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aem-GridColumn--default--12","image_1":"aem-GridColumn aem-GridColumn--default--12","title_v2_1":"aem-GridColumn aem-GridColumn--default--12","title_v2_2":"aem-GridColumn aem-GridColumn--default--12","image_3":"aem-GridColumn aem-GridColumn--default--12","image_2":"aem-GridColumn aem-GridColumn--default--12","blog_text_12":"aem-GridColumn aem-GridColumn--default--12","blog_text_13":"aem-GridColumn aem-GridColumn--default--12","blog_text_10":"aem-GridColumn aem-GridColumn--default--12","blog_text_11":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text_1":{"id":"blog-text-a39ec8e854","text":"\u003Cp\u003EAs of December, customers got a whole new level of insight into Snowflake query performance and query execution statistics when Snowflake \u003Ca href=\"https://www.snowflake.com/blog/programmatic-access-query-profile-public-preview/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Eannounced the public preview\u003C/a\u003E of the new \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/get_query_operator_stats.html?_ga=2.9008374.2115194657.1679951807-809365945.1616985065&_gac=1.90901096.1679952491.CjwKCAjw_YShBhAiEiwAMomsEPp9f8qXGVh2ByyvDpngDEJCzZ_Q_GOjMi2b1x_KshgucmJh1F63tRoCudkQAvD_BwE\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Eget_query_operator_stats\u003C/a\u003E function, opening up programmatic access to Snowflake query profiles and providing customers a whole new level of insight into Snowflake query performance and query execution statistics. \u003C/p\u003E\u003Cp\u003ESnowflake maintains its \u003Ca href=\"https://www.snowflake.com/blog/commitment-to-improving-economics-for-customers/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Ecommitment to customers\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/blog/data-platform-that-does-more-costs-less/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Eongoing platform improvements\u003C/a\u003E. Along with our \u003Ca href=\"https://www.snowflake.com/partners/technology-partners/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowflake technology partners\u003C/a\u003E (with \u003Ca href=\"https://medium.com/snowflake/introducing-the-snowflake-visual-table-clustering-explorer-6fbb66a15bd5\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Esimilar examples of innovation early last year\u003C/a\u003E), we will demonstrate how they use the new get_query_operator_stats function within their respective products and further empower all of our customers for ongoing success with Snowflake. \u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_1":{"id":"blog-title-943ac7e010","propertiesId":"faster-time-to-insights-when-analyzing-snowflake-queries","type":"heading3","lines":["Faster time to insights when analyzing Snowflake queries"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_2":{"id":"blog-text-32b1de70a4","text":"\u003Cp\u003EEvery Snowflake developer and administrator likely has a basic level of familiarity with our \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowsight\u003C/a\u003E UI, and how it can be used to \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-activity\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Emonitor activity\u003C/a\u003E within your Snowflake account, including \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-activity#query-history\" target=\"_blank\" rel=\"noreferrer noopener\"\u003EQuery History\u003C/a\u003E. These same individuals have also likely spent time \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-query-profile\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Eanalyzing queries using Query Profile\u003C/a\u003E. While incredibly powerful, there can also be a steep learning curve here, and the shape, complexity, and number of steps for any particular query can vary dramatically. Many users have found our \u003Ca href=\"https://www.snowflake.com/en/resources/learn/training/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Einstructor-led training\u003C/a\u003E courses and \u003Ca href=\"https://www.snowflake.com/summit/sessions/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowflake Summit breakout sessions\u003C/a\u003E (especially the “Operating and Optimizing Snowflake” learning track) provide the resources they need to be successful.