Top 5 Actions to Manage Interactive BI SLA on Hadoop

  • Mathematical functions for computing on one or more metrics
  • Over 2000 metrics which upon which the alerts can be executed
  • In-flight alerting, while the query is executing.
  • Complex combinatorial conditions across various metrics
  • Filters — avoid mission critical jobs for further actions
  1. Killing a particular application when it exceeds a duration, memory bound, or other metric bounds
  2. Reducing priority of the application to ensure mission-critical jobs are not suffering because of these issues.
  3. Resume/resubmission of the same job with appropriate parameters which may range from the size of the containers, to memory and in the case of spark jobs it may refer to the number of executors.
  4. Custom workflow integration examples such as addition of Spot Cloud instances, when there is a lack of capacity, which might cause outage in the case of concurrent users
  5. Intercept poorly written SQL’s preemptively




Thoughts and trends on data observability

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Clean Code Famous Quotes.

C++ Check if the user’s current active window is the desktop window — EnumDesktopWindows…

Setting Up the Working Environment using DevOps. (Production + Testing Environment)

What is the «Green Screen of Death» in Windows 10?

Go faster with Golang and LocalStack

Elevating the DeWi Grant Program

Big O Notation

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
The Data Observer

The Data Observer

Thoughts and trends on data observability

More from Medium

Change Data Capture by JDBC with FlinkSQL

Preparing for Data Warehouse Interview Part #1

Data Warehouse

How dose Apache SeaTunnel (Incubating) refactor the API to decouple with the computing engine?

Implementing a Data Lakehouse Architecture in AWS — Part 4 of 4