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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-98cd28db53","isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["리소스 최적화: 성능"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Matt 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-------------->\n","\u003Ch2\u003E개요\u003C/h2\u003E\n","\u003Cp\u003E이 \u003Ca href=\"/blog/understanding-snowflakes-resource-optimization-capabilities/\"\u003E블로그 포스트\u003C/a\u003E는 여러분이 Snowflake의 리소스 최적화 기능을 더 잘 이해할 수 있도록 합니다.\u003C/p\u003E\n","\u003Ch3\u003E성능\u003C/h3\u003E\n","\u003Cp\u003E이 가이드에 제공된 쿼리는 다양한 요인으로 인한 낮은 성능이 과도한 소비를 초래할 수도 있는 영역을 확인하는 것과 관계된 쿼리 설정과 실행에 도움을 주기 위한 것입니다.\u003C/p\u003E\n","\u003Ch3\u003E학습할 내용\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E성능을 개선할 수 있는 영역 확인 방법\u003C/li\u003E\u003Cli\u003E낮은 성능이 과도한 소비를 초래하는 워크로드 분석 방법\u003C/li\u003E\u003Cli\u003E스케일 업 또는 스케일 아웃으로 이점을 누릴 수 있는 웨어하우스 식별 방법\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E필요한 것\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/\"\u003ESnowflake\u003C/a\u003E 계정\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/sql-reference/account-usage.html#enabling-account-usage-for-other-roles\"\u003E계정 사용량 데이터 공유\u003C/a\u003E를 보기 위한 액세스\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E관련 자료\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E리소스 최적화: 설정 및 구성\u003C/li\u003E\u003Cli\u003E리소스 최적화: 사용량 모니터링\u003C/li\u003E\u003Cli\u003E리소스 최적화: 청구 메트릭\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003E쿼리 계층\u003C/h2\u003E\n","\u003Cp\u003E리소스 최적화 Snowflake 빠른 시작 내에 있는 각 쿼리 이름의 오른쪽에는 ‘(T*)’로 계층 지정이 있습니다.  다음 계층 설명은 이러한 지정을 더 잘 이해하는 데 도움이 됩니다.\u003C/p\u003E\n","\u003Ch3\u003E계층 1 쿼리\u003C/h3\u003E\n","\u003Cp\u003E본질적으로 계층 1 쿼리는 Snowflake의 리소스 최적화에 매우 중요합니다. 또한 각 고객이 규모, 업계, 위치 등에 관계없이 자신의 소비 모니터링에 대해 도움을 받기 위해 사용해야 합니다.\u003C/p\u003E\n","\u003Ch3\u003E계층 2 쿼리\u003C/h3\u003E\n","\u003Cp\u003E계층 2 쿼리는 리소스 최적화에 대한 추가적인 깊이를 제공하는 동시에 프로세스에서 중요한 역할을 수행합니다. 또한 모든 고객과 고객의 워크로드에 필수적이지는 않을지라도 과도한 소비가 확인되었을 수 있는 모든 추가 영역에 대한 추가적인 설명을 제공할 수 있습니다.\u003C/p\u003E\n","\u003Ch3\u003E계층 3 쿼리\u003C/h3\u003E\n","\u003Cp\u003E마지막으로 계층 3 쿼리는 Snowflake 소비를 최적화하는 데 있어서 모든 부분을 확인하고자 하는 고객이 사용할 수 있도록 설계되었습니다.  이러한 쿼리는 이 프로세스에서 여전히 매우 유용하지만 계층 1 및 2의 쿼리만큼 중요하지는 않습니다.\u003C/p\u003E\n","\u003Ch2\u003EData Ingest with Snowpipe and "Copy" (T1)\u003C/h2\u003E\n","\u003Ch6\u003E계층 1\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E이 쿼리는 Snowflake에서 각 테이블을 위해 모든 로드의 집계된 일별 요약을 반환합니다. 이는 평균 파일 크기, 총열, 총볼륨 및 수집 메서드(복사 또는 Snowpipe)를 나타냅니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E이러한 개괄적 정보를 통해 파일 크기가 이상적인 수집에 너무 작거나 큰지를 결정할 수 있습니다. 