Lambda SnapStart on AWS
For Python and.NET functions, AWS Lambda SnapStart is now widely accessible. With little to no code modifications needed in Python, C#, F#, and Powershell, it provides faster function startup performance, ranging from a few seconds to as little as a sub-second.On November 28, 2022, AWS launched Lambda SnapStart for Java functions, which can tenfold speed up starting. Lambda SnapStart enables you to reduce outlier latencies brought on by initializing functions without committing resources or time to complex performance optimizations.
Lambda SnapStart allows the snapshotted memory and disk state of any one-time initialization code that is, code that runs only the first time a Lambda function is called to be cached and reused. Using Firecracker microVM, Lambda takes a snapshot of the memory and disk state of the initialized execution environment, encrypts it, and caches it for low-latency access.
By initiating new execution environments from the cached snapshot instead of initializing them from scratch when you call the function version for the first time and as the invocations increase in frequency, Lambda reduces startup latency. Lambda SnapStart makes it easy to develop highly scalable and responsive Python and.NET applications with AWS Lambda.
Python functions may have a startup lag of several seconds due to initialization code. This can occur, for instance, when loading dependencies like LangChain, Numpy, Pandas, and DuckDB or when using frameworks like Flask or Django. For many functions that also use Lambda for machine learning (ML) inference, loading ML models during initialization can take tens of seconds, depending on the size of the model being used. Lambda SnapStart can reduce beginning latency in some scenarios from a few seconds to as little as a sub-second.
It assumes that the majority of use cases will benefit from.NET functions because just-in-time (JIT) compilation can take several seconds. The latency unpredictability associated with initializing Lambda services has long deterred customers from using.NET for AWS Lambda. For quick resumes, SnapStart stores a snapshot of a function's disk and memory conditions. Consequently, for most.NET functions, Lambda SnapStart will drastically lower latency variability.
How to get started with Lambda SnapStart for Python and.NET
To get started, use the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs to activate, update, and uninstall SnapStart for Python and.NET functions.Go to the AWS Lambda console's Functions page and choose your function to begin using Lambda SnapStart. Select Configuration, then General Configuration, and finally Edit. SnapStart options are displayed on the Edit basic settings page.
Lambda functions can be activated using Python 3.12 and later, as well as managed runtimes for.NET 8 and later. Choose Published versions, then click Save.
Every time you publish a new version of your function, Lambda initializes your code, takes a snapshot of the initialized execution environment, and caches the snapshot for low-latency access. You can utilize the function to confirm that SnapStart is turned on.
Use the AWS CLI command update-function-configuration with the –snap-start option to change the function settings.
To make a function version available to the public, use the publish-version command.
To confirm that SnapStart is enabled for the function version, input the version number and use the get-function-configuration command.
If the response shows that OptimizationStatus is On and State is Active, which means that SnapStart is active, then a snapshot is available for the specified function version.
Runtime hooks
Runtime hooks can be used to execute code that was performed either prior to Lambda creating a snapshot or following Lambda's restart of a function from a snapshot. Runtime hooks can be used to optimize your function's startup sequence (e.g., by preloading dependencies), update configuration or other metadata dynamically, clean up or release resources, and integrate with external systems or services (e.g., updating external state or sending notifications).Python runtime hooks are provided via the free source Snapshot Restore for Python package, which is a part of the Python controlled runtime. This library provides two decorators: @register_before_snapshot, which runs before Lambda creates a snapshot, and @register_after_restore, which runs when Lambda resumes a function from a snapshot.
This example of a Python handler shows how to run code both before and after checkpointing:
from snapshot_restore_py import register_before_snapshot, register_after_restore
def lambda_handler(event, context):
# handler code
@register_before_snapshot
def before_checkpoint():
# Logic to be executed before taking snapshots
@register_after_restore
def after_restore():
# Logic to be executed after restore
You can alternatively utilize the.NET runtime hooks that come with the Amazon.Lambda.Core package (version 2.5 or later) from NuGet. Two methods are available in this library: RegisterBeforeSnapshot(), which executes before to snapshot creation, and RegisterAfterRestore(), which executes following a function's resume from a snapshot.
Things to consider
Here are some things you should know about Lambda SnapStart:Handling uniqueness: Reusing the content produced by your initialization code in multiple execution settings won't make it stand out if it is unique and part of the snapshot. Let's say your code generates random numbers on its own, without the aid of pre-installed libraries, and doesn't cache any data, such DNS entries, that might expire at startup. Then, in order to keep SnapStart fresh, you need to create original material after initialization.
Performance tuning: To maximize performance, AWS recommends that you initialize resources that result in startup latency and preload dependencies in your initialization code instead than in the function handler. SnapStart's starting speed is enhanced by eliminating the latency caused by heavy class loading from the invocation pipeline.
Best practices for networking: Your function's connections from the initialization stage might not be in the same condition when Lambda resumes it from a snapshot. An AWS SDK frequently automatically reestablishes network connections.
Function monitoring: To obtain real-time telemetry data for extensions, you can utilize AWS X-Ray active tracing, Amazon CloudWatch log stream, the Telemetry API, Amazon API Gateway, and function URL metrics.
The following AWS regions now offer AWS Lambda SnapStart for Python and.NET functions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).
SnapStart's prices
SnapStart can cut the startup time for latency-sensitive apps from many seconds to as little as a sub-second. SnapStart takes a snapshot of your function's initialized memory (and disk) state and caches it to enable low-latency access. By beginning execution environments from this pre-initialized snapshot instead of starting them from scratch when your function is later called, Lambda saves startup time.A snapshot is created each time you publish a new version of your function that has SnapStart enabled. You will be charged for caching a snapshot while your function version is in use for at least three hours, and then per millisecond after that. The amount of memory you allocate to your function determines the cost. Additionally, each time Lambda recovers your snapshot to resume an execution environment, you pay a price that is determined by the amount of memory you dedicate to your function.
The cost of caching a snapshot for every function version you publish with SnapStart enabled and the cost of restoration for every instance of a function that is restored from a snapshot are the two types of SnapStart charges available for the Python and.NET managed runtimes. Therefore, eliminate any function versions that aren't in use to reduce your SnapStart cache costs. Go to the AWS Lambda price page for additional information about costs.
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