createTouch||"ontouchstart"in window||"onmsgesturechange"in window||navigator.msMaxTouchPoints,Q=document.getElementsByTagName("html")[0],ee={selector:".glightbox",elements:null,skin:"clean",theme:"clean",closeButton:!0,startAt:null,autoplayVideos:!0,autofocusVideos:!0,descPosition:"bottom",width:"900px",height:"506px",videosWidth:"960px",beforeSlideChange:null,afterSlideChange:null,beforeSlideLoad:null,afterSlideLoad:null,slideInserted:null,slideRemoved:null,slideExtraAttributes:null,onOpen:null,onClose:null,loop:!1,zoomable:!0,draggable:!0,dragAutoSnap:!1,dragToleranceX:40,dragToleranceY:65,preload:!0,oneSlidePerOpen:!1,touchNavigation:!0,touchFollowAxis:!0,keyboardNavigation:!0,closeOnOutsideClick:!0,plugins:!1,plyr:{css:"https://cdn.plyr.io/3.6.8/plyr.css",js:"https://cdn.plyr.io/3.6.8/plyr.js",config:{ratio:"16:9",fullscreen:{enabled:!0,iosNative:!0},youtube:{noCookie:!0,rel:0,showinfo:0,iv_load_policy:3},vimeo:{byline:!1,portrait:!1,title:!1,transparent:!1}}},openEffect:"zoom",closeEffect:"zoom",slideEffect:"slide",moreText:"See more",moreLength:60,cssEfects:{fade:{in:"fadeIn",out:"fadeOut"},zoom:{in:"zoomIn",out:"zoomOut"},slide:{in:"slideInRight",out:"slideOutLeft"},slideBack:{in:"slideInLeft",out:"slideOutRight"},none:{in:"none",out:"none"}},svg:{close:'',next:' ',prev:''},slideHTML:'
        \n
        \n
        \n
        \n
        \n
        \n
        \n

