Add concurrency

This commit is contained in:
dwrz
2026-10-27 12:41:06 +00:00
commit 2334f4eaf0
31 changed files with 43061 additions and 0 deletions
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package main
import (
"encoding/csv"
"io"
"os"
"sync"
"time"
)
// The free tier of the OpenWeatherMap API allows for 60 requests per minute.
// ∴ we limit our requests to 1 per second.
var interval = 1 * time.Second
func processCSV(
file *os.File, ztchan chan *ZipTemp,
) {
var (
limiter = time.Tick(interval)
wg sync.WaitGroup
)
reader := csv.NewReader(file)
for {
record, err := reader.Read()
if err == io.EOF {
break
}
if err != nil {
panic(err)
}
<-limiter
wg.Add(1)
go getZipTemp(&wg, record, ztchan)
}
wg.Wait()
close(ztchan)
}
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package main
import (
"fmt"
"time"
)
const (
title = "TOP TEN ZIP CODES BY HIGHEST CURRENT TEMPERATURE"
)
func display(ztchan chan []*ZipTemp, done chan struct{}) {
for {
locations, more := <-ztchan
if !more {
done <- struct{}{}
return
}
// Clear the screen.
print("\033[H\033[2J")
// Print the title and top ten.
fmt.Println(title)
fmt.Printf(
"Last update: %s.\n\n",
time.Now().Format(time.RFC1123),
)
for i, zt := range locations {
if zt == nil {
fmt.Printf("%d. No data\n", i+1)
continue
}
fmt.Printf(
"%d. %.02f°F %s (%s, %s)\n",
i+1,
zt.Temperature,
zt.ZipCode,
zt.City,
zt.State,
)
}
}
}
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package main
import (
"log"
"os"
"time"
)
type ZipTemp struct {
City string
Latitude string
Longitude string
State string
Temperature float64
Time time.Time
ZipCode string
}
func main() {
file, err := os.Open("./zipcodes-50.csv")
if err != nil {
log.Fatal(err)
}
defer file.Close()
// Set up channels.
var (
done = make(chan struct{})
ztchan = make(chan *ZipTemp)
ttchan = make(chan []*ZipTemp)
)
// Start the rank and display goroutines.
go rank(ztchan, ttchan)
go display(ttchan, done)
// Process the CSV.
processCSV(file, ztchan)
// Wait for the last display output.
<-done
}
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package main
func rank(ztchan chan *ZipTemp, ttchan chan []*ZipTemp) {
var topTen = make([]*ZipTemp, 10)
for {
zt, more := <-ztchan
if !more {
close(ttchan)
return
}
var newItem bool
for i, tt := range topTen {
if tt != nil && tt.Temperature > zt.Temperature {
continue
}
// We've found a zip code with a higher temperature.
// Insert it into the top ten.
topTen = append(topTen, nil)
copy(topTen[i+1:], topTen[i:])
topTen[i] = zt
// Drop the last value.
topTen = topTen[:10]
newItem = true
break
}
// If the top ten hasn't changed, process the next zip code.
if !newItem {
continue
}
// If the top ten has changed, message the display goroutine.
// Send a copy, to prevent race conditions.
var newTopTen = make([]*ZipTemp, 10)
for i, tt := range topTen {
if tt == nil {
continue
}
newTopTen[i] = tt
}
ttchan <- newTopTen
}
}
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#!/bin/bash
export OPEN_WEATHER_MAP_API_KEY="$(pass open-weather-map/api-key)"
go run ./*.go
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package main
import (
"sync"
"time"
"github.com/dwrz/talks/concurrency/pkg/weather"
)
func getZipTemp(
wg *sync.WaitGroup, record []string, ztchan chan *ZipTemp,
) {
defer wg.Done()
// Ignore malformed records.
if len(record) < 5 {
return
}
var zt = &ZipTemp{
City: record[3],
Latitude: record[1],
Longitude: record[2],
State: record[4],
ZipCode: record[0],
}
// Ignore zip codes we were not able to get the weather for.
