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several unknown factors relating to the frequency at which both the Snowflake internal
processes run and process interactions for information collation affect latency.
Micro-partitions are a difficult subject to address.
Based on just Snowflake-supplied information, I have found this chapter difficult to
write, so here I present the best interpretation of the available evidence.
In general, you see the Snowflake state transition holds true, though observability
proves difficult, if not impossible, to accurately define in time. Taking all factors into
consideration, the closest analog is to say that observability is eventually consistent with
expectations, but I cannot say exactly when consistency occurs.
In an ideal situation, you would observe the following behavior using
mocked-up data.
First, create a table with
data_retention_time_in_days = 1
.
Examine storage using the view
v_table_storage_metrics
. Figure 4-16 shows the
expected result where
active_GB
storage is allocated for the sample table.
Figure 4-16.
Sample table active storage at creation
After creation you update your table contents.
When latency has expired, you re-examine the storage. Figure 4-17 shows the
expected result where the
active_GB
value has changed.
Figure 4-17.
Sample table active storage after update
When micro-partitions have transitioned into Time Travel and latency has elapsed,
Figure 4-18 shows the expected result where the
time_travel_GB
value has changed.
ChaPTer 4
MiCro-ParTiTionS