ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: Distributed Task Scheduler: System Design Interview (Stripe & Amazon Offers)

เลือกโหมดฝึก

แต่ละโหมดจะเปิดห้องฝึกแบบเต็มจอ

324 ประโยค

หากประโยคสั้นหรือยาวเกินไป กดที่ Edit เพื่อปรับแก้

in this video we are going to design a distributed task

scheduler like aws step functions a distributed task scheduler is a system

that is designed to execute arbitrary code

or trigger events at a specific time in the future either once

or on a recurring basis designing a robust task scheduler requires solving complex distributed systems problems including high precision

so a tasks should execute exactly when they are supposed to, maintaining fault tolerance so no tasks are lost if a worker node crashes,

and avoiding duplicate executions in a highly concurrent environment.

And so this is a really popular system design that is asked in interviews by Meta, Amazon, and Microsoft.

And they love to ask it because it exposes your understanding of distributed system trade -offs, which is a great way of differentiating the good candidates from the great ones.

For those of you

that are struggling with system design I highly recommend you check

out Tech Prep where you can see the full write -up

and the interactive AI whiteboard

that walks you through the design exactly like how an interviewer would

and it helped me go from projections to offers from the likes of Stripe and Amazon.

There are also a lot of system design concepts and patterns

that may be new to you in this system design and

so one way to keep up is to use the AI

highlighter where you can highlight anything in the write -up

and it will give you a full explanation based on the context of this system design.

So without further ado let's jump into the requirements.

So for the functional requirements firstly we will need task submission

so users can submit one -off delayed tasks or recurring tasks e .g cron expressions.

For task execution the system must execute the provided task payload so this could be a HTTP webhook, a script or a message push at the scheduled time.

In terms of task management users can query the state of a task so it can be pending,

running, completed, failed or cancelled and users should be able to cancel an upcoming task.

We should also then have an execution history

so the system must store the output and execution logs of completed tasks for auditing purposes.

Then for the non -functional requirements firstly scalability the system must handle 10 000 plus task submissions per second

and peak execution bursts of 50 000 tasks per second in

terms of high availability we'll want 99 .99 uptime for task submission

and execution routing and then for reliability we want no data loss

so once the task is acknowledged as accepted it must be durably stored

and guaranteed to execute and then finally in terms of precision

and latency tasks should begin execution within 200 milliseconds of their scheduled time.

So now that we've looked at the requirements let's look at the data model we need to build to fulfill those requirements.

So to handle the contrasting demand of strict state management

and the massive write -heavy logging I think a polyglot persistence strategy is required for a system of this scale

and so what polyglot persistence simply means is

that we have to use different data storage types for different things and I'm going to walk through each of them now.

So the first thing will be our relational database which you can implement with Postgres.

So this will serve as the system's source of truth for task definitions, schedules and metadata and we rely heavily on its asset properties

and row level locking to manage the state machine of a task without race conditions.

And the core entities will include a task definition

so this is the core configuration object it stores the user's

intent the payload to be executed for example http webhook url

and body the retry policy and the scheduling logic it's either one off

or a cron expression next will be our task execution

so this represents a single distinct run of a task definition

and it acts as the primary state machine

so we'll go from pending to queued to running to either success

or failed and it includes an item potency key and tracks exactly

when the task was picked up by a worker and when it finished.

So this is really important and is the kind of core of our architecture.

Next we'll also need an in -memory store which we can implement with Redis and this is also super important.

So this will act as the high speed timer and scheduling buffer

and we will also utilize Redis sorted sets

so z sets where the score is in the epoch execution time

and this will allow the system to query for tasks

that need to run right now in o log n time bypassing the need to constantly scan the relational database.

Now if you don't know what red assorted sets are don't worry about

that we're going to explain those in detail later on.

We'll also need a message broker so something implemented with either Kafka

or RabbitMQ which will serve as the durable execution queue and

so it decouples the scheduling components from the worker pool allowing the system to absorb massive spikes in task executions

so back pressure without overwhelming the database

and finally we'll also need a wide column store

so something like Cassandra and this is used exclusively for execution logs

and history since a single recurring task might generate millions of

logs over its lifetime a high write throughput horizontally scalable no sql database is ideal for this

and so that's the data model so

if you understand these four data storage techniques

and why they're used you will be in a really good place to move on to our api design

so for a distributed task scheduler we can just use a classic restful API to interact with the data.

