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AWS Lambda vs Kafka: What are the differences?

Developers describe AWS Lambda as "Automatically run code in response to modifications to objects in Amazon S3 buckets, messages in Kinesis streams, or updates in DynamoDB". AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security. On the other hand, Kafka is detailed as "Distributed, fault tolerant, high throughput pub-sub messaging system". Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

AWS Lambda and Kafka are primarily classified as "Serverless / Task Processing" and "Message Queue" tools respectively.

Some of the features offered by AWS Lambda are:

  • Extend other AWS services with custom logic
  • Build custom back-end services
  • Completely Automated Administration

On the other hand, Kafka provides the following key features:

  • Written at LinkedIn in Scala
  • Used by LinkedIn to offload processing of all page and other views
  • Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)

"No infrastructure", "Cheap" and "Quick" are the key factors why developers consider AWS Lambda; whereas "High-throughput", "Distributed" and "Scalable" are the primary reasons why Kafka is favored.

Kafka is an open source tool with 12.5K GitHub stars and 6.7K GitHub forks. Here's a link to Kafka's open source repository on GitHub.

According to the StackShare community, AWS Lambda has a broader approval, being mentioned in 1002 company stacks & 585 developers stacks; compared to Kafka, which is listed in 501 company stacks and 451 developer stacks.

Advice on AWS Lambda and Kafka

Need advice on what platform, systems and tools to use.

Evaluating whether to start a new digital business for which we will need to build a website that handles all traffic. Website only right now. May add smartphone apps later. No desktop app will ever be added. Website to serve various countries and languages. B2B and B2C type customers. Need to handle heavy traffic, be low cost, and scale well.

We are open to either build it on AWS or on Microsoft Azure.

Apologies if I'm leaving out some info. My first post. :) Thanks in advance!

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Replies (2)
Anis Zehani

I recommend this : -Spring reactive for back end : the fact it's reactive (async) it consumes half of the resources that a sync platform needs (so less CPU -> less money). -Angular : Web Front end ; it's gives you the possibility to use PWA which is a cheap replacement for a mobile app (but more less popular). -Docker images. -Kubernetes to orchestrate all the containers. -I Use Jenkins / blueocean, ansible for my CI/CD (with Github of course) -AWS of course : u can run a K8S cluster there, make it multi AZ (availability zones) to be highly available, use a load balancer and an auto scaler and ur good to go. -You can store data by taking any managed DB or u can deploy ur own (cheap but risky).

You pay less money, but u need some technical 2 - 3 guys to make that done.

Good luck

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My advice will be Front end: React Backend: Language: Java, Kotlin. Database: SQL: Postgres, MySQL, Aurora NOSQL: Mongo db. Caching: Redis. Public : Spring Webflux for async public facing operation. Admin api: Spring boot, Hibrernate, Rest API. Build Container image. Kuberenetes: AWS EKS, AWS ECS, Google GKE. Use Jenkins for CI/CD pipeline. Buddy works is good for AWS. Static content: Host on AWS S3 bucket, Use Cloudfront or Cloudflare as CDN.

Serverless Solution: Api gateway Lambda, Serveless Aurora (SQL). AWS S3 bucket.

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Needs advice

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

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Replies (4)
Maheedhar Aluri

Kafka is an Enterprise Messaging Framework whereas Redis is an Enterprise Cache Broker, in-memory database and high performance database.Both are having their own advantages, but they are different in usage and implementation. Now if you are creating microservices check the user consumption volumes, its generating logs, scalability, systems to be integrated and so on. I feel for your scenario initially you can go with KAFKA bu as the throughput, consumption and other factors are scaling then gradually you can add Redis accordingly.

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Angular 2Angular 2

I first recommend that you choose Angular over AngularJS if you are starting something new. AngularJs is no longer getting enhancements, but perhaps you meant Angular. Regarding microservices, I recommend considering microservices when you have different development teams for each service that may want to use different programming languages and backend data stores. If it is all the same team, same code language, and same data store I would not use microservices. I might use a message queue, in which case RabbitMQ is a good one. But you may also be able to simply write your own in which you write a record in a table in MSSQL and one of your services reads the record from the table and processes it. The most challenging part of doing it yourself is writing a service that does a good job of reading the queue without reading the same message multiple times or missing a message; and that is where RabbitMQ can help.

