GSP416

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 can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

In this lab, you work in-depth with semi-structured data (ingesting JSON, Array data types) inside of BigQuery. Denormalizing your schema into a single table with nested and repeated fields can yield performance improvements, but the SQL syntax for working with array data can be tricky. You will practice loading, querying, troubleshooting, and unnesting various semi-structured datasets.

What you'll do

In this lab, you learn how to:

• Load and query semi-structured data including unnesting.
• Troubleshoot queries on semi-structured data.

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 are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials 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).

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• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that 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 dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud 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 Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   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 below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

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   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
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After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Open the BigQuery console

1. In the Google Cloud Console, select Navigation menu > BigQuery.

The Welcome to BigQuery in the Cloud Console message box opens. This message box provides a link to the quickstart guide and the release notes.

2. Click Done.

The BigQuery console opens.

Task 1. Create a new dataset to store the tables

1. In your BigQuery, click the three dots next to your Project ID and select Create dataset:

2. Name the new dataset fruit_store. Leave the other options at their default values (Data Location, Default Expiration).

3. Click Create dataset.

Task 2. Practice working with arrays in SQL

Normally in SQL you will have a single value for each row like this list of fruits below:

Row	Fruit
1	raspberry
2	blackberry
3	strawberry
4	cherry

What if you wanted a list of fruit items for each person at the store? It could look something like this:

Row	Fruit	Person
1	raspberry	sally
2	blackberry	sally
3	strawberry	sally
4	cherry	sally
5	orange	frederick
6	apple	frederick

In traditional relational database SQL, you would look at the repetition of names and immediately think of splitting the above table into two separate tables: Fruit Items and People. That process is called normalization (going from one table to many). This is a common approach for transactional databases like mySQL.

For data warehousing, data analysts often go the reverse direction (denormalization) and bring many separate tables into one large reporting table.

Now, you're going to learn a different approach that stores data at different levels of granularity all in one table using repeated fields:

Row	Fruit (array)	Person
1	raspberry	sally
	blackberry
	strawberry
	cherry
2	orange	frederick
	apple

What looks strange about the previous table?

• It's only two rows.
• There are multiple field values for Fruit in a single row.
• The people are associated with all of the field values.

What the key insight? The array data type!

An easier way to interpret the Fruit array:

Row	Fruit (array)	Person
1	[raspberry, blackberry, strawberry, cherry]	sally
2	[orange, apple]	frederick

Both of these tables are exactly the same. There are two key learnings here:

• An array is simply a list of items in brackets [ ]
• BigQuery visually displays arrays as flattened. It simply lists the value in the array vertically (note that all of those values still belong to a single row)

Try it yourself.

1. Enter the following in the BigQuery Query Editor:

#standardSQL SELECT ['raspberry', 'blackberry', 'strawberry', 'cherry'] AS fruit_array

2. Click Run.

3. Now try executing this one:

#standardSQL SELECT ['raspberry', 'blackberry', 'strawberry', 'cherry', 1234567] AS fruit_array

You should get an error that looks like the following:

Error: Array elements of types {INT64, STRING} do not have a common supertype at [3:1]

Arrays can only share one data type (all strings, all numbers).

4. Here's the final table to query against:

#standardSQL SELECT person, fruit_array, total_cost FROM `data-to-insights.advanced.fruit_store`;

5. Click Run.

6. After viewing the results, click the JSON tab to view the nested structure of the results.

Loading semi-structured JSON into BigQuery

What if you had a JSON file that you needed to ingest into BigQuery?

Create a new table fruit_details in the dataset.

1. Click on fruit_store dataset.

Now you will see the Create Table option.

Note: You may have to widen your browser window to see the Create table option.

2. Add the following details for the table:

• Source: Choose Google Cloud Storage in the Create table from dropdown.
• Select file from Cloud Storage bucket: cloud-training/data-insights-course/labs/optimizing-for-performance/shopping_cart.json
• File format: JSONL (Newline delimited JSON)

3. Call the new table fruit_details.

4. Check the checkbox of Schema (Auto detect).

5. Click Create table.

In the schema, note that fruit_array is marked as REPEATED which means it's an array.

Recap

• BigQuery natively supports arrays
• Array values must share a data type
• Arrays are called REPEATED fields in BigQuery

Click Check my progress to verify the objective. Create a new dataset and table to store our data

Task 3. Create your own arrays with ARRAY_AGG()

Don't have arrays in your tables already? You can create them!

1. Copy and paste the below query to explore this public dataset:

SELECT fullVisitorId, date, v2ProductName, pageTitle FROM `data-to-insights.ecommerce.all_sessions` WHERE visitId = 1501570398 ORDER BY date

2. Click Run and view the results.

Now, use the ARRAY_AGG() function to aggregate our string values into an array.

