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AWS comes with abounding apparatus that awning altered areas of concerns. But, best are not accepted purpose and bargain abundant to be activated correctly. Acclimated in the amiss context, they end up crumbling time, resources, money, and will actualize developer abrasion and frustrations for months.

Here are the top 5 AWS Services you should avoid:

Cognito sounds abundant on paper. It will booty affliction of user administration for you, including Google Sign-in, Facebook Login, and handle user role assignments, displace passwords, and ascertain countersign rules. It can do both Desktop and Mobile. It can assignment with accessible user pools, and clandestine user pools (to bind action clandestine resources).

Cognito will advice you save time by presenting its own UI back requesting permission from users.

Cognito has competitors: Auth0, OneLogin, Okta, to name a few. It is additionally accessible to apparatus all of those aloft login workflows yourself, accustomed abundant time and aggregation size.

But, if you do use Cognito for a while, animal edges will acknowledge themselves. You will ascertain that Cognito cannot be acclimated for Built-in Adaptable logins. Back you are authoritative a adaptable application, and attempting to get user to login via Facebook, if you had chip with Firebase, it will alert the user to accessible the Facebook App, and acquiesce user to artlessly authenticate/authorize (assuming they are already logged in on the Facebook App).

With Cognito, they are presented with an anchored WebView, bidding the user to log into Facebook afresh in the anchored WebView. This creates a lot of friction, so you book tickets to the aggregation allurement about Built-in Logins.

Cognito does affiance that it will assignment with any OAuth2 Identity provider. We anticipation this meant we could use it to login with WeChat, to abate abrasion in user allotment for Apps in China. After research, we concluded up activity with WeChat’s own SDK. There is no simple way of implementing simple amusing logins with Cognito.

If Adaptable User administration is in your requirement, you would do bigger with Firebase Amusing Login. It supports Facebook and Google built-in amusing logins out of the box, and gives you abundant abutment on the server ancillary to actualize the user almanac for WeChat users.

There is additionally the advantage of rolling your own affidavit action and use anniversary of the SDK provided by Google/Apple/Facebook/WeChat. This is the safest avenue if you can allow the time and engineering resource. But, if you are abbreviate on manpower/time, absolutely do not go with Cognito. It cannot bear in the Adaptable context.

I abhorrence CloudFormation. I abhorrence “Stacks.” Parameters are actual bulky to assignment with. The formatting of the arrangement files is cool verbose. Of course, the new YAML CF is bigger than the old JSON CF, but it’s still boxy to apprehend continued circuitous stacks. Additionally you still acquire an internet abounding of JSON CloudFormation as your anatomy of reference.

Circular annex errors are body crushing.

Do you charge to advertence the attributes of one ability in a altered stack? Can’t. You charge to do outputs. Okay what if an achievement of that assemblage changes and needs to be reflected in the added stack? You charge to bethink area it’s acclimated and accomplish chiral intervention.

Do you appetite to modularize a allotment of the basement so you can reclaim it beyond assorted resources? Sorry, you charge to use “nested stacks” so you can acquire alike added assemblage on endless that are activity to get ashore in IN_PROGRESS or ROLLBACK mode. To be clear, what I am adage is, do not use nested stacks.

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Lack of Alluvion apprehension or reconciliation. With abridgement of alluvion apprehension comes abundant uncertainty.

I don’t like all the Fn:sub, ! Ref, Fn:join. I candidly cannot brainstorm a accomplished basement with assorted accounts, VPCs, subnets, analytical access accurate absolutely in CloudFormation.

Luckily the band-aid is easy! Use Terraform! Terraform is cool alarming and we acquire been appliance it back aboriginal 2012. It’s billow agnostic. You acquire a abundant bore arrangement that you can use and advantage heavily after fearing for your life.

You can alike do argumentation on top of these modules to add or not add assets based on variables appliance terraform’s calculation attribute.

Here’ s a abundant commodity about alluvion with Terraform.

HCL is admirable to apprehend and write. Terraform plan is amazing for belief basement changes and giving you the aplomb that you are alteration what you expect. (Yes CF has changesets if you must).

Moral of the adventure is, use terraform to destress yourself. Use terraform so that you do not absorb a accomplished day annoyed over how you will possibly accomplish changes to a assemblage that is depended on from aloft and beneath by added stacks. Terraform plan. Terraform apply.

Many a startup has collapsed casualty to ElastiCache. It usually happens back the aggregation is under-staffed and hasty for a borderline and they blazon in Redis into the AWS console:

On the bureaucracy screen, you name your cluster, acquire the defaults.

Voila! Begin appliance Redis. Perfect.

Obviously it works great.

What’s the problem? It is expensive, and no one notices for months. We’ve apparent this appear a few times already back we did amount assay for a brace clients. Firstly, the absence gives you a array with able-bodied cache.r5.large instance. Secondly that cache.* prefix agency this instance costs $0.216/hr instead of $0.126/hr, a 71.4% premium. Again you ability anticipate you charge one for dev, qa, and prod. That all adds up to $$$. You don’t appetite to be the one who acquired the $3000 ElastiCache band account on the AWS bill.

Run a Docker redis in ec2 for a while first. Accomplish abiding it serves your purposes. Figure out how the allocation works for you. Bethink redis is meant to authority brief abstracts in memory, so amuse don’t amusement it as a DB. It’s best to architecture your arrangement bold that redis may or may not lose whatever is inside. Clustering or HA via Sentinel, can all appear after back you apperceive you charge it!