        \n
        \n
        \n
        \n
        \n
        \n
        ',lightboxHTML:''},te=function(){function e(){var i=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};t(this,e),this.customOptions=i,this.settings=l(ee,i),this.effectsClasses=this.getAnimationClasses(),this.videoPlayers={},this.apiEvents=[],this.fullElementsList=!1}return n(e,[{key:"init",value:function(){var e=this,t=this.getSelector();t&&(this.baseEvents=a("click",{onElement:t,withCallback:function(t,i){t.preventDefault(),e.open(i)}})),this.elements=this.getElements()}},{key:"open",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(0==this.elements.length)return!1;this.activeSlide=null,this.prevActiveSlideIndex=null,this.prevActiveSlide=null;var i=M(t)?t:this.settings.startAt;if(k(e)){var n=e.getAttribute("data-gallery");n&&(this.fullElementsList=this.elements,this.elements=this.getGalleryElements(this.elements,n)),I(i)&&(i=this.getElementIndex(e))<0&&(i=0)}M(i)||(i=0),this.build(),g(this.overlay,"none"==this.settings.openEffect?"none":this.settings.cssEfects.fade.in);var s=document.body,l=window.innerWidth-document.documentElement.clientWidth;if(l>0){var o=document.createElement("style");o.type="text/css",o.className="gcss-styles",o.innerText=".gscrollbar-fixer {margin-right: ".concat(l,"px}"),document.head.appendChild(o),h(s,"gscrollbar-fixer")}h(s,"glightbox-open"),h(Q,"glightbox-open"),J&&(h(document.body,"glightbox-mobile"),this.settings.slideEffect="slide"),this.showSlide(i,!0),1==this.elements.length?(h(this.prevButton,"glightbox-button-hidden"),h(this.nextButton,"glightbox-button-hidden")):(d(this.prevButton,"glightbox-button-hidden"),d(this.nextButton,"glightbox-button-hidden")),this.lightboxOpen=!0,this.trigger("open"),T(this.settings.onOpen)&&this.settings.onOpen(),K&&this.settings.touchNavigation&&B(this),this.settings.keyboardNavigation&&X(this)}},{key:"openAt",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;this.open(null,e)}},{key:"showSlide",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];f(this.loader),this.index=parseInt(t);var n=this.slidesContainer.querySelector(".current");n&&d(n,"current"),this.slideAnimateOut();var s=this.slidesContainer.querySelectorAll(".gslide")[t];if(c(s,"loaded"))this.slideAnimateIn(s,i),p(this.loader);else{f(this.loader);var l=this.elements[t],o={index:this.index,slide:s,slideNode:s,slideConfig:l.slideConfig,slideIndex:this.index,trigger:l.node,player:null};this.trigger("slide_before_load",o),l.instance.setContent(s,(function(){p(e.loader),e.resize(),e.slideAnimateIn(s,i),e.trigger("slide_after_load",o)}))}this.slideDescription=s.querySelector(".gslide-description"),this.slideDescriptionContained=this.slideDescription&&c(this.slideDescription.parentNode,"gslide-media"),this.settings.preload&&(this.preloadSlide(t+1),this.preloadSlide(t-1)),this.updateNavigationClasses(),this.activeSlide=s}},{key:"preloadSlide",value:function(e){var t=this;if(e<0||e>this.elements.length-1)return!1;if(I(this.elements[e]))return!1;var i=this.slidesContainer.querySelectorAll(".gslide")[e];if(c(i,"loaded"))return!1;var n=this.elements[e],s=n.type,l={index:e,slide:i,slideNode:i,slideConfig:n.slideConfig,slideIndex:e,trigger:n.node,player:null};this.trigger("slide_before_load",l),"video"==s||"external"==s?setTimeout((function(){n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}),200):n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}},{key:"prevSlide",value:function(){this.goToSlide(this.index-1)}},{key:"nextSlide",value:function(){this.goToSlide(this.index+1)}},{key:"goToSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(this.prevActiveSlide=this.activeSlide,this.prevActiveSlideIndex=this.index,!this.loop()&&(e<0||e>this.elements.length-1))return!1;e<0?e=this.elements.length-1:e>=this.elements.length&&(e=0),this.showSlide(e)}},{key:"insertSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1;t<0&&(t=this.elements.length);var i=new U(e,this,t),n=i.getConfig(),s=l({},n),o=i.create(),r=this.elements.length-1;s.index=t,s.node=!1,s.instance=i,s.slideConfig=n,this.elements.splice(t,0,s);var a=null,h=null;if(this.slidesContainer){if(t>r)this.slidesContainer.appendChild(o);else{var d=this.slidesContainer.querySelectorAll(".gslide")[t];this.slidesContainer.insertBefore(o,d)}(this.settings.preload&&0==this.index&&0==t||this.index-1==t||this.index+1==t)&&this.preloadSlide(t),0==this.index&&0==t&&(this.index=1),this.updateNavigationClasses(),a=this.slidesContainer.querySelectorAll(".gslide")[t],h=this.getSlidePlayerInstance(t),s.slideNode=a}this.trigger("slide_inserted",{index:t,slide:a,slideNode:a,slideConfig:n,slideIndex:t,trigger:null,player:h}),T(this.settings.slideInserted)&&this.settings.slideInserted({index:t,slide:a,player:h})}},{key:"removeSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1;if(e<0||e>this.elements.length-1)return!1;var