This resource optimization guide represents one module of the four contained in the series. These guides are meant to help customers better monitor and manage their credit consumption. Helping our customers build confidence that their credits are being used efficiently is key to an ongoing successful partnership. In addition to this set of Snowflake Quickstarts for Resource Optimization, Snowflake also offers community support as well as Training and Professional Services offerings. To learn more about the paid offerings, take a look at upcoming education and training.
This blog post can provide you with a better understanding of Snowflake's Resource Optimization capabilities.
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Setup & Configuration queries provide more proactive insight into warehouses that are not utilizing key features that can prevent runaway resource and cost consumption. Leverage these key queries listed below to identify warehouses which should be re-configured to leverage the appropriate features.
Each query within the Resource Optimization Snowflake Quickstarts will have a tier designation just to the right of its name as "(T*)". The following tier descriptions should help to better understand those designations.
At its core, Tier 1 queries are essential to Resource Optimization at Snowflake and should be used by each customer to help with their consumption monitoring - regardless of size, industry, location, etc.
Tier 2 queries, while still playing a vital role in the process, offer an extra level of depth around Resource Optimization and while they may not be essential to all customers and their workloads, it can offer further explanation as to any additional areas in which over-consumption may be identified.
Finally, Tier 3 queries are designed to be used by customers that are looking to leave no stone unturned when it comes to optimizing their consumption of Snowflake. While these queries are still very helpful in this process, they are not as critical as the queries in Tier 1 & 2.
Identifies all warehouses that do not have auto-resume enabled. Enabling this feature will automatically resume a warehouse any time a query is submitted against that specific warehouse. By default, all warehouses have auto-resume enabled.
Make sure all warehouses are set to auto resume. If you are going to implement auto suspend and proper timeout limits, this is a must or users will not be able to query the system.
SHOW WAREHOUSES ; SELECT "name" AS WAREHOUSE_NAME ,"size" AS WAREHOUSE_SIZE FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) WHERE "auto_resume" = 'false' ;
Identifies all warehouses that do not have auto-suspend enabled. Enabling this feature will ensure that warehouses become suspended after a specific amount of inactive time in order to prevent runaway costs. By default, all warehouses have auto-suspend enabled.
Make sure all warehouses are set to auto suspend. This way when they are not processing queries your compute footprint will shrink and thus your credit burn.
SHOW WAREHOUSES ; SELECT "name" AS WAREHOUSE_NAME ,"size" AS WAREHOUSE_SIZE FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) WHERE IFNULL("auto_suspend",0) = 0 ;
Identifies warehouses that have the longest setting for automatic suspension after a period of no activity on that warehouse.
All warehouses should have an appropriate setting for automatic suspension for the workload.
– For Tasks, Loading and ETL/ELT warehouses set to immediate suspension.
– For BI and SELECT query warehouses set to 10 minutes for suspension to keep data caches warm for end users
– For DevOps, DataOps and Data Science warehouses set to 5 minutes for suspension as warm cache is not as important to ad-hoc and highly unique queries.
SHOW WAREHOUSES ; SELECT "name" AS WAREHOUSE_NAME ,"size" AS WAREHOUSE_SIZE FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) WHERE "auto_suspend" >= 3600 // 3600 seconds = 1 hour ;
Identifies all warehouses without resource monitors in place. Resource monitors provide the ability to set limits on credits consumed against a warehouse during a specific time interval or date range. This can help prevent certain warehouses from unintentionally consuming more credits than typically expected.
Warehouses without resource monitors in place could be prone to excessive costs if a warehouse consumes more credits than anticipated. Leverage the results of this query to identify the warehouses that should have resource monitors in place to prevent future runaway costs.
SHOW WAREHOUSES ; SELECT "name" AS WAREHOUSE_NAME ,"size" AS WAREHOUSE_SIZE FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) WHERE "resource_monitor" IS NULL ;
Lists out all warehouses that are used by multiple ROLEs in Snowflake and returns the average execution time and count of all queries executed by each ROLE in each warehouse.
