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Amazon Redshift demo in Mode
March 5, 2019 · Refreshed 2 months ago

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SELECT DATE_TRUNC('day',o.occurred_at) AS date, r.name AS region, we.channel, SUM(o.gloss_qty) AS gloss_units, SUM(o.poster_qty) AS poster_units, SUM(o.standard_qty) AS standard_units, SUM(o.total_amt_usd) AS total_sales_usd, (SUM(o.gloss_qty) + SUM(o.poster_qty)) / NULLIF(SUM(o.total),0)::float AS non_standard_sales FROM demo_accounts a JOIN demo_orders o ON o.account_id = a.id AND o.occurred_at >= '2016-01-01' AND o.occurred_at < '2017-01-01' JOIN demo_sales_reps sr ON a.sales_rep_id = sr.id JOIN demo_region r ON sr.region_id = r.id JOIN demo_web_events_new we ON we.account_id = o.account_id AND o.occurred_at BETWEEN we.occurred_at AND we.occurred_at + interval '31 minutes' GROUP BY 1, 2, 3 ORDER BY 1
SELECT DATE_TRUNC('day',o.occurred_at) AS date, r.name AS region, we.channel, SUM(o.gloss_qty) AS gloss_units, SUM(o.poster_qty) AS poster_units, SUM(o.standard_qty) AS standard_units, SUM(o.total_amt_usd) AS total_sales_usd, (SUM(o.gloss_qty) + SUM(o.poster_qty)) / NULLIF(SUM(o.total),0)::float AS non_standard_sales FROM demo_accounts a JOIN demo_orders o ON o.account_id = a.id AND o.occurred_at >= '2016-01-01' AND o.occurred_at < '2017-01-01' JOIN demo_sales_reps sr ON a.sales_rep_id = sr.id JOIN demo_region r ON sr.region_id = r.id JOIN demo_web_events_new we ON we.account_id = o.account_id AND o.occurred_at BETWEEN we.occurred_at AND we.occurred_at + interval '31 minutes' GROUP BY 1, 2, 3 ORDER BY 1
/* This query reports the total sales amount by brand for a specific manufacturer (manufacturer ID 463) over time. Its performance is affected by the three table JOINs, GROUP BY and WHERE clauses. */ SELECT dt.d_date, item.i_brand_id brand_id, item.i_brand brand, sum(ss_ext_sales_price) sum_agg FROM date_dim dt, store_sales, item WHERE dt.d_date_sk = store_sales.ss_sold_date_sk AND store_sales.ss_item_sk = item.i_item_sk AND item.i_manufact_id = 436 GROUP BY dt.d_date, item.i_brand, item.i_brand_id ORDER BY dt.d_date, sum_agg DESC, brand_id
/* This query reports the total sales amount by brand for a specific manufacturer (manufacturer ID 463) over time. Its performance is affected by the three table JOINs, GROUP BY and WHERE clauses. */ SELECT dt.d_date, item.i_brand_id brand_id, item.i_brand brand, sum(ss_ext_sales_price) sum_agg FROM date_dim dt, store_sales, item WHERE dt.d_date_sk = store_sales.ss_sold_date_sk AND store_sales.ss_item_sk = item.i_item_sk AND item.i_manufact_id = 436 GROUP BY dt.d_date, item.i_brand, item.i_brand_id ORDER BY dt.d_date, sum_agg DESC, brand_id
/* This query reports total daily sales amount for a specific manufacturer (manufacturer ID 463). Its performance is affected by the three table JOINs, GROUP BY and WHERE clauses. */ SELECT dt.d_date, sum(ss_ext_sales_price) sum_agg FROM date_dim dt, store_sales , item WHERE dt.d_date_sk = store_sales.ss_sold_date_sk AND store_sales.ss_item_sk = item.i_item_sk AND item.i_manufact_id = 436 GROUP BY dt.d_date ORDER BY dt.d_date DESC
/* This query reports total daily sales amount for a specific manufacturer (manufacturer ID 463). Its performance is affected by the three table JOINs, GROUP BY and WHERE clauses. */ SELECT dt.d_date, sum(ss_ext_sales_price) sum_agg FROM date_dim dt, store_sales , item WHERE dt.d_date_sk = store_sales.ss_sold_date_sk AND store_sales.ss_item_sk = item.i_item_sk AND item.i_manufact_id = 436 GROUP BY dt.d_date ORDER BY dt.d_date DESC
/* This table contains data about events in the Acme Co. webapp. */ SELECT * FROM demo_company_webapp
/* This table contains data about events in the Acme Co. webapp. */ SELECT * FROM demo_company_webapp
SELECT lat AS lat, lng AS lng FROM demo_company_locations
