Understanding Your Snowflake Utilization, Part 1: Warehouse Profiling
Mar 28, 2017
Author: Kevin Ruan
Engineering, How to Use Snowflake
This is the first in a series of follow-up posts to Kent Graziano’s earlier post, Using the Snowflake Information Schema. This series will take a deeper dive into the Information Schema (Snowflake’s data dictionary) and show you some practical ways to use this data to better understand your usage of Snowflake.
As a Customer Success Engineer, much of my day-to-day activity involves profiling some of our most important customers and understanding exactly how the service is being used. I measure their compute credit usage, storage growth, and query performance. Many of these customers often ask me how they can implement similar profiling for their Snowflake account on their own.
The answer is to utilize the Information Schema. Aside from providing a set of detailed views into Snowflake’s metadata store, the Information Schema goes a step further and provides several powerful table functions that can be called directly in SQL. These functions can be used to return historical information about executed queries, data stored in databases and stages, and virtual warehouse (i.e. compute) usage.
In addition to these functions, I also recommend leveraging the recently implemented TABLE_STORAGE_METRICS view (also in the Information Schema) to dive even deeper into your analysis.
In this post, I will show you how to leverage these easy-to-use function to gather detailed information about the usage of your virtual warehouses. So let’s get started.
To profile your current warehouse usage, use the WAREHOUSE_LOAD_HISTORY and WAREHOUSE_METERING_HISTORY functions. A good way to think about the relationship between these two functions is that the first one shows how much work was done over a period of time (load) and the second one shows the cost for doing the work (metering).
The syntax for calling these functions is simple, and can be executed in the Worksheet in the Snowflake web interface. For example:
use warehouse mywarehouse; select * from table(information_schema.warehouse_load_history(date_range_start=>dateadd('hour',-1,current_timestamp()))); select * from table(information_schema.warehouse_metering_history(dateadd('hour',-1,current_timestamp()),current_timestamp()));
The above queries show warehouse load and credits used for the past hour for all your warehouses. Be sure to check out the Usage Notes section (in the documentation) for each function to understand all the requirements and rules. For example, the WAREHOUSE_LOAD_HISTORY function returns results in different intervals based on the timeframe you specify:
- 5-second intervals when the timeframe is less than 7 hours.
- 5-minute intervals when the timeframe is greater than 7 hours.
Here’s an example of the output from the WAREHOUSE_LOAD_HISTORY query against SNOWHOUSE, a warehouse that we use internally:
Per our documentation:
- AVG_RUNNING – Average number of queries executed.
- AVG_QUEUE_LOAD – Average number of queries queued because the warehouse was overloaded.
- AVG_QUEUE_PROVISION – Average number of queries queued because the warehouse was being provisioned.
- AVG_BLOCKED – Average number of queries blocked by a transaction lock.
And here’s an example of the output from the WAREHOUSE_METERING_HISTORY query against SNOWHOUSE:
Now that we know the amount of work that was performed during the time period (via WAREHOUSE_LOAD_HISTORY) and the cost per time period (via WAREHOUSE_METERING_HISTORY), we can perform a simple efficiency ratio calculation for a particular warehouse. This example returns this information for a warehouse named XSMALL:
with cte as ( select date_trunc('hour', start_time) as start_time, end_time, warehouse_name, credits_used from table(information_schema.warehouse_metering_history(dateadd('days',-1,current_date()),current_date())) where warehouse_name = 'XSMALL') select date_trunc('hour', a.start_time) as start_time, avg(AVG_RUNNING), avg(credits_used), avg(AVG_RUNNING) / avg(credits_used) * 100 from table(information_schema.warehouse_load_history(dateadd('days',-1,current_date()),current_date())) a join cte b on a.start_time = date_trunc('hour', a.start_time) where a.warehouse_name = 'XSMALL' group by 1 order by 1;
In the above query, we are treating the average of AVG_RUNNING as work and the average of CREDITS_USED as cost and we apply a simple efficiency ratio on both of these values. Feel free to experiment any way you like.
Next, let’s talk about the specific use of WAREHOUSE_LOAD_HISTORY in our example above:
select date_trunc('hour', start_time), hour(start_time), avg(avg_running) from table(information_schema.warehouse_load_history(date_range_start=>dateadd('day',-1,current_timestamp()))) group by date_trunc('hour', start_time), hour(start_time) order by date_trunc('hour', start_time) asc;
Here is the output:
In this case, I’m indeed asking for an average of an average. I’m grouping the values by hours so I can get a general overview of my warehouse workload. I can see my warehouse is working almost a full day. However, if I see some time gaps in this output, then I might do some additional investigation around those times and see if the warehouse should be doing work.
Another thing you can see in the output from this function is whether these time gaps repeat over a few days. If they do, then I would recommend that you script the warehouse to sleep when not in use (i.e. to save money), or enable AUTO_SUSPEND and AUTO_RESUME for that warehouse.
The Snowflake web interface also has a nice visual representation of this function (under the Warehouse tab):
For more details on this chart, see our online documentation.
Whether you use the visual chart or the manual query, for the four available metrics, pay particular attention to AVG_RUNNING. This should give you an idea how each warehouse performs. If you have split your workload across several different warehouses, it should tell you how well your queries are distributed.
AVG_QUEUE_LOAD and AVG_BLOCKED are also interesting and should provide you with good insight about how well your warehouses are sized. Keep in mind that queuing is not necessarily a bad thing and you shouldn’t expect zero queuing. The idea is to accept a certain amount of queuing per time period based on your usage requirements.
Using these metrics, you can determine what to do:
- Increasing the warehouse size will provide more throughput in processing the queries and thereby can help reduce the queuing time.
- Increasing the cluster count (if using a multi-cluster warehouse) will allow more concurrency, which should also help reduce queuing and blocking.
Finding an Underutilized Warehouse
Is there a warehouse that’s underutilized? For example, any similar sized warehouses being shared across several users could potentially be consolidated to a single warehouse. You can surface this information by comparing your AVG_RUNNING and AVG_QUEUE_LOAD scores across your warehouses:
- If you see a warehouse with a very low number of queries running, you may want to turn that warehouse off and redirect the queries to another less used warehouse.
- If a warehouse is running queries and queuing, perhaps it’s time to review your workflow to increase your warehouse sizes.
- If you have built your own client application to interface with Snowflake, reviewing your client scripts / application code should also reveal any biases towards one warehouse over another.
Putting it all together…
The Information Schema views and functions are provided to help you with dissecting your query, warehouse, and database usage. Actual implementation and analysis is based on your specific needs.
To dig in some more on these functions, you can check out our online documentation:
Look for Part 2 of this series in coming weeks where I will show you how to analyze your storage utilization.
Until then, please feel free to reach out to us at firstname.lastname@example.org. We would love to help you on your journey to the cloud. And keep an eye on this blog or follow us on Twitter (@snowflakedb) to keep up with all the news and happenings here at Snowflake Computing.