Lambda concurrency and throttling in AWS - Deep Dive

Overview - Lambda concurrency and throttling

What is it?

AWS Lambda concurrency is the number of function instances that can run at the same time. Throttling happens when the number of requests exceeds this limit, causing some requests to be delayed or rejected. This controls how many Lambda functions can process events simultaneously to protect resources. It helps manage performance and cost by limiting how many functions run together.

Why it matters

Without concurrency limits, too many Lambda functions could run at once, overwhelming other services or causing unexpected costs. Throttling prevents this by controlling the flow of requests, ensuring system stability and predictable performance. Without it, your application might slow down or fail during traffic spikes, hurting user experience and reliability.

Where it fits

Before learning this, you should understand basic AWS Lambda functions and event-driven computing. After this, you can explore advanced Lambda scaling strategies, reserved concurrency, and error handling. This topic fits into managing serverless application performance and cost control.

Mental Model

Core Idea

Lambda concurrency is like the number of checkout counters open in a store, and throttling is when customers have to wait because all counters are busy.

Think of it like...

Imagine a grocery store with a limited number of checkout lanes. Each lane can serve one customer at a time. If more customers arrive than lanes available, some must wait or leave. Lambda concurrency is the number of open lanes, and throttling is the waiting line or customers turned away.

┌───────────────┐
│ Incoming      │
│ Requests      │
└──────┬────────┘
       │
┌──────▼────────┐
│ Lambda        │
│ Concurrency   │
│ Limit (lanes) │
└──────┬────────┘
       │
┌──────▼────────┐      ┌───────────────┐
│ Running       │      │ Throttled     │
│ Functions     │      │ Requests      │
└───────────────┘      └───────────────┘

Build-Up - 7 Steps

1

Foundation

What is Lambda concurrency?

Concept: Introduce the basic idea of concurrency as simultaneous function executions.

AWS Lambda runs your code in response to events. Concurrency means how many copies of your function can run at the same time. For example, if concurrency is 5, up to 5 function instances can run simultaneously.

Result

You understand concurrency as the number of parallel Lambda executions possible.

Understanding concurrency as parallel execution capacity helps grasp how Lambda handles multiple requests.

2

Foundation

What causes throttling in Lambda?

Concept: Explain throttling as what happens when concurrency limits are reached.

If more requests come in than the concurrency limit allows, Lambda cannot run all functions immediately. The extra requests are throttled, meaning they are delayed or rejected until capacity frees up.

Result

You see throttling as a natural limit to protect resources when demand is too high.

Knowing throttling prevents overload helps you design reliable serverless apps.

3

Intermediate

Understanding unreserved and reserved concurrency

🤔Before reading on: do you think all Lambda functions share the same concurrency pool or have separate limits? Commit to your answer.

Concept: Introduce reserved concurrency as a way to guarantee capacity for specific functions.

By default, all Lambda functions share an unreserved concurrency pool, which is the account-wide limit. You can reserve concurrency for a function to guarantee it always has capacity and to limit how much it can use. Reserved concurrency subtracts from the total pool.

Result

You learn how to control concurrency per function to avoid interference and ensure availability.

Understanding reserved concurrency helps prevent one function from starving others and controls costs.

4

Intermediate

How throttling affects retries and errors

🤔Before reading on: do you think throttled Lambda requests fail immediately or retry automatically? Commit to your answer.

Concept: Explain Lambda's behavior on throttling including retries and error responses.

When Lambda throttles a request, it returns a throttling error (429). For asynchronous invocations, Lambda retries automatically with delays. For synchronous calls, the caller receives the error immediately. This affects how your application handles failures.

Result

You understand the impact of throttling on function reliability and error handling.

Knowing retry behavior guides how to design error handling and backoff strategies.

5

Intermediate

Using concurrency limits to protect downstream systems

🤔Before reading on: do you think concurrency limits only protect Lambda or also other services? Commit to your answer.

Concept: Show how concurrency limits help avoid overwhelming databases or APIs Lambda calls.

If your Lambda function calls a database or API, too many concurrent executions can overload those systems. Setting concurrency limits on Lambda controls the request rate to downstream services, preventing failures and maintaining stability.

Result

You see concurrency limits as a tool to protect the whole system, not just Lambda.

Understanding this helps design balanced, resilient architectures.

6

Advanced

Burst concurrency and scaling behavior

🤔Before reading on: do you think Lambda scales instantly to the concurrency limit or gradually? Commit to your answer.

Concept: Explain how Lambda handles sudden spikes with burst concurrency and gradual scaling.

Lambda allows a burst of concurrent executions up to a certain limit instantly, then scales more slowly to the full concurrency limit. This burst capacity depends on region and account. Understanding this helps predict cold starts and performance during traffic spikes.

Result

You grasp how Lambda scales under load and why some requests may be slower initially.

Knowing burst behavior helps optimize cold start impact and capacity planning.

7

Expert

Advanced concurrency controls: provisioned concurrency and throttling surprises

🤔Before reading on: do you think provisioned concurrency eliminates all cold starts and throttling? Commit to your answer.

Concept: Introduce provisioned concurrency and subtle throttling behaviors in complex setups.

Provisioned concurrency keeps function instances ready to avoid cold starts, improving latency. However, it still counts against your concurrency quota and can be throttled if limits are exceeded. Also, throttling can cascade in event source mappings, causing hidden delays. Experts monitor and tune these settings carefully.

Result

You learn how advanced concurrency features improve performance but require careful management.

Understanding these nuances prevents unexpected throttling and latency in production.

