Connecting Athena and S3 in same Cloudformation Stack

Question

From the documentation, AWS::Athena::NamedQuery, it is unclear how to attach Athena to an S3 bucket specified in the same stack.

If I had to guess from the example, I would imagine that you can write a template like,

Resources:
  MyS3Bucket:
    Type: AWS::S3::Bucket
       ... other params ...

  AthenaNamedQuery:
    Type: AWS::Athena::NamedQuery
    Properties:
      Database: "db_name"
      Name: "MostExpensiveWorkflow"
      QueryString: >
                    CREATE EXTERNAL TABLE db_name.test_table 
                    (...) LOCATION s3://.../path/to/folder/

Would a template like the above work? Upon stack creation, will the table db_name.test_table be available to run queries on?

Answer 1

Turns out the way you connect the S3 and Athena is to make a Glue table! How silly of me!! Of course Glue is how you connect things!

Sarcasm aside, this is a template that worked for me when using AWS::Glue::Table and AWS::Glue::Database,

Resources:
  MyS3Bucket:
    Type: AWS::S3::Bucket

  MyGlueDatabase:
    Type: AWS::Glue::Database
    Properties:
      DatabaseInput:
        Name: my-glue-database
        Description: "Glue beats tape"
      CatalogId: !Ref AWS::AccountId

  MyGlueTable:
    Type: AWS::Glue::Table
    Properties:
      DatabaseName: !Ref MyGlueDatabase
      CatalogId: !Ref AWS::AccountId
      TableInput:
        Name: my-glue-table
        Parameters: { "classification" : "csv" }
        StorageDescriptor:
          Location:
            Fn::Sub: "s3://${MyS3Bucket}/"
          InputFormat: "org.apache.hadoop.mapred.TextInputFormat"
          OutputFormat: "org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat"
          SerdeInfo:
            Parameters: { "separatorChar" : "," }
            SerializationLibrary: "org.apache.hadoop.hive.serde2.OpenCSVSerde"
          StoredAsSubDirectories: false
          Columns:
            - Name: column0
              Type: string
            - Name: column1
              Type: string

After this, the database and table were in the AWS Athena Console!