RESTful API is simple, widely used, stateless and they support caching which makes it a great candidate for our system.

So the first endpoint can be a create task endpoint.

So this will be a post to a v1 tasks.

It accepts a payload, execution time or a cron and a target and will return a task ID.

Next will be the get task status.

This will be a get request to the v1 tasks task id

and it will return the current state and metadata of that task.

A cancel task endpoint so this will be a delete at v1 tasks task id

and it marks a pending task as cancelled

and then finally for logs we can get a get logs endpoint

which will be a get to a v1 tasks task id executions

and this will return a paginated execution history of that task.

Obviously production system will have a lot more endpoints

but these are the kind of core endpoints to outline to your interviewer what's kind of core to this system

and what we'll need to cover as we go through our high -level system design.

So now that we've mentioned our data model and API design let's look at our high -level design.

So at a high level the system architecture can be separated in terms of accepting a task as well as executing it.

So the first part will be the submission path.

So this path focuses on securely receiving the task and durably persisting its schedule.

So here a client will upload it'll go to the gateway

this will route the incoming request it'll also authenticate the client

and enforce rate limits this will then get pushed to our task management service

which validates the payload and cron expressions

and it writes the task definition to the task database again Postgres with this state of pending and then

if the task is scheduled to run in the very near

100

future e .g within the next 15 minutes the service could

101

also push a lightweight reference to the task directly into the redis zset timer next is the execution path

102

and so this path focuses on time triggering the task

103

and hand and handing it off to worker nodes

104

so firstly we'll have relay nodes this will be a background batch process

105

that periodically queries the task database postgres for tasks scheduled to run in the near future for example 10 minutes

106

and stages them in the redis zset timer this prevents the database from being continuously hammered by high frequency polling

107

and then what we can have is we can have a fleet of lightweight scheduler nodes

108

which will continuously run a process let's say every 100 milliseconds

109

to pull the redis z set for tasks whose scheduled time is less than

110

or equal to the current time once we've pulled those tasks

111

off the redis z set we can then add them to a task queue

112

and so the task payload is fetched

113

and pushed onto this highly available message queue

114

and to prevent noisy neighbors where one customer submitting 50 000 tasks saturates the queue

115

and delays others tasks are rooted to tenant specific queues using a hash of the tenant id

116

and this allows workers to round robin between tenant queues ensuring

117

equitable compute distribution among our customers next we'll have our worker nodes

118

and so this is a fleet of horizontally scaled compute nodes

119

that consume messages from the queue execute the business logic eg

120

make the request to the target system the http webhook

121

and then report the successor failure to the task database

122

and then keep a historical log in cassandra

123

so pretty simple system it's not too crazy and so

124

while this high level design does separate our concerns it does contain critical flaws

125

so pulling our database efficiently we can dive deeper into

126

that workers can crash mid -execution leaving tasks stuck in limbo

127

which we'll have to deal with

128

and network retries could cause a payment task to execute twice

129

and so we have to deep diaper into all of these and show how we can resolve them.

130

So firstly let's look at Redis Z sets and how they're implemented so we have a better understanding.

131

So Redis sorted sets is an in -memory data structure that automatically orders unique elements by a specific score.

132

In this case the Unix execution timestamp allowing the scheduler to instantly identify

133

and extract tasks run in o log n time without constantly polling

134

and overwhelming our primary database and

135

so under the hood the way it's implemented is two data structures the first one is a hash map

136

and this is used for o1 point lookups

137

so the purpose of this is to get

138

that instant access via task id and

139

so the situation this would be used is like

140

if a user wants to cancel an upcoming task

141

or the system needs to check its exact scheduled time can check via the task

142

id to get its execution timestamp

143

and redis ignores time -based sorting entirely by consulting this internal hash map to find by key

144

and instantly return the execution timestamp in o of one time

145

so it doesn't have to traverse any list the second data structure is the skip list

146

and this is for o log n time -based polling and

147

so here this is the purpose of this is for fast traversal by execution timestamp.

148

And so the scenario we use this is like the schedule node polls for tasks ready to execute right now.

149

So we'll check for tasks that's execution time is less than or equal to right now.

150

And so obviously Redis cannot use the hash map here because it doesn't know which specific task IDs are right for execution.

