Lambda function for RDS Slow Query

Lambda functions are just another great tool provided by AWS to solve issues in a modern way! Using Lambda functions, you can run a micro service without a need to have a server and think of how to configure and maintain it!

There are lots of use cases for Lambda functions; here I used it to implement a service which sends alerts in case there is a slow query running in RDS. Of course slow queries are important for developers as it helps them to debug better and improve performance of the application. You can find the code here but there are some other things to be considered:

  • As you may know, there are some ways to trigger a Lambda function. In this case, using CloudWatch Events to schedule it periodically makes sense.
  • The lamda function should have some permissions to get RDS Logs and send alerts using SNS. To find out how to define required rules, please see this AWS documentation. You are also asked to do this when creating Lambda function.
  • There is a parameter named ‘distinguisher’ which is actually the keyword specifying the occurrence of slow query. For ‘Postgresql’ RDS it can be ‘
  • Parameters Group in RDS should be configured to log slow queries. To know how to do this please see AWS documentation or this guide:Enabling slow query log on Amazon RDS

ElasticSearch snapshot on S3

If you use ElasticSearch for Log analysis, you probably need to have backup and retirement strategy. It’s very handy to store a backup on a S3 bucket and configure lifecycle on that S3 bucket. I know there is a plugin (curator) that can do this but I preferred to use another approach and use ElasticSearch REST API’s. Here is a step to step guide about how to achieve this:

1) install AWS plugin:

https://www.elastic.co/guide/en/elasticsearch/plugins/current/cloud-aws.html

2) create repository in your Elasticsearch cluster:

curl -XPUT 'localhost:9200/_snapshot/backup_s3_repository?pretty' -d'
{
"type": "s3",
"settings": {
"bucket": "BUCKETNAME",
"region": "REGION",
"base_path": "DIRECTORY_NAME WITHIN BUCKET"
}
}'

Notes

  • AWS plugin should be installed on all nodes and services should be restarted to recognize plugin; otherwise you will get this error:

“Unknown [repository] type [s3]”

3) create snapshot:

curl -k -XPUT ‘https://localhost:9200/_snapshot/backup_s3_repository/snapshot_name?pretty?wait_for_completion=true’

4) create a cron job for taking snapshots (for step 3). You can skip `wait_for_completion=true` in cron job

5) Configure Lifecycle for that S3 bucket.

Healthcheck with Serverspec

As I mentioned in my previous post, I would like to introduce a project I have recently started which is a Chef cookbook about system healthcheck.

The idea is to generate a healthcheck page based on results of running Serverspec tests.  Specifically, it would be helpful for load balancers and more specifically for AWS ELB healthcheck. According to Serverspec: it tests your servers’ actual state by executing command locally, via SSH, via WinRM, via Docker API and so on. So you don’t need to install any agent softwares on your servers and can use any configuration management tools. But the true aim of Serverspec is to help refactoring infrastructure code. Load Balancers check health of system using this healthcheck URL and stop sending traffic if it’s not healthy. The generated health check page can be handy for this URL then. If self-check fails, it will return an error status code, otherwise 200 status code. In this cookbook, 2 concepts are used:

  • Infrastructure test as code using Serverspec: It’s best if we have a test-driven infrastructure as code but even if there is no code for infrastructure, this cookbook can still help!
  • Loadbalancer healthcheck: if healthchek page is comprehensive enough, this check can help in having a high available system. In case of AWS, when ELB is accompanied by Auto Scaling Group, a new healthy instance will replace the bad one if ELB notices an issue.

So, if you are interested in test driven coding especially in infrastructure, please have a look. Looking forward to hear your feedbacks.