weather, err := weather.GetGeo(zt.Latitude, zt.Longitude)
if err != nil {
return
}
zt.Temperature = weather.Temp
zt.Time = time.Now()
ztchan <- zt
}
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15336,40.260141,-79.992678,Gastonville,PA,Washington
64475,40.439298,-94.62247,Parnell,MO,Nodaway
62864,38.293269,-88.892273,Mount Vernon,IL,Jefferson
36049,31.754655,-86.291595,Luverne,AL,Crenshaw
30549,34.10601,-83.589021,Jefferson,GA,Jackson
49235,41.930577,-84.156209,Clayton,MI,Lenawee
21561,39.538568,-79.190791,Swanton,MD,Garrett
13165,42.892556,-76.882263,Waterloo,NY,Seneca
36473,31.466945,-87.044687,Range,AL,Conecuh
98181,47.432251,-121.803388,Seattle,WA,King
54874,46.568809,-91.935156,South Range,WI,Douglas
45840,41.015219,-83.664786,Findlay,OH,Hancock
97303,44.992731,-123.01672,Salem,OR,Marion
91709,34.839964,-115.967051,Chino Hills,CA,San Bernardino
68048,40.971961,-96.001244,Plattsmouth,NE,Cass
15327,39.764089,-79.937419,Dilliner,PA,Greene
38954,33.445684,-89.910076,Sidon,MS,Carroll
29581,34.000746,-79.102314,Nichols,SC,Horry
81504,39.123554,-108.589414,Grand Junction,CO,Mesa
39161,32.498294,-89.868563,Sandhill,MS,Rankin
85737,32.435158,-110.984499,Tucson,AZ,Pima
55365,45.200875,-93.888099,Monticello,MN,Wright
99176,47.12196,-117.433708,Thornton,WA,Whitman
72532,36.061757,-91.607603,Evening Shade,AR,Sharp
72145,35.277393,-91.732556,Searcy,AR,White
75662,32.410306,-94.793392,Kilgore,TX,Gregg
23272,37.524246,-77.493157,Richmond,VA,Richmond City
99023,47.557733,-117.183637,Mica,WA,Spokane
77802,30.620983,-96.319377,Bryan,TX,Brazos
05463,44.878094,-73.338597,Isle La Motte,VT,Grand Isle
27110,36.027482,-80.20728,Winston Salem,NC,Forsyth
33404,26.645895,-80.430269,West Palm Beach,FL,Palm Beach
84141,40.668068,-111.908297,Salt Lake City,UT,Salt Lake
32867,28.566338,-81.260818,Orlando,FL,Orange
78461,27.777,-97.463213,Corpus Christi,TX,Nueces
84123,40.65963,-111.919269,Salt Lake City,UT,Salt Lake
76299,33.20743,-97.116282,Roanoke,TX,Denton
24830,37.375246,-81.653889,Elbert,WV,Mcdowell
61914,39.638,-88.75367,Bethany,IL,Moultrie
13089,43.02143,-76.197701,Liverpool,NY,Onondaga
03774,44.077803,-72.014608,North Haverhill,NH,Grafton
01098,42.40494,-72.896133,Worthington,MA,Hampshire
14517,42.591883,-77.894474,Nunda,NY,Livingston
16743,41.881851,-78.457878,Port Allegany,PA,Mckean
10048,40.71254,-74.013289,New York,NY,New York
19375,39.901177,-75.750203,Unionville,PA,Chester
44608,40.665824,-81.592696,Beach City,OH,Stark
14883,42.203102,-76.471165,Spencer,NY,Tioga
34218,27.499515,-82.709871,Holmes Beach,FL,Manatee
75037,32.767268,-96.777626,Irving,TX,Dallas
1 15336 40.260141 -79.992678 Gastonville PA Washington
2 64475 40.439298 -94.62247 Parnell MO Nodaway
3 62864 38.293269 -88.892273 Mount Vernon IL Jefferson
4 36049 31.754655 -86.291595 Luverne AL Crenshaw
5 30549 34.10601 -83.589021 Jefferson GA Jackson
6 49235 41.930577 -84.156209 Clayton MI Lenawee
7 21561 39.538568 -79.190791 Swanton MD Garrett
8 13165 42.892556 -76.882263 Waterloo NY Seneca
9 36473 31.466945 -87.044687 Range AL Conecuh
10 98181 47.432251 -121.803388 Seattle WA King
11 54874 46.568809 -91.935156 South Range WI Douglas
12 45840 41.015219 -83.664786 Findlay OH Hancock
