AWS Certified Solutions Architect – Associate

I’ve been doing a Udemy course as a preparation for the AWS Certified Solutions Architect – Associate. These are my summary notes

AWS Certified Solutions Architect – Associate

Exam

  • 130 minutes
  • 60 questions
  • Results are between 100 – 1000, pass: 720
  • Scenario based qestions

IAM

  • Users
  • Groups
  • Roles
  • Policis

Users are part of Groups Resources have Roles : i.e, for an instance to connect to S3, it needs to have a role All the User groups and Roles get their permissions are through Policies, which are defined by json:

# God mode policy
{
    "Version":"2019-01-01",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "*",
            "Resource": "*"
        }
    ]
}

(Creating policies is not part of the exam)

General

  • IAM is cross-regional
  • "root account" is the account created with the first setup of the AWS account, and has complete Admin access.
  • New users have no permissions until assigned
  • New users are assinged Access Key Id and Secret Access Keys when created, for the api access.

S3

  • Key – Value Object based, with metadata and versioning
  • Has access control lists
  • Max 5TB file size
  • Buckets are universal namespace (https://s3-{region}.amazonaws.com/{bucket})

Consistency model:

  • Read After Write consistency – object will be availabe for readdirectly after being written
  • Eventual consistency for overwrite PUTS and DELETES

Storage Tiers/Classes

  • S3 Standatrd – 99.99% availability, 99.999999999% durability, cross-devices, cross-facilities redundancy, designed to sustain loss of 2 facilities at the same time.
  • S3 – IA (infrequently access) – for data accessed less frequently. Lower storage fee, but has a retrieval fee. S3 One Zone – IA : the same as IA, only in 1 AZ. (cheaper)
  • Glacier: Very cheap, archival only. Standard retrieval time takes 3 – 5 hours.

Cross Region Replication (CRR)

  • Requires versioning enaled on the source bucket

CloudFront

  • Edge Location – the location the content will be cached: Per AWS Region (They are not read only, you can write to them too, and they will replicate to the origin and from there to others)
  • Clearing cache cost money 🙂
  • Origin – The original file location: S3 bucket, EC2 instance, ELB, or Route53
  • Distribution – all the locations of the Edges you defined
  • Can distribute dynamic, static, streaming and interactive content (Web Distribution: most common, for websites; RTMP – media streaming)

EC2

Placment groups

Two types:

  1. Cluster placment group – A group of instances within a single AZ that need low latency / high throughput (i.e cassandra cluster). Only available for specific types.
  2. Spread placment group – A group of instances that need to be place seperatly from each other
  • Placment group name myst be unique within aws account
  • Only available for certain instance types
  • Recommended to use homogenous instances within placment group
  • You can’t merge placment groups
  • You can’t move an exisitng instance to a placment group, only create it into it

EFS

  • Supports NFSv4
  • Only pay for used storage
  • Scales up to petabytes
  • Support thousands of concurrent NFS connections
  • Data is stored across multiple AZ within region

Route 53

DNS overview

  • NS – Name Server record. Meaning, if I go to helloRoute53gurus.com, and I’m the first one to try it in my ISP, then the ISP server will ask the .com if it has NS recored for helloRoute53gurus. The .com will have a record that maps it to ns.awsdns.com. So it’ll go to ns.awsdns.com , which will direct it to Route53..\
  • A – short for Address – that’s the most basic record, and it’s the IP for the url
  • TTL – time to live – how long to keep in cache
  • CNAME – resolve one domain to another (can’t be used for ‘naked’ domain names, e.g: ‘www.google.com’ )
  • Alias – unique to Route53, the may resource records to Elastic Load Balancer, CloudFron, or S3 bucket that are configured as websites. They work like CNAME (www.example.com -> elb1234.elb.amazonaws.com)
  • MX record – email records
  • PTR Records – reverse lookups

ELB do not have predefined IPv4 addresses, you resolve them using a dns name. So basically, if you have the domain "example.com" and you want to direct it’s traffic to an ELB, you need to use an Alias (not a cname, because it’s a naked domain name!, and not an A record because it has no IP)

Routing Policies

  • Simple Routing – 1 record with multiple ips addresses, randomly returned. No health checks
  • Weighted Routing – 1 record with N% goes to one rcecord, and M% to another and so forth
  • Latency Based Routing – Route 53 will send to the region with the lowest latency
  • Failover Routing – Health check based Primary/Secondary routing: if the primary instance fails (health check = false), directs to the secondary
  • Geolocation Routing – config which geo location goes to which instance
  • Multivalue Answer Routing – Several records, each with ip addresses, and health check for each resource. The ips will return randomlly, so it’s good for disparsing traffic to different resources.

