Hi Team,
How can i learn more related to max_staleness in relation to materialized view as i am trying to create a materialized view with refresh interval of 15 mins but I want to have updated data in the view when user query that view so want to set max_staleness but i am not aware how it works and what parameter works and how and i am not able to find any explanations regarding this so it will be great help if any link can be provided.
Thanks in Advance.
Solved! Go to Solution.
Here is clarification on better understanding Refresh Frequency and Max Staleness in BigQuery Materialized Views:
Refresh Frequency:
Max Staleness:
max_staleness = INTERVAL "0:30:0" HOUR_TO_SECOND
, BigQuery will:
Key Point:
Clarification on Data Handling:
Recommendations:
max_staleness
to at least the same duration as the refresh interval (15 minutes or more) to ensure efficient use of cached data between scheduled refreshes.Additional Considerations:
Best Practices:
max_staleness
is not compatible with partitioning in BigQuery-managed storage.Understanding max_staleness in BigQuery Materialized Views
Functionality:
How It Works:
Parameters:
Creating a Materialized View with max_staleness:
CREATE MATERIALIZED VIEW my_view AS
SELECT * FROM my_table
WITH MAX_STALENESS 5 MINUTES;
For More Information:
Thanks for your response please help me if my below set up is correct. As per my requirement I need to create Materialize view which should refresh data every 15 mins saying that what should be max staleness and is the below statement is correct to say that materialize view refresh frequency is set to every 15 mins and the max staleness is 30 mins that means if the data is not refreshed in materialized view in last 30 mins and user is querying(say its around at 35 mins) data from materialized view then bigquery will combine data from cheche data and updated data from base table:
Please correct me if my understanding is wrong.
Here is clarification on better understanding Refresh Frequency and Max Staleness in BigQuery Materialized Views:
Refresh Frequency:
Max Staleness:
max_staleness = INTERVAL "0:30:0" HOUR_TO_SECOND
, BigQuery will:
Key Point:
Clarification on Data Handling:
Recommendations:
max_staleness
to at least the same duration as the refresh interval (15 minutes or more) to ensure efficient use of cached data between scheduled refreshes.Additional Considerations:
Best Practices:
max_staleness
is not compatible with partitioning in BigQuery-managed storage.Thanks for that detailed explanation it is really helpful 🙂
Hello, i stumbled on this thread and find it to be an amazing explanation thank you for that.
But i have two questions about what you said :
Can you explain more how this might impact the Query Performance ?
And what are the cost implications from Bigquery side with a low max_Staleness or a high max_Staleness , you should be querying the same (more or less few new rows from the refresh) table sizes ?
I have a use case of Datastream loading to Bigquery some tables so the max_Staleness is set by Datastream into bigquery table.
I want to know the cost impact will be on Datastream or Bigqurey with a low max_Staleness
Thanks in advance.
Hi @AlaB ,
Directly querying large base tables can indeed be resource-intensive and slow. Materialized views act as precomputed result sets, significantly accelerating query performance by eliminating the need to recalculate complex queries each time.
The max_staleness
setting controls how fresh your materialized view data is:
max_staleness
: Ensures near real-time data, but more frequent refreshes can introduce slight query latency during updates. This is usually minimal for well-designed views.max_staleness
: Allows the view to lag behind base tables, leading to faster queries but potentially slightly outdated results.Cost Implications
BigQuery costs are primarily driven by data scanned during queries and refreshes:
max_staleness
: More frequent refreshes mean more data scanned, potentially increasing costs, especially for large or rapidly changing base tables.max_staleness
: Less frequent refreshes reduce data scanned for updates, generally lowering costs.Datastream and Your Use Case
Since Datastream manages max_staleness
for your BigQuery tables, the cost impact is mainly on the BigQuery side. A lower max_staleness
might increase costs due to more frequent refreshes. However, weigh this against the value of fresher data for your specific needs.
Finding the Optimal Balance
The best max_staleness
depends on several factors:
Recommendations
max_staleness
: Begin with a value balancing freshness and cost, then monitor and adjust as needed.max_staleness
.Hi @ms4446
Thanks for this great explanation, yes i will take your advice to weigh in the tradeoff between freshness of data and cost.
All the Best.