151

Instead it goes to this skip list. And

152

so it uses the upper level express lanes to quickly skip

153

over millions of tasks scheduled far into the future like task C

154

and it drops down to the base level only

155

when it nears the target timestamp landing directly on the threshold of ready tasks to instantly extract them for the message queue.

156

So if we look at a quick example let's say we're polling for ready tasks

157

and the current timestamp is this one here on the screen.

158

Okay well the first thing we're going to do is start at level three start at our head

159

and then look right and when we look right we can see we have task c

160

which as you can see is greater than our current time stamp as we can see that's in the future

161

so we can ignore it so what do we do

162

so we drop down to the next level

163

and again we start out our head and we look to the right

164

and as you can see we move across

165

and we can see the time stamp is exactly equal to our current time stamp

166

and so therefore we know we have found our upper bound

167

and so what we now need to do is find all the elements up to

168

that upper bound and the way we do

169

that is drop down again a level and

170

because we're at the bottom level here we know this is a perfectly sorted link list

171

and so redis instantly knows

172

that any node from the head to task b is right for execution

173

so it sequentially traverses the level one extracting task a

174

and task b and returning them to the scheduler to be pushed to the kafka queue

175

so hopefully that makes sense

176

and again the primary architectural benefit here is we are able

177

to extract ready to run tasks in o log n time without constantly polling and overwhelming the primary database.

178

So next we need a look at high precision scheduling.

179

So running a select all from the tasks table in our

180

Postgres database containing billions of rows every second will instantly melt the database.

181

We need a way to track time efficiently to achieve that less than 200 milliseconds execution precision without database bottlenecking.

182

And so we will implement a push -pull time partitioned architecture.

183

So the first step, again, we have our luck ahead cron.

184

So this background relay worker will wake up every five minutes

185

and it'll query the Postgres database for all tasks scheduled to run in the next 10 minutes.

186

Then we'll have our Redis sorted set.

187

The relay will push these upcoming task IDs into the Redis Z set.

188

And the score in the Z set again is that exact Unix epoch timestamp of when the task should run.

189

Then we will have sub second polling.

190

So a fleet of lightweight scheduler nodes will pull the Redis Z set every 100 milliseconds.

191

looking for tasks where the score is less than or equal to the current timestamp.

192

Then we will have an atomic handoff, so using a Lua script.

193

So to prevent multiple scheduler nodes from pulling the exact same task during concurrent polling, we use a custom Lua script.

194

And because Redis executes Lua scripts as a single indivisible transaction on its single threaded event loop, it guarantees an atomic check and pop.

195

And so this means that the script queries for the ready tasks and immediately deletes them,

196

safely handing the tasks to exactly one scheduler to then push onto the message queue.

197

And then finally, to mitigate thundering herds.

198

So, for example, humans will naturally schedule cron jobs at clean intervals, for example, every midnight.

199

And so to prevent massive artificial latency spikes at the top of the hour, the schedule can inject optional execution jitter,

200

which will add a random number of seconds between, let's say, 0 and 300 delay for tasks

201

that do not require strict millisecond precision so that we can smooth out that load curve,

202

ensuring that we don't have massive spikes in executions.

203

And so the architectural benefit here is the heavy relational database is only queried sequentially in predictable batches,

204

while the high frequency sub -second polling and lock frequency concurrency control is handled entirely in memory by Redis.

205

Next, we need to look at our worker fault tolerance.

206

So a major distributed system problem is the stuck task.

207

So what happens if a worker node pulls a critical task from the queue, but the server suffers a hard crash before finishing?

208

Well, the task must not be lost, nor can it be duplicated prematurely.

209

So to guarantee at least once delivery and fault tolerance,

210

we rely on visibility timeouts and explicit acknowledgements natively supported by brokers like AWS SQS or RabbitMQ.

211

what we need is

212

when a worker pulls a message the broker does not delete

213

it instead it will update the metadata attribute on the message

214

for example we could have a field invisible underscore until timestamp and so

215

when other workers pull the queue the broker's internal query simply

216

filters out messages where the invisible until is greater than the current time

217

because that is currently being executed

218

and we should not touch it we'll also have the receipt handle

219

and so upon pulling the message the broker also gives the worker a unique temporary identifier

220

so this receipt handle or delivery tag and we'll see why that's important in just a second.

221

We'll also have heartbeats so this is the visibility extension so

222

if a task naturally takes a long time a background thread on the worker periodically pings the broker to overwrite

223

and extend the invisible until timestamp actively maintaining its exclusive lock and preventing other workers from touching it.