Under the Hood

AWS Lambda manages concurrency by creating separate execution environments for each function instance. The concurrency limit controls how many environments can run simultaneously. When a new request arrives, Lambda checks if it can start a new environment or reuse an existing one. If the limit is reached, Lambda throttles the request. Provisioned concurrency pre-creates environments to reduce startup time but still respects limits. Throttling triggers error responses or retries depending on invocation type.

Why designed this way?

Lambda was designed to provide scalable, event-driven compute without users managing servers. Concurrency limits protect shared cloud resources and downstream systems from overload. Provisioned concurrency was added to reduce cold start latency, balancing cost and performance. The design trades off instant scaling for system stability and predictable behavior.

┌───────────────┐
│ Incoming      │
│ Event         │
└──────┬────────┘
       │
┌──────▼────────┐
│ Check current │
│ concurrency   │
│ usage         │
└──────┬────────┘
       │
┌──────▼─────────────┐       ┌───────────────┐
│ If below limit      │──────▶│ Start new     │
│                     │       │ execution env │
└──────┬─────────────┘       └───────────────┘
       │
       │ If at limit
       ▼
┌───────────────┐
│ Throttle      │
│ request       │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does setting reserved concurrency to zero disable the function? Commit yes or no.

Common Belief: Setting reserved concurrency to zero just limits the function to zero concurrent executions but does not disable it.

Reality: Setting reserved concurrency to zero effectively disables the function because it cannot run any instances.

Why it matters: Misunderstanding this can cause unexpected downtime if reserved concurrency is set to zero unintentionally.

Quick: Does throttling always mean your Lambda function code is slow? Commit yes or no.

Common Belief: Throttling happens because the function code is slow and cannot keep up with requests.

Reality: Throttling is caused by concurrency limits, not function speed. Even fast functions can be throttled if concurrency limits are reached.

Why it matters: Blaming slow code for throttling can lead to wasted effort optimizing code instead of managing concurrency.

Quick: Can provisioned concurrency eliminate all cold starts? Commit yes or no.

Common Belief: Provisioned concurrency completely removes cold starts for Lambda functions.

Reality: Provisioned concurrency greatly reduces cold starts but does not eliminate them entirely, especially during scaling events or configuration changes.

Why it matters: Expecting zero cold starts can cause surprises in latency-sensitive applications.

Quick: Does throttling only affect the Lambda function itself? Commit yes or no.

Common Belief: Throttling only impacts the Lambda function and does not affect other parts of the system.

Reality: Throttling can cascade and cause delays or failures in event sources or downstream systems connected to Lambda.

Why it matters: Ignoring throttling's wider impact can cause hidden system bottlenecks and harder-to-debug failures.

Expert Zone

1

Reserved concurrency subtracts from the total account concurrency, so over-reserving can starve other functions.

2

Provisioned concurrency incurs cost even when functions are idle, so it must be balanced against performance needs.

3

Throttling behavior differs between synchronous and asynchronous invocations, affecting retry strategies and error handling.

When NOT to use

Avoid using high reserved concurrency for functions with unpredictable or low traffic; instead, rely on unreserved concurrency and autoscaling. For ultra-low latency needs, consider provisioned concurrency but monitor costs. If you need guaranteed throughput beyond Lambda limits, use container services like ECS or EKS.

Production Patterns

In production, teams set reserved concurrency to isolate critical functions and prevent noisy neighbors. They use provisioned concurrency for latency-sensitive APIs. Monitoring throttling metrics triggers alarms and auto-adjusts concurrency settings. Event source mappings are tuned to handle throttling gracefully with backoff and dead-letter queues.

Connections

Rate Limiting

Related concept controlling request flow to prevent overload

Understanding Lambda throttling helps grasp how rate limiting protects systems by controlling traffic bursts.

Thread Pool Management

Similar pattern of limiting concurrent tasks in software

Knowing Lambda concurrency is like thread pools clarifies how resource limits prevent overload in computing.

Traffic Control in Transportation

Analogous system managing flow to avoid congestion

Seeing concurrency limits as traffic lights helps understand how systems prevent jams by controlling flow.

Common Pitfalls

#1 Setting reserved concurrency too high for many functions

Wrong approach:

FunctionA reserved concurrency = 1000
FunctionB reserved concurrency = 1000
Account concurrency limit = 1000

Correct approach:

FunctionA reserved concurrency = 500
FunctionB reserved concurrency = 400
Account concurrency limit = 1000

Root cause:

Misunderstanding that reserved concurrency is deducted from the total account limit, causing resource starvation.

#2 Ignoring throttling errors in synchronous calls

Wrong approach:

Invoke Lambda synchronously without retry or error handling on 429 errors

Correct approach:

Implement retry logic with exponential backoff for synchronous Lambda invocations to handle throttling

Root cause:

Assuming throttling only affects asynchronous calls leads to unhandled failures.

#3 Expecting provisioned concurrency to remove all cold starts

Wrong approach:

Deploy provisioned concurrency and assume zero cold start latency in all cases

Correct approach:

Use provisioned concurrency but monitor and plan for occasional cold starts during scaling or updates

Root cause:

Overestimating provisioned concurrency capabilities causes unexpected latency.

Key Takeaways

Lambda concurrency limits control how many function instances run at the same time to protect resources and maintain stability.

Throttling occurs when requests exceed concurrency limits, causing delays or errors that affect application reliability.

Reserved concurrency guarantees capacity for critical functions but reduces the total available concurrency for others.

Provisioned concurrency reduces cold start latency but still counts against concurrency limits and costs money even when idle.

Understanding concurrency and throttling helps design scalable, resilient serverless applications that balance performance and cost.