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.

The dataset you'll use is an ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. You have a copy of that dataset for this lab and will explore the available fields and row for insights.

In this lab you will query partitioned datasets and create your own dataset partitions to improve query performance and reduce cost.

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Open 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 lists UI updates.

2. Click Done.

Task 1. Create a new dataset

First, you will create a dataset to store your tables.

1. In the left pane, under Explorer section, click on the View actions icon next to your project name (starts with qwiklabs-gcp-...), and then click CREATE DATASET.

2. Set the Dataset ID to ecommerce. Leave the other options at their default values (Data Location, Default table Expiration). Click CREATE DATASET.

Task 2. Creating tables with date partitions

A partitioned table is a table that is divided into segments, called partitions, that make it easier to manage and query your data. By dividing a large table into smaller partitions, you can improve query performance, and control costs by reducing the number of bytes read by a query.

Now you will create a new table and bind a date or timestamp column as a partition. Before we do that, let's explore the data in the non-partitioned table first.

Query webpage analytics for a sample of visitors in 2017

1. In the Query Editor, add the below query. Before running, note the total amount of data it will process as indicated next to the query validator icon: "This query will process 1.74 GB when run".

#standardSQL SELECT DISTINCT fullVisitorId, date, city, pageTitle FROM `data-to-insights.ecommerce.all_sessions_raw` WHERE date = '20170708' LIMIT 5

2. Click RUN.

The query returns 5 results.

Query webpage analytics for a sample of visitors in 2018

Let's modify the query to look at visitors for 2018 now.

1. In the Query Editor, add the below query:

#standardSQL SELECT DISTINCT fullVisitorId, date, city, pageTitle FROM `data-to-insights.ecommerce.all_sessions_raw` WHERE date = '20180708' LIMIT 5

The Query results will tell you how much data this query will process.

2. Click RUN.

Notice that the query still processes 1.74 GB even though it returns 0 results. Why? The query engine needs to scan all records in the dataset to see if they satisfy the date matching condition in the WHERE clause. It must look at each record to compare the date against the condition of ‘20180708'.

Additionally, the LIMIT 5 does not reduce the total amount of data processed, which is a common misconception.

Common use-cases for date-partitioned tables

Scanning through the entire dataset everytime to compare rows against a WHERE condition is wasteful. This is especially true if you only really care about records for a specific period of time like:

• All transactions for the last year
• All visitor interactions within the last 7 days
• All products sold in the last month

Instead of scanning the entire dataset and filtering on a date field like we did in the earlier queries, we will now setup a date-partitioned table. This will allow us to completely ignore scanning records in certain partitions if they are irrelevant to our query.

Create a new partitioned table based on date

1. Click "+" (SQL query) and add the below query, then RUN:

#standardSQL CREATE OR REPLACE TABLE ecommerce.partition_by_day PARTITION BY date_formatted OPTIONS( description="a table partitioned by date" ) AS SELECT DISTINCT PARSE_DATE("%Y%m%d", date) AS date_formatted, fullvisitorId FROM `data-to-insights.ecommerce.all_sessions_raw`

In this query, note the new option - PARTITION BY a field. The two options available to partition are DATE and TIMESTAMP. The PARSE_DATE function is used on the date field (stored as a string) to get it into the proper DATE type for partitioning.

2. Click on the ecommerce dataset, then select the new partiton_by_day table:

3. Click on the Details tab.

Confirm that you see:

• Partitioned by: Day
• Partitioning on: date_formatted

Note: Partitions within partitioned tables on your Qwiklabs account will auto-expire after 60 days from the value in your date column. Your personal GCP account with billing-enabled will let you have partitioned tables that don't expire.

For the purposes of this lab, the remaining queries will be ran against partitioned tables that have already been created.

Task 3. View data processed with a partitioned table

1. Run the below query, and note the total bytes to be processed:

#standardSQL SELECT * FROM `data-to-insights.ecommerce.partition_by_day` WHERE date_formatted = '2016-08-01'

This time ~25 KB or 0.025MB is processed, which is a fraction of what you queried.

2. Now run the below query, and note the total bytes to be processed:

#standardSQL SELECT * FROM `data-to-insights.ecommerce.partition_by_day` WHERE date_formatted = '2018-07-08'

You should see This query will process 0 B when run.

Why is there 0 bytes processed?

Task 4. Creating an auto-expiring partitioned table

Auto-expiring partitioned tables are used to comply with data privacy statutes, and can be used to avoid unnecessary storage (which you'll be charged for in a production environment). If you want to create a rolling window of data, add an expiration date so the partition disappears after you're finished using it.