볼륨을 크레딧 소비와 매핑할 수 있다면 어떤 테이블이 로드된 TB당 더 많은 크레딧을 소비하는지 결정할 수 있습니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT \n  TO_DATE(LAST_LOAD_TIME) as LOAD_DATE\n  ,STATUS\n  ,TABLE_CATALOG_NAME as DATABASE_NAME\n  ,TABLE_SCHEMA_NAME as SCHEMA_NAME\n  ,TABLE_NAME\n  ,CASE WHEN PIPE_NAME IS NULL THEN 'COPY' ELSE 'SNOWPIPE' END AS INGEST_METHOD\n  ,SUM(ROW_COUNT) as ROW_COUNT\n  ,SUM(ROW_PARSED) as ROWS_PARSED\n  ,AVG(FILE_SIZE) as AVG_FILE_SIZE_BYTES\n  ,SUM(FILE_SIZE) as TOTAL_FILE_SIZE_BYTES\n  ,SUM(FILE_SIZE)/POWER(1024,1) as TOTAL_FILE_SIZE_KB\n  ,SUM(FILE_SIZE)/POWER(1024,2) as TOTAL_FILE_SIZE_MB\n  ,SUM(FILE_SIZE)/POWER(1024,3) as TOTAL_FILE_SIZE_GB\n  ,SUM(FILE_SIZE)/POWER(1024,4) as TOTAL_FILE_SIZE_TB\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."COPY_HISTORY"\nGROUP BY 1,2,3,4,5,6\nORDER BY 3,4,5,1,2\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E스크린샷\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/resource-optimization-performance-optimization-kr/dataingestwithsnowpipe2.png\" alt=\"alt-tlext-here\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EScale Up vs. Out (Size vs. Multi-cluster) (T2)\u003C/h2\u003E\n","\u003Ch6\u003E계층 2\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003EMCW 설정 또는 더 큰 사이즈로 스케일 업하여 이점을 누릴 수 있는 웨어하우스 및 시간을 목록으로 나열하는 별도의 쿼리 2개입니다\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E웨어하우스에서 경합을 초래하는 웨어하우스, 시간 또는 사용자 재구성을 결정하기 위해 이 목록을 사용합니다\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--LIST OF WAREHOUSES AND DAYS WHERE MCW COULD HAVE HELPED\nSELECT TO_DATE(START_TIME) as DATE\n,WAREHOUSE_NAME\n,SUM(AVG_RUNNING) AS SUM_RUNNING\n,SUM(AVG_QUEUED_LOAD) AS SUM_QUEUED\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."WAREHOUSE_LOAD_HISTORY"\nWHERE TO_DATE(START_TIME) >= DATEADD(month,-1,CURRENT_TIMESTAMP())\nGROUP BY 1,2\nHAVING SUM(AVG_QUEUED_LOAD) >0\n;\n\n--LIST OF WAREHOUSES AND QUERIES WHERE A LARGER WAREHOUSE WOULD HAVE HELPED WITH REMOTE SPILLING\nSELECT QUERY_ID\n,USER_NAME\n,WAREHOUSE_NAME\n,WAREHOUSE_SIZE\n,BYTES_SCANNED\n,BYTES_SPILLED_TO_REMOTE_STORAGE\n,BYTES_SPILLED_TO_REMOTE_STORAGE / BYTES_SCANNED AS SPILLING_READ_RATIO\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."QUERY_HISTORY"\nWHERE BYTES_SPILLED_TO_REMOTE_STORAGE > BYTES_SCANNED * 5  -- Each byte read was spilled 5x on average\nORDER BY SPILLING_READ_RATIO DESC\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E스크린샷\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/resource-optimization-performance-optimization-kr/scaleupvsout1.png\" alt=\"alt-tlext-here\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EWarehouse Cache Usage (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E웨어하우스 캐시에서 스캔한 데이터의 비율을 나타내는 웨어하우스에 의해 분석된 모든 쿼리에 걸쳐 집계합니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E쿼리/보고에서 사용되었으며 낮은 비율을 가진 웨어하우스를 찾습니다. 이는 웨어하우스가 너무 빨리 일시 중단됨을 나타냅니다\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT WAREHOUSE_NAME\n,COUNT(*) AS QUERY_COUNT\n,SUM(BYTES_SCANNED) AS BYTES_SCANNED\n,SUM(BYTES_SCANNED*PERCENTAGE_SCANNED_FROM_CACHE) AS BYTES_SCANNED_FROM_CACHE\n,SUM(BYTES_SCANNED*PERCENTAGE_SCANNED_FROM_CACHE) / SUM(BYTES_SCANNED) AS PERCENT_SCANNED_FROM_CACHE\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."QUERY_HISTORY"\nWHERE START_TIME >= dateadd(month,-1,current_timestamp())\nAND BYTES_SCANNED > 0\nGROUP BY 1\nORDER BY 