2b:null 2c:["$","title",null,{"children":"SQL Course: Session 4 | guypporter | Observable"}] 31:T76f,testYourKnowledge([ { title: 'Window functions', backText: [`A window function is a SQL function where the input values are taken from a “window” of one or more rows in the results set of a SELECT statement.`, `A window can be defined by the number of rows (preceding and following), a date range (interval) or by a condition (partition by)`], question: "Window functions allow you to look at a window of records, whether by the number of rows, a date range, by some condition.", answer: true }, { title: 'WITH statements', backText: ['WITH statements are easy to write. All you have to do is use the WITH expression, provide a name to your temporary table, then write the query in parenthesis.', 'WITH statements are helpful for breaking up complicated queries into smaller parts. It can help make them more readable.'], question: 'WITH statements are difficult to write, and should be used sparingly.', answer: false }, { title: 'Query Optimization', backText: [`Writing optimal queries is very important! One, it can save you time to have a query that's better written, as it'll reduce the runtime, two, it can save money as some SQL services charge by the runtime.`, `It's also better for readability and shareability to have optimal queries, and when you're working at a large organization, those differences matter.`], question: `It doesn't matter how you write the query, as long as you return the correct results.`, answer: false }, { title: 'SQL Dialects', backText: [`Most SQL dialects are very similar. They have the same clauses (SELECT, FROM, WHERE, etc.) and many of the same functions.`, `To note, most differences in SQL dialects will be between the functions they have. For example, SQLite have very few functions compared to Snowflake.`], question: `Most SQL dialects are very similar.`, answer: true }, ], 300, 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2f:{"id":"376e978b65e4d317","slug":null,"title":"SQL Course: Session 4","thumbnail":null,"access_level":"public","create_time":"$D2023-07-14T14:52:11.093Z","update_time":"$D2023-07-14T14:52:11.093Z","view_time":null,"first_public_time":"$D2023-07-14T14:52:11.093Z","has_unpublished_edits":null,"fork":{"id":"987695cd750036f0","slug":"sql-course-session-4","title":"SQL Course: Session 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Course: Session 4","cells":[{"id":0,"value":"# SQL Course: Session 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"- [Session 4 slide deck](https://docs.google.com/presentation/d/16BYbJA9se4dmWJ-okvxrg3MkCyrSZ1rQzqEuCtFunLQ/edit?usp=sharing)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"For the hands-on portion of this course, we'll use the [Pizza Paradise](https://observablehq.com/@observablehq/pizza-paradise-data) dataset. To improve performance, I've recreated the database in this notebook.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"pizzeriaDB = DuckDBClient.of({\n orders: FileAttachment(\"orders-1.parquet\"),\n stores: FileAttachment(\"stores-1.csv\"),\n customers: FileAttachment(\"customers-1.csv\"),\n products: FileAttachment(\"products-1.csv\"),\n order_items: FileAttachment(\"orderItems-1.parquet\"),\n // Views\n orders_by_product_day: FileAttachment(\"orders_by_product_day.csv\"),\n california_stores: FileAttachment(\"california_stores.csv\"),\n nevada_stores: FileAttachment(\"nevada_stores.csv\"),\n california_daily_orders: FileAttachment(\"california_daily_orders.csv\"),\n nevada_daily_orders: FileAttachment(\"nevada_daily_orders.csv\")\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"note(md`__Note:__ This is a large, realistic mock dataset, so it may take a few seconds for it to load in your notebook.`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"---\n## Activity 1\n