SELECT lat AS lat, lng AS lng FROM demo_company_locations
<style> tab1 { padding-left: 2em; } body { background: #eee; min-height: 520px; } button { font-family: consolas, courier, monaco, menlo, monospace; } .ole, .elo { background: rgb(200, 240, 255); padding: 6px 36px; display: inline-block; font-size: 1.2em; border-radius: 4px; border: 0; cursor: pointer; color: #000; } .ct { text-align: center } .two { background: rgb(180, 220, 255); } .three { background: rgb(160, 200, 255); } .four { background: rgb(140, 180, 255); } .ole:hover, .elo:hover { background: dodgerblue; color: #fff; text-shadow: 1px 1px 1px #000; box-shadow: 0 0 0 #555; } .multiple_elements { padding: 30px 0 10px 0; text-align: center; } /*customizing tooltip color*/ /*right tooltip*/ .tooltip.right>.tooltip-arrow { border-right-color: dodgerblue; } /*bottom tooltip*/ .tooltip.bottom>.tooltip-arrow { border-bottom-color: dodgerblue; } /*top tooltip*/ .tooltip.top>.tooltip-arrow { border-top-color: dodgerblue; } /*left tooltip*/ .tooltip.left>.tooltip-arrow { border-left-color: dodgerblue; } /*tooltip inner*/ .tooltip>.tooltip-inner { background-color: dodgerblue; text-shadow: 0 1px 1px #000; font-weight: normal; } code { color: royalblue; background-color: azure; } @media (max-width:640px) { .ole, .elo { padding: 6px 12px; font-size: 12px; } } </style> <script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.5.10/clipboard.min.js"></script> <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script> <script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script> <link href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css" rel="stylesheet"> <link rel="stylesheet" href="https://mode.github.io/alamode/alamode.min.css"> <link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css"> <script src="https://cdn.rawgit.com/mode/playbook/master/assets/leaflet-heatmap.js"></script> <script src="https://mode.github.io/alamode/alamode.min.js"></script> <link rel="stylesheet" href="https://mode.github.io/alamode/alamode.min.css"> <script src="https://mode.github.io/alamode/alamode.min.js"></script> <div class="mode-header embed-hidden"> <h1>{{ title }}</h1> <p>{{ description }}</p> </div> <div class="mode-grid container"> <div class="row"> <div class="col-md-12"> <mode-text id="text_0f9b1fe5-e4a8-4ca6-ca03-649d3a8c7779" dataset="dataset" options="text_options"> <h2>Welcome! 👋🏽</h2> <p><br></p> <p>The charts below were produced using the <a href="http://www.tpc.org/tpcds/" target="_blank">TPC-DS performance benchmark</a> dataset and a few other publicly-available datasets. They query a demo Redshift instance using <a href="https://modeanalytics.com/" target="_blank">Mode</a>, a free analytics platform that connects SQL, Python, R, and built-in charts.</p> <p><br></p> <p><br></p> <p><br></p> <h5>WANT TO TRY OUT A DEMO REDSHIFT CLUSTER YOURSELF?</h5> <p>Skip to the bottom of this page for a few simple steps to get set up in Redshift and Mode in 15 minutes. Then you can try running these queries for yourself, or running your own queries against these datasets.</p> </mode-text> </div> </div> <div class="row"> <div class="col-md-12"> <mode-text id="text_be766f66-4f2a-4966-bc1b-e92d60c44dbd" dataset="dataset" options="text_options"> <p><span style="background-color: transparent;">Once your free cluster is available and connected in your free Mode account, simply click the ‘+’ icon in Mode to create a new query, and copy and paste the SQL here into the editor, to run it. You’ll see how fast the queries run against these common benchmarking datasets.