5\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003E스크린샷\u003C/h4\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/resource-optimization-performance-optimization-kr/warehousecacheusage.png\" alt=\"alt-tlext-here\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EHeavy Scanners (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E많은 양의 데이터를 스캔하는 쿼리를 실행하는 순서가 지정된 사용자 목록입니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E이는 사용자를 학습시키거나 클러스터링을 활성화할 수 있는 잠재적 기회입니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect \n  User_name\n, warehouse_name\n, avg(case when partitions_total > 0 then partitions_scanned / partitions_total else 0 end) avg_pct_scanned\nfrom   snowflake.account_usage.query_history\nwhere  start_time::date > dateadd('days', -45, current_date)\ngroup by 1, 2\norder by 3 desc\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EFull Table Scans by User (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E이러한 쿼리는 거의 모든 테이블 검색을 통해 대부분 쿼리를 실행하는 사용자 목록과 쿼리 자체의 목록입니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E이는 사용자를 학습시키거나 클러스터링을 활성화할 수 있는 잠재적 기회입니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E--who are the users with the most (near) full table scans\nSELECT USER_NAME\n,COUNT(*) as COUNT_OF_QUERIES\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."QUERY_HISTORY"\nWHERE START_TIME >= dateadd(month,-1,current_timestamp())\nAND PARTITIONS_SCANNED > (PARTITIONS_TOTAL*0.95)\nAND QUERY_TYPE NOT LIKE 'CREATE%'\ngroup by 1\norder by 2 desc;\n\n-- This gives all queries in the last month with nearly a full table scan :) > 95%, ordered by the worst offending\nSELECT * \nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."QUERY_HISTORY"\nWHERE START_TIME >= dateadd(month,-1,current_timestamp())\nAND PARTITIONS_SCANNED > (PARTITIONS_TOTAL*0.95)\nAND QUERY_TYPE NOT LIKE 'CREATE%'\nORDER BY PARTITIONS_SCANNED DESC\nLIMIT 50  -- Configurable threshold that defines "TOP N=50"\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ETop 10 Spillers Remote (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E원격 저장소로 분산되는 바이트 양에 따라 문제를 초래하는 상위 쿼리 10개를 식별합니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E이러한 쿼리는 더 많은 로컬 저장소와 메모리를 보유하고 있는 더 큰 웨어하우스에서 실행될 가능성이 매우 높습니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect query_id, substr(query_text, 1, 50) partial_query_text, user_name, warehouse_name, warehouse_size, \n       BYTES_SPILLED_TO_REMOTE_STORAGE, start_time, end_time, total_elapsed_time/1000 total_elapsed_time\nfrom   snowflake.account_usage.query_history\nwhere  BYTES_SPILLED_TO_REMOTE_STORAGE > 0\nand start_time::date > dateadd('days', -45, current_date)\norder  by BYTES_SPILLED_TO_REMOTE_STORAGE desc\nlimit 10\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EAutoClustering History & 7-Day Average (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E지난 한 해 동안 주별로 그룹화된 Auto-Clustering에 의해 소비된 일별 평균 크레딧입니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH CREDITS_BY_DAY AS (\nSELECT TO_DATE(START_TIME) as DATE\n,SUM(CREDITS_USED) as CREDITS_USED\n\n\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."AUTOMATIC_CLUSTERING_HISTORY"\n\nWHERE START_TIME >= dateadd(year,-1,current_timestamp()) \nGROUP