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Manipulating Tables with Delta Lake

This notebook provides a hands-on review of some basic functionality of Delta Lake.

Learning Objectives

By the end of this lab, you should be able to: - Execute standard operations to create and manipulate Delta Lake tables, including:



Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over.

%run ../Includes/Classroom-Setup-2.2L

Create a Table

In this notebook, we'll be creating a table to track our bean collection. Use the cell below to create a managed Delta Lake table named beans. Provide the following schema:

Field Name Field type
color STRING
grams FLOAT
delicious BOOLEAN
create table beans 
(name string, color string, grams float, delicious boolean)


We'll use Python to run checks occasionally throughout the lab. The following cell will return as error with a message on what needs to change if you have not followed instructions. No output from cell execution means that you have completed this step.

assert spark.table("beans"), "Table named `beans` does not exist"
assert spark.table("beans").columns == ["name", "color", "grams", "delicious"], "Please name the columns in the order provided above"
assert spark.table("beans").dtypes == [("name", "string"), ("color", "string"), ("grams", "float"), ("delicious", "boolean")], "Please make sure the column types are identical to those provided above"

Insert Data

Run the following cell to insert three rows into the table.

("black", "black", 500, true),
("lentils", "brown", 1000, true),
("jelly", "rainbow", 42.5, false)

Manually review the table contents to ensure data was written as expected.

select * from beans

Insert the additional records provided below. Make sure you execute this as a single transaction.

insert into beans values
('pinto', 'brown', 1.5, true),
('green', 'green', 178.3, true),
('beanbag chair', 'white', 40000, false)

Run the cell below to confirm the data is in the proper state.

assert spark.conf.get("") == "2", "Only 3 commits should have been made to the table"
assert spark.table("beans").count() == 6, "The table should have 6 records"
assert set(row["name"] for row in spark.table("beans").select("name").collect()) == {'beanbag chair', 'black', 'green', 'jelly', 'lentils', 'pinto'}, "Make sure you have not modified the data provided"

Update Records

A friend is reviewing your inventory of beans. After much debate, you agree that jelly beans are delicious.

Run the following cell to update this record.

UPDATE beans
SET delicious = true
WHERE name = "jelly"

You realize that you've accidentally entered the weight of your pinto beans incorrectly.

Update the grams column for this record to the correct weight of 1500.

update beans 
set grams = 1500
where name = 'pinto'

Run the cell below to confirm this has completed properly.

assert spark.table("beans").filter("name='pinto'").count() == 1, "There should only be 1 entry for pinto beans"
row = spark.table("beans").filter("name='pinto'").first()
assert row["color"] == "brown", "The pinto bean should be labeled as the color brown"
assert row["grams"] == 1500, "Make sure you correctly specified the `grams` as 1500"
assert row["delicious"] == True, "The pinto bean is a delicious bean"

Delete Records

You've decided that you only want to keep track of delicious beans.

Execute a query to drop all beans that are not delicious.

delete from beans
where delicious = false

Run the following cell to confirm this operation was successful.

assert spark.table("beans").filter("delicious=true").count() == 5, "There should be 5 delicious beans in your table"
assert spark.table("beans").filter("delicious=false").count() == 0, "There should be 0 delicious beans in your table"
assert spark.table("beans").filter("name='beanbag chair'").count() == 0, "Make sure your logic deletes non-delicious beans"

Using Merge to Upsert Records

Your friend gives you some new beans. The cell below registers these as a temporary view.

CREATE OR REPLACE TEMP VIEW new_beans(name, color, grams, delicious) AS VALUES
('black', 'black', 60.5, true),
('lentils', 'green', 500, true),
('kidney', 'red', 387.2, true),
('castor', 'brown', 25, false);

SELECT * FROM new_beans

In the cell below, use the above view to write a merge statement to update and insert new records to your beans table as one transaction.

Make sure your logic: - Match beans by name and color - Updates existing beans by adding the new weight to the existing weight - Inserts new beans only if they are delicious

merge into beans a
using new_beans b
on and a.color = b.color
when matched then 
update set grams = a.grams + b.grams
when not matched and b.delicious = true then
insert *

Run the cell below to check your work.

version = spark.sql("DESCRIBE HISTORY beans").selectExpr("max(version)").first()[0]
last_tx = spark.sql("DESCRIBE HISTORY beans").filter(f"version={version}")
assert"operation").first()[0] == "MERGE", "Transaction should be completed as a merge"
metrics ="operationMetrics").first()[0]
assert metrics["numOutputRows"] == "3", "Make sure you only insert delicious beans"
assert metrics["numTargetRowsUpdated"] == "1", "Make sure you match on name and color"
assert metrics["numTargetRowsInserted"] == "2", "Make sure you insert newly collected beans"
assert metrics["numTargetRowsDeleted"] == "0", "No rows should be deleted by this operation"

Dropping Tables

When working with managed Delta Lake tables, dropping a table results in permanently deleting access to the table and all underlying data files.

NOTE: Later in the course, we'll learn about external tables, which approach Delta Lake tables as a collection of files and have different persistence guarantees.

In the cell below, write a query to drop the beans table.

drop table beans

Run the cell below to assert that your table no longer exists.

assert spark.sql("SHOW TABLES LIKE 'beans'").collect() == [], "Confirm that you have dropped the `beans` table from your current database"

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