VPC

  • NAT Gateways – scaled up to 10G, no need to patch/add security groups/assign ip (automatic), they do need to be updates in the routing table (so they can go out via igw)
  • Network ACL –
    • It’s like a SG, in the subnet level.
    • Each subnet is associated with one, but default it’s blocking all in/out bound traffic. you can associate multiple subnets to the same ACL, but only 1 ACL per subnet.
    • The traffic rules are evaluated from the lowest value and up.
    • Unlike SG, opening port 80 for incoming will not allow outbound response on port 80. If you want to communicate on port 80, you’d have to define rule both for inbound and outbound. (Otherwise, it’ll go in and not out)
    • You can block IP addresses using ACL, you can’t with SG
  • ALB – you need at least 2 public subnets for an Application Load Balancer

Application Services

SQS

  • Distributed Pull based Messaging queue
  • Up to 256 Kb messages
  • Default retention: 4 days, max 14 days
  • Default promisese "at-least-once", "FIFO" promises exactly once with ordering
  • Can poll with timeout (like kafka)
  • Visibility – once message is consumed, it’s marked as "invisible" for 30 seconds (default, max is 12 hours), and if it’s not marked as "read" within that time frame, it returns to be visible and re-distributed to another consumer.

SWF – Simple Workflow Service

  • Kind on amazon ETL system, with Workers (who process jobs) and Deciders (who control the flow of jobs). The system enables dispatching of jobs to multiple workers (which makes it easily scalable), tracking the jobs status, and so forth.
  • SWF keeps track of all the tasks and events in an application (in SQS you’d have to do it manually)
  • Unlike SQS, In SWF a task is assigned only once and never duplicated (What happens if the job fails? IDK).
  • SWF enables you to incorportae human interaction – like, if someone needs to approve received messages, for example

SNS – Simple Notifications Services

Delivers notification too:

  • Push notifications

  • SMS

  • Email

  • SQS queue

  • Any http endpoint

  • Lamda functions

  • Messages are hosted in multiple AZ for redundancy

Messages are agregated by Topics, and recipients can dynamically subscribe to Topics.

Elastic Transcoder

  • Convert video files between formats – like formatiing video files to different formats for portable devices

API Gateway

Basically a front API for your lamda/internal APIs, with amazon capabilities:

  • API Caching – caching responses to an request api with TTL
  • Throttling requests to prevent attacks

Kinesis

3 types:

  • Streams – Kafka (Retention : up to 7 days) – Shards = partitions (?)
  • Firehose – Fully automated, no consumers, No retention, No shards – Can be written to s3 / elastic
  • Analytics – run SQL queries on the streams/firehose streams, and write the result to s3 /elsastic

Basic Lambda to call internal VPC api

Product team decided they wanted a specific event to happen every time a specific email address receives an email. The first option was to poll mail server and analyse all the received emails (yuck!).

The other option was to use AWS tools. In our case, forward the email to SES and call a lambda that will trigger our internal service with all the email meta-data – Which is exactly what we did.

Few pointers before the code:

  1. Lambda doesn’t load node dpendencies – so if you want to use some external packages except aws, you’d need to upload a zip with the dependencies and your code
  2. If you want to call an internal VPC resource, you need to:
    1. Assign the lambda to your VPC
    2. Assign the lambda a security group that will enable it to work from within the vpc

After you’ve assigned the Lambda to the VPC and setup the SG, the rest is easy:

const axios = require('axios'); //Loaded in the zip file!

console.log(`Url: http://${process.env.SERVICE_URL}`)

exports.handler = (event, context, callback) => {
    console.log(`Inside lambda: ${JSON.stringify(event)}` )
    
    axios.post(`http://${process.env.SERVICE_URL}`, event)
        .then((res) => {
            console.log(res);
            callback(null, res);
        })
        .catch((error) => {
            console.error(error);
            callback(error);
        });
        
    console.log(`Logging out`)    
};

