A while ago, we with the AWS catalog. covered the invocation (trigger) methods supported by Lambda and the integrations available Now we’re launching a series of articles to correlate these integration possibilities with common serverless architectural patterns (covered by this ). literature review In Part I, we will cover the category. and stay tuned for the next parts of the series. Orchestration & Aggregation Subscribe to our newsletter Pattern: Aggregator Purpose A single API is used to aggregate multiple downstream resources. Solutions Entry-point Lambda as a router to other Lambdas Requests come from API Gateway, which triggers a Lambda function (L1) synchronously using the . L1 then triggers multiple other Lambda functions (L2x). The invocation from L1 to L2x could be synchronous or asynchronous, depending on the use case. proxy integration model If the client expects data that comes from L2x, the invocation trigger should be synchronous. For write-only endpoints, when the client only expects a ‘200 - OK’ response, L1 can invoke L2x asynchronously and respond to the client immediately. One disadvantage of using synchronous invocations is that the L1 function will continue to be billed for each millisecond it awaits L2x functions results. See more in this tutorial. Serverless Trilemma API Gateway as a router, client as aggregator In some cases, the client could play the role of aggregator. Consider a frontend application under your control that requires data from multiple backend sources. A single API Gateway can be deployed with several endpoints, each routing to different L2x Lambda functions (also using the ). proxy integration model The client is then responsible for parallelizing calls to all required endpoints, collecting, and aggregating results. The main benefit of this approach is removing the double-billing factor. Watch this to learn more about this. tutorial about the Serverless Trilemma Architectural concerns 1. Timeout limits API Gateway and API services’ , which can create problems if the jobs expected from the Lambda functions take longer; REST HTTP timeout limit is 29 seconds The API service , which gives more room if your jobs require 30+ seconds; Web socket supports connections for up to 2 hours Keep in mind that the Aggregator pattern (or even Lambda itself) may not be suitable for long-running processes taking several minutes, in the first place; 2. Concurrency limits Having L1 and L2x running synchronously will eat up Lambda concurrency quota faster; Consider one L1 function and four L2x functions (L2a, L2b, L2c, L2d); each invocation to the API endpoint will consume 5 concurrency credits; that means 200 requests already exhausts the entire ; default quota of 1000 concurrent executions If you are allocating concurrency 3. Potential failures L1 should have logic in place to handle failures in any of the L2x functions; Bear in mind that the AWS Lambda platform ; the problem is that this retry will not respond to the L1 function since their connection will be lost by then; will already retry a failed L2x request automatically Lambda failures can be identified using AWS CloudWatch, but monitoring retries and linking to previous executions are possible on professional platforms such as ; Dashbird If L1 is manually invoking L2x again, you’ll be faced with at least three executions: 1 failed 1 retried by AWS 1 retried by the L1 function This has the potential to triple your costs of running L2x and there isn’t much you can do about it without big changes to the architectural pattern; Pattern: Data Lake Purpose Having a central, long-term data storage that is rarely modified and supports flexible, on-demand data query and transformation according to different access pattern requirements. Solutions Push-based approach API Gateway and Lambda (using ) can serve as a passive gate to receive requests with information for the data lake. Authorized applications would send the data in JSON format through a REST endpoint. The Lambda function is responsible for packing the data and uploading it to an S3 bucket. proxy integration This bucket will serve as the data lake storage. AWS Athena is used to stored in S3 on-demand. Athena can be accessed through or drivers (opens up for the usage of GUI analytical tools), an , or even the . query the JSON data JDBC ODBC HTTP API AWS CLI In case it’s needed, a second API endpoint and Lambda function could be used to receive data requests, query Athena and send data back to the client. The benefits of this approach are: Ability to use API Gateway powerful authentication and throttling features, which is important considering that ; Athena has short limits in terms of concurrency Decoupling client requests from the data lake query service; in case it’s required to migrate to a different solution in the future, it would be much easier to do so without causing any disruption to clients depending on the data lake; Event-driven approach In case the primary data storage service supports event-driven triggers, the Lambda function can consume data for the data lake in an asynchronous way. This is the case of DynamoDB and Aurora, for example. automatically as information is entered or modified in a table. in an event-driven way. DynamoDB Streams can trigger a Lambda function Aurora (MySQL compatible only) can similarly trigger a Lambda function The asynchronously triggered Lambda would then perform the same operations to store the data in S3. Optimizing storage for fast and cheap reads JSON is a universal and easy to use structured data format, but not optimized for large scale data consumption. Athena queries will be orders of magnitude faster and cheaper with . columnar formats such as Apache Parquet An to transform JSON data into a columnar format, but AWS Kinesis would probably fit better in a serverless stack like ours. The Firehose service can supported by Athena. In this case, the data is . EMR Cluster could be used convert incoming JSON data into popular columnar formats delivered directly into S3 from Kinesis An API Gateway can also be used in front of Kinesis Firehose with the , which is beneficial for security and concurrency control purposes. AWS-type integration Architectural concerns 1. Concurrency limits There are concurrency limitations for all services listed here ( , and ), and scalability mismatches can defeat the architectural pattern; API Gateway Lambda Kinesis Firehose For example, API Gateway may ingest up to 10,000 concurrent requests, but Kinesis will start throttling way below that; It is important to model the architecture to handle peaks in demand; , for example, can increase the scalability capabilities; ; using SQS between API Gateway and Lambda or Kinesis a more scalable alternative to API Gateway is an Application Load Balancer 2. Query scalability limits Athena tables are only metadata projection of the data stored in S3, it does not store information in itself; S3 also has its limits in terms of maximum ; if Athena queries need to read data from too many objects, S3 may not be able to serve them; requests per second Kinesis Firehose is also a good option here since it’s able to concatenate multiple records in a single S3 object; 3. Data access and security Although we could have all our data bundled together and accessed by anyone, this will rarely be a good practice from an access security standpoint; A good practice is to have multiple ; Athena Tables pointing to different S3 object locations Let’s say we have human resources, logistics, and financial information within an organization and would like to keep data access restricted only to people from within each department; A prefix can be added to each S3 object to enable different access patterns within various Athena tables, such as ‘ s3://bucket/logistics/object-name’; When uploading data from Lambda to S3 it is easy to add such prefixes and ; Kinesis Firehose also support custom prefixes for S3 object names Wrapping up This was the first article in a series about Lambda triggers and architectural design patterns. We’ve covered some patterns within the category. In the coming weeks, we’ll cover more patterns in the same category, such as Fan-in/Fan-out, Queue-bases Load Leveling, Finite-state Machine. Orchestration & Aggregation Other categories of patterns will come as well, such as , , , and . Event-Management Availability Communication Authorization patterns to stay tuned for the next parts in this series! Subscribe to our newsletter In case you are looking for a solution to help you build well-architected serverless applications, cross-references your cloud stack against industry best practices to suggest performance and architectural improvements. You can today, no credit card required. Dashbird Insights try the service for free Previously published at https://dashbird.io/blog/complete-guide-lambda-triggers-design-patterns-part-1/

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Serverless Architecture: Lambda Triggers and Design Patterns [Part 1]

Serverless Architecture: Lambda Triggers and Design Patterns [Part 1]

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