aws-elk-billing

Overview

aws-elk-billing is a combination of configuration snippets and tools to assist with indexing AWS programatic billing access files(CSV's) and visualizing the data using Kibana.

Currently it supports AWS Cost and Usage Report type, although it might work for other AWS Billing Report Types which contains some extra columns along with all the columns from AWS Cost and Usage Report.

You can create AWS Cost and Usage Report at https://console.aws.amazon.com/billing/home#/reports Make sure that it contains the following dimensions only (Don't include Resource IDs)

• Account Identifiers
• Invoice and Bill Information
• Usage Amount and Unit
• Rates and Costs
• Product Attributes
• Pricing Attributes
• Cost Allocation Tags

Follow instructions at http://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/detailed-billing-reports.html#turnonreports

Architecture

There are Four Docker containers.

1. elasticsearch:5-alpine
2. priceboard/docker-alpine:kibana (https://github.com/PriceBoardIn/docker-alpine/tree/master/alpine-kibana)
3. Logstash:5-alpine
4. aws-elk-billing (Refer: Dockerfile of this repository)

Integration among the 4 containers is done with docker-compose.yml

Primary Components

Task	Files
Logstash configuration	logstash.conf
Kibana configuration	kibana.yml
Elasticsearch index mapping	aws-billing-es-template.json
Indexing Kibana dashboard	kibana/orchestrate_dashboard.sh
Indexing Kibana visualisation	kibana/orchestrate_visualisation.sh
Indexing Kibana default index (This file is just for reference purpose, we will automate this part eventually)	kibana/orchestrate_kibana.sh
Parsing the aws-billing CSV's and sending to logstash	main.go
Connecting the dots: Wait for ELK Stack to start listening on their respective ports, downloads, extracts the latest compressed billing report from S3, XDELETE previous index of the current month, Index mapping, Index kibana_dashboard, Index kibana_visualization and finally executes main.go	orchestrate.py
Integrating all 4 containers	Dockerfile, docker-compose.yml

Getting Started

Clone the Repository and make sure that no process is listening to the ports used by all these dockers.

Ports	Process
9200, 9300	Elasticsearch
5601	Kibana
5140	Logstash

Set S3 credentials and AWS Billing bucket and directory name

Rename prod.sample.env to prod.env and provide values for the following keys AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, S3_BUCKET_NAME, S3_REPORT_PATH

S3_BUCKET_NAME = S3 bucket name (Refer the image above)

S3_REPORT_PATH = Report path (Refer the image above)

S3_REPORT_NAME = Report name (Refer the image above)

prod.env is added in .gitignore so that you don't push your credentials upstream accidentally.

Run Docker

The entire process is automated through scripts and docker. All the components would be downloaded automatically inside your docker

1. sudo docker-compose up -d

2. View Kibana at http://localhost:5601

   2.1 Use the index pattern as aws-billing-* and select the time field as lineItem/UsageStartDate

   2.2 Kibana AWS Billing Dashboard http://localhost:5601/app/kibana#/dashboard/AWS-Billing-DashBoard

   2.3 For MAC replace localhost with the ip of docker-machine To find IP of docker-machine docker-machine ip default

3 . sudo docker-compose stop to shutdown all the docker containers.

4 . sudo docker-compose down to shutdown and remove all the files from docker. Note: Next time you do a docekr-compose up every thing will start from scratch. Use this if you see some problems in your data or ES is timing out.

Gotchas

• aws-elk-billing container will take time while running the following two process [Filename: orchestrate.py].
  1. Downloading and extracting AWS Billing report from AWS S3.
  2. Depending on the size of AWS Billing CSV report main.go will take time to index all the data to Elasticsearch via Logstash.
• You can view the dashboard in kibana, even while main.go is still indexing the data.
• In order to index new data, you'll have to run docker-compose up -d again.

Feedback

We'll love to hear feedback and ideas on how we can make it more useful. Just create an issue.

Thanks