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1":{"id":"image-14aee77780","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d98c923d-d173-422d-9d5a-e06425107cb6/unnamed-4.png?quality=85&preferwebp=true","width":"512","height":"244",":type":"snowflake-site/components/image"},"blog_text_3":{"id":"blog-text-45b79cb84a","text":"\u003Cp\u003EWe continue to hear from many customers that they want even more. And even though our \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/account-usage/query_history\" target=\"_blank\" rel=\"noreferrer noopener\"\u003EACCOUNT_USAGE.QUERY_HISTORY\u003C/a\u003E view provides several (but not all) of these same statistics, it never provided the same level of detail for individual queries that was available in Query Profile—until now! For the first time ever, customers are able to use the new get_query_operator_stats function to analyze Snowflake Query Profiles in a programmatic fashion. For years, our documentation has helped customers identify and better understand some of the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-activity#common-query-problems-identified-by-query-profile\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Ecommon query problems identified by Query Profile\u003C/a\u003E, including:\u003C/p\u003E\u003Cul class=\"wp-block-list\"\u003E\n\u003Cli\u003EExploding joins\u003C/li\u003E\n\n\n\n\u003Cli\u003EUNION without ALL\u003C/li\u003E\n\n\n\n\u003Cli\u003EQueries too large to fit in memory (most often evidenced by “spilling”)\u003C/li\u003E\n\n\n\n\u003Cli\u003EInefficient \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions#query-pruning\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Epruning\u003C/a\u003E (most often evidenced by large table scans)\u003C/li\u003E\n\u003C/ul\u003E\u003Cp\u003EHistorically, most of these common problems could only be diagnosed by viewing individual query profiles within the Snowflake web UI, one at a time. And even then, these problems have not always been easily noticeable to the untrained eye.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2":{"id":"image-95b8597ba1","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--6077ae23-f73f-4111-8f98-d72db72769c0/unnamed-5.png?quality=85&preferwebp=true","width":"512","height":"225",":type":"snowflake-site/components/image"},"title_v2_2":{"id":"blog-title-39a68d5adf","propertiesId":"snowflake-technology-partner-solution-accelerators-for-programmatic-query-profiling","type":"heading3","lines":["Snowflake technology partner solution accelerators for programmatic query profiling"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_4":{"id":"blog-text-b4f42307ea","text":"\u003Cp\u003EWe are excited to highlight several Snowflake technology partners who have integrated this new Snowflake feature into their products, either as an add-on to previously existing Snowflake accelerators, or as entirely new product functionality. Some of these partner solutions build upon \u003Ca href=\"https://medium.com/snowflake/deep-performance-analysis-with-the-new-query-operator-stats-in-snowflake-74837971c5d3\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Epreviously mentioned approaches to analyze queries in bulk\u003C/a\u003E. All of these solutions offer new, easy-to-use approaches for viewing individual query statistics, quickly highlighting any of the common query problems that may exist. Several also offer helpful tips to improve performance for the individual query being analyzed, whether that is an adjustment to the SQL itself (for example, adding or correcting a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/querying-joins\" target=\"_blank\" rel=\"noreferrer noopener\"\u003EJOIN operator\u003C/a\u003E or adding \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/constructs/where\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Efilters\u003C/a\u003E to reduce the size of your data set); \u003Ca href=\"https://medium.com/snowflake/introducing-the-snowflake-visual-table-clustering-explorer-6fbb66a15bd5\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Ere-examining a table’s structure\u003C/a\u003E (for example, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/table-considerations#when-to-set-a-clustering-key\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Ecluster keys\u003C/a\u003E or sort order); \u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-considerations#warehouse-resizing-improves-performance\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Eincreasing the size of the virtual warehouse\u003C/a\u003E; or utilizing newer Snowflake features such as \u003Ca href=\"/en/blog/customers-can-leverage-faster-more-efficient-search/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESearch Optimization\u003C/a\u003E or the \u003Ca href=\"https://www.snowflake.com/blog/query-acceleration-service-now-generally-available/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003EQuery Acceleration