</span></p> <p><br></p> <p><br></p> </mode-text> </div> </div> <div class="mode-grid container"> <button type="button" class="btn btn-primary elo" data-toggle="popover" data-placement="right" data-original-title="Chart SQL" data-content="<p><b>SELECT</b><br/><tab1> </tab1>dt.d_date,<br/> <tab1> </tab1>item.i_brand_id,<br/> <tab1> </tab1>brand_id,<br/> <tab1> </tab1>item.i_brand brand,<br/> <tab1> </tab1>sum(ss_ext_sales_price) sum_agg <br/> <b>FROM</b> <br/> <tab1> </tab1> date_dim dt,<br/> <tab1> </tab1> store_sales,<br/> <tab1> </tab1> item<br/> <b>WHERE</b> <br/> <tab1> </tab1> dt.d_date_sk = store_sales.ss_sold_date_sk <br/> <tab1> </tab1> <b>AND</b> store_sales.ss_item_sk = item.i_item_sk <br/> <tab1> </tab1> <b>AND</b> item.i_manufact_id = 436 <br/> <b>GROUP BY</b><br/> <tab1> </tab1> dt.d_date, <br/> <tab1> </tab1> item.i_brand, <br/> <tab1> </tab1> item.i_brand_id <br/> <b>ORDER BY</b> <br/> <tab1> </tab1> dt.d_date, <br/> <tab1> </tab1> sum_agg DESC, <br/> <tab1> </tab1> brand_id</p>"> ? </button> <div class="row"> <div class="col-md-12"> <mode-chart id="chart_4b13f4e56e52" dataset="dataset" options="chart_options"></mode-chart> </div> </div> <div class="row"> <div class="col-md-12"> <mode-text id="text_87107615-7371-44e0-8677-6ae0fe32d9ec" dataset="dataset" options="text_options"> <h3>What is this? ⬆</h3> <p><br></p> <p>This stacked bar chart is based on <a href="http://www.tpc.org/tpc_documents_current_versions/pdf/tpc-ds_v2.10.0.pdf" target="_blank">query 6</a> from the TCP-DS set, and shows total sales during a given month, broken down by item.</p> <p><br></p> </mode-text> </div> </div> <div class="row" data-row-height="medium"> <div class="col-md-6"> <mode-text id="text_17f95b83-64be-46da-e46f-5cf3e9a66f12" dataset="dataset" options="text_options"> <p><br></p> <p><br></p> <p><img src="data:image/png;base64,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"></p> </mode-text> </div> <div class="col-md-6"> <mode-text id="text_1d3385f5-991a-4d91-bc85-786f95e7e20a" dataset="dataset" options="text_options"> <h4>EXPLORE THE CHART</h4> <p><br></p> <p>For a more granular look at this data, click on a segment of the chart above. You can drilldown from the monthly view to a view by weekday.</p> <p><br></p> <p><br></p> <p>Or, roll the data up into quarterly or yearly view by clicking on the minus button in the bottom right corner. 📊</p> <p><br></p> </mode-text> </div> </div> <div class="row"> <div class="col-md-12"> <mode-text id="text_9d0b5be2-61df-4d22-8b46-18c6c6940c83" dataset="dataset" options="text_options"> <h4>EXPLORE THE SQL</h4> <p>To see the SQL code yourself, click the question mark above the chart.</p> <p><br></p> </mode-text> </div> </div> <div class="row" data-row-height="medium"> <div class="col-md-6"> <mode-text id="text_a5c06484-e9b4-4c32-dd59-c9797d78671f" dataset="dataset" options="text_options"> <h3>Now, explore another chart + query ➡</h3> <p><br></p> <p>This big number is based on TCP-DS query 3 and reports the total daily sales.</p> <p><br></p> <p>The trendline at the bottom of the chart shows fluctuations in daily sales over time.</p> <p><br></p> <p>To learn to build these charts yourself in Mode, visit the <a href="https://community.modeanalytics.com/gallery/big-number-sparkline/" target="_blank">Gallery</a>.</p> <p><br></p> </mode-text> </div> <div class="mode-grid container"> <button type="button" class="btn btn-primary elo" data-toggle="popover" data-placement="right" data-original-title="Chart SQL" data-content="<p><b>SELECT</b><br/><tab1> </tab1>dt.d_date,<br/> <tab1> </tab1>sum(ss_ext_sales_price) sum_agg <br/> <b>FROM</b> <br/> <tab1> </tab1> date_dim dt,<br/> <tab1> </tab1> store_sales,<br/> <tab1> </tab1> item<br/> <b>WHERE</b> <br/> <tab1> </tab1> dt.d_date_sk = store_sales.ss_sold_date_sk <br/> <tab1> </tab1> <b>AND</b> store_sales.ss_item_sk = item.i_item_sk <br/> <tab1> </tab1> <b>AND</b> item.i_manufact_id = 436 <br/> <b>GROUP BY</b><br/> <tab1> </tab1> dt.d_date, <br/> <b>ORDER BY</b> <br/> <tab1> </tab1> dt.d_date DESC <br/>"> ? </button> <div class="col-md-6"> <mode-chart id="chart_8793f8f4a960" dataset="dataset" options="chart_options"></mode-chart> </div> </div> <div class="row"> <div class="col-md-12"> </div> </div> <div class="row" data-row-height="small"> <div class="col-md-12"> <mode-text id="text_7dfe6623-2f1b-41d8-e6f8-660143bf9ad2" dataset="dataset" options="text_options"> <h3>Can I make a map? 