13 97303 44.992731 -123.01672 Salem OR Marion
14 91709 34.839964 -115.967051 Chino Hills CA San Bernardino
15 68048 40.971961 -96.001244 Plattsmouth NE Cass
16 15327 39.764089 -79.937419 Dilliner PA Greene
17 38954 33.445684 -89.910076 Sidon MS Carroll
18 29581 34.000746 -79.102314 Nichols SC Horry
19 81504 39.123554 -108.589414 Grand Junction CO Mesa
20 39161 32.498294 -89.868563 Sandhill MS Rankin
21 85737 32.435158 -110.984499 Tucson AZ Pima
22 55365 45.200875 -93.888099 Monticello MN Wright
23 99176 47.12196 -117.433708 Thornton WA Whitman
24 72532 36.061757 -91.607603 Evening Shade AR Sharp
25 72145 35.277393 -91.732556 Searcy AR White
26 75662 32.410306 -94.793392 Kilgore TX Gregg
27 23272 37.524246 -77.493157 Richmond VA Richmond City
28 99023 47.557733 -117.183637 Mica WA Spokane
29 77802 30.620983 -96.319377 Bryan TX Brazos
30 05463 44.878094 -73.338597 Isle La Motte VT Grand Isle
31 27110 36.027482 -80.20728 Winston Salem NC Forsyth
32 33404 26.645895 -80.430269 West Palm Beach FL Palm Beach
33 84141 40.668068 -111.908297 Salt Lake City UT Salt Lake
34 32867 28.566338 -81.260818 Orlando FL Orange
35 78461 27.777 -97.463213 Corpus Christi TX Nueces
36 84123 40.65963 -111.919269 Salt Lake City UT Salt Lake
37 76299 33.20743 -97.116282 Roanoke TX Denton
38 24830 37.375246 -81.653889 Elbert WV Mcdowell
39 61914 39.638 -88.75367 Bethany IL Moultrie
40 13089 43.02143 -76.197701 Liverpool NY Onondaga
41 03774 44.077803 -72.014608 North Haverhill NH Grafton
42 01098 42.40494 -72.896133 Worthington MA Hampshire
43 14517 42.591883 -77.894474 Nunda NY Livingston
44 16743 41.881851 -78.457878 Port Allegany PA Mckean
45 10048 40.71254 -74.013289 New York NY New York
46 19375 39.901177 -75.750203 Unionville PA Chester
47 44608 40.665824 -81.592696 Beach City OH Stark
48 14883 42.203102 -76.471165 Spencer NY Tioga
49 34218 27.499515 -82.709871 Holmes Beach FL Manatee
50 75037 32.767268 -96.777626 Irving TX Dallas
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package main
import (
"fmt"
"sync"
)
func main() {
var balance int = 100
// var mu sync.Mutex
fmt.Println("Initial balance:", balance)
// Apply concurrent adjustments to the balance.
// The sum of these adjustments is 0.
// We should see no changes in balance at the end.
var adjustments = []int{
-8, -4, -2, 0, 2, 4, 8,
-8, -4, -2, 0, 2, 4, 8,
-8, -4, -2, 0, 2, 4, 8,
-8, -4, -2, 0, 2, 4, 8,
-8, -4, -2, 0, 2, 4, 8,
-8, -4, -2, 0, 2, 4, 8,
}
var wg sync.WaitGroup
wg.Add(len(adjustments))
for _, adjustment := range adjustments {
go func(adjustment int) {
defer wg.Done()
// mu.Lock()
balance += adjustment
// mu.Unlock()
}(adjustment)
}
wg.Wait()
fmt.Println("Final balance:", balance)
}
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#!/bin/bash
for (( ; ; )) do
go run ./main.go
printf "\n"
sleep 0.5
done
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package main
import (
"fmt"
"time"
)
var cities = []string{
"Chicago",
"Dallas",
"Houston",
"Los Angeles",
"New York",
"Philadelphia",
"Phoenix",
"San Antonio",
"San Diego",
"San Jose",
}
func main() {
now := time.Now()
printTemperatures(cities)
fmt.Printf(
"\nTook %v to get temperatures sequentially.\n\n",
time.Since(now),
)
// Reset the timer.