BY 1\nORDER BY 2 DESC \n  )\n  \nSELECT DATE_TRUNC('week',DATE)\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\nFROM CREDITS_BY_DAY\nGROUP BY 1\nORDER BY 1\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EMaterialized Views History & 7-Day Average (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E지난 한 해 동안 주별로 그룹화된 Materialized Views에 의해 소비된 일별 평균 크레딧입니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH CREDITS_BY_DAY AS (\nSELECT TO_DATE(START_TIME) as DATE\n,SUM(CREDITS_USED) as CREDITS_USED\n\n\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."MATERIALIZED_VIEW_REFRESH_HISTORY"\n\nWHERE START_TIME >= dateadd(year,-1,current_timestamp()) \nGROUP BY 1\nORDER BY 2 DESC \n  )\n  \nSELECT DATE_TRUNC('week',DATE)\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\nFROM CREDITS_BY_DAY\nGROUP BY 1\nORDER BY 1\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESearch Optimization History & 7-Day Average (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E지난 한 해 동안 주별로 그룹화된 Search Optimization에 의해 소비된 일별 평균 크레딧입니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH CREDITS_BY_DAY AS (\nSELECT TO_DATE(START_TIME) as DATE\n,SUM(CREDITS_USED) as CREDITS_USED\n\n\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."SEARCH_OPTIMIZATION_HISTORY"\n\nWHERE START_TIME >= dateadd(year,-1,current_timestamp()) \nGROUP BY 1\nORDER BY 2 DESC \n  )\n  \nSELECT DATE_TRUNC('week',DATE)\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\nFROM CREDITS_BY_DAY\nGROUP BY 1\nORDER BY 1\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ESnowpipe History & 7-Day Average (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E지난 한 해 동안 주별로 그룹화된 Snowpipe에 의해 소비된 일별 평균 크레딧입니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH CREDITS_BY_DAY AS (\nSELECT TO_DATE(START_TIME) as DATE\n,SUM(CREDITS_USED) as CREDITS_USED\n\n\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."PIPE_USAGE_HISTORY"\n\nWHERE START_TIME >= dateadd(year,-1,current_timestamp()) \nGROUP BY 1\nORDER BY 2 DESC \n  )\n  \nSELECT DATE_TRUNC('week',DATE)\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\nFROM CREDITS_BY_DAY\nGROUP BY 1\nORDER BY 1\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EReplication History & 7-Day Average (T3)\u003C/h2\u003E\n","\u003Ch6\u003E계층 3\u003C/h6\u003E\n","\u003Ch4\u003E설명:\u003C/h4\u003E\n","\u003Cp\u003E지난 한 해 동안 주별로 그룹화된 Replication에 의해 소비된 일별 평균 크레딧입니다.\u003C/p\u003E\n","\u003Ch4\u003E결과 해석 방법:\u003C/h4\u003E\n","\u003Cp\u003E한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\u003C/p\u003E\n","\u003Ch4\u003E주요 스키마:\u003C/h4\u003E\n","\u003Cp\u003EAccount_Usage\u003C/p\u003E\n","\u003Ch4\u003ESQL\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH CREDITS_BY_DAY AS (\nSELECT TO_DATE(START_TIME) as DATE\n,SUM(CREDITS_USED) as CREDITS_USED\n\n\nFROM "SNOWFLAKE"."ACCOUNT_USAGE"."REPLICATION_USAGE_HISTORY"\n\nWHERE START_TIME >= dateadd(year,-1,current_timestamp()) \nGROUP BY 1\nORDER BY 2 DESC \n  )\n  \nSELECT DATE_TRUNC('week',DATE)\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\nFROM CREDITS_BY_DAY\nGROUP BY 1\nORDER BY 1\n;\n\u003C/code\u003E\u003C/pre\u003E"],"title":"리소스 최적화: 성능","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- --------------\u003E\r\n## 개요\r\n\r\n이 [블로그 포스트](/blog/understanding-snowflakes-resource-optimization-capabilities/)는 여러분이 Snowflake의 리소스 최적화 기능을 더 잘 이해할 수 있도록 합니다.