{"id":4839,"date":"2020-04-19T19:17:28","date_gmt":"2020-04-19T19:17:28","guid":{"rendered":"https:\/\/www.testpreptraining.com\/tutorial\/?page_id=4839"},"modified":"2020-04-19T19:17:28","modified_gmt":"2020-04-19T19:17:28","slug":"optimize-for-performance-and-cost-google-professional-data-engineer-gcp","status":"publish","type":"page","link":"https:\/\/www.testpreptraining.ai\/tutorial\/optimize-for-performance-and-cost-google-professional-data-engineer-gcp\/","title":{"rendered":"Optimize for Performance  and Cost Google Professional Data Engineer GCP"},"content":{"rendered":"<p><strong>Partitioned table<\/strong><\/p>\n<ul>\n<li>a special table that is divided into segments, called partitions,<\/li>\n<li>make it easier to manage and query data.<\/li>\n<li>By dividing a large table improve query performance, and control costs<\/li>\n<li>partition tables by:\n<ul>\n<li>Ingestion time<\/li>\n<li>Date\/timestamp<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><strong>Clustered tables<\/strong><\/p>\n<ul>\n<li>table data is automatically organized based on the contents of one or more columns in the table\u2019s schema.<\/li>\n<li>The columns specified are used to colocate related data.<\/li>\n<li>order of columns is important for the sort order of the data.<\/li>\n<li>Clustering improve the performance of queries<\/li>\n<li>During write data is sorted using the values in clustering columns.<\/li>\n<li>clustering is allowed over a partitioned table.<\/li>\n<li>Use clustering if<\/li>\n<li>data is already partitioned on a date, timestamp, or integer column.<\/li>\n<li>using filters or aggregation against particular columns in queries.<\/li>\n<li>clustering is based on ingestion time\n<ul>\n<li>date\/timestamp<\/li>\n<li>integer range<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Views<\/strong><\/p>\n<ul>\n<li>a virtual table defined by a SQL query.<\/li>\n<li>A view is queried it in the same way you query a table.<\/li>\n<li>query results of view contain data only from the tables and fields specified in the query that defines the view.<\/li>\n<li>can query views in BigQuery by\n<ul>\n<li>Query editor box in the Cloud Console<\/li>\n<li>Compose Query option in the classic BigQuery web UI<\/li>\n<li>BigQuery command-line tool&#8217;s bq query command<\/li>\n<li>BigQuery REST API to programmatically call the jobs.query or query-type jobs.insert methods<\/li>\n<li>BigQuery client libraries<\/li>\n<\/ul>\n<\/li>\n<li>Also use view as a data source for a visualization<\/li>\n<li>views limitations\n<ul>\n<li>The dataset that contains view and the dataset that contains the tables referenced by the view must be in the same location.<\/li>\n<li>cannot run a BigQuery job that exports data from a view.<\/li>\n<li>cannot use the TableDataList JSON API method to retrieve data from a view.<\/li>\n<li>cannot reference query parameters in views.<\/li>\n<li>If schema change after view is created, the reported schema will be inaccurate<\/li>\n<li>cannot include a user-defined function in the SQL query that defines a view.<\/li>\n<li>cannot reference a view in a wildcard table query.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Reservations<\/strong><\/p>\n<ul>\n<li>two pricing models<\/li>\n<li>On-demand pricing: You pay only for the data scanned by queries.<\/li>\n<li>Flat-rate pricing: Offers predictable and consistent month-to-month costs.<\/li>\n<li>By default, billed as per on-demand pricing model.<\/li>\n<li>With BigQuery Reservations, can switch to the flat-rate pricing model by purchasing commitments<\/li>\n<li>commitments are dedicated portions of query processing capacity measured in slots with cost of all bytes processed is included in the flat-rate price.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>INFORMATION_SCHEMA<\/strong><\/p>\n<ul>\n<li>For on-demand pricing, queries against INFORMATION_SCHEMA views 10 MB is the minimum billing amount<\/li>\n<li>For flat-rate pricing, queries against INFORMATION_SCHEMA views and tables consume purchased BigQuery slots.<\/li>\n<li>INFORMATION_SCHEMA queries are not cached<\/li>\n<li>charged each time you run an INFORMATION_SCHEMA query,<\/li>\n<li>not charged storage fees for the INFORMATION_SCHEMA views.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Partitioned table a special table that is divided into segments, called partitions, make it easier to manage and query data. By dividing a large table improve query performance, and control costs partition tables by: Ingestion time Date\/timestamp Clustered tables table data is automatically organized based on the contents of one or more columns in the&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[617],"tags":[706,711,619,623,622,618,621],"class_list":["post-4839","page","type-page","status-publish","hentry","category-google-gcp","tag-bigquery","tag-bigquery-optimize-for-performance-and-cost","tag-data-engineer","tag-gcp","tag-google-certification","tag-google-cloud","tag-professional-data-engineer"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Optimize for Performance and Cost Google Professional Data Engineer GCP - Testprep Training Tutorials<\/title>\n<meta name=\"description\" content=\"Google Cloud Certified Professional Data Engineer Tutorial, dumps, brief notes on Optimize for Performance and Cost\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.testpreptraining.ai\/tutorial\/optimize-for-performance-and-cost-google-professional-data-engineer-gcp\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Optimize for Performance and Cost Google Professional Data Engineer GCP - 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