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© Andrew Carruthers 2024
A. Carruthers, Tuning the Snowflake Data Cloud, https://doi.org/10.1007/979-8-8688-0379-6_10
CHAPTER 10
Optimizing Performance
In previous chapters, you investigated Snowflake performance tuning from different
perspectives and established a clear understanding through practical investigation
and hands-on examples. This final chapter brings all of your learnings together into
one place with the intention of providing a pragmatic guide to aid in your future
investigations.
I do not claim to cover 100 percent of all possible scenarios or Snowflake
performance issues in this chapter. With ever-expanding platform capability, Snowflake
continually finds ways to improve performance, improve available information,
and deliver tooling to improve processes. Its goals are to provide a starting point
for investigation and provide contextual information to open up new pathways for
investigation.
I have attempted to make this chapter light on code but provide template code
samples and summary queries. Many expanded code examples are contained in
previous chapters; my hope is that this book will be well used over time!
Tuning must be regarded as a continual activity. Treat the root cause, not the
symptoms!
Naturally some information overlaps occur when investigating performance issues;
this chapter is no exception. There is no single “right path” to begin an investigation.
Your entry point will depend upon what you already know and the context, whether
planning a new application, investigating a newly reported issue, or remediating a
known issue. Intersections between different investigation paths offer insight into new
avenues, possibilities, and opportunities for both learning and improvements to be
made. I encourage both investigation and experimentation. You will often learn more
through failure than success!