\r\n\r\n### 성능\r\n\r\n이 가이드에 제공된 쿼리는 다양한 요인으로 인한 낮은 성능이 과도한 소비를 초래할 수도 있는 영역을 확인하는 것과 관계된 쿼리 설정과 실행에 도움을 주기 위한 것입니다.\r\n\r\n### 학습할 내용\r\n\r\n- 성능을 개선할 수 있는 영역 확인 방법\r\n- 낮은 성능이 과도한 소비를 초래하는 워크로드 분석 방법\r\n- 스케일 업 또는 스케일 아웃으로 이점을 누릴 수 있는 웨어하우스 식별 방법\r\n\r\n### 필요한 것\r\n\r\n- [Snowflake](/) 계정\r\n- [계정 사용량 데이터 공유](https://docs.snowflake.com/ko/sql-reference/account-usage.html#enabling-account-usage-for-other-roles)를 보기 위한 액세스\r\n\r\n### 관련 자료\r\n\r\n- 리소스 최적화: 설정 및 구성\r\n- 리소스 최적화: 사용량 모니터링\r\n- 리소스 최적화: 청구 메트릭\r\n\r\n## 쿼리 계층\r\n\r\n리소스 최적화 Snowflake 빠른 시작 내에 있는 각 쿼리 이름의 오른쪽에는 ‘(T*)’로 계층 지정이 있습니다.  다음 계층 설명은 이러한 지정을 더 잘 이해하는 데 도움이 됩니다.\r\n\r\n### 계층 1 쿼리\r\n\r\n본질적으로 계층 1 쿼리는 Snowflake의 리소스 최적화에 매우 중요합니다. 또한 각 고객이 규모, 업계, 위치 등에 관계없이 자신의 소비 모니터링에 대해 도움을 받기 위해 사용해야 합니다.\r\n\r\n### 계층 2 쿼리\r\n\r\n계층 2 쿼리는 리소스 최적화에 대한 추가적인 깊이를 제공하는 동시에 프로세스에서 중요한 역할을 수행합니다. 또한 모든 고객과 고객의 워크로드에 필수적이지는 않을지라도 과도한 소비가 확인되었을 수 있는 모든 추가 영역에 대한 추가적인 설명을 제공할 수 있습니다.\r\n\r\n### 계층 3 쿼리\r\n\r\n마지막으로 계층 3 쿼리는 Snowflake 소비를 최적화하는 데 있어서 모든 부분을 확인하고자 하는 고객이 사용할 수 있도록 설계되었습니다.  이러한 쿼리는 이 프로세스에서 여전히 매우 유용하지만 계층 1 및 2의 쿼리만큼 중요하지는 않습니다.\r\n\r\n## Data Ingest with Snowpipe and \"Copy\" (T1)\r\n\r\n###### 계층 1\r\n\r\n#### 설명:\r\n\r\n이 쿼리는 Snowflake에서 각 테이블을 위해 모든 로드의 집계된 일별 요약을 반환합니다. 이는 평균 파일 크기, 총열, 총볼륨 및 수집 메서드(복사 또는 Snowpipe)를 나타냅니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n이러한 개괄적 정보를 통해 파일 크기가 이상적인 수집에 너무 작거나 큰지를 결정할 수 있습니다. 볼륨을 크레딧 소비와 매핑할 수 있다면 어떤 테이블이 로드된 TB당 더 많은 크레딧을 소비하는지 결정할 수 있습니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nSELECT \r\n  TO_DATE(LAST_LOAD_TIME) as LOAD_DATE\r\n  ,STATUS\r\n  ,TABLE_CATALOG_NAME as DATABASE_NAME\r\n  ,TABLE_SCHEMA_NAME as SCHEMA_NAME\r\n  ,TABLE_NAME\r\n  ,CASE WHEN PIPE_NAME IS NULL THEN 'COPY' ELSE 'SNOWPIPE' END AS INGEST_METHOD\r\n  ,SUM(ROW_COUNT) as ROW_COUNT\r\n  ,SUM(ROW_PARSED) as ROWS_PARSED\r\n  ,AVG(FILE_SIZE) as AVG_FILE_SIZE_BYTES\r\n  ,SUM(FILE_SIZE) as TOTAL_FILE_SIZE_BYTES\r\n  ,SUM(FILE_SIZE)/POWER(1024,1) as TOTAL_FILE_SIZE_KB\r\n  ,SUM(FILE_SIZE)/POWER(1024,2) as TOTAL_FILE_SIZE_MB\r\n  ,SUM(FILE_SIZE)/POWER(1024,3) as TOTAL_FILE_SIZE_GB\r\n  ,SUM(FILE_SIZE)/POWER(1024,4) as TOTAL_FILE_SIZE_TB\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"COPY_HISTORY\"\r\nGROUP BY 1,2,3,4,5,6\r\nORDER BY 3,4,5,1,2\r\n;\r\n```\r\n\r\n#### 스크린샷\r\n\r\n![alt-tlext-here](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/resource-optimization-performance-optimization-kr/dataingestwithsnowpipe2.png)\r\n\r\n## Scale Up vs. Out (Size vs. Multi-cluster) (T2)\r\n\r\n###### 계층 2\r\n\r\n#### 설명:\r\n\r\nMCW 설정 또는 더 큰 사이즈로 스케일 업하여 이점을 누릴 수 있는 웨어하우스 및 시간을 목록으로 나열하는 별도의 쿼리 2개입니다\r\n\r\n#### 결과 해석 방법:\r\n\r\n웨어하우스에서 경합을 초래하는 웨어하우스, 시간 또는 사용자 재구성을 결정하기 위해 이 목록을 