now = time.Now()
printTemperaturesAsync(cities)
fmt.Printf(
"\nTook %v to get temperatures concurrently.\n\n",
time.Since(now),
)
}
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package main
import (
"fmt"
"sync"
"github.com/dwrz/talks/concurrency/pkg/weather"
)
const (
// Hardcoded padding for longest city name, Philadelphia.
message = "%-12s %.2f°F\n"
)
func printTemperatures(cities []string) {
for _, city := range cities {
cityWeather, err := weather.GetCity(city)
if err != nil {
fmt.Printf(
"error getting weather for %s: %v\n", city, err,
)
continue
}
fmt.Printf(message, city, cityWeather.Temp)
}
}
func printTemperaturesAsync(cities []string) {
var weatherReports = make(chan string, len(cities))
var wg sync.WaitGroup
wg.Add(len(cities))
for _, city := range cities {
go func(city string) {
defer wg.Done()
cityWeather, err := weather.GetCity(city)
if err != nil {
fmt.Printf(
"error getting weather for %s: %v\n",
city, err,
)
return
}
weatherReports <- fmt.Sprintf(
message, city, cityWeather.Temp,
)
}(city)
}
wg.Wait()
close(weatherReports)
for report := range weatherReports {
fmt.Print(report)
}
}
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#!/bin/bash
export OPEN_WEATHER_MAP_API_KEY="$(pass open-weather-map/api-key)"
go run ./*.go
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module github.com/dwrz/talks/concurrency
go 1.14
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package weather
type response struct {
Temperatures temperatures `json:"main"`
}
type temperatures struct {
Temp float64 `json:"temp"`
}
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package weather
import (
"encoding/json"
"fmt"
"io/ioutil"
"net/http"
"net/url"
"os"
)
const (
baseURL = "https://api.openweathermap.org/data/2.5/weather"
)
var key = func() string {
key := os.Getenv("OPEN_WEATHER_MAP_API_KEY")
if key == "" {
panic("missing open weather map api key")
}
return key
}()
type Weather struct {
Temp float64
}
func GetCity(city string) (*Weather, error) {
params := fmt.Sprintf(
"appid=%s&q=%s&units=imperial", key, url.QueryEscape(city),
)
u := fmt.Sprintf("%s?%s", baseURL, params)
res, err := http.Get(u)
if err != nil {
return nil, err
}
defer res.Body.Close()
if res.StatusCode != http.StatusOK {
body, _ := ioutil.ReadAll(res.Body)
return nil, fmt.Errorf("%s (%d)", body, res.StatusCode)
}
var response response
if err := json.NewDecoder(res.Body).Decode(&response); err != nil {
return nil, err
}
return &Weather{Temp: response.Temperatures.Temp}, nil
}
func GetGeo(lat, lng string) (*Weather, error) {
params := fmt.Sprintf(
"appid=%s&lat=%s&lon=%s&units=imperial", key, lat, lng,
)
url := fmt.Sprintf("%s?%s", baseURL, params)
res, err := http.Get(url)
if err != nil {
return nil, err
}
defer res.Body.Close()
if res.StatusCode != http.StatusOK {
body, _ := ioutil.ReadAll(res.Body)
return nil, fmt.Errorf("%s (%d)", body, res.StatusCode)
}
var response response
if err := json.NewDecoder(res.Body).Decode(&response); err != nil {
return nil, err
}
return &Weather{Temp: response.Temperatures.Temp}, nil
}
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* 1. Concurrency
- David Wen Riccardi-Zhu
- Senior Software Engineer @ Good Uncle
- https://github.com/dwrz
- dwrz@dwrz.net
Slides and code: https://github.com/dwrz/talks/concurrency.
* 2. Why
You'll likely encounter it at work:
- Client requests to your backend
- Database and API requests made by your backend
- Frontend rendering
* 3. Why
Performance Benefits
- I/O versus CPU bound applications.