t=this.slidesContainer&&this.slidesContainer.querySelectorAll(".gslide")[e];t&&(this.getActiveSlideIndex()==e&&(e==this.elements.length-1?this.prevSlide():this.nextSlide()),t.parentNode.removeChild(t)),this.elements.splice(e,1),this.trigger("slide_removed",e),T(this.settings.slideRemoved)&&this.settings.slideRemoved(e)}},{key:"slideAnimateIn",value:function(e,t){var i=this,n=e.querySelector(".gslide-media"),s=e.querySelector(".gslide-description"),l={index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,slideNode:this.prevActiveSlide,slideIndex:this.prevActiveSlide,slideConfig:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].slideConfig,trigger:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].node,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},o={index:this.index,slide:this.activeSlide,slideNode:this.activeSlide,slideConfig:this.elements[this.index].slideConfig,slideIndex:this.index,trigger:this.elements[this.index].node,player:this.getSlidePlayerInstance(this.index)};if(n.offsetWidth>0&&s&&(p(s),s.style.display=""),d(e,this.effectsClasses),t)g(e,this.settings.cssEfects[this.settings.openEffect].in,(function(){i.settings.autoplayVideos&&i.slidePlayerPlay(e),i.trigger("slide_changed",{prev:l,current:o}),T(i.settings.afterSlideChange)&&i.settings.afterSlideChange.apply(i,[l,o])}));else{var r=this.settings.slideEffect,a="none"!==r?this.settings.cssEfects[r].in:r;this.prevActiveSlideIndex>this.index&&"slide"==this.settings.slideEffect&&(a=this.settings.cssEfects.slideBack.in),g(e,a,(function(){i.settings.autoplayVideos&&i.slidePlayerPlay(e),i.trigger("slide_changed",{prev:l,current:o}),T(i.settings.afterSlideChange)&&i.settings.afterSlideChange.apply(i,[l,o])}))}setTimeout((function(){i.resize(e)}),100),h(e,"current")}},{key:"slideAnimateOut",value:function(){if(!this.prevActiveSlide)return!1;var e=this.prevActiveSlide;d(e,this.effectsClasses),h(e,"prev");var t=this.settings.slideEffect,i="none"!==t?this.settings.cssEfects[t].out:t;this.slidePlayerPause(e),this.trigger("slide_before_change",{prev:{index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,slideNode:this.prevActiveSlide,slideIndex:this.prevActiveSlideIndex,slideConfig:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].slideConfig,trigger:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].node,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},current:{index:this.index,slide:this.activeSlide,slideNode:this.activeSlide,slideIndex:this.index,slideConfig:this.elements[this.index].slideConfig,trigger:this.elements[this.index].node,player:this.getSlidePlayerInstance(this.index)}}),T(this.settings.beforeSlideChange)&&this.settings.beforeSlideChange.apply(this,[{index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},{index:this.index,slide:this.activeSlide,player:this.getSlidePlayerInstance(this.index)}]),this.prevActiveSlideIndex>this.index&&"slide"==this.settings.slideEffect&&(i=this.settings.cssEfects.slideBack.out),g(e,i,(function(){var t=e.querySelector(".ginner-container"),i=e.querySelector(".gslide-media"),n=e.querySelector(".gslide-description");t.style.transform="",i.style.transform="",d(i,"greset"),i.style.opacity="",n&&(n.style.opacity=""),d(e,"prev")}))}},{key:"getAllPlayers",value:function(){return this.videoPlayers}},{key:"getSlidePlayerInstance",value:function(e){var t="gvideo"+e,i=this.getAllPlayers();return!(!O(i,t)||!i[t])&&i[t]}},{key:"stopSlideVideo",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}console.log("stopSlideVideo is deprecated, use slidePlayerPause");var i=this.getSlidePlayerInstance(e);i&&i.playing&&i.pause()}},{key:"slidePlayerPause",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}var i=this.getSlidePlayerInstance(e);i&&i.playing&&i.pause()}},{key:"playSlideVideo",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}console.log("playSlideVideo is deprecated, use slidePlayerPlay");var i=this.getSlidePlayerInstance(e);i&&!i.playing&&i.play()}},{key:"slidePlayerPlay",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}var i=this.getSlidePlayerInstance(e);i&&!i.playing&&(i.play(),this.settings.autofocusVideos&&i.elements.container.focus())}},{key:"setElements",value:function(e){var t=this;this.settings.elements=!1;var i=[];e&&e.length&&o(e,(function(e,n){var s=new U(e,t,n),o=s.getConfig(),r=l({},o);r.slideConfig=o,r.instance=s,r.index=n,i.push(r)})),this.elements=i,this.lightboxOpen&&(this.slidesContainer.innerHTML="",this.elements.length&&(o(this.elements,(function(){var e=m(t.settings.slideHTML);t.slidesContainer.appendChild(e)})),this.showSlide(0,!0)))}},{key:"getElementIndex",value:function(e){var t=!1;return o(this.elements,(function(i,n){if(O(i,"node")&&i.node==e)return t=n,!0})),t}},{key:"getElements",value:function(){var e=this,t=[];this.elements=this.elements?this.elements:[],!I(this.settings.elements)&&E(this.settings.elements)&&this.settings.elements.length&&o(this.settings.elements,(function(i,n){var s=new U(i,e,n),o=s.getConfig(),r=l({},o);r.node=!1,r.index=n,r.instance=s,r.slideConfig=o,t.push(r)}));var i=!1;return this.getSelector()&&(i=document.querySelectorAll(this.getSelector())),i?