Ansible vs Terraform vs CloudFormation

Studying for my AWS professional certifications introduced me to the wonderfully difficult to read world of JSON and CloudFormation templates. Once I discovered you can write them in YAML they became a bit less intimidating, and on the whole pretty amazing. CF templates were my first introduction to Infrastructure as Code, and it has been a lot of fun trying auto reproduce my school’s infrastructure in another region using them. I also had a chance to work with them on my log aggregation project, and wrote a template to install my log transport function, which was pretty cool.

I picked up Terraform as I was applying for a job which had it as a requirement, and I found it easier to pierce than CF templates, mainly because the syntax and variables were easier to read than straight JSON ( I didn’t know about YAML CF templates till later), and I could comment the terraform templates easily, where you can’t comment a JSON file. So, most of my IoC scripts are in Terraform.

I just started working with ansible to solve a server deployment issue at my school where I am deploying a container cluster. I seized on ansible to manage the configurations, and it made deploying the 5 servers a snap.

What I didn’t realize at the time is that ansible can be used for so much more than configuration management. To paraphrase an old Steve Martin joke, it’s like ansible has a module for everything! I had a friend, who uses ansible in production, take a look at my first playbook, and he showed me that instead of treating it like a glorified init script, ansible could create resources and deploy containers as easy as terraform and cloudformation.

Mind blown!

So, my challenge is to create all of my IoC code in each flavor (ansible, terraform and cloudformation) and build automation pipelines for each. As I get them built and tested, I’ll post anonymized versions here. What an adventure!

ansible : docker client

This is my first ansible playbook which is used to provision some linux boxes to run as docker platforms. This playbook speeds up the deployment and configuration management considerably, especially since there are 5 boxes at separate sites to manage.

--- #post install configuration for docker use
- hosts: localserver
  remote_user: root 
  become: su
  gather_facts: no
  connection: ssh

  - name: 'selinux permissive'
    lineinfile: dest=/etc/selinux/config regexp="^SELINUX=" line="SELINUX=permissive"

  - name: 'add docker ce repo'
      dest: /etc/yum.repos.d/
      flat: yes
      fail_on_missing: no

  - name: 'update package list'
      update_cache: yes 
      name: '*' 
      state: latest

  - name: 'add packages'
       - epel-release
       - yum-utils
       - device-mapper-persistent-data
       - docker-ce
       - python-pip
      state: latest

  - name: 'install docker-compose'
    pip: >

  - name: 'add centos to docker group'
     name: centos
     groups: docker
     append: yes

  - name: 'add daemon.json'
      src: /mnt/c/Users/soops/playbooks/dockerServer/daemon.json
      dest: /etc/docker/daemon.json
      owner: root
      group: root
      mode: 0644

  - name: 'enable and restart docker'
      name: docker
      enabled: yes
      state: restarted
      daemon_reload: yes

  - name: 'stop postfix'
      name: postfix
      enabled: no
      state: stopped

  - name: 'start portainer container'
      name: portainer
      state: started
      restart_policy: always
      ports: 9000:9000
      docker_host: unix://var/run/docker.sock
      image: portainer/portainer
      command: --no-auth

  - name: 'website test'
      name: testWebserver
      state: started
      ports: 80:80
      docker_host: unix://var/run/docker.sock
      image: httpd
      volumes: /home/centos/html/:/usr/local/apache2/htdocs/

I learned a couple of things here while hacking this together once I had a friend who uses ansible professionally review it:

  • There’s a module for that. Avoid shell commands like the plague, and look up your command’s corresponding module. Using shell commands relies on sequence, ansible is a declarative structured tool so task statements should be tests of state and stand alone without regard to location in the script.
  • Use -C to test each statement without changing anything on the managed hosts.
  • Use –syntax-check to validate your playbook without executing it.
  • Use pip to install your ansible. I used the apt-get method for my Ubuntu for Windows, and I could only get I kept getting syntax errors for statements that I knew to be true, and after hours of staring at the screen I found out that it meant the module wasn’t supported in the ansible version being used. Installing ansible through pip gave me the current 2.7.6 version.