Service\u003C/a\u003E (QAS). The basic approach for all of these partner solutions involves first querying \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/account-usage/query_history\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY\u003C/a\u003E, joined to \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/account-usage/sessions\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESNOWFLAKE.ACCOUNT_USAGE.SESSIONS\u003C/a\u003E to include the different client tool names that may be executing queries on Snowflake. For example:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1":{"id":"code-snippet-1aecefa14b","language":"sql","codeSnippet":"SELECT (columns_of_interest),\r\n      PARSE_JSON(s.CLIENT_ENVIRONMENT):APPLICATION::string as APPLICATION_NAME\r\nFROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY q\r\nINNER JOIN SNOWFLAKE.ACCOUNT_USAGE.SESSIONS s\r\n   ON q.SESSION_ID = s.SESSION_ID\r\nLEFT OUTER JOIN SNOWFLAKE.ACCOUNT_USAGE.QUERY_ACCELERATION_ELIGIBLE qas\r\n   ON q.QUERY_ID = qas.QUERY_ID\r\n  \r\nWHERE q.END_TIME \u003E= DATE_TRUNC('week', CURRENT_DATE) - INTERVAL '14 DAY'\r\n/* additional optional parameters */\r\nAND q.QUERY_TYPE = 'SELECT'\r\nAND q.WAREHOUSE_NAME IS NOT NULL\r\nAND q.EXECUTION_STATUS = 'SUCCESS' ","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_5":{"id":"blog-text-3564871e62","text":"\u003Cp\u003ENext, when executing the function to analyze an individual query, it may be helpful to run it within a common table expression (CTE) first, checking for certain conditions and other problems right within the SQL. Note that the exact SQL commands within these queries are only suggestions, and will be very similar to the approaches used within the partner solution accelerators, Readers should feel free to experiment and ultimately use what works best for them.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_2":{"id":"code-snippet-5934b0ad2a","codeSnippet":"with query_stats as(\r\nselect\r\n   QUERY_ID,\r\n   STEP_ID,\r\n   OPERATOR_ID,\r\n   PARENT_OPERATOR_ID,\r\n   OPERATOR_TYPE,\r\n   OPERATOR_STATISTICS,\r\n   EXECUTION_TIME_BREAKDOWN,\r\n   OPERATOR_ATTRIBUTES,\r\n   EXECUTION_TIME_BREAKDOWN:overall_percentage::float as OPERATOR_EXECUTION_TIME,\r\n   OPERATOR_STATISTICS:output_rows output_rows,\r\n   OPERATOR_STATISTICS:input_rows input_rows,\r\n   CASE WHEN operator_statistics:input_rows\u003E0 THEN operator_statistics:output_rows / operator_statistics:input_rows ELSE 0 END as row_multiple,\r\n\r\n   /* look for queries too large to fit into memory */\r\n   OPERATOR_STATISTICS:spilling:bytes_spilled_local_storage bytes_spilled_local,\r\n   OPERATOR_STATISTICS:spilling:bytes_spilled_remote_storage bytes_spilled_remote,\r\n  \r\n   operator_statistics:io:percentage_scanned_from_cache::float percentage_scanned_from_cache,\r\n\r\n   operator_attributes:table_name::string tablename,\r\n   OPERATOR_STATISTICS:pruning:partitions_scanned partitions_scanned,\r\n   OPERATOR_STATISTICS:pruning:partitions_total partitions_total,\r\n   OPERATOR_STATISTICS:pruning:partitions_scanned/OPERATOR_STATISTICS:pruning:partitions_total::float as partition_scan_ratio,\r\n\r\n   /* COMMON QUERY PROBLEMS IDENTIFIED BY QUERY PROFILE */\r\n\r\n       /* 1) EXPLODING JOIN */\r\n       CASE WHEN row_multiple \u003E 1 THEN 1 ELSE 0 END AS EXPLODING_JOIN,\r\n\r\n       /* 2) \"UNION WITHOUT ALL\" */\r\n       CASE WHEN OPERATOR_TYPE = 'UnionAll' and lag(OPERATOR_TYPE) over (ORDER BY OPERATOR_ID) = 'Aggregate' THEN 1 ELSE 0 END AS UNION_WITHOUT_ALL,\r\n\r\n       /* 3) Queries Too Large to Fit in Memory */\r\n       CASE WHEN bytes_spilled_local\u003E0 OR bytes_spilled_remote\u003E0 THEN 1 ELSE 0 END AS QUERIES_TOO_LARGE_MEMORY,\r\n      \r\n       /* 4) Inefficient Pruning (numbers can be changed to fit your use case) */\r\n       CASE WHEN partition_scan_ratio \u003E= .8 AND partitions_total \u003E= 20000 THEN 1 ELSE 0 END AS INEFFICIENT_PRUNING_FLAG\r\n\r\nfrom table(get_query_operator_stats('your_query_id'))\r\nORDER BY STEP_ID,OPERATOR_ID\r\n)\r\nSELECT\r\n   QUERY_ID,\r\n   SYSTEM$ESTIMATE_QUERY_ACCELERATION('your_query_id'),\r\n   STEP_ID,\r\n   OPERATOR_ID,\r\n   PARENT_OPERATOR_ID,\r\n   OPERATOR_TYPE,\r\n   OPERATOR_STATISTICS,\r\n   EXECUTION_TIME_BREAKDOWN,\r\n   OPERATOR_ATTRIBUTES,\r\n   OPERATOR_EXECUTION_TIME,\r\n   OUTPUT_ROWS,\r\n   INPUT_ROWS,\r\n   ROW_MULTIPLE,\r\n   BYTES_SPILLED_LOCAL,\r\n   BYTES_SPILLED_REMOTE,\r\n   PERCENTAGE_SCANNED_FROM_CACHE,\r\n   TABLENAME,\r\n   PARTITIONS_SCANNED,\r\n   PARTITIONS_TOTAL,\r\n   PARTITION_SCAN_RATIO,\r\n   EXPLODING_JOIN,\r\n   UNION_WITHOUT_ALL,\r\n   QUERIES_TOO_LARGE_MEMORY,\r\n   INEFFICIENT_PRUNING_FLAG\r\nFROM query_stats;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"code_snippet_3":{"id":"code-snippet-49787a86aa","fileName":"","language":"","codeSnippet":"","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_6":{"id":"blog-text-9999b267a1","text":"\u003Cp\u003ELearn more about each of the following partner solutions by clicking on the links below, and try them today. \u003C/p\u003E\u003Cp\u003E\u003Cem\u003ENote: listed partners are sorted alphabetically.