Explore the available NOAA weather data tables

1. In the left menu, in Explorer, click on + ADD and select Star a project by name > Enter project name.

2. Enter bigquery-public-data and click Star.

3. Expand bigquery-public-data and search for noaa_gsod.

4. Scroll through the tables in the noaa_gsod dataset (which are manually sharded and not partitioned).

1. First, copy and paste this below query to Query editor:

#standardSQL SELECT DATE(CAST(year AS INT64), CAST(mo AS INT64), CAST(da AS INT64)) AS date, (SELECT ANY_VALUE(name) FROM `bigquery-public-data.noaa_gsod.stations` AS stations WHERE stations.usaf = stn) AS station_name, -- Stations may have multiple names prcp FROM `bigquery-public-data.noaa_gsod.gsod*` AS weather WHERE prcp < 99.9 -- Filter unknown values AND length(_TABLE_SUFFIX) = 4 AND CAST(_TABLE_SUFFIX AS int64) >= 2018 AND prcp > 0 -- Filter stations/days with no precipitation AND CAST(_TABLE_SUFFIX AS int64) >= 2018 ORDER BY date DESC -- Where has it rained/snowed recently LIMIT 10

Notice that the table wildcard * used in the FROM clause to limit the amount of tables referred to in the TABLE_SUFFIX filter.

Notice that although a LIMIT 10 was added, this still does not reduce the total amount of data scanned (about 457.5 MB) since there are no partitions yet.

2. Click RUN.

3. Confirm the date is properly formatted and the precipitation field is showing non-zero values.

Task 5. Your turn: Create a partitioned table

• Modify the previous query to create a table with the below specifications:

  • Table name: ecommerce.days_with_rain
  • Use the date field as your PARTITION BY
  • For OPTIONS, specify partition_expiration_days = 60
  • Add the table description = "weather stations with precipitation, partitioned by day"

Your query should look like this:

#standardSQL CREATE OR REPLACE TABLE ecommerce.days_with_rain PARTITION BY date OPTIONS ( partition_expiration_days=60, description="weather stations with precipitation, partitioned by day" ) AS SELECT DATE(CAST(year AS INT64), CAST(mo AS INT64), CAST(da AS INT64)) AS date, (SELECT ANY_VALUE(name) FROM `bigquery-public-data.noaa_gsod.stations` AS stations WHERE stations.usaf = stn) AS station_name, -- Stations may have multiple names prcp FROM `bigquery-public-data.noaa_gsod.gsod*` AS weather WHERE prcp < 99.9 -- Filter unknown values AND length(_TABLE_SUFFIX) = 4 AND CAST(_TABLE_SUFFIX AS int64) >= 2018 AND prcp > 0 -- Filter AND CAST(_TABLE_SUFFIX AS int64) >= 2018

Confirm data partition expiration is working

1. To confirm you are only storing data from 60 days in the past up until today, run the DATE_DIFF query to get the age of your partitions, which are set to expire after 60 days.

Below is a query which tracks the average rainfall for the NOAA weather station in Wakayama, Japan which has significant precipitation.

2. Add this query and run it:

#standardSQL # avg monthly precipitation SELECT AVG(prcp) AS average, station_name, date, CURRENT_DATE() AS today, DATE_DIFF(CURRENT_DATE(), date, DAY) AS partition_age, EXTRACT(MONTH FROM date) AS month FROM ecommerce.days_with_rain WHERE station_name = 'WAKAYAMA' #Japan GROUP BY station_name, date, today, month, partition_age ORDER BY date DESC; # most recent days first

Task 6. Confirm the oldest partition_age is at or below 60 days

1. Update the ORDER BY clause to show the oldest partitions first.

2. Add this query and run it:

#standardSQL # avg monthly precipitation SELECT AVG(prcp) AS average, station_name, date, CURRENT_DATE() AS today, DATE_DIFF(CURRENT_DATE(), date, DAY) AS partition_age, EXTRACT(MONTH FROM date) AS month FROM ecommerce.days_with_rain WHERE station_name = 'WAKAYAMA' #Japan GROUP BY station_name, date, today, month, partition_age ORDER BY partition_age DESC Note: Your results will vary if you re-run the query in the future, as the weather data, and your partitions, are continuously updated.

Congratulations!

You've successfully created and queried partitioned tables in BigQuery.

End your lab

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