224

We'll also have the ACK which is the final deletion and

225

so upon successful completion the worker sends the receipt handle

226

which we saw previously back to the broker in an ACK request

227

and the broker uses this handle to locate the message in O of one time

228

and permanently hard delete it from its memory or disk so that it is never picked up again by any other worker.

229

And then in the case of handling the crash well

230

if a worker crashes it stops sending the heartbeats

231

and never sends the ACK and so once the invisible until timestamp expires

232

and it's in the past the message naturally becomes visible again for a healthy worker node to claim

233

and process and so this is a really nice way of handling

234

that worker fault tolerance that most candidates won't be able to answer.

235

Next we need to look at execution guarantees and

236

because our fault tolerance mechanism relies on timeouts

237

and retries we guarantee at least once execution however this means

238

that a task might run twice and

239

so strict exactly once delivery is theoretically impossible over an unreliable

240

network therefore we must implement idempotency to make duplicate executions harmless

241

so we need optimistic concurrency control

242

so before a worker executes a payload it attempts to update the task status in postgres

243

so you'll update the status to running

244

and increment its version number by one right and so this means

245

if the worker crashed previously

246

and a duplicate worker picks up the same task the database will reject the second update

247

because the status is no longer pending

248

and the worker knows to safely discard the duplicate and again

249

that version number acts as a unique constraint

250

so once one worker increments it from one to two the second worker's update fails

251

because it will still be looking for version one and

252

so this implements the optimistic locking ensuring exactly one worker can claim a task even under high concurrency

253

and the version number means the check and update operation truly is atomic

254

and we'll also need client side item potency keys

255

so for HTTP webhook tasks the scheduler will inject an item

256

potency key header generated from the task id plus the execution timestamp into the outbound request

257

and the downstream system receiving the webhook is responsible for using this key to ignore duplicate HTTP calls

258

and so by using these mechanisms we are improving our execution guarantees.

259

And then finally looking at scaling our storage layer

260

because handling 10 ,000 plus continuous task commissions per second pushes the limits of single node architectures.

261

This translates to 10 ,000 plus database inserts operations per second plus B3 index updates all

262

while serving continuous select queries from the relay components.

263

And so to prevent severe CPU and disk IO bottlenecks both the relational database

264

and the in -memory Redis cluster must be horizontally partitioned

265

so firstly looking at postgres you know

266

while a heavily provisioned single postgres primary can technically peak at 10 000 tps running it at

267

that threshold and production guarantees latency spikes and lock contention and

268

so firstly we'll need horizontal sharding

269

so we distribute the right load across multiple independent postgresql clusters

270

for a multi -tenant sas platform we shard based on tenant id

271

and this ensures data locality

272

so all tasks for specific customer reside on the same database node keeping transactions highly localized

273

and by provisioning 10 distinct nodes the load drops to a highly manageable 1000 tps per primary

274

and then we'll also have time -based table partitioning

275

so as the system scales to billions of executed tasks the

276

active database shard will become bloated slowing down the look ahead cron queries

277

and so we can mitigate this by partitioning the task tables by month

278

or week and

279

so this allows the system to quickly drop historical partitions archiving

280

them to cold storage like s3 without running expensive delete queries and keeping the active b3 index incredibly small and fast

281

next looking at redis and so

282

because redis operates entirely in memory a single instance can handle

283

100 000 simple operations per second however a single node creates a single point of failure

284

and a massive single sorted set degrades performance and

285

so we can see time complexity duration so because we rely on zadd

286

and z range by score queries both of

287

which operate o log n time as a single z set

288

grows to millions of elements the single threaded redis event loop will begin to block

289

and so therefore we will need time bucketed keys

290

so instead of one global schedule task zset we can partition

291

zsets by time bucket for example tasks minute you know 2 30

292

and tasks minute 2 31 and as well as

293

that we can also have a hash slot distribution

294

so by deploying a redis cluster these time bucketed keys are deterministically hashed

295

and distributed across redis primary nodes and this horizontally scales both the memory footprint

296

and the olog n compute overhead ensuring this layer never blocks under massive concurrency

297

and so look we've gone deep on you know high precision scheduling worker fault tolerance execution guarantees

298

and scaling the storage layer so

299

if you need know these things these are the core things interviewers are going to ask you