I need to work on roles, triggers and variables, but this has vastly simplified my server deployment and configuration problem, and given me an added tool to deploy containers that I can try with Jenkins.

EFS Provisioned Throughput

I always see recommendations to avoid EFS when running webservers, when it looks like a much better solution than copying documents back and forth to s3. Our public and private web servers have always been particularly low volume, so I never really noticed unacceptable lag. If our pages load in a couple of seconds, not a problem. However, over the last couple of weeks our web server has been performing dog slow. At first I thought is was an apache 2.4 tuning problem, and I was wracking my brain trying different KeepAlive and MinServer directive values, to no avail. I also suspected a plugin problem (when in WordPress, beware the plugin) but it wasn’t until I did a du -f -d 1 to see how much space the plugins were taking up (in case one had blown up) and it took forever to complete the command. Ah ha!

The original EFS mount was set to ‘burst’ mode, so clearly we were exhausting the throughput with just a couple of servers. I set the mount to ‘provisioned’ at 10MiB, and that solved the latency problem. It will cost us about $60 a month for 10MiB (cheap for you all, but that’s real money for us charter school people), but now when I stress test the web server with Jmeter and 400 simultaneous users, the system barely notices, and scales out accordingly. The EFS mount can scale to 1024 MiB, so I imagine that can be pretty beefy for a large scaled service. Now I can go get some sleep!

CodeCommit : example

This is a visualizer snapshot of the main repo my current organization uses to organize their code, showing the recent commits and branch merges.

lambda : python3 : log filter and transport serverless function

This lambda function is deployed via cloud formation template. It is composed of the following parts:

  • A yaml config file that is sourced from an s3 bucket at time of execution. It includes instructions on how to build job rules to extend the function’s capacity.
  • A library of filtering, parsing, and messaging functions
  • A simple lambda_function handler that is triggered by SQS messages posted when a log file is posted to an s3 bucket.
  • cloudformation template

 The function handles 10 messages at a time, filters out files we don’t want, identifies file types we do want, wraps them as syslog messages, then compresses the file and posts it to a bucket, which will get pulled on premise by a local worker script.

The yaml config file

# This document is an abstraction of the pattern match and job function logic of the lambda function.
# The 'job' rules define how the function will parse logs.
# Each job is a log type that is received from AWS accounts that have implemented log aggregation and forwarding
# Each log is tested for gzip compression, unzipped if necessary, and loaded into memory. All files are treated as
# plain text files for the purposes of job definition.
# The data structure is as follows:
# job##: a simple name for the rule. Must be unique, but not necessarily sequential
# name: a plain language name for the job that is not processed by the function
# match_variable: a pattern that the function will use to identify the log
# service_variable: a service that will be added to the syslog message to determine the log type.
# action variables: an ordered list of steps the function will take to process the log.
# The callable methods work as follows:
#   json: treats the file as a json object on each line
#   firehose: json objects on a single line. The method looks for touching braces "}{" and breaks them into lines "}\n{"
#   text_wrap: handles text logs regardless of form, wraps them as a syslog message
# all logs that meet the job rules get parsed, gzipped and sent to the filtered s3 bucket.
# all logs that fail to get parsed are written to the region's DynamoDB table, AWSLogsFilterExceptions
# Periodic review of the table will show logs that are not being processed, and a new set of job rules can be
# added to this file. The configuration will be updated on the next execution of the lambda function.
# Periodic review of the dead letter queue will also be useful to identify logs that are not being processed, but these
# be a rare exception. We will need to ascertain why the log was not written to the database table.
# This file is kept in the s4://<redacted> bucket, which supports versioning. If a change is made that
# breaks the lambda functions, you can restore the previous version using the AWS S3 management console.
    job01 :
        name: cloudtrail
        match_variable: cloudtrail/
        service_variable: ''
           - json

    job02 :
        name: rds
        match_variable: rds/
        service_variable: ''
           - text_wrap

    job03 :
        name: firehose
        match_variable: firehose/
        service_variable: ''
           - firehose
           - json

    job04 :
        name: config
        match_variable: config/
        service_variable: ''
           - json

