Querying Data in S3 with Amazon Athena

1 hour
  • 3 Learning Objectives

About this Hands-on Lab

Welcome to this hands-on AWS lab for querying data in Amazon S3 with Amazon Athena. This lab allows you to practice analyzing data stored in S3 using SQL queries in Athena.

Web servers are often fronted by a global content delivery network (CDN), such as Amazon CloudFront, to accelerate delivery of websites, APIs, media content, and other web assets.

In this lab, you will bequerying data in S3 with Amazon Athena. CloudFront accesss logs have been pre-generated for you as part of the lab.

As part of our web server infrastructure, we’re using Amazon CloudFront to deliver content to consumers with low latency.

CloudFront generates access logs at each of its global edge locations, and delivers those raw logs to a bucket in S3. These raw logs are not optimized for efficient querying, however.

We use AWS Glue to run a job which divides the overall data into small partitions. This allows queries to run much faster by reducing the number of files to scan. The Glue job then converts each partition into a columnar format to reduce storage cost and increase the efficiency of scans by Amazon Athena.

The Glue transformation step is already done for you. The optimized CloudFront access logs are stored in an S3 bucket in this lab environment.

The optimized logs are in a format called [Apache Parquet](https://parquet.apache.org/).

In this lab, you’ll be analyzing these [optimized CloudFront access logs](https://github.com/awslabs/athena-glue-service-logs/blob/master/athena_glue_service_logs/cloudfront.py) using Amazon Athena. Athena is an interactive query service that can help you analyze data for various AWS services, including CloudFront.

You will be creating a table, loading the data partitions into the table, and querying the data in the table using SQL.

More info on Glue and partitioning data [here](https://docs.aws.amazon.com/athena/latest/ug/partitions.html).

NOTE: There is a current issue in this lab. As a work around the issue, when prompted, instead of clicking Create Table, click on Tutorial in the upper right. Then click next twice, and you should be where you need to be to create the database from your s3 bucket.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Create a Table from S3 Bucket Metadata
  1. Navigate to the Amazon Athena service:
    • Click Get Started if this is our first trip into Athena, otherwise continue to #2
  2. First, add an S3 location for your queries by clicking on the ‘Before you run your first query, you need to set up a query result location in Amazon S3.‘ link
  3. Paste in the S3 Bucket ARN we copied earlier, being sure to remove "arn:aws:s3:::" from the beginning of the data we paste in and including a trailling slash
  4. Once the S3 location is properly configured you will notice the Run query button has been made active.
  5. In the query editor paste the following query, then press Ctrl+Enter to run the query:
    CREATE database aws_service_logs
  6. Under Tables, select Create Table > from S3 bucket data.
  7. Step 1: Name and Location:
    • Database: aws_service_logs
    • Table: cf_access_optimized
    • Location: s3://Name of the generated S3 bucket/ (including trailing slash)
  8. Step 2: Data Format
    • Select Parquet
  9. Step 3: Columns
    • Bulk add columns using this data:
      time timestamp, location string, bytes bigint, requestip string, method string, host string, uri string, status int, referrer string, useragent string, querystring string, cookie string, resulttype string, requestid string, hostheader string, requestprotocol string, requestbytes bigint, timetaken double, xforwardedfor string, sslprotocol string, sslcipher string, responseresulttype string, httpversion string
  10. Step 4: Partitions
    • Column Name: year, Column Type: string
    • Column Name: month, Column Type: string
    • Column Name: day, Column Type: string
    • Click Create table
  11. Click Run query on the generated SQL statement. Ensure the S3 bucket location in the query matches the one generated in your lab environment.
Add Partition Metadata
  1. Open a new query tab
  2. Run the following query: MSCK REPAIR TABLE aws_service_logs.cf_access_optimized
  3. Verify the partitions were created with the following query: SELECT count(*) AS rowcount FROM aws_service_logs.cf_access_optimized. You should see 207535 rows present in the table.
  4. Run the following query: SELECT * FROM aws_service_logs.cf_access_optimized LIMIT 10
Query the Total Bytes Served in a Date Range
  1. Perform the following query:
    SELECT SUM(bytes) AS total_bytes
    FROM aws_service_logs.cf_access_optimized
    WHERE time BETWEEN TIMESTAMP '2018-11-02' AND TIMESTAMP '2018-11-03'
  2. Observe the value for total_bytes equals 87310409.

Additional Resources

Make sure you are in the us-east-1 region throughout this lab.

NOTE: This lab will be replaced soon. On the step to create a table: Click on "Connect data source" in the left navigation; on the right side of Data Source, then make sure to select "Query data in Amazon S3" for where the data is located. Also select "AWS Glue data catalog" for the metadata catalog and click "Next". Then we will select the "Add a table and enter schema info manually." Then the choice will update to "Continue to add table." After that add the db name, Table name, and s3 URL as in the video.

When prompted to bulk add column definitions, you may use this data to save time:

time timestamp, location string, bytes bigint, requestip string, method string, host string, uri string, status int, referrer string, useragent string, querystring string, cookie string, resulttype string, requestid string, hostheader string, requestprotocol string, requestbytes bigint, timetaken double, xforwardedfor string, sslprotocol string, sslcipher string, responseresulttype string, httpversion string

CloudFront raw access logs are stored in a CSV format called Web Distribution Log File Format.

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