\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_3":{"id":"image-a0cfdd03fe","additionalClasses":"img","lazyEnabled":true,"alt":"acceldata","src":"https://www.snowflake.com/content/snowflake-site/global/en/blog/new-approaches-visualizing-snowflake-query-statistics/_jcr_content/root/responsivegrid/container_main_content/flexible_column_container/flexible_column_content_container_1/responsivegrid_content/image_3.coreimg.svg/1778868304754/acceldata.svg",":type":"snowflake-site/components/image"},"blog_text_7":{"id":"blog-text-ee31d578a8","text":"\u003Cp\u003E\u003Ca href=\"https://www.acceldata.io/snowflake?utm_source=blog&utm_campaign=content&utm_medium=snowflakequerymed\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EAcceldata’s data observability platform\u003C/a\u003E empowers data teams with deep insights into compute, spend, data reliability, pipelines, and users.\u003C/p\u003E\u003Cp\u003EBy leveraging Snowflake’s comprehensive \u003Ca href=\"https://www.acceldata.io/blog/optimize-queries-for-query-history-metadata-with-acceldatas-data-observability-platform?utm_source=blog&utm_campaign=content&utm_medium=snowflakequerymed\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Emetadata on query history and operator statistics\u003C/a\u003E, alongside Acceldata’s advanced query fingerprinting techniques, users can attain a deeper comprehension of their workloads and pinpoint the most resource-intensive or unoptimized queries. This functionality provides detailed insights into statistics such as elapsed time, scanned partitions, cache utilization, and more for grouped queries. Query grouping also streamlines the process of diagnosing performance issues by enabling users to compare selected executions of grouped queries, complete with all relevant metrics. Users can \u003Ca href=\"https://www.acceldata.io/blog/maximize-snowflake-warehouse-efficiency-rightsize-query-analysis?utm_source=blog&utm_campaign=content&utm_medium=snowflakequerymed\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Edetect, understand, optimize, and enhance the performance of their query workloads\u003C/a\u003E, ultimately improving the reliability and performance of their Snowflake Data Cloud.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_4":{"id":"image-c95a61ea78","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a8630909-c338-4b62-b5d8-532323358940/acceldata-query-studio.gif?quality=85&preferwebp=true","width":"2872","height":"1682",":type":"snowflake-site/components/image"},"image_5":{"id":"image-7583e25155","additionalClasses":"img","lazyEnabled":true,"alt":"astrato","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--7bf85be0-c4ac-4c2f-ad11-2015c3fd6499/astrato-logo.png?quality=85&preferwebp=true","width":"784","height":"230",":type":"snowflake-site/components/image"},"blog_text_8":{"id":"blog-text-a96fdc03de","text":"\u003Cp\u003ENative to Snowflake, Astrato is a cloud BI solution that enables all users to create custom data applications that actively drive better business decisions. The example below shows how a user can analyze the metadata of Snowflake’s Query History by aggregation and drill down. The user can also harness Snowflake’s powerful new function to collect operator statistics to identify and mark the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-query-profile#common-query-problems-identified-by-query-profile\" target=\"_blank\" rel=\"noreferrer noopener\"\u003Efour common types\u003C/a\u003E of query inefficiency: \u003Cstrong\u003ECartesian,\u003C/strong\u003E \u003Cstrong\u003Eunion\u003C/strong\u003E, \u003Cstrong\u003Ememory\u003C/strong\u003E, and \u003Cstrong\u003Epruning\u003C/strong\u003E. Although Astrato’s generated SQL queries meet the above efficiency criteria as built-in, users can now find inefficient or expensive query requests, such as from custom client’s SQL, with a few interactions.\u003C/p\u003E\u003Cp\u003EIn addition, we \u003Ca href=\"https://astrato.io/blog/introducing-query-profile-insights-with-snowflake/?utm_campaign=PTS-2023-Q2-Snowflake%20Blog%20Feature-Global&utm_source=snowflake&utm_medium=blog\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eadded more details\u003C/a\u003E around the query execution and indicators for common types of inefficiency to Astrato’s query insights panel to help users to save time and money with the most efficient queries possible.