    job05 :
        name: flowlog
        match_variable: flowlog/
        service_variable: ''
           - text_wrap

    job06 :
        name: elb
        match_variable: elasticloadbalancing/
        service_variable: ''
           - text_wrap

    job07 :
        name: serveraccesslogs
        match_variable: serveraccesslogs/
        service_variable: ''
           - text_wrap

    job08 :
        name: mq
        match_variable: mq/
        service_variable: ''
           - text_wrap

    job09 :
        name: redshift
        match_variable: redshift/
        service_variable: ''
           - text_wrap

    job10 :
        name: cloudfront
        match_variable: cloudfront/
        service_variable: ''
           - text_wrap

    job11 :
        name: ec2
        match_variable: ec2/
        service_variable: ''
           - json

    job12 :
        name: emr
        match_variable: elasticmapreduce/
        service_variable: ''
           - text_wrap

    job13 :
        name: elb2
        match_variable: elb/
        service_variable: ''
           - text_wrap

    job14 :
        name: etl
        match_variable: etl/
        service_variable: ''
           - text_wrap

    job15 :
        name: s3
        match_variable: s3/
        service_variable: ''
           - text_wrap

        name: aurora
        match_variable: aurora/
        service_variable: ''
        - text_wrap

# filter variable. These are key path patterns that we drop to avoid redundant logs and files that lock up the parser
    - CloudTrail-Digest
    - ConfigWritabilityCheckFile
    - DS_Store
    - .trm
    - .trc
    - <example key path>
    - <rest removed for privacy>

# aggregate_accounts variable for those that combine their logs in one account before replication. Note in key path where
# actual account number shows, split by '/'
        name: <redacted1>
        agg_account: '<example1>'
        match_position: '1'
        name: <redacted2>
        agg_account: '<redacted2>'
        match_position: '2'
        name: <redacted3>
        agg_account: '<redacted3>'
        match_position: '2'

The Library File

Lambda function to process sqs events, filter messages based on key paths,
load filtered messages into memory,parse formats to syslog,
and write syslog messages to a gzipped s3 object, and drop the event generating sqs message.

import re
import ast
import gzip
import json
from datetime import datetime
import urllib
import boto3
import botocore
import os
import yaml
import math
import uuid

REGION = os.environ.get('AWS_REGION')

# setup vars
config_bucket = '<redacted>'
yaml_config = 'config.yaml'
s3 = boto3.client('s3')
config_handle = s3.get_object(Bucket=config_bucket, Key=yaml_config)
config_data = body = config_handle.get('Body').read()
yaml_data = yaml.load(config_data)
FILTERS = yaml_data['FILTERS']
aggregate_accounts = yaml_data['aggregate_accounts']

class Message:
    """ Pull objects and variables plus event data. """

    def __init__(self, sqs_object, record, in_queue, out_queue):
        """ receives a single record from event. """
        self.sqs = sqs_object
        self.message_id = record["messageId"]
        self.receipt_handle = record["receiptHandle"]
        # not in testing
        body = ast.literal_eval(record["body"])
        self.msg_key_path = body["Records"][0]["s3"]["object"]["key"]
        # self.msg_key_path = record["body"]["Records"]["s3"]["object"]["key"]
        self.in_queue = in_queue
        self.out_queue = out_queue
        self.tag = 'message: initialized'
        #print('key: ', self.msg_key_path)

    def filter_message(self):
        filters keypath against list to drop files we don't want
        return_value = ''
        global FILTERS
        for my_filter in FILTERS:
            if, self.msg_key_path):
                #print('matched: ', self.msg_key_path)
                self.tag = 'message: filtered'
                return_value = True
        if return_value:
            return True
            #print('file not matched: ', self.msg_key_path)
            return False

    def drop_message(self):
        removes sqs msg from queue after successful file processing
            return True

        except botocore.exceptions.ClientError as error:
            exception_string = 'Raised exception: error: {0}'.format(error) + '\n'
            print('can not drop message:', exception_string)
            return False

    def write_sqs_message_to_out_queue(self, sqs_object, out_queue, data):
        Writes a message to specified / dlq queue. Have to replace single quotes to meet json formatting

            replace_single_quotes = re.sub('\'', '\"', data)
            sqs = sqs_object
            return True

        except botocore.exceptions.ClientError as error:
            exception_string = 'Raised exception: error: {0}'.format(error) + '\n'
            print('can not write message to out_queue:', exception_string)
            return False

class File:

