It’s almost like a myth – one that I’ve heard people talk about, but never actually seen myself. The “shock absorber” is a pretty clever data flow design pattern to ingest data where a regular ETL process would choke on the throughput or spikes. The idea is to use a buffer table to capture incoming data, and then run an asynchronous process that loads that data in batches from the buffer into its intended target table.
While I’ve seen whitepapers and blog posts mention the concept loosely along with claims of “7x or 10x performance”, none of them go into technical detail on how it’s done, so I decided to try my hand at it.
I’ve compiled my findings, along with some pre-baked framework code if you want to try building something yourself. Professional driver on closed roads. It’s gonna get pretty technical.
Combine that with Eitan Blumin’s post yesterday and you’d think it were buffer week.
This shock absorber pattern works well for warehouse loading, especially when you’re trickle-loading data into columnstore indexes and don’t want to have open rowgroups slowing everything down.