(o(i,(function(i,n){var s=new U(i,e,n),o=s.getConfig(),r=l({},o);r.node=i,r.index=n,r.instance=s,r.slideConfig=o,r.gallery=i.getAttribute("data-gallery"),t.push(r)})),t):t}},{key:"getGalleryElements",value:function(e,t){return e.filter((function(e){return e.gallery==t}))}},{key:"getSelector",value:function(){return!this.settings.elements&&(this.settings.selector&&"data-"==this.settings.selector.substring(0,5)?"*[".concat(this.settings.selector,"]"):this.settings.selector)}},{key:"getActiveSlide",value:function(){return this.slidesContainer.querySelectorAll(".gslide")[this.index]}},{key:"getActiveSlideIndex",value:function(){return this.index}},{key:"getAnimationClasses",value:function(){var e=[];for(var t in this.settings.cssEfects)if(this.settings.cssEfects.hasOwnProperty(t)){var i=this.settings.cssEfects[t];e.push("g".concat(i.in)),e.push("g".concat(i.out))}return e.join(" ")}},{key:"build",value:function(){var e=this;if(this.built)return!1;var t=document.body.childNodes,i=[];o(t,(function(e){e.parentNode==document.body&&"#"!==e.nodeName.charAt(0)&&e.hasAttribute&&!e.hasAttribute("aria-hidden")&&(i.push(e),e.setAttribute("aria-hidden","true"))}));var n=O(this.settings.svg,"next")?this.settings.svg.next:"",s=O(this.settings.svg,"prev")?this.settings.svg.prev:"",l=O(this.settings.svg,"close")?this.settings.svg.close:"",r=this.settings.lightboxHTML;r=m(r=(r=(r=r.replace(/{nextSVG}/g,n)).replace(/{prevSVG}/g,s)).replace(/{closeSVG}/g,l)),document.body.appendChild(r);var d=document.getElementById("glightbox-body");this.modal=d;var g=d.querySelector(".gclose");this.prevButton=d.querySelector(".gprev"),this.nextButton=d.querySelector(".gnext"),this.overlay=d.querySelector(".goverlay"),this.loader=d.querySelector(".gloader"),this.slidesContainer=document.getElementById("glightbox-slider"),this.bodyHiddenChildElms=i,this.events={},h(this.modal,"glightbox-"+this.settings.skin),this.settings.closeButton&&g&&(this.events.close=a("click",{onElement:g,withCallback:function(t,i){t.preventDefault(),e.close()}})),g&&!this.settings.closeButton&&g.parentNode.removeChild(g),this.nextButton&&(this.events.next=a("click",{onElement:this.nextButton,withCallback:function(t,i){t.preventDefault(),e.nextSlide()}})),this.prevButton&&(this.events.prev=a("click",{onElement:this.prevButton,withCallback:function(t,i){t.preventDefault(),e.prevSlide()}})),this.settings.closeOnOutsideClick&&(this.events.outClose=a("click",{onElement:d,withCallback:function(t,i){e.preventOutsideClick||c(document.body,"glightbox-mobile")||u(t.target,".ginner-container")||u(t.target,".gbtn")||c(t.target,"gnext")||c(t.target,"gprev")||e.close()}})),o(this.elements,(function(t,i){e.slidesContainer.appendChild(t.instance.create()),t.slideNode=e.slidesContainer.querySelectorAll(".gslide")[i]})),K&&h(document.body,"glightbox-touch"),this.events.resize=a("resize",{onElement:window,withCallback:function(){e.resize()}}),this.built=!0}},{key:"resize",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;if((e=e||this.activeSlide)&&!c(e,"zoomed")){var t=y(),i=e.querySelector(".gvideo-wrapper"),n=e.querySelector(".gslide-image"),s=this.slideDescription,l=t.width,o=t.height;if(l<=768?h(document.body,"glightbox-mobile"):d(document.body,"glightbox-mobile"),i||n){var r=!1;if(s&&(c(s,"description-bottom")||c(s,"description-top"))&&!c(s,"gabsolute")&&(r=!0),n)if(l<=768)n.querySelector("img");else if(r){var a=s.offsetHeight,u=n.querySelector("img");u.setAttribute("style","max-height: calc(100vh - ".concat(a,"px)")),s.setAttribute("style","max-width: ".concat(u.offsetWidth,"px;"))}if(i){var g=O(this.settings.plyr.config,"ratio")?this.settings.plyr.config.ratio:"";if(!g){var v=i.clientWidth,f=i.clientHeight,p=v/f;g="".concat(v/p,":").concat(f/p)}var m=g.split(":"),x=this.settings.videosWidth,b=this.settings.videosWidth,S=(b=M(x)||-1!==x.indexOf("px")?parseInt(x):-1!==x.indexOf("vw")?l*parseInt(x)/100:-1!==x.indexOf("vh")?o*parseInt(x)/100:-1!==x.indexOf("%")?l*parseInt(x)/100:parseInt(i.clientWidth))/(parseInt(m[0])/parseInt(m[1]));if(S=Math.floor(S),r&&(o-=s.offsetHeight),b>l||S>o||ob){var w=i.offsetWidth,T=i.offsetHeight,C=o/T,k={width:w*C,height:T*C};i.parentNode.setAttribute("style","max-width: ".concat(k.width,"px")),r&&s.setAttribute("style","max-width: ".concat(k.width,"px;"))}else i.parentNode.style.maxWidth="".concat(x),r&&s.setAttribute("style","max-width: ".concat(x,";"))}}}}},{key:"reload",value:function(){this.init()}},{key:"updateNavigationClasses",value:function(){var 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      • + + + + + Maps and memory leaks (#28) + + + + +
        • + + + + + + + + + +
      • + + + + + Writing inaccurate benchmarks (#89) + + + + +
        • + + + + + + + + +
      • @@ -845,7 +896,7 @@

