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Troubleshooting Common SQL Errors with BigQuery

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Troubleshooting Common SQL Errors with BigQuery

50 minutes Free

GSP408

Google Cloud self-paced labs logo

Overview

BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL, and you can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

A newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store has been loaded into BigQuery. You have a copy of that dataset for this lab and will explore the available fields and row for insights.

This lab steps you through the logic of troubleshooting queries. It provides activities within the context of a real-world scenario. Throughout the lab, imagine you're working with a new data analyst on your team, and they've provided you with their queries below to answer some questions on your ecommerce dataset. Use the answers to fix their queries to get a meaningful result.

What you'll learn

In this lab, learn how to perform the following tasks:

  • Query the data-to-insights public dataset

  • Use the BigQuery Query editor to troubleshoot common SQL errors

  • Use the Query Validator

  • Troubleshoot syntax and logical SQL errors

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google Console button
    • Time remaining
    • The temporary credentials that you must use for this lab
    • Other information, if needed, to step through this lab
  2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Tip: Arrange the tabs in separate windows, side-by-side.

    Note: If you see the Choose an account dialog, click Use Another Account.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

Task 1. Open BigQuery and Pin a project to the Resource tree

  1. Click Navigation menu Navigation menu icon > BigQuery.

The Welcome to BigQuery in the Cloud Console message box opens.

Note: The Welcome to BigQuery in the Cloud Console message box provides a link to the quickstart guide and UI updates.
  1. Click Done.

  2. BigQuery public datasets are not displayed by default. To open the public datasets project, copy data-to-insights.

  3. Click Add Data > Star a project by name then paste the data-to-insights name.

The data-to-insights project is listed in the Explorer section.

Task 2. BigQuery Query editor

For each activity in the following sections, this lab provides queries with common errors for you to troubleshoot. The lab directs you what to look at and suggests how to correct the syntax and return meaningful results.

To follow along with the troubleshooting and suggestions, copy and paste the query into the BigQuery Query editor. If there are errors, you see a red exclamation point at the line containing the error and in the query validator (bottom corner).

BigQuery Query editor

If you run the query with the errors, the query fails and the error is specified in the Job information.

Query failed infobox

When the query is error free, you see a green checkmark in the query validator. When you see the green checkmark, click Run to run the query to view what you get for results.

Green checkmark in the query validator

Note: For information about syntax, see Standard SQL Query Syntax.

Task 3. Find the total number of customers who went through checkout

Your goal in this section is to construct a query that gives you the number of unique visitors who successfully went through the checkout process for your website. The data is in the rev_transactions table which your data analyst team has provided. They have also given you example queries to help you get started in your analysis but you're not sure they're written correctly.

Troubleshoot queries that contain query validator, alias, and comma errors

  • Look at the below query and answer the following question:

#standardSQL SELECT FROM `data-to-inghts.ecommerce.rev_transactions` LIMIT 1000

  • What about this updated query?

#standardSQL SELECT * FROM [data-to-insights:ecommerce.rev_transactions] LIMIT 1000

  • What about this query that uses Standard SQL?

#standardSQL SELECT FROM `data-to-insights.ecommerce.rev_transactions`

  • What about now? This query has a column:

#standardSQL SELECT fullVisitorId FROM `data-to-insights.ecommerce.rev_transactions`

  • What about now? The following query has a page title:

#standardSQL SELECT fullVisitorId hits_page_pageTitle FROM `data-to-insights.ecommerce.rev_transactions` LIMIT 1000

  • What about now? The missing comma has been corrected.

#standardSQL SELECT fullVisitorId , hits_page_pageTitle FROM `data-to-insights.ecommerce.rev_transactions` LIMIT 1000

Answer: This returns results, but are you sure visitors aren't counted twice? Also, returning only one row answers the question of how many unique visitors reached checkout. In the next section you find a way to aggregate your results.

Troubleshoot queries that contain logic errors, GROUP BY statements, and wildcard filters

  • Aggregate the following query to answer the question: How many unique visitors reached checkout?

#standardSQL SELECT fullVisitorId , hits_page_pageTitle FROM `data-to-insights.ecommerce.rev_transactions` LIMIT 1000
  • What about this? An aggregation function, COUNT(), was added:

#standardSQL SELECT COUNT(fullVisitorId) AS visitor_count , hits_page_pageTitle FROM `data-to-insights.ecommerce.rev_transactions`

  • In this next query, GROUP BY and DISTINCT statements were added:

#standardSQL SELECT COUNT(DISTINCT fullVisitorId) AS visitor_count , hits_page_pageTitle FROM `data-to-insights.ecommerce.rev_transactions` GROUP BY hits_page_pageTitle

Results Results table

Great! The results are good, but they look strange.