사용합니다\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\n--LIST OF WAREHOUSES AND DAYS WHERE MCW COULD HAVE HELPED\r\nSELECT TO_DATE(START_TIME) as DATE\r\n,WAREHOUSE_NAME\r\n,SUM(AVG_RUNNING) AS SUM_RUNNING\r\n,SUM(AVG_QUEUED_LOAD) AS SUM_QUEUED\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"WAREHOUSE_LOAD_HISTORY\"\r\nWHERE TO_DATE(START_TIME) \u003E= DATEADD(month,-1,CURRENT_TIMESTAMP())\r\nGROUP BY 1,2\r\nHAVING SUM(AVG_QUEUED_LOAD) \u003E0\r\n;\r\n\r\n--LIST OF WAREHOUSES AND QUERIES WHERE A LARGER WAREHOUSE WOULD HAVE HELPED WITH REMOTE SPILLING\r\nSELECT QUERY_ID\r\n,USER_NAME\r\n,WAREHOUSE_NAME\r\n,WAREHOUSE_SIZE\r\n,BYTES_SCANNED\r\n,BYTES_SPILLED_TO_REMOTE_STORAGE\r\n,BYTES_SPILLED_TO_REMOTE_STORAGE / BYTES_SCANNED AS SPILLING_READ_RATIO\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"QUERY_HISTORY\"\r\nWHERE BYTES_SPILLED_TO_REMOTE_STORAGE \u003E BYTES_SCANNED * 5  -- Each byte read was spilled 5x on average\r\nORDER BY SPILLING_READ_RATIO DESC\r\n;\r\n```\r\n\r\n#### 스크린샷\r\n\r\n![alt-tlext-here](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/resource-optimization-performance-optimization-kr/scaleupvsout1.png)\r\n\r\n## Warehouse Cache Usage (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n웨어하우스 캐시에서 스캔한 데이터의 비율을 나타내는 웨어하우스에 의해 분석된 모든 쿼리에 걸쳐 집계합니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n쿼리/보고에서 사용되었으며 낮은 비율을 가진 웨어하우스를 찾습니다. 이는 웨어하우스가 너무 빨리 일시 중단됨을 나타냅니다\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nSELECT WAREHOUSE_NAME\r\n,COUNT(*) AS QUERY_COUNT\r\n,SUM(BYTES_SCANNED) AS BYTES_SCANNED\r\n,SUM(BYTES_SCANNED*PERCENTAGE_SCANNED_FROM_CACHE) AS BYTES_SCANNED_FROM_CACHE\r\n,SUM(BYTES_SCANNED*PERCENTAGE_SCANNED_FROM_CACHE) / SUM(BYTES_SCANNED) AS PERCENT_SCANNED_FROM_CACHE\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"QUERY_HISTORY\"\r\nWHERE START_TIME \u003E= dateadd(month,-1,current_timestamp())\r\nAND BYTES_SCANNED \u003E 0\r\nGROUP BY 1\r\nORDER BY 5\r\n;\r\n```\r\n\r\n#### 스크린샷\r\n\r\n![alt-tlext-here](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/resource-optimization-performance-optimization-kr/warehousecacheusage.png)\r\n\r\n## Heavy Scanners (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n많은 양의 데이터를 스캔하는 쿼리를 실행하는 순서가 지정된 사용자 목록입니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n이는 사용자를 학습시키거나 클러스터링을 활성화할 수 있는 잠재적 기회입니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nselect \r\n  User_name\r\n, warehouse_name\r\n, avg(case when partitions_total \u003E 0 then partitions_scanned / partitions_total else 0 end) avg_pct_scanned\r\nfrom   snowflake.account_usage.query_history\r\nwhere  start_time::date \u003E dateadd('days', -45, current_date)\r\ngroup by 1, 2\r\norder by 3 desc\r\n;\r\n```\r\n\r\n## Full Table Scans by User (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n이러한 쿼리는 거의 모든 테이블 검색을 통해 대부분 쿼리를 실행하는 사용자 목록과 쿼리 자체의 목록입니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n이는 사용자를 학습시키거나 클러스터링을 활성화할 수 있는 잠재적 기회입니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\n--who are the users with the most (near) full table