300

so knowing them in depth will really bulletproof you

301

when you go into your interview and

302

so to finish up let's just walk through the complete architecture one last time

303

so you've kind of got that one mental snapshot of understanding how everything works together

304

so a client will submit a payload to the API gateway

305

that will then get routed to the task management service which writes

306

that persistent configuration to Postgres the relay service every few minutes

307

in a batch process will pull tasks scheduled for the near future from Postgres

308

and stage them to our Redis Z set keyed by their

309

precise execution epoch time high frequency scheduler nodes then continuously pull Redis

310

and when a task's time arrives a Lua script atomically pops it from redis

311

and pushes it onto a partitioned kafka

312

or rabbit mqq utilizing tenant -based routing to avoid noisy neighbors

313

a worker node claims a task triggering a visibility timeout the

314

worker then updates the database state to running using optimistic locking

315

to prevent concurrent execution races the worker then executes the arbitrary code

316

or http webhook injecting an item potency key to protect downstream systems from network retries

317

and the worker then sends an act to the queue to permanently delete the message updates the state

318

and postgres to complete it

319

and flushes the execution logs asynchronously into the cassandra wide column

320

store for permanent auditing to check out the full write -up

321

and the ai whiteboard head over to techprep .app

322

and hopefully you got some value out of this

323

if you did please like and subscribe

324

and share with a friend it helps the channel out a lot and i will see you in the next one

ดาวน์โหลดแอป

ทุกอย่างที่ต้องใช้เพื่อพูดได้คล่อง

AI ให้คะแนนการออกเสียงให้คะแนนทุกประโยค

ฝึก IPAเชี่ยวชาญทุกเสียง

คำศัพท์สร้างคลังคำของคุณ

เกมคำศัพท์เล่นไปเรียนไป

สแกน QR

Download on theApp Store Get it onGoogle Play

ช่องเดียวกัน

Rate Limiter System Design in Under 60 Seconds #techprep #programming #systemdesign

Daily ConversationUniversity StudentsWorking Adults

FOR YOU

วิดีโอแนะนำ

264 ประโยค

System Design Mock Interview: Design WhatsApp

Business EnglishInterview EnglishUniversity Students

198 ประโยค

2025 DevOps, SRE, Cloud Engineer Interview Questions: Ace Your Interview with This Guide

Business EnglishInterview EnglishUniversity Students

277 ประโยค

Introduction to RTOS Part 1 - What is a Real-Time Operating System (RTOS)? | Digi-Key Electronics

Business EnglishUniversity StudentsWorking Adults

257 ประโยค

A/B Testing in Data Science Interviews by a Google Data Scientist | DataInterview

Business EnglishInterview EnglishUniversity Students

42 ประโยค

System Design: How to Build an API Rate Limiter

Business EnglishUniversity StudentsWorking Adults

239 ประโยค

Excellent 2 Years AWS DevOps Engineer Mock Interview with Questions and Answers | Cloud | Docker

Business EnglishInterview EnglishUniversity Students

คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

บทเรียนฝึกพูดระดับ C1 นี้ใช้วิดีโอ “Distributed Task Scheduler: System Design Interview” เป็นสื่อ คำที่ถูกพูดซ้ำบ่อยที่สุด: task, execution, worker, redis, database วิดีโอนี้มี 324 ประโยค และ 4079 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 22:56 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 178 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน มีเพียง 73% ของคำที่อยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ คำศัพท์จึงค่อนข้างยาก

คำศัพท์สำคัญในวิดีโอนี้

คำที่ยากที่สุด 15 คำในวิดีโอ พร้อมคำอ่านและความหมาย:

คำศัพท์	คำอ่าน	ความหมาย
queue คำนาม	/kju/	คิว
execute คำกริยา	/ˈɛksɪˌkjuːt/	ประหาร
query คำนาม	/ˈkwɪɹ.i/	สอบถาม
broker คำนาม	/ˈbɹoʊkɚ/	นายหน้า
hash คำนาม	/ˈhæʃ/	ความยุ่งเหยิง
invisible คำคุณศัพท์	/ɪnˈvɪzəb(ə)l/	ล่องหน
epoch คำนาม	/ˈɛp.ək/	ยุค, กาล
timer คำนาม	/ˈtaɪmɚ/	เครื่องจับเวลา
optimistic คำคุณศัพท์	/ˌɒptɪˈmɪstɪk/	มองโลกในแง่ดี
inject คำกริยา	/ɪnˈd͡ʒɛkt/	ฉีดยา
sequentially คำกริยาวิเศษณ์	/səˈkwɛntʃəli/	อย่างเป็นลำดับ
whiteboard คำนาม	/ˈwaɪtbɔɹd/	ไวท์บอร์ด, กระดานขาว
noisy คำคุณศัพท์	/ˈnɔɪzi/	ดัง
arbitrary คำคุณศัพท์	/ˈɑː.bɪ.tɹə.ɹi/	ดุลยพินิจ
disk คำนาม	/dɪsk/	ดิสก์, จักร