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_6":{"id":"image-975adc4f6c","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e6ef8375-04ee-4207-8daa-b5460e860080/astrato-query-insights-data-app.gif?quality=85&preferwebp=true","width":"4094","height":"2356",":type":"snowflake-site/components/image"},"image_7":{"id":"image-cc3ada3abd","additionalClasses":"img","lazyEnabled":true,"alt":"bluesky","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f2c879aa-e918-4dfe-b0b6-4dd4687be84d/bluesky.png?quality=85&preferwebp=true","width":"500","height":"368",":type":"snowflake-site/components/image"},"blog_text_9":{"id":"blog-text-055028bed5","text":"\u003Cp\u003E\u003Ca href=\"https://www.getbluesky.io/company?utm_source=snowflake.com&utm_medium=referral+&utm_campaign=Snowflakecoblog&utm_id=snowflakemarketing+\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EBluesky\u003C/a\u003E is your co-pilot for Snowflake workload optimization, providing \u003Ca href=\"https://www.getbluesky.io/technology?utm_source=snowflake.com&utm_medium=referral+&utm_campaign=Snowflakecoblog&utm_id=snowflakemarketing+\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eperformance and cost governance features\u003C/a\u003E for continuous spend monitoring and optimization. Bluesky is unique in the market as it provides deep technical insights into the performance and \u003Ca href=\"https://www.getbluesky.io/technology?utm_source=snowflake.com&utm_medium=referral+&utm_campaign=Snowflakecoblog&utm_id=snowflakemarketing+\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Ecost aspects of query workloads\u003C/a\u003E (including query profiles and lineage), and suggests optimization parameters for warehouse and storage settings.\u003C/p\u003E\u003Cp\u003EWith Bluesky, companies can identify the most resource-intensive queries and optimize accordingly. Whether it’s a straightforward warehouse misconfiguration, or an advanced recommendation on clustering keys, Bluesky focuses on prioritizing insights and lending practical advice.\u003C/p\u003E\u003Cp\u003EThe following example shows a query that is missing a join key, and therefore produces a Cartesian product that has an outsized output in terms of records as well as results in a high cost.\u003C/p\u003E\u003Cp\u003ELaying out the original and proposed query side by side—thereby providing a graphical view of the execution path with actual figures and a human readable explanation—is a straightforward way to understand the source of inefficiency and revise the query for better data quality, performance, and lower cost. You can learn more about Bluesky’s solution \u003Ca href=\"https://www.getbluesky.io/resources/bluesky-one-pager?utm_source=snowflake.com&utm_medium=referral+&utm_campaign=Snowflakecoblog&utm_id=snowflakemarketing+\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_8":{"id":"image-65d1f33b4d","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8863847c-b61d-4fe2-8d0c-d51c5df90c99/bluesky-missing-join-2.gif?quality=85&preferwebp=true","width":"1724","height":"866",":type":"snowflake-site/components/image"},"image_9":{"id":"image-c1c782cfe6","additionalClasses":"img","lazyEnabled":true,"alt":"domo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--826afb14-fb7a-4d1b-9de7-e885bb5779b1/domo.png?quality=85&preferwebp=true","width":"400","height":"400",":type":"snowflake-site/components/image"},"blog_text_10":{"id":"blog-text-a1cc94bb2b","text":"\u003Cp\u003E\u003Ca href=\"https://www.domo.com/partners/snowflake\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EDomo\u003C/a\u003E is a data experience platform providing rich self-serve analytics capabilities that natively integrate with Snowflake. \u003Ca href=\"https://www.domo.com/appstore/apps?q=snowflake\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EDomo’s Snowflake Query Investigator app\u003C/a\u003E helps identify potential “problem queries” through visualizations that summarize query volume and history, and rank how queries have performed in relation to each other and over time.\u003C/p\u003E\u003Cp\u003EUsers can drill into a comprehensive view of query operator stats data to quickly investigate potential issues, and can customize logic from within Domo to apply different tolerance thresholds for management purposes. For example, a category of “inefficient pruning” can be created in the data, defined as queries where more than 50% of partitions were scanned and more than 50,000 partitions exist. This custom logic can be altered by those with editing rights, can power visualizations of the offending queries, and can trigger automated Domo alerts via email or SMS to the users responsible for Snowflake administration and optimization.