  • Filter to just "Checkout Confirmation" in the results:

#standardSQL SELECT COUNT(DISTINCT fullVisitorId) AS visitor_count , hits_page_pageTitle FROM `data-to-insights.ecommerce.rev_transactions` WHERE hits_page_pageTitle = "Checkout Confirmation" GROUP BY hits_page_pageTitle

Click Check my progress to verify the objective. Find the total number of customers went through checkout

Task 4. List the cities with the most transactions with your ecommerce site

Troubleshoot ordering, calculated fields, and filtering after aggregating errors

  1. Complete the partially written query:

SELECT geoNetwork_city, totals_transactions, COUNT( DISTINCT fullVisitorId) AS distinct_visitors FROM `data-to-insights.ecommerce.rev_transactions` GROUP BY

Possible solution:

#standardSQL SELECT geoNetwork_city, SUM(totals_transactions) AS totals_transactions, COUNT( DISTINCT fullVisitorId) AS distinct_visitors FROM `data-to-insights.ecommerce.rev_transactions` GROUP BY geoNetwork_city
  1. Update your previous query to order the top cities first.

Possible solution:

#standardSQL SELECT geoNetwork_city, SUM(totals_transactions) AS totals_transactions, COUNT( DISTINCT fullVisitorId) AS distinct_visitors FROM `data-to-insights.ecommerce.rev_transactions` GROUP BY geoNetwork_city ORDER BY distinct_visitors DESC
  1. Update your query and create a new calculated field to return the average number of products per order by city.

Possible solution:

#standardSQL SELECT geoNetwork_city, SUM(totals_transactions) AS total_products_ordered, COUNT( DISTINCT fullVisitorId) AS distinct_visitors, SUM(totals_transactions) / COUNT( DISTINCT fullVisitorId) AS avg_products_ordered FROM `data-to-insights.ecommerce.rev_transactions` GROUP BY geoNetwork_city ORDER BY avg_products_ordered DESC

Results

Results table

Filter your aggregated results to only return cities with more than 20 avg_products_ordered.

  • What's wrong with the following query?

#standardSQL SELECT geoNetwork_city, SUM(totals_transactions) AS total_products_ordered, COUNT( DISTINCT fullVisitorId) AS distinct_visitors, SUM(totals_transactions) / COUNT( DISTINCT fullVisitorId) AS avg_products_ordered FROM `data-to-insights.ecommerce.rev_transactions` WHERE avg_products_ordered > 20 GROUP BY geoNetwork_city ORDER BY avg_products_ordered DESC

Possible solution:

#standardSQL SELECT geoNetwork_city, SUM(totals_transactions) AS total_products_ordered, COUNT( DISTINCT fullVisitorId) AS distinct_visitors, SUM(totals_transactions) / COUNT( DISTINCT fullVisitorId) AS avg_products_ordered FROM `data-to-insights.ecommerce.rev_transactions` GROUP BY geoNetwork_city HAVING avg_products_ordered > 20 ORDER BY avg_products_ordered DESC

Click Check my progress to verify the objective. List the cities with the most transactions with your ecommerce site

Task 5. Find the total number of products in each product category

Find the top selling products by filtering with NULL values

  1. What's wrong with the following query? How can you fix it?

#standardSQL SELECT hits_product_v2ProductName, hits_product_v2ProductCategory FROM `data-to-insights.ecommerce.rev_transactions` GROUP BY 1,2

  1. What is wrong with the following query?

#standardSQL SELECT COUNT(hits_product_v2ProductName) as number_of_products, hits_product_v2ProductCategory FROM `data-to-insights.ecommerce.rev_transactions` WHERE hits_product_v2ProductName IS NOT NULL GROUP BY hits_product_v2ProductCategory ORDER BY number_of_products DESC

  1. Update the previous query to only count distinct products in each product category.

Possible solution:

#standardSQL SELECT COUNT(DISTINCT hits_product_v2ProductName) as number_of_products, hits_product_v2ProductCategory FROM `data-to-insights.ecommerce.rev_transactions` WHERE hits_product_v2ProductName IS NOT NULL GROUP BY hits_product_v2ProductCategory ORDER BY number_of_products DESC LIMIT 5

Note:
  • (not set) could indicate the product has no category
  • ${productitem.product.origCatName} is front-end code to render the category which may indicate the Google Analytics tracking script is firing before the page is fully-rendered
  • Click Check my progress to verify the objective. Find the total number of products in each product category

    Congratulations!

    You troubleshot and fixed broken queries in BigQuery standard SQL. Remember to use the Query Validator for incorrect query syntax but also to be critical of your query results even if your query executes successfully.

    Finish your quest

    This self-paced lab is part of the BigQuery Basics for Data Analysts quest. A quest is a series of related labs that form a learning path. Completing this quest earns you a badge to recognize your achievement. You can make your badge or badges public and link to them in your online resume or social media account. Enroll in this quest and get immediate completion credit. Refer to the Google Cloud Skills Boost catalog for all available quests.

    Looking for a hands-on challenge lab to demonstrate your BigQuery skills and validate your knowledge? On completing this quest, finish this additional challenge lab to receive an exclusive Google Cloud digital badge.

    Take your next lab

    Continue your Quest with the next lab, Explore and Create Reports with Data Studio.

    Check out other labs to learn more about BigQuery:

    Next steps / Learn more

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    Manual Last Updated: August 9, 2022

    Lab Last Tested: May 05, 2022

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