กริยาวลีที่คุณจะได้ยิน

คำศัพท์	คำอ่าน	ความหมาย
pick up คำกริยา		ยก, หยิบ
wake up คำกริยา	/ˌweɪk ˈʌp/	ตื่น, ตื่นนอน

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้าง	ในวิดีโอ
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ	is designed · are lost · is asked
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน	we've looked · we've mentioned · we've pulled
Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ	offs, which is · RabbitMQ which will · caching which makes

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 40 ครั้ง เช่น we'll, we're, we've ให้พูดแบบสั้นตามที่ได้ยิน

เสียง “sh” และ “zh”: scheduler /ˈʃɛdjələ/, precision /pɹɪˈsɪʒ.ən/, hash /ˈhæʃ/, partition /pɑɹˈtɪ.ʃən/, submission /səbˈmɪʃən/
คำยาว — ลงเสียงหนักให้ถูกพยางค์: concurrency /kəŋˈkʌɹənsi/, millisecond /ˈmɪlɪˌsɛkənd/, invisible /ɪnˈvɪzəb(ə)l/, metadata /ˈmɛt.əˌdeɪ.tə/, visibility /ˌvɪz.əˈbɪl.ɪ.ti/

เสียงที่คนไทยมักออกเสียงยาก:

เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: tolerance /ˈtɑ.lə.ɹəns/, hash /ˈhæʃ/, invisible /ɪnˈvɪzəb(ə)l/, restful /ˈɹɛs(t)fʊl/, persistence /pəˈsɪst(ə)ns/
/v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: invisible /ɪnˈvɪzəb(ə)l/, overwhelm /ˌoʊ.vɚˈwɛlm/, visibility /ˌvɪz.əˈbɪl.ɪ.ti/, interviewer /ˈɪntɚvjuɚ/
/z/ — เสียงก้อง ไม่ใช่ /s/: invisible /ɪnˈvɪzəb(ə)l/, visibility /ˌvɪz.əˈbɪl.ɪ.ti/, noisy /ˈnɔɪzi/

วิธีฝึกกับวิดีโอนี้

ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง queue, execute, query เป็นพิเศษ

เทคนิค Shadowing คืออะไร?

Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →

ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: Distributed Task Scheduler: System Design Interview (Stripe & Amazon Offers)

เลือกโหมดฝึก

ดาวน์โหลดแอป

Rate Limiter System Design in Under 60 Seconds #techprep #programming #systemdesign

System Design Mock Interview: Design WhatsApp

2025 DevOps, SRE, Cloud Engineer Interview Questions: Ace Your Interview with This Guide

Distributed Transactions Explained: 2 Phase Commit vs Saga Pattern

Concurrency in Low-level Design Interviews w/ Staff Engineer

Claude Code: The Advanced Guide (99% of Devs Skip These Features)

Incident Management Process: A Step by Step guide

[2026] TOEFL iBT Speaking Take an Interview | Step-by-Step Strategies #0008

How to Answer System Design Interview Questions (Complete Guide)

Introduction to RTOS Part 1 - What is a Real-Time Operating System (RTOS)? | Digi-Key Electronics

A/B Testing in Data Science Interviews by a Google Data Scientist | DataInterview

System Design: How to Build an API Rate Limiter

Excellent 2 Years AWS DevOps Engineer Mock Interview with Questions and Answers | Cloud | Docker

คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

คำศัพท์สำคัญในวิดีโอนี้

กริยาวลีที่คุณจะได้ยิน

ไวยากรณ์ในวิดีโอนี้

การออกเสียงที่ควรระวัง

วิธีฝึกกับวิดีโอนี้

เทคนิค Shadowing คืออะไร?