Hey VS anteon

We can reproduce the failure with curl or hey tool, so that we have many failed UpdateProduct Lambda functions.

Then I sent several hundreds create product requests via the hey tool like :

As in the previous article we use hey tool to perform the load test like this

The results of the experiment below were based on reproducing approximately 100 cold starts for the duration of our experiment which ran for approximately 1 hour. For it (and all experiments from my previous articles) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman

The results of the experiment to retrieve the existing product from the database by its id see GetProductByIdViaAuroraServerlessV2DataApiHandler with Lambda function with 1024 MB memory setting were based on reproducing more than 100 cold and approximately 10.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. We won't enable SnapStart on the Lambda function first.

The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts with experiment which ran for approximately 1 hour. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. I ran all these experiments for all 3 scenarios using 2 different compilation options in template.yaml each:

In our experiment we'll re-use the application introduced in part 9 for this. There are basically 2 Lambda functions which both respond to the API Gateway requests and retrieve product by id received from the API Gateway from DynamoDB. One Lambda function GetProductByIdWithPureJava21Lambda can be used with and without SnapStart and the second one GetProductByIdWithPureJava21LambdaAndPriming uses SnapStart and DynamoDB request invocation priming. We'll measure cold and warm starts using the following memory settings in MBs : 256, 512, 768, 1024, 1536 and 2048. I also put the cold starts measured in the part 12 into the tables to see both cold and warm starts in one place. The results of the experiment below were based on reproducing more than 100 cold and approximately 100.000 warm starts for the duration of our experiment which ran for approximately 1 hour. Here is the code for the sample application. For it (and experiments from my previous article) I used the load test tool hey, but you can use whatever tool you want, like Serverless-artillery or Postman. Abbreviation c is for the cold start and w is for the warm start.

Por último, podemos executar os testes de carga usando hey.

For running our experiments to provoke anomalies we'll use the stress test tool. You can use the tool of your choice (like Gatling, JMeter, Fiddler or Artillery), I personally prefer to use the tool hey as it is easy to use and similar to curl. On Linux this tool can be installed by executing

DDosify: https://github.com/ddosify/ddosify

We are thrilled to announce the release of Ddosify Self-Hosted on GitHub today. Unlike the Ddosify Engine, this version features a No-code UI and supports distributed traffic generation.

Ddosify is built with ease of use and flexibility in mind, and it supports popular web protocols. It allows you to define and customize test scenarios, generate realistic loads, and monitor performance metrics in real time.

We have organized this write-up into two parts to demonstrate two different features of Ddosify. In the first part, we will perform a load test on a GET endpoint that accepts base and target currency and returns their exchange rate of them. The rand() utility method is used to send different currencies on each request. In the second part, we will test a POST endpoint that performs exchange operations. We will use a CSV file that contains test data stored on our Test App's Database. Then we import this CSV file into Ddosify to replay the same transactions stored on DB, but in high concurrency. In both parts, we will gain insights into the reliability of our exchange API across high traffic.