Product Update: An Easy Way To Find The Cause of Disk Usage Spikes in Amazon Redshift

In August we worked on a few new features in intermix.io that make your life as a data engineer easier when working with Amazon Redshift.

For example, “Disk full” errors can be one of the performance issues you may encounter. And so we’ve made it easier to find the cause of disk usage spikes in Amazon Redshift. It will help you to avoid those dreadful “disk full” errors.

How To Find The Cause of Disk Usage Spikes

In your intermix.io dashboard (if you don’t have one, sign-up here for a free 2-week trial), go to the Storage Analysis tab. Click anywhere on the Schema chart to get the list of tables for that exact time. The tables are sorted by growth, so the ones which are causing the usage spike show up at the top of the list.

Disk Usage Spikes in intermix.io

UI Updates

Table info is now consolidated into “Discover -> Tables”.

  • We consolidated Skew & Stale Statistics information are into a single page “Table Analysis”.
  • In Table Analysis you can now save table filters, stream table data to Amazon CloudWatch (see our Metric Stream integration), and see all tables in one location.
  • Our Storage Analysis does a better job of handling clusters with lots of schemas

Next Release

We are working to provide insights into table access patterns to answer:

  • a table is growing fast – which queries and Apps are inserting into it?
  • what are my “cold tables” which haven’t been used in a while? Most popular tables being used the most?
  • queries in this App are slowing down – which table(s) is it touching?

As always – we’d love to hear your feedback, so please get in touch over chat or email.

Upcoming Events

We’ll be speaking at a number of events in the next few months.

September 29th – Paul is speaking @ DataEngConf (Barcelona)
November 26th to 30th – We have a booth @ AWS Re:Invent (Las Vegas)

That’s it for September updates! And if you want to get more data engineering news, consider signing up for our weekly newsletter “SF Data Weekly” with over 5,000 readers.

Paul Lappas

Paul Lappas

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