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_10":{"id":"image-982fea9f01","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a2328641-fad5-4a13-a3d4-c6855f7cb066/domo-snowflake-query-analyzer-video.gif?quality=85&preferwebp=true","width":"2602","height":"1586",":type":"snowflake-site/components/image"},"markup_editor":{"id":"markup-editor-497242fab5","title":"page css","cssContent":".img{max-width:500px}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"image_11":{"id":"image-ce9305696a","additionalClasses":"img","lazyEnabled":true,"alt":"qlik","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--679dcc0c-7b6a-447f-b0a8-bf06e42cf25c/qlik.png?quality=85&preferwebp=true","width":"1200","height":"570",":type":"snowflake-site/components/image"},"blog_text_11":{"id":"blog-text-a8c77be1e9","text":"\u003Cp\u003E\u003Ca href=\"https://www.qlik.com/snowflake\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EQlik\u003C/a\u003E provides a complimentary \u003Ca href=\"https://github.com/Qlik-PE/Snowflake-Usage-Analysis-Dashboard\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003ESnowflake Utilization\u003C/a\u003E solution to help organizations keep track of their Snowflake usage and spending. Even if you have multiple tenants, Qlik’s solution fully supports as many tenants as you need to track. Recently, the company made updates to this solution to take advantage of the new function in Snowflake called “get_query_operator_stats.”\u003C/p\u003E\u003Cp\u003EWith this updated Qlik solution for Snowflake, users can easily identify and select a query of interest and get all the associated execution statistics live in a visually presented “flow.” The solution can represent each operation type with an image icon, and it color-codes any operation that needs attention. If the query involves an exploding join, UNION without ALL, inefficient pruning, or causes disk spillage, the solution quickly and clearly presents that information and provides a direct link to Snowflake documentation for further information.\u003C/p\u003E\u003Cp\u003EIn addition to being partners, Qlik is a Snowflake customer and utilizes this Snowflake Utilization solution internally to track and manage usage on all of its Snowflake tenants. The Snowflake Utilization solution is available for free, and Qlik solution architects are able to help joint customers get it configured and running.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_12":{"id":"image-3b39d93f64","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--cfc6debe-88fb-42a6-bca6-31c69a65a8a5/qlik-snowflakequerystatsanimated-1-1.gif?quality=85&preferwebp=true","width":"1916","height":"936",":type":"snowflake-site/components/image"},"image_13":{"id":"image-3f73f4e237","additionalClasses":"img","lazyEnabled":true,"alt":"powerbi","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1248e072-55a7-4efb-a9c3-3a9e43b30a2e/power-bi-icon.png?quality=85&preferwebp=true","width":"3840","height":"2160",":type":"snowflake-site/components/image"},"blog_text_12":{"id":"blog-text-5f54284e91","text":"\u003Cp\u003EIn Power BI we have connected to a Snowflake account using \u003Ca href=\"https://medium.com/snowflake/best-practices-for-using-power-bi-in-directquery-mode-with-snowflake-bfd1312ca7ab\" target=\"_blank\" rel=\"noreferrer noopener\"\u003EDirect Query\u003C/a\u003E and then used dynamic SQL within Power Query to pass the query ID into the Snowflake function. This retrieves the stats of the query ID in question.\u003C/p\u003E\u003Cp\u003E\u003Ca href=\"https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-dynamic-m-query-parameters\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EDynamic M\u003C/a\u003E parameters allow Power BI to \u003Ca href=\"https://medium.com/p/c7c1d576dc21/edit#:~:text=https%3A//blog.crossjoin.co.uk/category/dynamic%2Dm%2Dparameters/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Edynamically pass the Query ID\u003C/a\u003E into the SQL statement, which allows the user to interactively choose the query ID from a list—which will in turn call the function and retrieve the results instantly. This solution is available \u003Ca