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We found that the CNCF landscape is a good advisor when working going into the cloud / microservices space: When choosing a technology one important criteria to me is if it is cloud native or not. Neither Redis, RabbitMQ nor Kafka is cloud native. The try to adapt but will be replaced eventually with technologies that are cloud native.

We have gone with NATS and have never looked back. We haven't spend a single minute on server maintainance in the last year and the setup of a cluster is way too easy. With the new features NATS incorporates now (and the ones still on the roadmap) it is already and will be sooo much mure than Redis, RabbitMQ and Kafka are. It can replace service discovery, load balancing, global multiclusters and failover, etc, etc.

Your thought might be: But I don't need all of that! Well, at the same time it is much more leightweight than Redis, RabbitMQ and especially Kafka.

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Amit Mor
Software Architect at Payoneer · | 3 upvotes · 305.3K views

I think something is missing here and you should consider answering it to yourself. You are building a couple of services. Why are you considering event-sourcing architecture using Message Brokers such as the above? Won't a simple REST service based arch suffice? Read about CQRS and the problems it entails (state vs command impedance for example). Do you need Pub/Sub or Push/Pull? Is queuing of messages enough or would you need querying or filtering of messages before consumption? Also, someone would have to manage these brokers (unless using managed, cloud provider based solution), automate their deployment, someone would need to take care of backups, clustering if needed, disaster recovery, etc. I have a good past experience in terms of manageability/devops of the above options with Kafka and Redis, not so much with RabbitMQ. Both are very performant. But also note that Redis is not a pure message broker (at time of writing) but more of a general purpose in-memory key-value store. Kafka nowadays is much more than a distributed message broker. Long story short. In my taste, you should go with a minialistic approach and try to avoid either of them if you can, especially if your architecture does not fall nicely into event sourcing. If not I'd examine Kafka. If you need more capabilities than I'd consider Redis and use it for all sorts of other things such as a cache.

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Pramod Nikam
Co Founder at Usability Designs · | 2 upvotes · 208.8K views
Needs advice
Apache ThriftApache Thrift

I am looking into IoT World Solution where we have MQTT Broker. This MQTT Broker Sits in one of the Data Center. We are doing a lot of Alert and Alarm related processing on that Data, Currently, we are looking into Solution which can do distributed persistence of log/alert primarily on remote Disk.

Our primary need is to use lightweight where operational complexity and maintenance costs can be significantly reduced. We want to do it on-premise so we are not considering cloud solutions.

We looked into the following alternatives:

Apache Kafka - Great choice but operation and maintenance wise very complex. Rabbit MQ - High availability is the issue, Apache Pulsar - Operational Complexity. NATS - Absence of persistence. Akka Streams - Big learning curve and operational streams.

So we are looking into a lightweight library that can do distributed persistence preferably with publisher and subscriber model. Preferable on JVM stack.

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Replies (1)
Naresh Kancharla
Staff Engineer at Nutanix · | 4 upvotes · 206.1K views

Kafka is best fit here. Below are the advantages with Kafka ACLs (Security), Schema (protobuf), Scale, Consumer driven and No single point of failure.

Operational complexity is manageable with open source monitoring tools.

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Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

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Replies (4)
Tarun Batra
Back End Developer at instabox · | 7 upvotes · 260.3K views

RabbitMQ is great for queuing and retrying. You can send the requests to your backend which will further queue these requests in RabbitMQ (or Kafka, too). The consumer on the other end can take care of processing . For a detailed analysis, check this blog about choosing between Kafka and RabbitMQ.

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Trevor Rydalch
Software Engineer at · | 6 upvotes · 260.2K views

Well, first off, it's good practice to do as little non-UI work on the foreground thread as possible, regardless of whether the requests take a long time. You don't want the UI thread blocked.

This sounds like a good use case for RabbitMQ. Primarily because you don't need each message processed by more than one consumer. If you wanted to process a single message more than once (say for different purposes), then Apache Kafka would be a much better fit as you can have multiple consumer groups consuming from the same topics independently.

Have your API publish messages containing the data necessary for the third-party request to a Rabbit queue and have consumers reading off there. If it fails, you can either retry immediately, or publish to a deadletter queue where you can reprocess them whenever you want (shovel them back into the regular queue).