    """ Processes s3 object from msg_key_path. """
    def __init__(self, s3_object, msg_key_path, in_bucket, out_bucket):
        self.s3 = s3_object
        self.msg_key_path = msg_key_path
        self.in_bucket = in_bucket
        self.out_bucket = out_bucket
        self.service_name = ''
        self.owner_id = ''
        self.tag = 'file: initialized'

# check these
    def parse_body_json_to_syslog(self):
        output_data = ''
        accountID = 'account-' + self.owner_id
        my_time ='%Y-%m-%dT%H:%M:%S%Z')
        service_name = self.service_name
        my_data = str(self.body).split('\n')
        self.datetime = my_time

        for data in my_data:
            my_data = json.loads(data)
            message = my_time + ' ' + str(accountID) + ' ' + str(service_name) + ' ' + str(my_data) + '\n'
            output_data += message
        self.body = output_data
        return True

    def determine_if_agg_account(self):
        for key in aggregate_accounts.keys():
            match =, aggregate_accounts[key]['agg_account'])
            split_path = self.msg_key_path.split('/')

            if match:
                self.owner_id = split_path[int(aggregate_accounts[key]['match_position'])]
                self.owner_id = split_path[0]
        return self.owner_id

    def determine_service_name(self, service_name):
        self.service_name = service_name
        return True

    def fix_body_firehose(self):
        data = re.sub('}{', '}\n{', self.body)
        self.tag = 'body: each line json'
        self.body = data
        return True

    def parse_ascii_body_to_syslog(self):
        output_data = ''
        accountID = 'account-' + self.owner_id
        my_time ='%Y-%m-%dT%H:%M:%S%Z')
        service_name = self.service_name
        self.datetime = my_time
        output_data = my_time + ' ' + str(accountID) + ' ' + str(service_name) + ' ' + str(self.body)
        self.body = output_data
        return True

# these are reliable
    def load_s3_object_to_body(self):
       Loads the file's contents to memory for manipulation
        self.msg_key_path = urllib.parse.unquote(self.msg_key_path)
        file_handle = self.s3.get_object(Bucket=self.in_bucket, Key=self.msg_key_path)
            self.body = file_handle.get('Body').read()
            self.tag = 'body: loaded'
            return True
        except botocore.exceptions.ClientError as error:
            exception_string = 'Raised exception: error: {0}'.format(error) + '\n'
            print('can not load file from path: ', self.msg_key_path, ' ', exception_string)
            return False

    def put_s3_object(self):
        """ Write loaded data to filtered bucket. """
            self.s3.put_object(Bucket=self.out_bucket, Key=self.msg_key_path, Body=self.body)
            return True
            print('could not put: ', self.msg_key_path)
            return False

    def check_body_is_gzip(self):
            test_gzip = gzip.decompress(self.body)
            self.tag = 'body: gzip'
            return True
            return False

    def check_body_is_ascii(self):
            data = self.body.decode()
            test_ascii = isinstance(data, data)
            if test_ascii:
                self.tag = 'body: ascii'
                return True
            return False

    def gunzip_body(self):
        data = gzip.decompress(self.body)
        self.body = data.decode('utf-8')
        self.tag = 'body: unzipped'
        return True

    def gzip_body(self):
        data = self.body
        encoded_data = data.encode('utf-8')
        self.body = gzip.compress(encoded_data)
        self.tag = 'body: compressed to gz'
        return True

    def add_syslog_suffix_to_path(self):
        self.msg_key_path = self.msg_key_path + '.syslog'
        self.tag = 'file: .syslog'
        return True

    def add_gz_suffix_to_path(self):
        self.tag = 'file: .gz'
        self.msg_key_path = self.msg_key_path + '.gz'
        return True