- Typically outweigh gains from optimized data structures and algorithms.
- Hardware limitations.
- Rate of single core performance improvement is slowing.
- More cores.
* 4. Why
Expressive
* 5. Why
Difficult to do correctly.
Easy to do incorrectly.
* 6. Goals
- Give you a rough sense of what concurrency is.
- Provide context to help you understand how it's made possible.
- Practical demonstration, to expose some of the trade-offs.
* 7. Three Parts
1. Fundamentals
2. Application
3. Infrastructure
* 8. Definition
/Concurrency/
1. A running together in place or time.
2. Simultaneous occurrence.
OK starting point, but:
Time, and simultaneity, is /complicated/.
A second is a duration, not an instant.
Two things can happen in the same second and not be simultaneous.
In computer science, this nuance matters.
* 9. Time
|----------------------+----------------+----------------|
| System Event | Actual Latency | Scaled Latency |
|----------------------+----------------+----------------|
| One CPU cycle | 0.4 ns | 1 s |
| Level 1 cache access | 0.9 ns | 2 s |
| Level 2 cache access | 2.8 ns | 7 s |
| Level 3 cache access | 28 ns | 1 min |
| Main memory access | ~100 ns | 4 min |
| NVMe SSD I/O | ~25 μs | 17 hrs |
| SSD I/O | 50–150 μs | 1.5–4 days |
| Rotational disk I/O | 1–10 ms | 1–9 months |
| Internet: SF to NYC | 65 ms | 5 years |
| Internet: SF to HK | 141 ms | 11 years |
|----------------------+----------------+----------------|
Source: [[https://www.prowesscorp.com/computer-latency-at-a-human-scale/]]
* 10. Example
- Get the current temperature of ten most populous cities in the US.
- First, /sequentially./
- Then, /concurrently/.
[[file:cmd/temperatures/run.sh]]
* 11. Example
[[file:images/concurrent-requests.jpg]]
* 12. Cooking Recipes
- Step-by-step
- Concurrent
- Pre-heat oven
- Boil water
- Timers
* 13. Multitasking
Computers can "simultaneously" work on:
- Different /parts/ of a recipe
- Different /recipes/
* 14. htop
* 15. How?
- Operating System's *Scheduler*
- Hardware (interrupts)
- Not just multiple cores!
* 16. NB: Concurrency != Parallelism
First consumer multi-core CPUs released in mid-2000's.
Even before that, computers were able to multitask with one core.
* 17. Cooperative Scheduling
[[file:images/cooperative-scheduling.jpg]]
* 18. Preemptive Scheduling
[[file:images/preemptive-scheduling.jpg]]
* 19. Preemptive Scheduling
[[file:images/preemptive-scheduling-tree.jpg]]
* 20. Single Core
[[file:images/single-core-concurrency.jpg]]
* 21. Multi Core
[[file:images/preemptive-scheduling-multi-core.jpg]]
* 22. Concurrency versus Parallelism
- Parallelism :: performing computations at the same moment.
- Multiple cores.
- Multiples computers.
- Kind of like having multiple cooks in a kitchen.
- Concurrency :: performing computations over inter-leaving, non-sequential time periods.
- Single or multi-core.
* 23. Processes and Threads
Computers can "simultaneously" work on:
- Different /parts/ of a recipe
- Different /recipes/
- Process :: a program in execution (recipe).
- Thread :: smallest sequence of programmed instructions that can be managed independently by a scheduler (part of a recipe).
* 24. Processes and Threads
Processes
- Have their own memory space
- Instructions
- Constants
- Stack
- Heap
- Start out with one thread (the /main/ thread).
- Can fork -- create a child process.
- Inter-Process Communication (IPC)
* 25. Processes and Threads
Threads (a.k.a, lightweight processes)
- Share:
- Memory
- Instructions
- Open files (descriptors)
- Signals and signal handlers
- Etcetera...
- Less resource intensive than processes.
* 26. Processes and Threads
[[file:images/process-vs-thread.jpg]]
Source: https://stackoverflow.com/questions/16354460/forking-vs-threading/16354658
* 27. htop
Press ~H~ to show threads.