        100 Go Mistakes and How to Avoid Them

        Community space of 100 Go Mistakes and How to Avoid Them, published by Manning in 2022.

        -

        +

        Description

        If you're a Go developer looking to improve your skills, this book is for you. With a focus on practical examples, 100 Go Mistakes and How to Avoid Them covers a wide range of topics from concurrency and error handling to testing and code organization. You'll learn to write more idiomatic, efficient, and maintainable code and become a proficient Go developer.

        Quotes

        @@ -949,11 +1000,11 @@
        - + - + \ No newline at end of file diff --git a/site/chapter-1/index.html b/site/chapter-1/index.html index 1a3265c..f04ff53 100644 --- a/site/chapter-1/index.html +++ b/site/chapter-1/index.html @@ -89,7 +89,18 @@ - + @@ -717,6 +728,46 @@ +
      • + + + + + Maps and memory leaks (#28) + + + + +
        • + + + + + + + + + +
      • + + + + + Writing inaccurate benchmarks (#89) + + + + +
        • + + + + + + + + +
      • @@ -996,7 +1047,7 @@

        This book aims to help accelerate our journey toward proficiency by delving into 100 Go mistakes.

        100 Go mistakes

        Why should we read a book about common Go mistakes? Why not deepen our knowledge with an ordinary book that would dig into different topics?

