Web Log Analytics with Amazon Kinesis Data Streams Proxy using Amazon API Gateway

This repository provides you cdk scripts and sample code on how to implement a simple web analytics system.
Below diagram shows what we are implementing.

The cdk.json file tells the CDK Toolkit how to execute your app.

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .venv directory. To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

$ python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

$ source .venv/bin/activate

If you are a Windows platform, you would activate the virtualenv like this:

% .venv\Scripts\activate.bat

Once the virtualenv is activated, you can install the required dependencies.

(.venv) $ pip install -r requirements.txt

Upload Lambda Layer code

Before deployment, you should uplad zipped code files to s3 like this:

(.venv) $ aws s3api create-bucket --bucket your-s3-bucket-name-for-lambda-layer-code --region region-name
(.venv) $ ./build-aws-lambda-layer-package.sh your-s3-bucket-name-for-lambda-layer-code

⚠️ To create a bucket outside of the us-east-1 region, aws s3api create-bucket command requires the appropriate LocationConstraint to be specified in order to create the bucket in the desired region. For more information, see these examples.

⚠️ Make sure you have Docker installed.

For example,

(.venv) $ aws s3api create-bucket --bucket lambda-layer-resources --region us-east-1
(.venv) $ ./build-aws-lambda-layer-package.sh lambda-layer-resources

For more information about how to create a package for Amazon Lambda Layer, see here.

Deploy

Before to synthesize the CloudFormation template for this code, you should update cdk.context.json file.
In particular, you need to fill the s3 location of the previously created lambda lay codes.

For example,

{
  "firehose_data_tranform_lambda": {
    "s3_bucket_name": "lambda-layer-resources",
    "s3_object_key": "var/fastavro-lib.zip"
  },
  ....
}

Now you are ready to synthesize the CloudFormation template for this code.

(.venv) $ export CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
(.venv) $ export CDK_DEFAULT_REGION=$(aws configure get region)
(.venv) $ cdk synth --all

Use cdk deploy command to create the stack shown above.

(.venv) $ cdk deploy --require-approval never --all

After all CDK stacks are successfully deployed, make sure that the AWS Lambda function merging many small files to a few of large parquet files is granted appropriate LakeFormation permissions.

Go checking Amazon Lake Formation Web console

Otherwise, you need to grant appropriate LakeFormation permissions to the AWS Lambda function merging many small files to a few of large parquet files by running the following commands:

(.venv) $ MERGE_SMALL_FILES_JOB_ROLE_ARN=$(aws cloudformation describe-stacks \
            --stack-name WebAnalyticsMergeSmallFiles | \
            jq -r '.Stacks[0].Outputs[] | \
            select(.OutputKey | endswith("LambdaExecRoleArn")) | \
            .OutputValue')
(.venv) $ aws lakeformation grant-permissions \
              --principal DataLakePrincipalIdentifier=${MERGE_SMALL_FILES_JOB_ROLE_ARN} \
              --permissions CREATE_TABLE DESCRIBE ALTER DROP \
              --resource '{ "Database": { "Name": "mydatabase" } }'
(.venv) $ aws lakeformation grant-permissions \
              --principal DataLakePrincipalIdentifier=${MERGE_SMALL_FILES_JOB_ROLE_ARN} \
              --permissions SELECT DESCRIBE ALTER INSERT DELETE DROP \
              --resource '{ "Table": {"DatabaseName": "mydatabase", "TableWildcard": {}} }'

ℹ️ mydatabase is the database for access logs specified as OLD_DATABASE and NEW_DATABASE in the cdk.context.json file.

ℹ️ WebAnalyticsMergeSmallFiles is the CDK Stack name to create the lambda function merging small files to large one by running Amazon Athena Create Table As Select(CTAS) query.