href=\"https://aka.ms/snowflakepbixdynamicm\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_14":{"id":"image-e6f968a618","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--6e98c14c-481f-400f-b285-af4281f88c30/power-bi-snowflake-query-profile.gif?quality=85&preferwebp=true","width":"1378","height":"768",":type":"snowflake-site/components/image"},"image_15":{"id":"image-e2b68f07e5","additionalClasses":"img","lazyEnabled":true,"alt":"sigma","src":"https://www.snowflake.com/content/snowflake-site/global/en/blog/new-approaches-visualizing-snowflake-query-statistics/_jcr_content/root/responsivegrid/container_main_content/flexible_column_container/flexible_column_content_container_1/responsivegrid_content/image_15.coreimg.svg/1778868221138/sigma-logo-black.svg",":type":"snowflake-site/components/image"},"blog_text_13":{"id":"blog-text-a12f81c383","text":"\u003Cp\u003ESigma is an analytics and business intelligence platform purpose built for the cloud. Sigma sits directly on top of Snowflake, and passes machine-generated SQL to Snowflake for execution. Sigma includes a robust set of Snowflake monitoring \u003Ca href=\"https://www.sigmacomputing.com/interactive-demos/snowflake-performance-monitoring-template?utm_source=partner&utm_medium=referral&utm_campaign=partner_snowflake_blog&utm_content=blog_2023_04_New_Approach_to_Visualizing_Query_Stats-demos/snowflake-performance-monitoring-template\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Etemplates\u003C/a\u003E free for every customer, built right into its platform. These Snowflake templates assist users with monitoring their Data Cloud implementation, providing insights into query performance, warehouse usage, user activity, and \u003Ca href=\"https://www.sigmacomputing.com/interactive-demos/snowflake-cost-monitoring-template?utm_source=partner&utm_medium=referral&utm_campaign=partner_snowflake_blog&utm_content=blog_2023_04_New_Approach_to_Visualizing_Query_Stats\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003Eoverall cost\u003C/a\u003E optimization.\u003C/p\u003E\u003Cp\u003ESigma’s users can take this analysis a step further with a Sigma Query Profile Workbook, which allows users to optimize performance at the individual query level. Sigma’s Query Profile Workbook for Snowflake assists users in identifying resource-intensive queries and ways to optimize them. The workbook includes high-level account statistics including longest running queries, Snowflake credits consumed, and warehouse usage greater than the 7 day average. Users of the workbook can see live views of the QUERY_HISTORY view located in the ACCOUNT_USAGE schema of every Snowflake instance to identify queries of interest. When a query is selected and pasted into the query ID box, Sigma will call Snowflake’s GET_QUERY_OPERATOR_STATS function, and display the query profile. As the query profile populates, Sigma will evaluate the profile for performance optimizations that could be made for a query, including warehouse sizing, poorly performing UNION and JOIN statements, as well as queries that could benefit from using Snowflake Query Acceleration Service.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_16":{"id":"image-741cff3297","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--6de63483-91f9-4095-a5ed-1e617345d4dd/sigma-query-profile-workbook--1-.gif?quality=85&preferwebp=true","width":"1632","height":"940",":type":"snowflake-site/components/image"},"image_17":{"id":"image-f693739820","additionalClasses":"img","lazyEnabled":true,"alt":"stremalit","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ea1a4dbe-2897-445d-a025-7a5fa518c056/streamlit-logo-primary-colormark-darktext.png?quality=85&preferwebp=true","width":"2181","height":"1276",":type":"snowflake-site/components/image"},"blog_text_14":{"id":"blog-text-bfeda43c36","text":"\u003Cp\u003ESnowflake developers and administrators may have heard about \u003Ca href=\"https://www.snowflake.com/blog/snowflake-to-acquire-streamlit/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowflake’s acquisition of Streamlit\u003C/a\u003E. This blog post highlights new approaches to visualizing data using Snowflake technology partner tools, and would be incomplete without Streamlit. With tens of thousands of developers, Streamlit empowers Python practitioners to build and deploy interactive applications and share them with colleagues. The Streamlit Snowflake query profiling app is built upon the same Snowflake data sources as the other examples. It allows the user to interactively explore queries of interest and highlights potential problems within seconds of retrieval.