You can find all the event types here: Sample Events

The SES -> Lambda invocation only send the Email’s meta-data. If you want to have the email content, you’d need to use SNS (so SES -> SNS Topic -> Lambda), but bear in mind that SNS only supports emails up to 150K, so for anything larger, you’d need to move to S3

Postgres – Logging queries

We had an issue with some JQL queries returning weird results from the db, so we wanted to see exactly what’s arriving to the psql service. To see that:

  1. Edit the config file: /var/lib/postgresql/data/postgresql.conf

  2. Unmark and change the following:

#logging_collector = off                # Enable capturing of stderr and csvlog
                                        # into log files. Required to be on for
                                        # csvlogs.
                                        # (change requires restart)

# These are only used if logging_collector is on:
#log_directory = 'pg_log'               # directory where log files are written,
                                        # can be absolute or relative to PGDATA
#log_filename = 'postgresql-%Y-%m-%d_%H%M%S.log'        # log file name pattern,
                                        # can include strftime() escapes
[...]
log_statement = 'none'                   # none, ddl, mod, all
  • The logging_collector should be set to on to enable logging
  • The log_statement should be set to all to enable query logging
  • The log_directory and log_filename can stay the same, depends on what you want.

So your line should look like:

#logging_collector = on                # Enable capturing of stderr and csvlog
                                        # into log files. Required to be on for
                                        # csvlogs.
                                        # (change requires restart)

# These are only used if logging_collector is on:
#log_directory = 'pg_log'               # directory where log files are written,
                                        # can be absolute or relative to PGDATA
#log_filename = 'postgresql-%Y-%m-%d_%H%M%S.log'        # log file name pattern,
                                        # can include strftime() escapes
[...]
log_statement = 'all'                   # none, ddl, mod, all

Now restart your service, and you’re good to go : the logs will be at /var/lib/postgresql/data/pg_log

Don’t run this on production, as it will seriously fuck up your performance!

AWS Re:Invent 2018 – My Top Sessions

I’m planning to upload a different post on each one of the sessions I liked at the Re:Invent 2018, but for now, just to have everything at one place, here is the short list:

SVR322 – From Monolith to Modern Apps: Best Practices

We are a lean team consisting of developers, lead architects, business analysts, and a project manager. To scale our applications and optimize costs, we need to reduce the amount of undifferentiated heavy lifting (e.g., patching, server management) from our projects. We have identified AWS serverless services that we will use. However, we need approval from a security and cost perspective. We need to build a business case to justify this paradigm shift for our entire technology organization. In this session, we learn to migrate existing applications and build a strategy and financial model to lay the foundation to build everything in a truly serverless way on AWS.

SlideShare

ARC337 – Closing Loops and Opening Minds: How to Take Control of Systems, Big and Small

Whether it’s distributing configurations and customer settings, launching instances, or responding to surges in load, having a great control plane is key to the success of any system or service. Come hear about the techniques we use to build stable and scalable control planes at Amazon. We dive deep into the designs that power the most reliable systems at AWS. We share hard-earned operational lessons and explain academic control theory in easy-to-apply patterns and principles that are immediately useful in your own designs.

Slideshare

ARC403 – Resiliency Testing: Verify That Your System Is as Reliable as You Think”

In this workshop, we illustrate how to set up your own resiliency testing. We set up a simple three-tier architecture and explore the failure modes with Bash and Python scripts. To participate, you need an account that can run AWS CloudFormation, AWS Step Functions, AWS Lambda, Application Load Balancers, Amazon EC2, Amazon RDS (MySQL), and the AWS Database Migration Service, and Route53.

(Sorry, couldn’t find youtube / slides 😦 )

ARC335 – Failing Successfully in the Cloud: AWS Approach to Resilient Design

AWS global infrastructure provides the tools customers need to design resilient and reliable services. In this session, we discuss how to get the most out of these tools.

(Sorry, couldn’t find youtube / slides 😦 )

SRV305 – Inside AWS: Technology Choices for Modern Applications

AWS offers a wide range of cloud computing services and technologies, but we rarely give opinions about which services and technologies customers should choose. When it comes to building our own services, our engineering groups have strong opinions, and they express them in the technologies they pick. Join Tim Bray, senior principal engineer, to hear about the high-level choices that developers at AWS and our customers have to make. Here are a few: Are microservices always the best choice? Serverless, containers, or serverless containers? Is relational over? Is Java over? The talk is technical and based on our experience in building AWS services and working with customers on their cloud-native apps.