3. Copy and paste the below query to explore this public dataset:

SELECT fullVisitorId, date, ARRAY_AGG(v2ProductName) AS products_viewed, ARRAY_AGG(pageTitle) AS pages_viewed FROM `data-to-insights.ecommerce.all_sessions` WHERE visitId = 1501570398 GROUP BY fullVisitorId, date ORDER BY date

4. Click Run and view the results

5. Next, use the ARRAY_LENGTH() function to count the number of pages and products that were viewed:

SELECT fullVisitorId, date, ARRAY_AGG(v2ProductName) AS products_viewed, ARRAY_LENGTH(ARRAY_AGG(v2ProductName)) AS num_products_viewed, ARRAY_AGG(pageTitle) AS pages_viewed, ARRAY_LENGTH(ARRAY_AGG(pageTitle)) AS num_pages_viewed FROM `data-to-insights.ecommerce.all_sessions` WHERE visitId = 1501570398 GROUP BY fullVisitorId, date ORDER BY date

6. Next, deduplicate the pages and products so you can see how many unique products were viewed by adding DISTINCT to ARRAY_AGG():

SELECT fullVisitorId, date, ARRAY_AGG(DISTINCT v2ProductName) AS products_viewed, ARRAY_LENGTH(ARRAY_AGG(DISTINCT v2ProductName)) AS distinct_products_viewed, ARRAY_AGG(DISTINCT pageTitle) AS pages_viewed, ARRAY_LENGTH(ARRAY_AGG(DISTINCT pageTitle)) AS distinct_pages_viewed FROM `data-to-insights.ecommerce.all_sessions` WHERE visitId = 1501570398 GROUP BY fullVisitorId, date ORDER BY date

Click Check my progress to verify the objective. Execute the query to see how many unique products were viewed

Recap

You can do some pretty useful things with arrays like:

• finding the number of elements with ARRAY_LENGTH(<array>)
• deduplicating elements with ARRAY_AGG(DISTINCT <field>)
• ordering elements with ARRAY_AGG(<field> ORDER BY <field>)
• limiting ARRAY_AGG(<field> LIMIT 5)

Task 4. Query tables containing arrays

The BigQuery Public Dataset for Google Analytics bigquery-public-data.google_analytics_sample has many more fields and rows than our course dataset data-to-insights.ecommerce.all_sessions. More importantly, it already stores field values like products, pages, and transactions natively as ARRAYs.

1. Copy and paste the below query to explore the available data and see if you can find fields with repeated values (arrays):

SELECT * FROM `bigquery-public-data.google_analytics_sample.ga_sessions_20170801` WHERE visitId = 1501570398

2. Run the query.

3. Scroll right in the results until you see the hits.product.v2ProductName field (multiple field aliases are discussed shortly).

The amount of fields available in the Google Analytics schema can be overwhelming for analysis.

4. Try to query just the visit and page name fields like before:

SELECT visitId, hits.page.pageTitle FROM `bigquery-public-data.google_analytics_sample.ga_sessions_20170801` WHERE visitId = 1501570398

You will get an error: Error:Cannot access field page on a value with type ARRAY<STRUCT<hitNumber INT64, time INT64, hour INT64, ...>> at [3:8]

Before you can query REPEATED fields (arrays) normally, you must first break the arrays back into rows.

For example, the array for hits.page.pageTitle is stored currently as a single row like:

['homepage','product page','checkout']

and it needs to be:

['homepage', 'product page', 'checkout']

How do you do that with SQL?

Answer: Use the UNNEST() function on your array field:

SELECT DISTINCT visitId, h.page.pageTitle FROM `bigquery-public-data.google_analytics_sample.ga_sessions_20170801`, UNNEST(hits) AS h WHERE visitId = 1501570398 LIMIT 10

We'll cover UNNEST() more in detail later but for now just know that:

• You need to UNNEST() arrays to bring the array elements back into rows
• UNNEST() always follows the table name in your FROM clause (think of it conceptually like a pre-joined table)

Click Check my progress to verify the objective. Execute the query to use the UNNEST() on array field

Task 5. Introduction to STRUCTs

You may have wondered why the field alias hit.page.pageTitle looks like three fields in one separated by periods. Just as ARRAY values give you the flexibility to go deep into the granularity of your fields, another data type allows you to go wide in your schema by grouping related fields together. That SQL data type is the STRUCT data type.

The easiest way to think about a STRUCT is to consider it conceptually like a separate table that is already pre-joined into your main table.

A STRUCT can have:

• One or many fields in it
• The same or different data types for each field
• It's own alias

Sounds just like a table right?