* 28. Languages and Runtimes
- ~C~, ~C++~, ~Rust~ allow for fine-grained control of processes and threads.
- ~JavaScript~: single-threaded (for your code), runtimes (Chrome, nodejs) implement concurrency via an event loop.
- ~Go~: runtime multi-threading, multiplexes goroutines onto threads.
* 29. nodejs
[[file:images/nodejs-event-loop.png]]
Source: [[https://medium.com/preezma/node-js-event-loop-architecture-go-deeper-node-core-c96b4cec7aa4]]
* 30. Go
[[file:images/go-scheduler.png]]
Source: https://freethreads.net/2019/01/23/go-runtime-scheduler-design-internals/
* 31. Quick Review
- Sequential versus Concurrent Steps
- Compare with recipes.
- Concurrency versus Parallelism
- Multiple cooks.
- Operating System Scheduler
- Processes and Threads
- Languages and Runtimes
* 32. Part 2: Using Concurrency
Typical use cases:
- Network I/O
- Requests between clients and servers.
- Requests to a database.
- Requests between microservices.
- Requests to external APIs.
- User interfaces
- Capturing user input
- Rendering output
- Similar, but independent computations.
- Solving problems that are best expressed with concurrency.
* 33. JavaScript
- Callbacks
- Promises
- async/await
What is the output of the following? Why?
#+begin_src javascript
console.log('start');
setTimeout(() => console.log('callback 1'), 0);
console.log('middle');
setTimeout(() => console.log('callback 2'), 0);
console.log('end');
#+end_src
* 34. Go
1. Shared Memory with Mutex (Mutual Exclusion)
- Pitfall: synchronization, debugging.
2. Message Passing (Channels and Goroutines)
- Pitfall: resources usage, complexity.
* 35. Mutex Example
Concurrent updates to a bank account balance.
Code:
[[file:cmd/mutex/main.go]]
Run: [[file:cmd/mutex/run.sh]]
- Without mutex.
- With mutex.
* 36. Data Races
[[file:images/data-race.jpg]]
* 37. Mutex
/Mutual Exclusion/
- Purpose: protect a critical section of code.
- Effect: serialization.
* 38. Race Detector
- Without mutex.
- With mutex.
* 39. Tradeoffs: Validating Correctness, Testing, Debugging
- Non-deterministic behavior
- Works fine on one run, fatal bug in another.
- Up to how the scheduler(s) order things at execution time.
- Concealed bugs
- Heisenbugs
* 40. Message Passing
Proverbs:
#+begin_quote
Don't communicate by sharing memory, share memory by communicating.
Channels orchestrate, mutexes serialize.
#+end_quote
* 41. Requirements
- Read CSV
- 50 in our example
- Full list is ~42,000
- Get temperature with OpenWeatherMap API
- 60 requests per minute
- Need to rate limit our API requests.
- Display running list of top ten temperatures.
* 42. Concurrency Hallmarks
- Repeatedly processing similar data (lines of CSV)
- Network I/O
- UI I/O
* 43. Message Passing Example
Run: [[file:cmd/csp/run.sh]]
* 44. Code Walkthrough
[[file:cmd/csp/main.go]]
* 45. Channels and Goroutines
[[file:images/message-passing-example-diagram.jpg]]
* 46. Channels and Goroutines
[[file:images/go-scheduler.jpg]]
* 47. Scaling
What if:
- We wanted to process the entire list of 42,000 zip codes?
- We had access to the Enterprise tier of the Open Weather Map API (200,000 requests per minute).
Could we just get rid of the limiter?
* 48. Resources
Probably not, because:
- OS limit on open files (~ulimit~)
- Ask me how I know...
- Server load
- Network load
- CPU
- Ranking goroutine becomes a chokepoint.
- Data structures and algorithms could help here.
- Goroutines are cheap, but not free.
* 49. Possible Solutions
- Use a semaphore to limit concurrency.
- Buffers
* 50. Designing and Managing Concurrency
- Size of input matters.
