Step 6 - Preload the items for the table Scan exercise

Reminder: All commands are executed in the shell console connected to the EC2 instance, not your local machine. (If you are not sure you can always validate going back to step 1)

In the upcoming Exercise #2 we will discuss table scan and its best practices. In this step, let’s populate the table with 1 million items in preparation for that exercise.

Run the command to create a new table:

aws dynamodb create-table --table-name logfile_scan \
--attribute-definitions AttributeName=PK,AttributeType=S AttributeName=GSI_1_PK,AttributeType=S AttributeName=GSI_1_SK,AttributeType=S \
--key-schema AttributeName=PK,KeyType=HASH \
--provisioned-throughput ReadCapacityUnits=5000,WriteCapacityUnits=5000 \
--tags Key=workshop-design-patterns,Value=targeted-for-cleanup \
--global-secondary-indexes "IndexName=GSI_1,\

This command will create a new table and one GSI with the following definition:

Table: logfile_scan

  • Key schema: HASH
  • Table RCU = 5000
  • Table WCU = 5000
  • GSI(s):
    • GSI_1 (3000 RCU, 5000 WCU) - Allows for parallel or sequential scans of the access logs. Sorted by status code and timestamp.
Attribute name (Type) Special attribute? Attribute use case Sample attribute value
PK (STRING) Hash key Holds the request id for the access log request#104009
GSI_1_PK (STRING) GSI 1 hash key A shard key, with values 0-N, to allow log searches shard#3
GSI_1_SK (STRING) GSI 1 sort key Sorts the logs hierarchically, from status code -> date -> hour 200#2019-09-21#01

Run the command to wait until the table becomes Active:

aws dynamodb wait table-exists --table-name logfile_scan

Populate the table

Run the following command to load the server logs data into the logfile_scan table. It will load 1,000,000 rows to the table.

nohup python logfile_scan &

nohup is used to run the process in the background, and disown allows the load to continue in case you are disconnected.

The following command will take about ten minutes to complete. It will run in the background.

Run pgrep -l python to verify the script is loading data in the background.

pgrep -l python


3257 python

The process id - the 4 digit number in the above example - will be different for everyone.

The script will continue to run in the background while you work on the next exercise.

You have completed the SETUP!