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

Run Test

1. Run GET /streams method to invoke ListStreams in Kinesis

   $ curl -X GET https://your-api-gateway-id.execute-api.us-east-1.amazonaws.com/v1/streams
   

   The response is:

   {
     "HasMoreStreams": false,
     "StreamNames": [
       "PUT-Firehose-aEhWz"
     ],
     "StreamSummaries": [
       {
         "StreamARN": "arn:aws:kinesis:us-east-1:123456789012:stream/PUT-Firehose-aEhWz",
         "StreamCreationTimestamp": 1661612556,
         "StreamModeDetails": {
           "StreamMode": "ON_DEMAND"
         },
         "StreamName": "PUT-Firehose-aEhWz",
         "StreamStatus": "ACTIVE"
       }
     ]
   }
   

2. Generate test data.

   (.venv) $ pip install -r requirements-dev.txt
   (.venv) $ python src/utils/gen_fake_data.py --max-count 5 --stream-name PUT-Firehose-aEhWz --api-url 'https://your-api-gateway-id.execute-api.us-east-1.amazonaws.com/v1' --api-method records
   [200 OK] {"EncryptionType":"KMS","FailedRecordCount":0,"Records":[{"SequenceNumber":"49633315260289903462649185194773668901646666226496176178","ShardId":"shardId-000000000003"}]}
   [200 OK] {"EncryptionType":"KMS","FailedRecordCount":0,"Records":[{"SequenceNumber":"49633315260289903462649185194774877827466280924390359090","ShardId":"shardId-000000000003"}]}
   [200 OK] {"EncryptionType":"KMS","FailedRecordCount":0,"Records":[{"SequenceNumber":"49633315260223001227053593325351479598467950537766600706","ShardId":"shardId-000000000000"}]}
   [200 OK] {"EncryptionType":"KMS","FailedRecordCount":0,"Records":[{"SequenceNumber":"49633315260245301972252123948494224242560213528447287314","ShardId":"shardId-000000000001"}]}
   [200 OK] {"EncryptionType":"KMS","FailedRecordCount":0,"Records":[{"SequenceNumber":"49633315260223001227053593325353897450107179933554966530","ShardId":"shardId-000000000000"}]}
   

3. Creating and loading a table with partitioned data in Amazon Athena

   Go to Athena on the AWS Management console.

   • (step 1) Specify the workgroup to use

     To run queries, switch to the appropriate workgroup like this:

   • (step 2) Create a database

     In order to create a new database called mydatabase, enter the following statement in the Athena query editor and click the Run button to execute the query.

     CREATE DATABASE IF NOT EXISTS mydatabase
     

   • (step 3) Create a table

     Copy the following query into the Athena query editor, replace the xxxxxxx in the last line under LOCATION with the string of your S3 bucket, and execute the query to create a new table.

     CREATE EXTERNAL TABLE mydatabase.web_log_json (
       `userId` string,
       `sessionId` string,
       `referrer` string,
       `userAgent` string,
       `ip` string,
       `hostname` string,
       `os` string,
       `timestamp` timestamp,
       `uri` string)
     PARTITIONED BY (
       `year` int,
       `month` int,
       `day` int,
       `hour` int)
     ROW FORMAT SERDE
       'org.openx.data.jsonserde.JsonSerDe'
     STORED AS INPUTFORMAT
       'org.apache.hadoop.mapred.TextInputFormat'
     OUTPUTFORMAT
       'org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat'
     LOCATION
       's3://web-analytics-xxxxx/json-data'
     

     If the query is successful, a table named web_log_json is created and displayed on the left panel under the Tables section.

     If you get an error, check if (a) you have updated the LOCATION to the correct S3 bucket name, (b) you have mydatabase selected under the Database dropdown, and (c) you have AwsDataCatalog selected as the Data source.

     ℹ️ If you fail to create the table, give Athena users access permissions on mydatabase through AWS Lake Formation, or you can grant anyone using Athena to access mydatabase by running the following command:

     (.venv) $ aws lakeformation grant-permissions \
                   --principal DataLakePrincipalIdentifier=arn:aws:iam::{account-id}:user/example-user-id \
                   --permissions CREATE_TABLE DESCRIBE ALTER DROP \
                   --resource '{ "Database": { "Name": "mydatabase" } }'
     (.venv) $ aws lakeformation grant-permissions \
                   --principal DataLakePrincipalIdentifier=arn:aws:iam::{account-id}:user/example-user-id \
                   --permissions SELECT DESCRIBE ALTER INSERT DELETE DROP \
                    --resource '{ "Table": {"DatabaseName": "mydatabase", "TableWildcard": {}} }'
     

   • (step 4) Load the partition data

     Run the following query to load the partition data.