        -

        In a 2011 article, neuroscientists proved that the best time for brain growth is when we’re facing mistakes. Haven’t we all experienced the process of learning from a mistake and recalling that occasion after months or even years, when some context related to it? As presented in another article, by Janet Metcalfe, this happens because mistakes have a facilitative effect. The main idea is that we can remember not only the error but also the context surrounding the mistake. This is one of the reasons why learning from mistakes is so efficient.

        +

        In a 2011 article, neuroscientists proved that the best time for brain growth is when we’re facing mistakes. 1 Haven’t we all experienced the process of learning from a mistake and recalling that occasion after months or even years, when some context related to it? As presented in another article, by Janet Metcalfe, this happens because mistakes have a facilitative effect. 2 The main idea is that we can remember not only the error but also the context surrounding the mistake. This is one of the reasons why learning from mistakes is so efficient.

        To strengthen this facilitative effect, this book accompanies each mistake as much as possible with real-world examples. This book isn’t only about theory; it also helps us get better at avoiding mistakes and making more well-informed, conscious decisions because we now understand the rationale behind them.

        Unknown

        @@ -1014,7 +1065,7 @@

      We introduce each mistake category next.

      Bugs

      -

      The first type of mistake and probably the most obvious is software bugs. In 2020, a study conducted by Synopsys estimated the cost of software bugs in the U.S. alone to be over $2 trillion.

      +

      The first type of mistake and probably the most obvious is software bugs. In 2020, a study conducted by Synopsys estimated the cost of software bugs in the U.S. alone to be over $2 trillion. 3

      Furthermore, bugs can also lead to tragic impacts. We can, for example, mention cases such as Therac-25, a radiation therapy machine produced by Atomic Energy of Canada Limited (AECL). Because of a race condition, the machine gave its patients radiation doses that were hundreds of times greater than expected, leading to the death of three patients. Hence, software bugs aren’t only about money. As developers, we should remember how impactful our jobs are.

      This book covers plenty of cases that could lead to various software bugs, including data races, leaks, logic errors, and other defects. Although accurate tests should be a way to discover such bugs as early as possible, we may sometimes miss cases because of different factors such as time constraints or complexity. Therefore, as a Go developer, it’s essential to make sure we avoid common bugs.

      Needless complexity

      @@ -1040,6 +1091,20 @@
    • Go is simple to learn but not easy to master. This is why we need to deepen our knowledge to make the most effective use of the language.
    • Learning via mistakes and concrete examples is a powerful way to be proficient in a language. This book will accelerate our path to proficiency by exploring 100 common mistakes.
    +
    +
    +
      +
    1. +

      J. S. Moser, H. S. Schroder, et al., “Mind Your Errors: Evidence for a Neural Mechanism Linking Growth Mindset to Adaptive Posterror Adjustments,” Psychological Science, vol. 22, no. 12, pp. 1484–1489, Dec. 2011. ↩

      +
      2. +
    2. +

      J. Metcalfe, “Learning from Errors,” Annual Review of Psychology, vol. 68, pp. 465–489, Jan. 2017. ↩

      +
      3. +
    3. +

      Synopsys, “The Cost of Poor Software Quality in the US: A 2020 Report.” 2020. https://news.synopsys.com/2021-01-06-Synopsys-Sponsored-CISQ-Research-Estimates-Cost-of-Poor-Software-Quality-in-the-US-2-08-Trillion-in-2020. ↩

      +
      4. +
    +
    @@ -1109,11 +1174,11 @@
    - + - + \ No newline at end of file diff --git a/site/external/index.html b/site/external/index.html index c178cfe..ae7b5c9 100644 --- a/site/external/index.html +++ b/site/external/index.html @@ -89,7 +89,18 @@ - + @@ -662,6 +673,46 @@ +
  • + + + + + Maps and memory leaks (#28) + + + + +
    • + + + + + + + + + +
  • + + + + + Writing inaccurate benchmarks (#89) + + + + +
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  • - - Slice and memory leaks (#26) + + Slices and memory leaks (#26) -