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":47,"value":"### Window Functions\n- Create a row number for each product partitioned by category and ordered by the launch date.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":149,"value":"SELECT row_number() over (partition by Category order by Launch) as \"Row Number\"\n , *\n FROM products ","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:pizzeriaDB"},{"id":143,"value":"- Rank products by category based on the number of orders.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":151,"value":"SELECT products.name\n , products.size\n , products.category \n , count(distinct orders.id) as \"Number of Orders\"\n , rank() over (partition by products.category order by count(distinct orders.id) DESC) as \"Order Rank\"\n FROM products \n JOIN order_items\n ON products.SKU = order_items.SKU\n JOIN orders\n ON order_items.orderId = orders.id \n GROUP BY 1, 2, 3\n","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:pizzeriaDB"},{"id":202,"value":"- Write a query that finds the first products orders by each year.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":221,"value":"SELECT products.name\n , products.size\n , products.category \n , date_part('year', orders.orderDate::date)::string as year_name\n , row_number() over (partition by year_name order by orderDate asc) as row_num\n FROM products \n JOIN order_items\n ON products.SKU = order_items.SKU\n JOIN orders\n ON order_items.orderId = orders.id \n ORDER BY row_num, year_name \n LIMIT 3","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:pizzeriaDB"},{"id":12,"value":"---\n## Activity 2\n
\n\n### WITH statements\n- Write a query that creates WITH statement for Utah stores, and count sales by month for just Utah stores, filtering with a JOIN.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":210,"value":"WITH utahStores AS (\n SELECT *\n FROM stores\n WHERE state = 'Utah'\n)\n\n SELECT date_trunc('month', orders.orderDate::date) as \"Month Name\"\n , count(distinct orders.id) as \"Number of Orders\"\n FROM orders\n JOIN utahStores\n ON orders.storeId = utahStores.id\n GROUP BY 1","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:pizzeriaDB"},{"id":95,"value":"- Make a 7-day moving average of the number of Hawaiian pizza orders, using a WITH statement to pre-aggregate orders by day.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":98,"value":"WITH hawaiianOrders AS (\n SELECT orders.orderDate::date as order_date\n , count(distinct orders.id) as num_orders\n FROM products \n JOIN order_items\n ON products.SKU = order_items.SKU\n JOIN orders\n ON order_items.orderId = orders.id \n WHERE products.name like '%Hawaiian%'\n GROUP BY 1\n ORDER BY 1 ASC\n), returnResults AS (\n SELECT *\n , avg(num_orders) over (\n order by order_date asc\n range between interval 3 days preceding \n and interval 3 days following\n ) as moving_avg\n FROM hawaiianOrders\n)\n\nSELECT * \n FROM returnResults\n","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:pizzeriaDB"},{"id":13,"value":"---\n## Activity 3\n
\n- Optimize this query following the simple principles: filter data early, create subsets with WITH, use LEFT JOINs only when necessary, be careful for duplicate rows.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":235,"value":"WITH newData AS (\n SELECT orders.id\n , products.name\n , products.Size\n FROM orders\n JOIN order_items \n ON orders.id = order_items.orderId\n JOIN products\n ON order_items.SKU = products.SKU\n WHERE Category = 'Vegetarian'\n \n)\n\nSELECT name\n , Size\n , count(distinct id)\n FROM newData\n GROUP BY 1, 2","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:pizzeriaDB"},{"id":242,"value":"","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:pizzeriaDB"},{"id":16,"value":"---\n## Wrap Up\nTest your knowledge with these short questions.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":17,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":19,"value":"

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":20,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"import {note} from \"@observablehq/callout-boxes\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"import { showMe } from \"@observablehq/show-me\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"import {testYourKnowledge} from \"@observablehq/test-your-knowledge-cards\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"SELECT avg(amount) as avg_amount\n FROM groceries\n WHERE category = 'Fruits'","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"var:groceries"},{"id":51,"value":"groceries = [\n{category: 'Fruits', value: 'Apple', amount: 10},\n{category: 'Fruits', value: 'Orange', amount: 6},\n{category: 'Fruits', value: 'Pear', amount: 2},\n{category: 'Fruits', value: 'Banana', amount: 20},\n{category: 'Vegetables', value: 'Corn', amount: 9},\n{category: 'Vegetables', value: 'Potato', amount: 15},\n{category: 'Vegetables', value: 'Artichoke', amount: 5},\n{category: 'Vegetables', value: 'Leek', amount: 2},\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"color = d3.schemeBlues[7]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W32","stdlib":"1"}} 30:"$2f:body"