     MSCK REPAIR TABLE mydatabase.web_log_json;
     

     After you run this command, the data is ready for querying.

     Instead of MSCK REPAIR TABLE command, you can use the ALTER TABLE ADD PARTITION command to add each partition manually.

     For example, to load the data in

     s3://web-analytics-xxxxx/json-data/year=2023/month=01/day=10/hour=06/

     you can run the following query.

     ALTER TABLE mydatabase.web_log_json ADD IF NOT EXISTS
     PARTITION (year=2023, month=1, day=10, hour=6)
     LOCATION 's3://web-analytics-xxxxx/json-data/year=2023/month=01/day=10/hour=06/';
     

   • (Optional) (step 5) Check partitions

     Run the following query to list all the partitions in an Athena table in unsorted order.

     SHOW PARTITIONS mydatabase.web_log_json;
     

4. Run test query

   Enter the following SQL statement and execute the query.

   SELECT COUNT(*)
   FROM mydatabase.web_log_json;
   

5. Merge small files into large one

   When real-time incoming data is stored in S3 using Kinesis Data Firehose, files with small data size are created.
   To improve the query performance of Amazon Athena, it is recommended to combine small files into one large file.
   Also, it is better to use columnar dataformat (e.g., Parquet, ORC, AVRO, etc) instead of JSON in Amazon Athena.
   To run these tasks periodically, the AWS Lambda function that executes Athena's Create Table As Select (CTAS) query has been deployed.
   Now we create an Athena table to query for large files that are created by periodical merge files task.

   CREATE EXTERNAL TABLE mydatabase.web_log_parquet (
     `userId` string,
     `sessionId` string,
     `referrer` string,
     `userAgent` string,
     `ip` string,
     `hostname` string,
     `os` string,
     `timestamp` timestamp,
     `uri` string)
   PARTITIONED BY (
     `year` int,
     `month` int,
     `day` int,
     `hour` int)
   ROW FORMAT SERDE
     'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
   STORED AS INPUTFORMAT
     'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
   OUTPUTFORMAT
     'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
   LOCATION
     's3://web-analytics-xxxxx/parquet-data'
   

   After creating the table and once merge files task is completed, the data is ready for querying.

Clean Up

Delete the CloudFormation stack by running the below command.

(.venv) $ cdk destroy --force --all

Useful commands

• cdk ls list all stacks in the app
• cdk synth emits the synthesized CloudFormation template
• cdk deploy deploy this stack to your default AWS account/region
• cdk diff compare deployed stack with current state
• cdk docs open CDK documentation

Enjoy!

References

• Web Analytics
• Tutorial: Create a REST API as an Amazon Kinesis proxy in API Gateway
• Streaming Data Solution for Amazon Kinesis
  
• Serverless Patterns Collection
• aws-samples/serverless-patterns
• Building fine-grained authorization using Amazon Cognito, API Gateway, and IAM
• AWS Lake Formation - Create a data lake administrator
• AWS Lake Formation Permissions Reference
• Tutorial: Schedule AWS Lambda Functions Using CloudWatch Events
• Amazon Athena Workshop
• Curl Cookbook
• fastavro - Fast read/write of AVRO files
• Apache Avro Specification
• How to create a Lambda layer using a simulated Lambda environment with Docker

  $ cat <<EOF > requirements-Lambda-Layer.txt
  > fastavro==1.6.1
  > EOF
  $ docker run -v "$PWD":/var/task "public.ecr.aws/sam/build-python3.11" /bin/sh -c "pip install -r requirements-Lambda-Layer.txt -t python/lib/python3.11/site-packages/; exit"
  $ zip -r fastavro-lib.zip python > /dev/null
  $ aws s3 mb s3://my-bucket-for-lambda-layer-packages
  $ aws s3 cp fastavro-lib.zip s3://my-bucket-for-lambda-layer-packages/
  

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.