scans\r\nSELECT USER_NAME\r\n,COUNT(*) as COUNT_OF_QUERIES\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"QUERY_HISTORY\"\r\nWHERE START_TIME \u003E= dateadd(month,-1,current_timestamp())\r\nAND PARTITIONS_SCANNED \u003E (PARTITIONS_TOTAL*0.95)\r\nAND QUERY_TYPE NOT LIKE 'CREATE%'\r\ngroup by 1\r\norder by 2 desc;\r\n\r\n-- This gives all queries in the last month with nearly a full table scan :) \u003E 95%, ordered by the worst offending\r\nSELECT * \r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"QUERY_HISTORY\"\r\nWHERE START_TIME \u003E= dateadd(month,-1,current_timestamp())\r\nAND PARTITIONS_SCANNED \u003E (PARTITIONS_TOTAL*0.95)\r\nAND QUERY_TYPE NOT LIKE 'CREATE%'\r\nORDER BY PARTITIONS_SCANNED DESC\r\nLIMIT 50  -- Configurable threshold that defines \"TOP N=50\"\r\n;\r\n```\r\n\r\n## Top 10 Spillers Remote (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n원격 저장소로 분산되는 바이트 양에 따라 문제를 초래하는 상위 쿼리 10개를 식별합니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n이러한 쿼리는 더 많은 로컬 저장소와 메모리를 보유하고 있는 더 큰 웨어하우스에서 실행될 가능성이 매우 높습니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nselect query_id, substr(query_text, 1, 50) partial_query_text, user_name, warehouse_name, warehouse_size, \r\n       BYTES_SPILLED_TO_REMOTE_STORAGE, start_time, end_time, total_elapsed_time/1000 total_elapsed_time\r\nfrom   snowflake.account_usage.query_history\r\nwhere  BYTES_SPILLED_TO_REMOTE_STORAGE \u003E 0\r\nand start_time::date \u003E dateadd('days', -45, current_date)\r\norder  by BYTES_SPILLED_TO_REMOTE_STORAGE desc\r\nlimit 10\r\n;\r\n```\r\n\r\n## AutoClustering History & 7-Day Average (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n지난 한 해 동안 주별로 그룹화된 Auto-Clustering에 의해 소비된 일별 평균 크레딧입니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nWITH CREDITS_BY_DAY AS (\r\nSELECT TO_DATE(START_TIME) as DATE\r\n,SUM(CREDITS_USED) as CREDITS_USED\r\n\r\n\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"AUTOMATIC_CLUSTERING_HISTORY\"\r\n\r\nWHERE START_TIME \u003E= dateadd(year,-1,current_timestamp()) \r\nGROUP BY 1\r\nORDER BY 2 DESC \r\n  )\r\n  \r\nSELECT DATE_TRUNC('week',DATE)\r\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\r\nFROM CREDITS_BY_DAY\r\nGROUP BY 1\r\nORDER BY 1\r\n;\r\n```\r\n\r\n## Materialized Views History & 7-Day Average (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n지난 한 해 동안 주별로 그룹화된 Materialized Views에 의해 소비된 일별 평균 크레딧입니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nWITH CREDITS_BY_DAY AS (\r\nSELECT TO_DATE(START_TIME) as DATE\r\n,SUM(CREDITS_USED) as CREDITS_USED\r\n\r\n\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"MATERIALIZED_VIEW_REFRESH_HISTORY\"\r\n\r\nWHERE START_TIME \u003E= dateadd(year,-1,current_timestamp()) \r\nGROUP BY 1\r\nORDER BY 2 DESC \r\n  )\r\n  \r\nSELECT DATE_TRUNC('week',DATE)\r\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\r\nFROM CREDITS_BY_DAY\r\nGROUP BY 1\r\nORDER BY 1\r\n;\r\n```\r\n\r\n## Search Optimization History & 7-Day Average (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n지난 한 해 동안 주별로 그룹화된 Search Optimization에 