Explore a dataset with STRUCTs

1. To open the bigquery-public-data dataset, click +ADD and then select Star a project by name and enter the name bigquery-public-data

2. Click Star.

The bigquery-public-data project is listed in the Explorer section.

3. Open bigquery-public-data.

4. Find and open google_analytics_sample dataset.

5. Click the ga_sessions_ (366) table.

6. Start scrolling through the schema and answer the following question by using the find feature of your browser.

As you can imagine, there is an incredible amount of website session data stored for a modern ecommerce website.

The main advantage of having 32 STRUCTs in a single table is it allows you to run queries like this one without having to do any JOINs:

SELECT visitId, totals.*, device.* FROM `bigquery-public-data.google_analytics_sample.ga_sessions_20170801` WHERE visitId = 1501570398 LIMIT 10 Note: The .* syntax tells BigQuery to return all fields for that STRUCT (much like it would if totals.* was a separate table we joined against).

Storing your large reporting tables as STRUCTs (pre-joined "tables") and ARRAYs (deep granularity) allows you to:

• Gain significant performance advantages by avoiding 32 table JOINs
• Get granular data from ARRAYs when you need it but not be punished if you don't (BigQuery stores each column individually on disk)
• Have all the business context in one table as opposed to worrying about JOIN keys and which tables have the data you need

Task 6. Practice with STRUCTs and arrays

The next dataset will be lap times of runners around the track. Each lap will be called a "split".

1. With this query, try out the STRUCT syntax and note the different field types within the struct container:

#standardSQL SELECT STRUCT("Rudisha" as name, 23.4 as split) as runner

Row	runner.name	runner.split
1	Rudisha	23.4

What do you notice about the field aliases? Since there are fields nested within the struct (name and split are a subset of runner) you end up with a dot notation.

What if the runner has multiple split times for a single race (like time per lap)?

With an array of course!

2. Run the below query to confirm:

#standardSQL SELECT STRUCT("Rudisha" as name, [23.4, 26.3, 26.4, 26.1] as splits) AS runner

Row	runner.name	runner.splits
1	Rudisha	23.4
		26.3
		26.4
		26.1

To recap:

• Structs are containers that can have multiple field names and data types nested inside.
• Arrays can be one of the field types inside of a Struct (as shown above with the splits field).

Practice ingesting JSON data

1. Create a new dataset titled racing.

2. Click on racing dataset and click Create table.

Note: You may have to widen your browser window to see the Create table option.

• Source: select Google Cloud Storage under Create table from dropdown.
• Select file from Cloud Storage bucket: cloud-training/data-insights-course/labs/optimizing-for-performance/race_results.json
• File format: JSONL (Newline delimited JSON)
• In Schema, click on Edit as text slider and add the following:

[ { "name": "race", "type": "STRING", "mode": "NULLABLE" }, { "name": "participants", "type": "RECORD", "mode": "REPEATED", "fields": [ { "name": "name", "type": "STRING", "mode": "NULLABLE" }, { "name": "splits", "type": "FLOAT", "mode": "REPEATED" } ] } ]

3. Call the new table race_results.

4. Click Create table.

5. After the load job is successful, preview the schema for the newly created table:

Which field is the STRUCT? How do you know?

The participants field is the STRUCT because it is of type RECORD.

Which field is the ARRAY?

The participants.splits field is an array of floats inside of the parent participants struct. It has a REPEATED Mode which indicates an array. Values of that array are called nested values since they are multiple values inside of a single field.

Click Check my progress to verify the objective. Create a dataset and a table to ingest JSON data

Practice querying nested and repeated fields

1. Let's see all of our racers for the 800 Meter race:

#standardSQL SELECT * FROM racing.race_results

How many rows were returned?

Answer: 1

What if you wanted to list the name of each runner and the type of race?

2. Run the below schema and see what happens:

#standardSQL SELECT race, participants.name FROM racing.race_results

Error: Cannot access field name on a value with type ARRAY<STRUCT<name STRING, splits ARRAY<FLOAT64>>>> at [2:27]

Much like forgetting to GROUP BY when you use aggregation functions, here there are two different levels of granularity. One row for the race and three rows for the participants names. So how do you change this...

Row	race	participants.name
1	800M	Rudisha
2	???	Makhloufi
3	???	Murphy

...to this:

Row	race	participants.name
1	800M	Rudisha
2	800M	Makhloufi
3	800M	Murphy

In traditional relational SQL, if you had a races table and a participants table what would you do to get information from both tables? You would JOIN them together. Here the participant STRUCT (which is conceptually very similar to a table) is already part of your races table but is not yet correlated correctly with your non-STRUCT field "race".

Can you think of what two word SQL command you would use to correlate the 800M race with each of the racers in the first table?

Answer: CROSS JOIN

Great!