    def strip_path_of_suffix(self):
            self.msg_key_path = re.sub('.gz', '', self.msg_key_path)
            self.msg_key_path = re.sub('.json', '', self.msg_key_path)
            self.msg_key_path = re.sub('.syslog', '', self.msg_key_path)
            return True
            return False

    def buffer_msg_size(self):
        content_length = len(self.body)
        content = self.body.split('\n')
        owner_id = self.owner_id
        service_name = self.service_name
        my_timestamp = self.datetime
        main_body = ''

        for line in content:
            line_length = len(line)
            if line_length < 8000:
                main_body += line + "\n\n"
                my_uuid = self.my_random_sequence()
                part_count = math.ceil(line_length / 8000)
                print('part_count: ', part_count)
                for x in range(0, part_count):
                    if x == part_count:
                        submessage = line[(x * 8000):0]
                        submessage = str(my_timestamp) + " account-" + str(owner_id) + " " + service_name + " " + submessage + " sequence " + str(my_uuid) + " part " + str(x + 1) + " of " + str(part_count) + "\n\n"
                        main_body += submessage
                        submessage = line[(x * 8000):((x + 1) * 8000)]
                        submessage = str(my_timestamp) + " account-" + str(owner_id) + " " + service_name + " " + submessage + " sequence " + str(my_uuid) + " part " + str(x + 1) + " of " + str(part_count) + "\n\n"
                        main_body += submessage
        self.body = main_body
        except botocore.exceptions.ClientError as error:
            exception_string = 'Raised exception: error: {0}'.format(error) + '\n'
            print('can not write message to out_queue:', exception_string)

    def parse_exception(self):
        # can let message process 4 times and fail to dlq, or copy directly to dlq, &/or write to ddb table
        line_split = self.msg_key_path.split("/")
        accountID = line_split[0]
        now ="%Y-%m-%dT%H:%M:%S:%f")
        ddb = boto3.client('dynamodb')
        table_name = 'AwsLogsFilterExceptions'
        if self.check_body_is_gzip() == True:
        # limiting body message to < 400kb, max limit of ddb entry
        self.body = str(self.body[0:3000])
        item = {
            'accountID': {'S': accountID},
            'eventDateTime': {'S': now},
            'region': {'S': REGION},
            'msg_key_path': {'S': self.msg_key_path},
            'tag': {'S': self.tag},
            'body_data': {'S': self.body},

            return True
        except botocore.exceptions.ClientError as error:
            print('can not write to ddb' + 'error: {0}'.format(error))
            return False

    def my_random_sequence(self,string_length=5):
        sequence = str(uuid.uuid4()).upper()
        return sequence[0:string_length]

    def transport_syslog_body_to_gz_s3(self):
        return True

The Lambda Handler

def lambda_handler(event, context):
    Flow control for aws-logs-filter.
    import aws_logs_filter
    import boto3
    import botocore
    import re
    import os
    import yaml

    # setup vars
    config_bucket = '<redacted>'
    yaml_config = 'config.yaml'
    s3 = boto3.client('s3')
    config_handle = s3.get_object(Bucket=config_bucket, Key=yaml_config)
    config_data = config_handle.get('Body').read()
    yaml_data = yaml.load(config_data)

    REGION = os.environ.get('AWS_REGION')
    FILTERS = yaml_data['FILTERS'] # TODO see if this use of FILTERS is necessary, called as function in aws_logs_filter
    jobs = yaml_data['jobs']
    aggregate_accounts = yaml_data['aggregate_accounts']

    in_bucket = '<redacted>' + REGION
    out_bucket = '<redacted>-filtered-' + REGION

    sqs = boto3.client('sqs')
    in_queue = 'https://sqs.' + REGION + '<redacted>/Log-Aggregation-Queue'
    out_queue = 'https://sqs.' + REGION + '<redacted>/Log-Aggregation-Dead-Letter-Queue'

    ddb = boto3.client('dynamodb')
    table_name = 'AwsLogsFilterExceptions'

    sent_flag = 'no'

    for record in event['Records']:

            message = aws_logs_filter.Message(sqs, record, in_queue, out_queue)
            # print(message.tag)
        except botocore.exceptions.ClientError as error:
            exception_string = 'Raised exception: error: {0}'.format(error) + '\n'
            print('can not retrieve message:', exception_string)

            filtered_result = message.filter_message()
            # print(message.tag)
        except botocore.exceptions.ClientError as error:
            exception_string = 'Raised exception: error: {0}'.format(error) + '\n'
            print('can not filter:', exception_string)

