page43
23
•
Clustering
•
Total micro-partitions
•
Micro-partition overlap values
•
Micro-partition depth
•
Column
•
Max/min value range
•
The number of distinct values
•
NULL
count
•
Subcolumn
•
Statistics for common paths in semi-structured data
Snowflake caches statistics within the Cloud Services layer. Statistics are used
as inputs to the optimizer cost model and for micro-partition pruning; both are
discussed later.
Summary
In this chapter, I laid out the rationale for this book while determining the core focus:
our primary objective is to focus on cost optimization for all Snowflake activity, not just
single query tuning.
I drew comparisons between provision-based models and consumption-based
models, noting the focus shift to security, cost, and performance. Your decisions on how
to approach application development and migration have material implications for cost
optimizations. When replicating data sets, replication costs can far exceed the initial cost
of generating the data sets.
I then covered how SQL is parsed, noting the statement parsing order and some
potential issues you may encounter before discussing optimizer statistics.
Having established baseline information in preparation for looking deeper into
performance tuning, it’s time to move on to the next chapter.
ChaptEr 1
tuning thE SnowFlakE Data ClouD