Couldn’t find slides, but someone blogged about it here

AWS Re:Invent 2018 – Recap

On November 2018 I was on my first AWS Re:Invent convention in Vegas. This was an amazing experience, which I highly recommend to anyone working with AWS (don’t we all?).

The sheer size of the convention (50K people!), the volume of sessions and products and above all, the amazing diversity of occupations and fields people came from was mind blowing.

Following is a short recap of the lessons I learned my first time in AWS Re:Invent – from registration to what not to miss (and what you can feel free to miss) in the event, and how to survive it.

Question 1: How much does it costs?

The registration fee is $1,800, and staying in a nice hotel for 6 nights was ~$800. In addition, you have 6 days of not getting any work done, plus flights .

Question 2: Is it worth it?

For me, as a DevOps/Developer with our entire fleet hosted on AWS – Totally. But I must say that the most valuable things I learned at Re:Invent was not so much the AWS services tutorials/announcements, but the sessions where professionals from around the world shared their experiences with moving/building/expanding to AWS infrastructure. If you want to convince your supervisor why it’s good, AWS even have a ready justification letter 🙂

Question 3: OK, I’m in. Should I register in advance to sessions? How? Where?

So, registration to sessions is really the weak side of the Re:Invent convention. The website looks like a relic from the 1990’s, searching is very hard and unintuitive, the calendar option only appeared a week after the registration was opened – seriously, terrible.

After you got over you shock that this is the entry point to what is the largest developers convention I know, few tips:

  • Most big sessions are held more than once, so can find them on other days/venues
  • A lot of sessions are broadcasted live in different venues (and even in the same venue) – So if you couldn’t find a seat, you still have a chance to see it.
  • The system won’t let you schedule 2 sessions less the 30 minutes apart if they are in different venues – take that into consideration.
  • Registration to sessions ends fast. I had all my desired sessions opened in different tabs, and the moment the registration opened I clicked “Register” on each one of them – and still didn’t get a seat in some.
  • New sessions and additional screenings are added all the time during the convention, and people replace and free their seats. Keep your “favourite” lists and check daily if something interesting has opened up.
  • Sessions level – anything lower than 300 is very basic. Only go if it’s something totally new for you / you’re new to AWS
  • Session types:
    • Workshops – vary significantly in their value: Some of them a really good, but in most of them you’re just following a github-hosted tutorial and have 2 AWS personal going around and assisting you with technical issues. I must say most of the workshops weren’t very valuable to me
    • Chalk-talk – Most chalk talks I’ve been at had 2 very experienced engineers, sharing their experiences on various topics. These were some of my best sessions.

Question 4: What to see?

Re:Invent really has a lot of extracurricular activities (Bar crawl, races, 4k runs, the expo, and so forth). I admit I haven’t attended to most of them – I arrived late Sunday night and had a 10 hours time diff to get over, so most nights I was in a zombie state, and I’m not very good networker. If you are (and you’re not jet-legged to death) – go!

The Expo: I guess you’ve heard all the urban legends of the wonderful land of expo, where swag is abundant and freely given. Well, it’s true, a lot of things are freely given, but you will have to stand in line for hours for some tech-labeled-socks. For the really valuable things, you’d have to compete with people, register to listen to some sales pitch you’re not interested at, and generally waste your time. My recommendation – skip it. If you have a free hour at the Venetian, go have a look – but trust me, no need to plan your schedule around it.

The Quad, however, is waaaay more interesting. You get a chance to play and build robotic legos and other things!

re:Play Party: I’ve only been to one, but I must say it wasn’t that impressive. I mean, go – it’s already paid for in your ticket, but let’s say I didn’t have any remorse for leaving early….

Question 5: What to wear? Where to eat? How to get around?

Unless you’re presenting something – snickers, jeans, and a t-shirt. Get a light jacket for the over-conditioned lecture halls and the rides between places, but most of the time the temperature is really office-like. (You’ll spend most of your time indoors anyway)

The food halls are enormous, but the food is really good – they always have gluten free choices, btw! – and food and drinks are abundant , to the point where you get snack when getting off the shuttles. I haven’t been to any of the breakfasts, only lunches, but I guess the standard is the same. Basically, you’ll only have to eat dinner on your own expense.