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Guillaume Maka
Full Stack Web Developer · | 2 upvotes · 259.7K views

As far as I understand, Kafka is a like a persisted event state manager where you can plugin various source of data and transform/query them as event via a stream API. Regarding your use case I will consider using RabbitMQ if your intent is to implement service inter-communication kind of thing. RabbitMQ is a good choice for one-one publisher/subscriber (or consumer) and I think you can also have multiple consumers by configuring a fanout exchange. RabbitMQ provide also message retries, message cancellation, durable queue, message requeue, message ACK....

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In my opinion RabbitMQ fits better in your case because you don’t have order in queue. You can process your messages in any order. You don’t need to store the data what you sent. Kafka is a persistent storage like the blockchain. RabbitMQ is a message broker. Kafka is not a good solution for the system with confirmations of the messages delivery.

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Needs advice

Hello! [Client sends live video frames -> Server computes and responds the result] Web clients send video frames from their webcam then on the back we need to run them through some algorithm and send the result back as a response. Since everything will need to work in a live mode, we want something fast and also suitable for our case (as everyone needs). Currently, we are considering RabbitMQ for the purpose, but recently I have noticed that there is Redis and Kafka too. Could you please help us choose among them or anything more suitable beyond these guys. I think something similar to our product would be people using their webcam to get Snapchat masks on their faces, and the calculated face points are responded on from the server, then the client-side draw the mask on the user's face. I hope this helps. Thank you!

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Replies (3)
Jordi Martínez
Senior software architect at Bootloader · | 3 upvotes · 210.8K views

For your use case, the tool that fits more is definitely Kafka. RabbitMQ was not invented to handle data streams, but messages. Plenty of them, of course, but individual messages. Redis is an in-memory database, which is what makes it so fast. Redis recently included features to handle data stream, but it cannot best Kafka on this, or at least not yet. Kafka is not also super fast, it also provides lots of features to help create software to handle those streams.

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For this kind of use case I would recommend either RabbitMQ or Kafka depending on the needs for scaling, redundancy and how you want to design it.

Kafka's true value comes into play when you need to distribute the streaming load over lot's of resources. If you were passing the video frames directly into the queue then you'd probably want to go with Kafka however if you can just pass a pointer to the frames then RabbitMQ should be fine and will be much simpler to run.

Bear in mind too that Kafka is a persistent log, not just a message bus so any data you feed into it is kept available until it expires (which is configurable). This can be useful if you have multiple clients reading from the queue with their own lifecycle but in your case it doesn't sound like that would be necessary. You could also use a RabbitMQ fanout exchange if you need that in the future.

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I've used all of them and Kafka is hard to set up and maintain. Mostly is a Java dinosaur that you can set up and. I've used it with Storm but that is another big dinosaur. Redis is mostly for caching. The queue mechanism is not very scalable for multiple processors. Depending on the speed you need to implement on the reliability I would use RabbitMQ. You can store the frames(if they are too big) somewhere else and just have a link to them. Moving data through any of these will increase cost of transportation. With Rabbit, you can always have multiple consumers and check for redundancy. Hope it clears out your thoughts!

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Decisions about AWS Lambda and Kafka

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.
The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

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Pros of AWS Lambda
Pros of Kafka
  • 127
    No infrastructure
  • 82
  • 69
  • 57
  • 47
    No deploy, no server, great sleep
  • 9
    AWS Lambda went down taking many sites with it
  • 6
    Event Driven Governance
  • 5
    Auto scale and cost effective
  • 5
    Extensive API
  • 5
    Easy to deploy
  • 4
    VPC Support
  • 2
    Integrated with various AWS services
  • 122
  • 116
  • 87
  • 81
  • 65
  • 36
  • 19
  • 15
    Open source
  • 10
    Written in Scala and java. Runs on JVM
  • 6
    Message broker + Streaming system
  • 4
    Avro schema integration
  • 2
    Suport Multiple clients
  • 2
  • 2
  • 2
    Partioned, replayable log
  • 1
  • 1
    Extremely good parallelism constructs
  • 1
    Simple publisher / multi-subscriber model
  • 1

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Cons of AWS Lambda
Cons of Kafka
  • 5
    Cant execute ruby or go
  • 0
    Can't execute PHP w/o significant effort
  • 27
    Non-Java clients are second-class citizens
  • 26
    Needs Zookeeper
  • 7
    Operational difficulties
  • 2
    Terrible Packaging

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What are some alternatives to AWS Lambda and Kafka?
Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.
Azure Functions
Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
AWS Elastic Beanstalk
Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
AWS Step Functions
AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
Google App Engine
Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
See all alternatives