        # simple filter function, mostly relevant to us-west-2, but some global entries
        if filtered_result is True:
            print('dropped message: ', message.msg_key_path)

        # main processing flow control
        if filtered_result is False:
            print('loading message:', message.msg_key_path)
                file = aws_logs_filter.File(s3, message.msg_key_path, in_bucket, out_bucket)
                print('could not load file into memory')
                # could result in dlq messages from unprocessed from break.
                # TODO fix so this fails gracefully

                if file.check_body_is_gzip() == True:
                elif file.check_body_is_gzip() == False:

                print('file is unparsable: ', file.msg_key_path)

            for key in jobs.keys():
                #print('account id: ',file.owner_id)
                match_variable = jobs[key]['match_variable']
                action_variables = jobs[key]['action_variables']
                service_variable = jobs[key]['service_variable']
                # TODO make sure ownerID variables are in place

                match =, message.msg_key_path, re.IGNORECASE)

                if match:
                    print("service: ", file.service_name)
                    action_count = len(action_variables)
                    #print('action_count: ', action_count)
                    for i in range(0, action_count):

                        if action_variables[i] == 'firehose':

                        elif action_variables[i] == 'json':

                        elif action_variables[i] == 'text_wrap':
                        #print('end of action #', i)
                    sent_flag = "yes"

            if sent_flag == "yes":
                print('sent: ', sent_flag)
                print('write to ddb as error')
                result = file.parse_exception()
                if result is True:
                # if false, no db entry, message goes to dead letter for further processing

Cloudformation template

AWSTemplateFormatVersion: 2010-09-09
Description: >-
    Creates an instance of for filtering, wrapping and transporting
    log files so they can be pulled on-premise for further processing.

        Description: The bucket where you uploaded the zip file
        Type: String
        Description: the name of the zip file you uploaded
        Type: String
        Description: Name of this instance of the function (must be unique).
        Type: String
        Type: AWS::Lambda::Function
                    Ref: LambdaCodeBucket
                    Ref: LambdaCodeFile
                Ref: FunctionInstanceName
            MemorySize: 512
            Role: arn:aws:iam::<redacted>:role/lambda_filter_log_agg_sqs_queue
            Timeout: 900
            Description: >-
                pulls log file, wraps as syslog, segments and sequences long messages
                and puts compressed file to filter bucket
            Runtime: python3.6
        Type: AWS::Lambda::EventSourceMapping
            BatchSize: 10
            Enabled: true
            #change above to true for production
                    - ''
                      - 'arn:aws:sqs:'
                      - Ref: 'AWS::Region'
                      - ':<redacted>:Log-Aggregation-Queue'
                Ref: AWSLogsFilter           

lambda : python3 : backupRotation

This is a simple function that rotates the backup files in an s3 bucket to maintain a 30 day archive. Some variables have obfuscated.

import boto3
from datetime import datetime, timedelta
today_date = str("%y%m%d"))
one_month_delta = - timedelta(days=31)
one_month_date = str(one_month_delta.strftime("%y%m%d"))
s3 = boto3.client('s3')
my_bucket = 'mybucket'
object_list = s3.list_objects(Bucket = my_bucket)
file_list = []
for object in object_list['Contents']:
for file in file_list:
    if one_month_date in file:
        s3.delete_object(Bucket= my_bucket, Key = file)

cloudformation : yaml : simple load balancer

We are in the process of rebuilding our instrastructure stack as code from the original configurations that were all hand crafted in the console. The first and easiest was the student web server load balancer. Some variables have been changed to avoid tampering.