의해 소비된 일별 평균 크레딧입니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nWITH CREDITS_BY_DAY AS (\r\nSELECT TO_DATE(START_TIME) as DATE\r\n,SUM(CREDITS_USED) as CREDITS_USED\r\n\r\n\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"SEARCH_OPTIMIZATION_HISTORY\"\r\n\r\nWHERE START_TIME \u003E= dateadd(year,-1,current_timestamp()) \r\nGROUP BY 1\r\nORDER BY 2 DESC \r\n  )\r\n  \r\nSELECT DATE_TRUNC('week',DATE)\r\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\r\nFROM CREDITS_BY_DAY\r\nGROUP BY 1\r\nORDER BY 1\r\n;\r\n```\r\n\r\n## Snowpipe History & 7-Day Average (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n지난 한 해 동안 주별로 그룹화된 Snowpipe에 의해 소비된 일별 평균 크레딧입니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nWITH CREDITS_BY_DAY AS (\r\nSELECT TO_DATE(START_TIME) as DATE\r\n,SUM(CREDITS_USED) as CREDITS_USED\r\n\r\n\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"PIPE_USAGE_HISTORY\"\r\n\r\nWHERE START_TIME \u003E= dateadd(year,-1,current_timestamp()) \r\nGROUP BY 1\r\nORDER BY 2 DESC \r\n  )\r\n  \r\nSELECT DATE_TRUNC('week',DATE)\r\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\r\nFROM CREDITS_BY_DAY\r\nGROUP BY 1\r\nORDER BY 1\r\n;\r\n```\r\n\r\n## Replication History & 7-Day Average (T3)\r\n\r\n###### 계층 3\r\n\r\n#### 설명:\r\n\r\n지난 한 해 동안 주별로 그룹화된 Replication에 의해 소비된 일별 평균 크레딧입니다.\r\n\r\n#### 결과 해석 방법:\r\n\r\n한 해에 걸친 일별 평균에서 변칙을 찾습니다. 소비 급증 또는 변화를 조사할 기회입니다.\r\n\r\n#### 주요 스키마:\r\n\r\nAccount_Usage\r\n\r\n#### SQL\r\n\r\n```sql\r\nWITH CREDITS_BY_DAY AS (\r\nSELECT TO_DATE(START_TIME) as DATE\r\n,SUM(CREDITS_USED) as CREDITS_USED\r\n\r\n\r\nFROM \"SNOWFLAKE\".\"ACCOUNT_USAGE\".\"REPLICATION_USAGE_HISTORY\"\r\n\r\nWHERE START_TIME \u003E= dateadd(year,-1,current_timestamp()) \r\nGROUP BY 1\r\nORDER BY 2 DESC \r\n  )\r\n  \r\nSELECT DATE_TRUNC('week',DATE)\r\n,AVG(CREDITS_USED) as AVG_DAILY_CREDITS\r\nFROM CREDITS_BY_DAY\r\nGROUP BY 1\r\nORDER BY 1\r\n;\r\n```",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo 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계층\u003C/h2\u003E","\u003Ch2\u003EData Ingest with Snowpipe and \"Copy\" (T1)\u003C/h2\u003E","\u003Ch2\u003EScale Up vs. Out (Size vs. Multi-cluster) (T2)\u003C/h2\u003E","\u003Ch2\u003EWarehouse Cache Usage (T3)\u003C/h2\u003E","\u003Ch2\u003EHeavy Scanners (T3)\u003C/h2\u003E","\u003Ch2\u003EFull Table Scans by User (T3)\u003C/h2\u003E","\u003Ch2\u003ETop 10 Spillers Remote (T3)\u003C/h2\u003E","\u003Ch2\u003EAutoClustering History & 7-Day Average (T3)\u003C/h2\u003E","\u003Ch2\u003EMaterialized Views History & 7-Day Average (T3)\u003C/h2\u003E","\u003Ch2\u003ESearch Optimization History & 7-Day Average (T3)\u003C/h2\u003E","\u003Ch2\u003ESnowpipe History & 7-Day Average (T3)\u003C/h2\u003E","\u003Ch2\u003EReplication History & 7-Day Average 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href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-86c84d4f7a","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatus\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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