If execution times or query counts across roles within a single warehouse are wildly different it might be worth segmenting those users into separate warehouses and configuring each warehouse to meet the specific needs of each workload
SELECT * FROM ( SELECT WAREHOUSE_NAME ,ROLE_NAME ,AVG(EXECUTION_TIME) as AVERAGE_EXECUTION_TIME ,COUNT(QUERY_ID) as COUNT_OF_QUERIES ,COUNT(ROLE_NAME) OVER(PARTITION BY WAREHOUSE_NAME) AS ROLES_PER_WAREHOUSE FROM "SNOWFLAKE"."ACCOUNT_USAGE"."QUERY_HISTORY" where to_date(start_time) >= dateadd(month,-1,CURRENT_TIMESTAMP()) group by 1,2 ) A WHERE A.ROLES_PER_WAREHOUSE > 1 order by 5 DESC,1,2 ;
Users in the Snowflake platform that have not logged in in the last 30 days
Should these users be removed or more formally onboarded?
SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.USERS WHERE LAST_SUCCESS_LOGIN < DATEADD(month, -1, CURRENT_TIMESTAMP()) AND DELETED_ON IS NULL;
Users that have never logged in to Snowflake
Should these users be removed or more formally onboarded?
SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.USERS WHERE LAST_SUCCESS_LOGIN IS NULL;
Roles that have not been used in the last 30 days
Are these roles necessary? Should these roles be cleaned up?
SELECT R.* FROM SNOWFLAKE.ACCOUNT_USAGE.ROLES R LEFT JOIN ( SELECT DISTINCT ROLE_NAME FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY WHERE START_TIME > DATEADD(month,-1,CURRENT_TIMESTAMP()) ) Q ON Q.ROLE_NAME = R.NAME WHERE Q.ROLE_NAME IS NULL and DELETED_ON IS NULL;
Warehouses that have not been used in the last 30 days
Should these warehouses be removed? Should the users of these warehouses be enabled/onboarded?
SHOW WAREHOUSES; select * from table(result_scan(last_query_id())) a left join (select distinct WAREHOUSE_NAME from SNOWFLAKE.ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY WHERE START_TIME > DATEADD(month,-1,CURRENT_TIMESTAMP()) ) b on b.WAREHOUSE_NAME = a."name" where b.WAREHOUSE_NAME is null;
Statement timeouts provide additional controls around how long a query is able to run before cancelling it. Using this feature will ensure that any queries that get hung up for extended periods of time will not cause excessive consumption of credits.
Show parameter settings at the Account, Warehouse, and User Session levels.
SHOW PARAMETERS LIKE 'STATEMENT_TIMEOUT_IN_SECONDS' IN ACCOUNT; SHOW PARAMETERS LIKE 'STATEMENT_TIMEOUT_IN_SECONDS' IN WAREHOUSE <warehouse-name>; SHOW PARAMETERS LIKE 'STATEMENT_TIMEOUT_IN_SECONDS' IN USER <username>;
This parameter is set at the account level by default. When the parameter is also set for both a warehouse and a user session, the lowest non-zero value is enforced.
Indicates whether the offset for the stream is positioned at a point earlier than the data retention period for the table (or 14 days, whichever period is longer). Change data capture (CDC) activity cannot be returned for the table.
To return CDC activity for the table, recreate the stream. To prevent a stream from becoming stale, consume the stream records within a transaction during the retention period for the table.
SHOW STREAMS; select * from table(result_scan(last_query_id())) where "stale" = true;
Returns a list of task executions that failed.
Revisit these task executions to resolve the errors.
select * from snowflake.account_usage.task_history WHERE STATE = 'FAILED' and query_start_time >= DATEADD (day, -7, CURRENT_TIMESTAMP()) order by query_start_time DESC ;
Returns an ordered list of the longest running tasks
revisit task execution frequency or the task code for optimization
select DATEDIFF(seconds, QUERY_START_TIME,COMPLETED_TIME) as DURATION_SECONDS ,* from snowflake.account_usage.task_history WHERE STATE = 'SUCCEEDED' and query_start_time >= DATEADD (day, -7, CURRENT_TIMESTAMP()) order by DURATION_SECONDS desc ;