- Resources
- Pipelines
- Chokepoints
- Backpressure
- Complexity
* 51. Quick Review
Techniques for implementing and managing concurrency:
- Event loop
- Mutexes
- Communication (channels and goroutines)
Concurrency Pitfalls:
- Synchronization
- Resource usage
- Debugging
- Complexity
- Need to think differently about concurrent code.
* 52. Part 3: Infrastructure Concurrency
Concurrent applications running concurrently on distributed computers.
- Client Application
- Backend Application
- Databases
- Host and Network Infrastructure
* 53. Student Context
- Simple architectures:
- Monolithic
- Single DB
- Few concurrent users
* 54. Professional Context
- Complex, sometimes messy architectures.
- Containers
- Queues
- Workers
- Tech Debt
- Multiple Servers
- Microservices
- Multiple datastores
- Distributed databases
- Many concurrent users
- Running longer (more vulnerable to failures, downtime)
* 55. Example
[[file:images/infrastructure-data-race.jpg]]
* 56. Review
- What concurrency is
- Sequential versus concurrent
- Comparison with recipes
- Compared to parallelism
- How it is implemented
- Operating System Scheduler and Interrupts
- Languages and Runtimes
* 57. Review
- When, why, and how to use it
- I/O + UI
- Similar, independent computations
- Expressiveness
- Pitfalls
- Synchronization
- Resources Usage
- Debugging
- Complexity
* 58. Review
- Infrastructure
- Data Race
* 59. Conclusion
- Goals:
- Give you a rough sense of what concurrency is.
- Provide context to help you understand what's going on.
- Practical exposure, to expose some of the trade-offs.
- Awareness and Sensitivity
- Remember:
- /Premature optimization is the root of all evil/.
- Tradeoffs and economics (resources, costs, developer time).
- Learning is continuous...
* 60. Further Reading
- [[https://slikts.github.io/concurrency-glossary/][Concurrency Glossary]]
- [[https://blog.golang.org/waza-talk][Concurrency is not parallelism]]
- [[https://en.wikipedia.org/wiki/Executable_and_Linkable_Format][Executable and Linkable Format]]
- [[https://www.youtube.com/watch?v=KBZlN0izeiY][GopherCon 2017: Kavya Joshi - Understanding Channels]]
- [[http://web.mit.edu/6.005/www/fa15/][MIT 6.005: Software Construction]]
- Chapters 19, 20, 22, 23
- [[https://www.gopl.io/][The Go Programming Language]]
- [[https://en.wikipedia.org/wiki/Concurrency_(computer_science)][Wikipedia: Concurrency]]
- [[https://en.wikipedia.org/wiki/Concurrency_control][Wikipedia: Concurrency Control]]
- [[https://en.wikipedia.org/wiki/Concurrent_computing][Wikipedia: Concurrent Computing]]
- [[https://en.wikipedia.org/wiki/Deadlock][Wikipedia: Deadlock]]
- [[https://en.wikipedia.org/wiki/Dining_philosophers_problem][Wikipedia: Dining Philosophers Problem]]
- [[https://www.backblaze.com/blog/whats-the-diff-programs-processes-and-threads/][What’s the Diff: Programs, Processes, and Threads]]
- [[https://www.youtube.com/watch?v=8aGhZQkoFbQ&t=13s][What the heck is the event loop anyway? | Philip Roberts]]
- [[https://en.wikipedia.org/wiki/Heisenbug][Wikipedia: Heisenbug]]
- [[https://en.wikipedia.org/wiki/Interrupt][Wikipedia: Interrupt]]
- [[https://en.wikipedia.org/wiki/Libuv][Wikipedia: Libuv]]
- [[https://en.wikipedia.org/wiki/Process_(computing)][Wikipedia: Process]]
- [[https://en.wikipedia.org/wiki/Scheduling_(computing)][Wikipedia: Scheduling]]
- [[https://en.wikipedia.org/wiki/Therac-25][Wikipedia: Therac-25]]
- [[https://en.wikipedia.org/wiki/Thread_(computing)][Wikipedia: Thread]]
- [[https://github.com/danicat/pacgo][pacgo: A Pac Man clone written in Go]]
- [[https://medium.com/preezma/node-js-event-loop-architecture-go-deeper-node-core-c96b4cec7aa4][node.js event loop architecture]]