Improve Latency with Kinesis Producer Library (KPL) on ECS Fargate

It seems like you're dealing with a scenario where your ECS Fargate tasks have enough CPU and RAM resources but are still experiencing high latency, which isn't improving significantly with an increase in the number of tasks. Since you’re using the Kinesis Producer Library (KPL) and the problem could be network-related, there are a few areas to explore.

1. Potential Causes for High Latency Despite Low CPU/RAM Usage Network Latency/Bandwidth Network bandwidth could definitely be a bottleneck, especially when you're handling a high volume of requests. The communication between your ECS tasks and Kinesis might not be as fast as required due to network constraints, even if the CPU/RAM are underutilized.

Diagnosing:

AWS VPC Flow Logs: Review your VPC flow logs for network traffic bottlenecks. Container Insights: Look for network-related metrics such as NetworkPacketsIn and NetworkPacketsOut to understand whether your Fargate tasks are experiencing network congestion. Elastic Network Interface (ENI) Limits: Fargate tasks are typically limited to the network performance of their respective ENIs. Ensure that ENI limits aren't being hit.

Task Configuration (Task Size) Your ECS tasks with 1 vCPU and 2 GiB RAM might be adequate for light workloads, but the asynchronous nature of KPL might require more compute capacity to handle high throughput efficiently. If you're hitting a network or I/O bottleneck (especially when waiting for Kinesis acknowledgment), additional CPU or memory might be needed.

Improvement Suggestion: Consider increasing the CPU and memory allocations for your ECS tasks. This might allow more efficient handling of concurrent requests and reduce the queuing and delay associated with processing KPL requests.

Kinesis Producer Library (KPL) Configuration KPL can introduce latency depending on its configuration (e.g., the buffer size, retries, and batching). Misconfiguring the KPL could cause delays in record submission, resulting in higher latency.

Diagnosing: Review KPL's configuration to make sure that buffer size and retry logic aren't too aggressive. Consider adjusting batching parameters (such as maxBatchSize and maxRecordSize). Tune the flush interval to ensure KPL is sending records promptly.

KPL Daemon Overhead The KPL runs as a background process (a daemon) that batches records before sending them to Kinesis. This can introduce some delay, especially under load, if the daemon is overwhelmed or not configured optimally.

Improvement Suggestion: Consider tuning the number of KPL daemons running. Ensure that you're running a sufficient number of threads for handling record batching and transmission without blocking API requests.

2. Why Does Increasing the Number of Tasks Not Improve Performance? When you increase the number of ECS tasks from 12 to 24, you would typically expect the load to be distributed better, improving response times. However, there might be several reasons why this doesn’t improve the latency:

Application-Level Bottleneck: If your application (API) is still waiting for Kinesis acknowledgment (even though you’re using asynchronous processing), adding more tasks might not help much since the bottleneck could be at the level of Kinesis transmission or waiting for resources to become available for KPL to process.

Task Queuing: If the tasks are queuing up to submit records to Kinesis (e.g., through the KPL), increasing the number of tasks may not significantly affect the process since the bottleneck is at the KPL or network level, not at the ECS task level.

Kinesis Throughput Limitations: Kinesis has throughput limits that could be another limiting factor. If you're sending a high volume of records to Kinesis, it’s possible that Kinesis itself becomes a bottleneck and isn't able to process the records as quickly as your ECS tasks can generate them.

3. Could Network Bandwidth Be the Bottleneck? Yes, network bandwidth could certainly be a bottleneck, especially if you're running your ECS Fargate tasks in an environment with insufficient bandwidth or if you're pushing data to Kinesis over a long distance.

Diagnosing Network Bottleneck: Use Amazon CloudWatch to monitor network traffic and metrics such as NetworkIn and NetworkOut on your ECS Fargate tasks. Check for network-related throttling by inspecting VPC Flow Logs. If you're sending a high volume of data to Kinesis, consider enabling Enhanced Monitoring for Kinesis to track throughput.

Improvement Suggestions: If your Fargate tasks are in an AWS VPC, ensure that the VPC is provisioned with sufficient network bandwidth. Consider using Amazon Kinesis Data Streams Enhanced Fan-out to get dedicated throughput for consumers, thus reducing the impact of network congestion. Ensure your ECS tasks are not running in public subnets with limited bandwidth and latency.

4. Optimization Suggestions for Latency Optimize KPL Usage Batching: Review KPL’s batch settings. If you’re batching too aggressively, this could lead to delays in acknowledging records. Use Multiple KPL Daemons: For higher throughput, run multiple KPL daemons in parallel, each handling different batches of records. Tune the Buffering Parameters: Ensure the buffer size, record size, and flushing intervals are optimized for your use case.

Scale ECS Task Resources Increase CPU/RAM: As mentioned earlier, increasing the CPU and memory resources available to your ECS Fargate tasks could help handle more concurrent requests and reduce delays from processing records through KPL.

Monitor and Optimize the Load Balancer ALB Latency: Since you’re using an Application Load Balancer (ALB), make sure the ALB itself is not adding significant latency by analyzing its response time and throughput.

Target Group Configuration: Ensure that the ALB target group is properly configured for the Fargate tasks and is distributing traffic efficiently across tasks.

Review Kinesis Throughput Limits Shard Limits: Ensure you’re not hitting Kinesis throughput limits. If needed, increase the number of shards or enable enhanced fan-out for your Kinesis stream.

regards, M Zubair https://zeonedge.com