    Type: AWS::ElasticLoadBalancing::LoadBalancer
- "us-west-2a"
- "us-west-2b"
- "us-west-2c"
     - i-myinstance
  - LoadBalancerPort: '80'
    InstancePort: '80'
    Protocol: HTTP
    Target: HTTP:80/healthy.html
    HealthyThreshold: '2'
    UnhealthyThreshold: '5'
    Interval: '10'
    Timeout: '5'
    - subnet-1
    - subnet-2
    - subnet-3
    - sg-mysecgroup

terrafom: managing dns

I use terraform and CodeCommit to manage our dns changes for each of my current organization’s zones. The specific variables are changed for publishing, and I have edited for brevity by showing a single example of each record type. This has significantly simplified our dns management processes. We use an s3 bucket to maintain state, so each of the site techs can make changes without stepping on each other.

/*  180129 -
    Deploy for Route53
sets the region
provider "aws" {
    region = "us-west-2"
makes state document an s3 object
terraform {
  backend "s3" {
    bucket  = "myzone.terraform.state"
    encrypt = true
    key     = "global/dns/"
    region  = "us-west-2"
variable "ttl" {
    default = "60" # for rapid propagation of changes
variable "zone_id" {
    default = "myzoneid"

resource "aws_route53_record" "apex" {
    zone_id = "myzoneid"
    name = ""
    type = "A"
    ttl = "${var.ttl}"
    records = ["myip"]
resource "aws_route53_record" "print" {
    zone_id = "${var.zone_id}"
    name = ""
    type = "CNAME"
    ttl = "${var.ttl}"
    records = [""]

terraform : instantiating a webserver

This script is an example of applying infrastructure as code principles to managing cloud servers. This script code be used as a step to pre-baking an AMI for production use, however it is currently used to rebuild the server as needed, rather than interactively patching one.

/*  180228 -
    Deploy new web server
    into prodVPC 
    for testing 

# sets the region
provider "aws" {
    region = "us-west-2"

#variables changed for publishing
resource "aws_instance" "webServerDev" {
    ami = "ami-standardAMI" 
    instance_type = "m4.large" # pubProd-b
    subnet_id = "subnet-mysubnet"
    vpc_security_group_ids = ["sg-mysecgroup"]
    associate_public_ip_address = true
    key_name = "mykey"
    iam_instance_profile = "myrole"

    ebs_block_device {
        device_name             = "/dev/xvda"
        delete_on_termination   = true
        volume_type             = "gp2"
        #volume_type            ="io1"
        volume_size             = "20"
#        iops                    = "1000"