Getting around the venues is extremely easy with the shuttles. Before I arrived I heard from a lot of people that in previous years the shuttles were really bad, and that I should base my mobility on Uber – but at least this year I can attest that the shuttles were rapid, fast and convenient.

General Tips

  • DO NOT buy coffee at Starbucks. They have (good) coffee/tea/soda stands everywhere around the lecture halls. Save your money and time (the queues are infinite)
  • Constantly fill you water bottle (They have refill stands everywhere)
  • Carry a lip balm on your person. Vegas is dry as hell.
  • There are electricity outlets literally everywhere, and the wifi was surpassingly good.

How to handle runtime exceptions in Kafka Streams

We’ve been using Kafka Streams (1.1.0, Java) as the backbone of our μ-services architecture. We’ve switched to stream mainly because we wanted the exactly-once processing guarantee.

Lately we’ve been having several runtime exceptions that killed the entire stream library and our μ-service.

So the main question was – is this the way to go? After a few back and forth, we realized that the best way to test this is by checking:

  1. What would Kafka do?
  2. Do we still keep our exactly-once promise?

What does Kafka do?

This document is the Kafka Stream Architecture design. After the description of the StreamThread , StreamTask and StandbyTask, there’s a discussion about Exceptions handling, the gist of which is as follows:

First, we can distinguish between recoverable and fatal exceptions. Recoverable exception should be handled internally and never bubble out to the user. For fatal exceptions, Kafka Streams is doomed to fail and cannot start/continue to process data. […] We should never try to handle any fatal exceptions but clean up and shutdown

So, if Kafka threw a Throwable at us, it basically means that the library is doomed to fail, and won’t be able to process data. In our case, since the entire app is built around Kafka, this means killing the entire μ-service, and letting the deployment mechanism just re-deploy another one automatically.

Do we still keep our exactly-once promise?

Now we’re faced with the question whether or not this behaviour might harm our hard-earned exactly-once guarantee.
To answer that question, we first need to understand when the exactly-once is applicable.

exactly-once is applicable from the moment we’re inside the stream – meaning, our message arrived at the first topic, T1. So everything that happens before that is irrelevant: the producer who pushed the message to T1 in the first time could have failed just before sending it, and the message will never arrive (so not even at-least-once is valid) – so this is something we probably need to handle, but that doesn’t have anything to do with streams.

Now, let’s say our message, M, is already inside topic T1. Hooray!

Now we can either fail before reading it, while processing it, and after pushing it.

  • If we failed before reading it, we’re fine. The μ-service will go up again, will use the same appId, and we’ll read the message.
  • If we read it and failed before we even started processing it, we’ll never send the offset commit, so again, we’re fine.
  • If we failed during processing it, again, we’ll never reach the point of updating the offsets (because we commit the processed message together with the consumer offset – so if one didn’t happen, neither did the other)
  • If we failed after sending it – again, we’re fine: even if we didn’t get the ack, both the consumer offset and the new transformed/processed message are out.

Uncaught Exception Handlers

Kafka has 2 (non-overlapping) ways to handle uncaught exceptions:

  • KafkaStreams::setUncaughtExceptionHandler – this function allows you to register an uncaught exception handler, but it will not prevent the stream from dying: it’s only there to allow you to add behaviour to your app in case such an exception indeed happens. This is a good way to inform the rest of your app that it needs to shut itself down / send message somewhere.

  • ProductionExceptionHandler – You can implement this class and add it via the properties: StreamsConfig.DEFAULT_PRODUCTION_EXCEPTION_HANDLER_CLASS_CONFIG – but in this case, you will need to decide if the stream can keep going or not, and I feel this requires very deep understanding of the internals of the streams, and I’m still not sure when exactly you would want that.

Conclusion

For us, using k8s deployments, with n number of pods of each service being automatically scaled all the time, the best way to handle runtime/unchecked exceptions is to make sure our app goes down with the Kafka Stream library (using the KafkaStreams::setUncaughtExceptionHandler), and letting the deployment service take care of starting the app again.