3. Now try running this:

#standardSQL SELECT race, participants.name FROM racing.race_results CROSS JOIN participants # this is the STRUCT (it is like a table within a table)

Table name "participants" missing dataset while no default dataset is set in the request.

Even though the participants STRUCT is like a table, it is still technically a field in the racing.race_results table.

4. Add the dataset name to the query:

#standardSQL SELECT race, participants.name FROM racing.race_results CROSS JOIN race_results.participants # full STRUCT name

5. And click Run.

Wow! You've successfully listed all of the racers for each race!

Row	race	name
1	800M	Rudisha
2	800M	Makhloufi
3	800M	Murphy
4	800M	Bosse
5	800M	Rotich
6	800M	Lewandowski
7	800M	Kipketer
8	800M	Berian

6. You can simplify the last query by:

• Adding an alias for the original table
• Replacing the words "CROSS JOIN" with a comma (a comma implicitly cross joins)

This will give you the same query result:

#standardSQL SELECT race, participants.name FROM racing.race_results AS r, r.participants

If you have more than one race type (800M, 100M, 200M), wouldn't a CROSS JOIN just associate every racer name with every possible race like a cartesian product?

Answer: No. This is a correlated cross join which only unpacks the elements associated with a single row. For a greater discussion, see working with ARRAYs and STRUCTs

Recap of STRUCTs:

• A SQL STRUCT is simply a container of other data fields which can be of different data types. The word struct means data structure. Recall the example from earlier: STRUCT(``"Rudisha" as name, [23.4, 26.3, 26.4, 26.1] as splits``)`` AS runner
• STRUCTs are given an alias (like runner above) and can conceptually be thought of as a table inside of your main table.
• STRUCTs (and ARRAYs) must be unpacked before you can operate over their elements. Wrap an UNNEST() around the name of the struct itself or the struct field that is an array in order to unpack and flatten it.

Task 7. Lab question: STRUCT()

Answer the below questions using the racing.race_results table you created previously.

Task: Write a query to COUNT how many racers were there in total.

• To start, use the below partially written query:

#standardSQL SELECT COUNT(participants.name) AS racer_count FROM racing.race_results Note: Remember you will need to cross join in your struct name as an additional data source after the FROM.

Possible solution:

#standardSQL SELECT COUNT(p.name) AS racer_count FROM racing.race_results AS r, UNNEST(r.participants) AS p

Row	racer_count
1	8

Answer: There were 8 racers who ran the race.

Click Check my progress to verify the objective. Execute the query to COUNT how many racers were there in total

Task 8. Lab question: Unpacking arrays with UNNEST( )

Write a query that will list the total race time for racers whose names begin with R. Order the results with the fastest total time first. Use the UNNEST() operator and start with the partially written query below.

• Complete the query:

#standardSQL SELECT p.name, SUM(split_times) as total_race_time FROM racing.race_results AS r , r.participants AS p , p.splits AS split_times WHERE GROUP BY ORDER BY ; Note:

• You will need to unpack both the struct and the array within the struct as data sources after your FROM clause.
• Be sure to use aliases where appropriate.

Possible solution:

#standardSQL SELECT p.name, SUM(split_times) as total_race_time FROM racing.race_results AS r , UNNEST(r.participants) AS p , UNNEST(p.splits) AS split_times WHERE p.name LIKE 'R%' GROUP BY p.name ORDER BY total_race_time ASC;

Row	name	total_race_time
1	Rudisha	102.19999999999999
2	Rotich	103.6

Click Check my progress to verify the objective. Execute the query that will list the total race time for racers whose names begin with R

Task 9. Filter within array values

You happened to see that the fastest lap time recorded for the 800 M race was 23.2 seconds, but you did not see which runner ran that particular lap. Create a query that returns that result.

• Complete the partially written query:

#standardSQL SELECT p.name, split_time FROM racing.race_results AS r , r.participants AS p , p.splits AS split_time WHERE split_time = ;

Possible solution:

#standardSQL SELECT p.name, split_time FROM racing.race_results AS r , UNNEST(r.participants) AS p , UNNEST(p.splits) AS split_time WHERE split_time = 23.2;

Row	name	split_time
1	Kipketer	23.2

Click Check my progress to verify the objective. Execute the query to see which runner ran fastest lap time

Congratulations!

You've successfully ingested JSON datasets, created ARRAYs and STRUCTs, and unnested semi-structured data for insights.

Next steps / Learn more

• For additional reading, refer to Working with Arrays.
• Check out these labs:
  • Creating Date-Partitioned Tables in BigQuery
  • Predict Taxi Fare with a BigQuery ML Forecasting Model
  • Ingesting New datasets into BigQuery

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Manual Last Updated December 10, 2024

Lab Last Tested December 10, 2024

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