🌎</h3> <p><br></p> <p>You sure can!</p> <p><br></p> <p>The heat map below displays where user activity of Acme App occurs across the United States. Zoom in or out with your mouse to inspect different locations.</p> <p><br></p> <p>To learn to build maps yourself in Mode, check out <a href="https://community.modeanalytics.com/gallery/geographic-heat-map/" target="_blank">this gallery page</a>.</p> <p><br></p> </mode-text> </div> </div> <div class="col-md-12"> <div id="map"></div> </div> <div class="row" data-row-height="small"> <div class="col-md-12"> <mode-text id="text_51f2295f-0f4f-49e8-9b54-ed4ceec59af1" dataset="dataset" options="text_options"> <h3>OK, let's see some custom visualizations! ⬇</h3> <p><br></p> <p>To follow your users' journey through your application, you can create a custom sunburst chart.</p> <p><br></p> <p>The chart below visualizes user journeys through the Acme App. Hover over the circles by moving outwards from the center to see what percentage of your app's users take a given journey.</p> <p><br></p> </mode-text> </div> </div> <div class="row"> <div class="col-md-12"> <div id="sunburst"></div> </div> </div> <div class="row"> <div class="col-md-12"> <mode-text id="text_fa3d4381-ec24-48ed-982e-3ff8f195a6ee" dataset="dataset" options="text_options"> <h3>Ready to try it yourself? 📈</h3> <p>Want to explore how Redshift performs with the TCP-DS dataset yourself? Try out Redshift by using Mode right in your browser:</p> <p><br></p> <p>1) <a href="https://drive.google.com/file/d/0B3_s3OEZV-4YZ3k0X0VCSjRVTkZyTmZTUEdOLUg4cmYtdnVF/view?usp=sharing" target="_blank">Launch a free Redshift cluster</a> already loaded with the TCP-DS data. Creating the cluster should take 5-10 minutes.</p> <p><br></p> <p>2) While you wait, <a href="https://modeanalytics.com/signup" target="_blank">sign up</a> for a free Mode account.</p> <p><br></p> <p>3) When prompted to connect a database, select "Amazon Redshift" and enter the credentials for Redshift provided in the AWS console (<a href="https://docs.google.com/document/d/1iV6zJWZZTKCBnALfsaS8e0frcst95Z9vlG9DrlvfqK0/edit?usp=sharing" target="_blank">this</a> document illustrates where to get them).</p> <p><br></p> <p>That's it! Now you can use Mode's SQL editor to query TCP-DS yourself, right in your browser. For a quick overview of how Mode works, check out <a href="https://mode.com/help/articles/reports/" target="_blank">this doc</a>--you'll be off and running in no time.</p> <p><br></p> <p>Happy analyzing!</p> </mode-text> </div> </div> </div> <script> alamode.bigNumberSparkline({ "chart_id": "chart_8793f8f4a960", "query_name": "Total daily sales", "value_column": "sum_agg", "x_axis_column": "d_date" }) </script> <script> alamode.leafletMap({ title: "User Locations", html_element: "#map", height: 500, lat_column: "lat", lng_column: "lng", label_column: "city", query_name: "User locations", center_lat: 34, center_lng: -115, starting_zoom: 5, dot_size: .4, dot_opacity: .7 }) </script> <script> alamode.sunburstChart({ query_name: "Webapp events", html_element: "#sunburst", title: "How users move through the Web Application", event_columns: ["e1", "e2", "e3", "e4", "e5"], event_counts: "occurances" }) </script> <script> $(document).ready(function() { $("body").tooltip({ selector: "[data-toggle='tooltip']", container: "body" }) .popover({ selector: "[data-toggle='popover']", container: "body", html: true }); }); </script> </div> </div>
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