![]() Analyze log data: DevOps Guru uses machine learning algorithms to analyze log data from your RDS database instances. You can specify the frequency of monitoring, the type of data to collect, and the specific database instances to monitor.Ĥ. Configure monitoring: Next, you need to configure the monitoring settings for DevOps Guru. Enable DevOps Guru: To enable DevOps Guru, you need to sign in to the AWS Management Console, navigate to the DevOps Guru service, and select the database instances you want to monitor.ģ. Additionally, DevOps Guru integrates with other AWS services, such as Amazon CloudWatch, Amazon S3, and Amazon Kinesis, to provide a comprehensive view of the RDS environment.ġ.Set up AWS accounts: To use DevOps Guru, you need to have an AWS account and the necessary permissions to use Amazon RDS.Ģ. The recommendations can help customers improve the performance, reliability, and security of their RDS database instances. The recommendations provided by DevOps Guru are based on best practices from AWS and the wider community of AWS users. DevOps Guru continuously monitors the RDS environment and sends notifications in real-time when it detects issues or potential issues. ![]() It uses machine learning algorithms to detect performance anomalies, identify potential operational issues, and provide actionable recommendations. This can help customers to resolve database issues faster and prevent them from occurring in the first place, improving the overall reliability and performance of their RDS-powered applications.ĪWS DevOps Guru for RDS works by analyzing log files, metrics, and events related to an RDS database instance. DevOps Guru analyzes logs, metrics, and events to detect performance anomalies, identify potential operational issues, and provide actionable recommendations to improve database performance and reliability. I haven't been able to confirm the resolution yet, but they said that restarting any existing tasks should be all it takes to fix the issue.AWS DevOps Guru for Amazon RDS can improve a customer's experience by providing automated and real-time operational insights and recommendations for their RDS instances. They offered $400 of AWS credits for both the investigative work and the increased AWS bill incurred by working around the issue for the last few months. UPDATE (3/18/19): Amazon said that they're now seeing consistent performance across the two task configurations in the us-west-2 region. UPDATE (1/10/19): Amazon is looking into this. UPDATE (1/10/19): cat /cpu/procinfo shows here that a 4-GB container has twice as many processors as a 2-GB container. Investigating now and posting any updates here. UPDATE (1/10/19): turns out this doesn't repro in the London region for me, only in us-west. I'm completely out of ideas at this point and hoping someone can help out! □ This could be specific to NodeJS at this point given that sysbench showed equal processing power despite having vastly difference Fargate resources. Many other things that ended up having no impact (e.g. Sysbench metrics of three different Fargate setups (1 vCPU / 2 GB RAM, 2 vCPU / 4 GB RAM, and 4 vCPU / 8 GB RAM)-all three produced almost equal results of ~350 events per second. Various NodeJS V8 options related to garbage collectionĬode that allocates lots of memory on the heap Why would a t2.micro instance (1 GB RAM) produce roughly the same results as the 4-GB Fargate instance? What could cause the performance to more than double between the 2-GB and 4-GB Fargate instances? This means that there's very little garbage collection happening, and the container is far from starved for memory (I see no paging locally, but then again, I can't repro the issue locally). Here are the Fargate execution results: vCPUįor laughs, I also ran it on a t2.micro instance (1 vCPU, 1 GB RAM) that had enough CPU credits to burst to 100% CPU, and I got 42.4K ops/ms.įinally, here's a crucial fact: the container runs with an almost-constant 12 megabytes of RAM. Alternatively, run it locally via docker run -it adam13531/fargate_perf_test:latest (although this doesn't repro locally even when supplying -cpus=1 -memory=1024m to Docker). Launch a container in Fargate with the configurations listed in the table below. Make Dockerfile alongside main.js with these contents.īuild, tag, and push the Docker image. I still haven't figured out the root cause, but I've produced a minimal repro and some new data. Since then, I've been investigating full-time. (update from 3/18/19 at the bottom of this post original contents kept as-is for archiving)įive days ago, I posted this.
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