Kinesis, in Amazon’s own words:

Streaming abstracts such as video processing/uploading are a acceptable fit for Kinesis. But, not all data/records/events should go into Kinesis. It is not a accepted purpose action accident bus or queue.

A little context:

We already abutting on a activity midway, and acquire to abutment an ETL activity that included Kinesis in the deployment. The appliance calm baby json records, and blimp them into Kinesis with the python boto3 api . On the added side, artisan action active central EC2/ECS were affairs these annal with boto3 and processing them. We again apparent that retrieving annal out of Kinesis Streams back you acquire assorted artisan is non-trivial.

Imagine if you are active 4 workers, and they are all alert to the aforementioned Kinesis beck for events. How do we anticipate them from alive on the aforementioned events? In acceptable queueing systems, you use locks and buyer columns to ensure anniversary assignment is handled by alone one worker. Amazon SQS will mark a bulletin as active back one of the artisan is alive on the message, so that added workers do not do alike work.

To apparatus the aforementioned arrangement with Kinesis Stream, you charge apparatus KCL, which is accounting in Java (nothing amiss with Java). The KCL abettor will be spun up as a additional apparition action in the background, and accelerate contest to your appliance back it itself receives events.

This means: all of your python/ruby/node docker images that handles annal from Kinesis charge install Java dependencies and run the KCL java action in the background. KCL communicates with the capital affairs via std-in/out/err, so logging from the capital appliance becomes ambiguous as well! If you acquire a bug in the application, you will not be able to book out accordant logs to STDOUT, because it will account errors in the KCL agent.

Furthermore, in adjustment to acquire assorted workers, Kinesis Streams crave you to use assorted shards. Anniversary artisan will accomplish affirmation to a shard. Added shards = added bills. This artisan to atom mapping is stored in DynamoDB! So, to recap: in adjustment to use Kinesis as an action queue, you must:

It is both bulky and difficult to administer Kinesis beck correctly, abnormally back you are attractive for an action queue. You would do bigger by appliance SNS/SQS combination, or appliance a queuing framework that sits on top of Redis or acceptable databases.

Lambdas are abundant for the afterward tasks:

Lambda is abhorrent for:

A backup for REST API endpoints.

Take an archetype of online book arcade REST api:

A complete centralized REST API endpoints can and will acquire hundreds of REST routes.

Normally, a aggregation may accept an API aperture or Nginx to arise assorted microservices, one for books, one for users, one for carts, etc. Or, they ability astute up and use a brace and put all 4 modules into one backend repository.

With the Lambda server-less paradigm, you end up with 1 lambda action per route.

Each deployment now agency you are blame updates for hundreds of lambda resources.

It becomes difficult to analysis distinct routes and attending at the cipher in AWS’s Lambda UI. Anniversary lambda is configured via Ambiance variables individually. If you acquire 5 variables to affix into a MySQL RDS instance, it aback agency you acquire 5 * 100 routes = 500 variables to configure central the billow accumulation template.

Furthermore, anniversary of them will try to authorize database access back administration requests. There is actually no out of box way to allotment assets amid lambdas for beginners.

Next, QA comes to you and ask about a backend to use with QA environments. You advance to add “QA-” prefix to all lambda names and attending at the lambda dashboard. Now, you acquire alert as abounding as baby lambdas.

Next, DevOps comes to you and ask about a backend to use with Production environments. You advance to add “Prod-” prefix to all lambda names and attending at the dashboard. Now, you acquire thrice as abounding as baby lambdas.

You again ascertain bugs aural your code, arch to appeal abettor throwing HTTP 500 errors, barter accuse about things not working. You dig into CLOUD WATCH logs. We acquire three sets of logs all intermixed together. This is unreadable.

A brace of weeks later, you ascertain that a assistant arrested in cipher such that lambdas are now calling anniversary added recursively. This is absolutely bad, because we no best apperceive how continued a appeal can booty now to complete. This is not a Lambda alone problem, microservices can alarm anniversary added by mistakes (and this is why we should use Monoliths back we can).

QA comes to you, they are allurement how they can run the absolute backend assemblage locally with docker (A reasonable affair to ask), so they can run selenium or cucumber tests adjoin it. How would you do it back you acquire several hundreds routes, anniversary angry up in a baby lambda? You can’t. Your laptop is not able abundant to run all of the routes at once.

DevOps comes to you, we would additionally like to run assemblage tests in CircleCI or Jenkins to analysis your affection annex afore it gets alloyed in. But, we acquire bound accretion assets and cannot circuit up hundreds of baby docker processes aloof to run the assemblage tests.

What should acquire backward as a Monoliths or baby and abstracted microservices are now absurd to run all at already in a bounded environment.

Do not attack to use Lambda for REST API endpoints if you abundance your commitment time, sanity, and assignment throughput. Use a approved web framework for implementing REST APIs: express/flask/spring/go-revel/rails. They all assignment as intended, and you will acquire an easier time appliance them.

Well there you go. 5 AWS Services we adulation to hate. Obviously there are able means to use all of these, but if you are new, footstep anxiously or apprentice some architecture patterns from experts!

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Last Updated: March 25th, 2020 by admin
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