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_18":{"id":"image-ffee1bda14","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--87f2e7f0-16f2-4953-af43-4faf8bad738a/streamlit.gif?quality=85&preferwebp=true","width":"1920","height":"870",":type":"snowflake-site/components/image"},"image_19":{"id":"image-96d13f9572","additionalClasses":"img","lazyEnabled":true,"alt":"tableau","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c885e72d-b14d-48a1-a11f-bf5157075a26/tableau-sf.png?quality=85&preferwebp=true","width":"525","height":"143",":type":"snowflake-site/components/image"},"blog_text_15":{"id":"blog-text-b12de44125","text":"\u003Cp\u003ETableau released a \u003Ca href=\"https://exchange.tableau.com/products/667\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003ESnowflake Cost and Performance Monitoring Dashboard\u003C/a\u003E in 2022 that uses Snowflake account usage tables to analyze the cost and performance of a Snowflake instance. The workbook includes error tracking analysis, storage and warehouse cost analysis, slow-running queries analysis, and more.\u003C/p\u003E\u003Cp\u003EUsing the new programmatic access to query profiler stats feature in Snowflake, customers are now able to use a new version of the Snowflake Cost and Performance Monitoring Dashboard that adds query profiler analysis.\u003C/p\u003E\u003Cp\u003EUsing custom SQL and a parameter called “QueryID,” Tableau is able to retrieve the query profiler stats in real time upon selection of a mark on the “Slow Running Queries” Dashboard. These stats are then parsed into the four different categories of common reasons for slow queries, and show up in the “Query Profiler Analysis” Sheet as True/False values. Each category is calculated using a Tableau calculated field. Viewers of this dashboard can then see the query string that was run; the user that ran it; warehouse, database, and schema, and more. This requires no additional setup in Snowflake.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_20":{"id":"image-578eb6a1c5","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--51186c81-98f2-44b4-af39-ed6e05fdeb3c/tableau-query-profiler.gif?quality=85&preferwebp=true","width":"1404","height":"954",":type":"snowflake-site/components/image"},"image_21":{"id":"image-267a4c07a3","additionalClasses":"img","lazyEnabled":true,"alt":"thoughtspot","src":"https://www.snowflake.com/content/snowflake-site/global/en/blog/new-approaches-visualizing-snowflake-query-statistics/_jcr_content/root/responsivegrid/container_main_content/flexible_column_container/flexible_column_content_container_1/responsivegrid_content/image_21.coreimg.svg/1778868239848/thoughtspot-logo-black-with-r.svg",":type":"snowflake-site/components/image"},"blog_text_16":{"id":"blog-text-3e4dff0507","text":"\u003Cp\u003EThoughtSpot is the AI-Powered Analytics company. With natural language and AI, ThoughtSpot empowers everyone in an organization to ask data questions, get answers, and take action. ThoughtSpot is intuitive enough for anyone to use, yet built to handle large, complex data at cloud scale.\u003C/p\u003E\u003Cp\u003EThe \u003Ca href=\"https://www.thoughtspot.com/product/spotapps\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003EThoughtSpot SpotApp for Snowflake Query Profiling\u003C/a\u003E is an AI-powered analytics accelerator that provides a holistic view of query performance in the Snowflake Data Cloud. Easy to deploy through a guided implementation process, the SpotApp gives users a proactive look into problematic queries as well as the ability to drill into underlying issues such as latency, failed execution, exploding joins, and more. The SpotApp even provides tips to resolve issues and make future queries more efficient.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_22":{"id":"image-7f3b0a8675","lazyEnabled":true,"alt":"","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--aca1952f-b6b6-4614-b49b-488906f6980a/thoughtspot-snowflake-query-profiling-spotapp-1.gif?quality=85&preferwebp=true","width":"3584","height":"1930",":type":"snowflake-site/components/image"},"blog_text_17":{"id":"blog-text-e2c4b9ebd6","text":" \u003Cp\u003E\u003Cstrong\u003ETry it out!\u003C/strong\u003E\u003C/p\u003E\u003Cp\u003EWe invite Snowflake developers and administrators everywhere to become more familiar with the partner accelerators described above.  After connecting the partner solution to your Snowflake account, view a list of recent queries from \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/account-usage/query_history\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY\u003C/a\u003E, and take a deeper dive into individual queries. The new get_query_operator_stats function provides customers a whole new level of insight into Snowflake query performance and query execution statistics. 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