    volume_tags = {
        Name = "webServerDev"
    # bootstraps the server
    user_data = <<-EOF
        # add apache 2.2
        sudo yum install httpd -y
        sudo mkdir -p /tmp/php7
        cd /tmp/php70
        # abb webstatic and load modules
        sudo yum install latest.rpm -y
        sudo yum install --enablerepo=webtatic php70w -y
        sudo yum install php70w-opcache php70w-xml php70w-ldap php70w-pdo php70w-mysqlnd php70w-gd php70w-pecl-apcu php70w-mbstring php70w-imap geoip mod_geoip -y
        # sudo echo "<?php echo phpinfo(); ?>" > /var/www/html/index.php
        sudo yum install openldap-clients -y
        sudo yum update -y
        cd /tmp
        sudo chmod +x /tmp/
        sudo /tmp/ --region us-west-2 --non-interactive --configfile s3://mys3bucket/awslogs/awslogs.conf
        sudo mkdir /etc/awslogs
        sudo aws s3 cp s3://mys3bucket/webServer/etc/awslogs/awslogs.conf /etc/awslogs/.
        sudo service awslogs stop
        sudo service awslogs start
        cd ~
        # adds prebuilt v-hosts.conf
        sudo aws s3 cp s3://mys3bucket/webServer/etc/httpd/conf.d/v-hosts.conf /etc/httpd/conf.d/v-hosts.conf
        sudo aws s3 cp s3://mys3bucket/webServer/etc/httpd/conf.d/geoip.conf /etc/httpd/conf.d/geoip.conf
        sudo sed -i "s/DirectoryIndex\ index.html\ index.html.var/DirectoryIndex\ index.html\ index.php/g" /etc/httpd/conf/httpd.conf
        #set NameVirtualHost to work in httpd.conf
        sudo sed -i "s/\#NameVirtualHost\ \*\:80/NameVirtualHost\ \*\:80/g" /etc/httpd/conf/httpd.conf
        sudo sed -i "s/memory_limit\ =\ 128M/memory_limit\ =\ 256M/g" /etc/php.ini
        sudo sed -i "s/upload_max_filesize\ =\ 2M/upload_max_filesize\ =\ 10M/g" /etc/php.ini
        #load the /home/www directory
        sudo mkdir /home/www
        #sudo aws s3 cp --recursive s3://mys3bucket/webServer/wwwSync /home/www/
        sudo chown -R apache:apache /home/www
        sudo service httpd start
        # adding dump scripts, ldap & mysql dependencies
        # need to make sure security group access for ldap and db
        mkdir /home/ec2-user/scripts
        echo "aws s3 sync /etc s3://mys3bucket/webServer/etc/; aws s3 sync /home s3://mys3bucket/webServer/home/" > /home/ec2-user/scripts/
        sudo chmod +x /home/ec2-user/
        echo "aws s3 sync /var/log s3://mys3bucket/webServer/" > /home/ec2-user/scripts/
        sudo chmod +x /home/ec2-user/
        sudo chmod 777 /etc/crontab
        sudo echo "*/5 * * * * root /home/ec2-user/scripts/" >> /etc/crontab
        sudo echo "*/5 * * * * root /home/ec2-user/scripts/" >> /etc/crontab
        sudo chmod 644 /etc/crontab
        # loading postfix, killing sendmail, starting servics
        sudo chkconfig sendmail off
        sudo service sendmail stop
        sudo yum install postfix -y
        sudo aws s3 cp s3://mys3bucket/webServer/etc/postfix/ /etc/postfix/
        sudo chkconfig postfix on
        sudo service postfix start
        sudo chkconfig httpd on
        sudo service httpd start

# names the instance
    tags {
        Name = "webServerTesting"


# attach to elb
resource "aws_elb_attachment" "webServerAttachment" {
    elb = "webServer"
    instance = "{}"

# sets dns A record
resource "aws_route53_record" "testServer" {
    zone_id = "myzoneid"
    name = "testwebserver.mydomain"
    type = "A"
    ttl = "300"
    records = ["${aws_instance.webServerDev.public_ip}"]

# sends public ip to terminal STDOUT
output "public ip" {
    value = "${aws_instance.webServerDev.public_ip}"

python: boto3 : multi-threading

This script takes a list of AWS account numbers and scans each one for deployed AWS services, then writes the data to a table. The is the library outlined here. This is my first multi-threaded script, which cut down the task of scanning 275 AWS accounts from 4 hours to 12 minutes.

""" flow control for AWS account service interrogation, using multithreading to speed up the process. """
from datetime import datetime
from multiprocessing import Process
import time
import boto3
import icstools
tempDateTime ="%Y-%m-%d")
dynamodb = boto3.client('dynamodb')
tablename = 'awsAccountAudit' + '-' + str(tempDateTime)
table = icstools.createTable(dynamodb=dynamodb, tablename=tablename)
time.sleep(30) # give time for table to instantiate
open removed-for-privacy/has-readOnly-role.txt as list
s3 = boto3.resource('s3')
obj = s3.Object('removed-for-privacy', 'has-readOnly-role.txt')
body = obj.get()['Body'].read()
hasRoleList = body.decode().split('\n')

def accountIterate(session, id):
    question = icstools.AwsAccount(session, id)
        result = question.ec2Services()
        icstools.writeDB(dynamodb, tablename, result)
        print('no ec2 results in ', id)

--- truncated for brevity. each service in library is called and output written to table

# multiprocess array
procs = []
for newID in hasRoleList:
    session = icstools.assumeRoleSession(newID)
    # create processes for each account iteration
    proc = Process(target=accountIterate, args=(session, newID))
run each proc in list of procs
for proc in procs: