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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. AWS Lambda vs Apache Flink

AWS Lambda vs Apache Flink

OverviewDecisionsComparisonAlternatives

Overview

AWS Lambda
AWS Lambda
Stacks26.0K
Followers18.8K
Votes432
Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K

AWS Lambda vs Apache Flink: What are the differences?

Differences between AWS Lambda and Apache Flink

  1. Execution Model: AWS Lambda follows an event-driven execution model, where functions are triggered by events and run in short-lived containers. On the other hand, Apache Flink follows a stream processing model, where data is processed continuously in real-time or batch mode.

  2. Data Processing: AWS Lambda is primarily designed for running small, stateless functions in response to events. It is well-suited for simple data transformations or event-driven tasks. In contrast, Apache Flink is a powerful distributed stream processing framework that can handle large-scale data streams and complex processing workflows.

  3. Scalability: AWS Lambda provides automatic scaling, allowing functions to dynamically scale based on the incoming workload. It can handle bursts of traffic and scale down when not in use. Apache Flink, being a distributed processing framework, inherently provides scalability by allowing the parallel execution of tasks across nodes in a cluster.

  4. State Management: AWS Lambda is stateless by design and does not provide built-in storage for maintaining state between function invocations. It relies on external storage services like AWS DynamoDB or S3. In contrast, Apache Flink offers built-in support for managing state, allowing for efficient processing of stateful computations such as maintaining user sessions or aggregating values over time.

  5. Batch Processing vs. Stream Processing: AWS Lambda is primarily focused on executing functions in response to events, which is suitable for real-time or near real-time processing. Apache Flink, on the other hand, can handle both batch and stream processing workloads, allowing for the efficient processing of large volumes of data in both real-time and offline scenarios.

  6. Ecosystem and Integrations: AWS Lambda integrates seamlessly with other AWS services and offers a wide range of event sources, making it easy to build serverless applications within the AWS ecosystem. Apache Flink, being a general-purpose stream processing framework, can integrate with various data sources and systems, including Apache Kafka, Hadoop, and more, providing flexibility in data integration.

In Summary, AWS Lambda is an event-driven execution service suitable for small, stateless functions, while Apache Flink is a distributed stream processing framework designed for handling large-scale data processing and supporting both batch and stream processing workflows.

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Advice on AWS Lambda, Apache Flink

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

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

357k views357k
Comments
Mark
Mark

Nov 2, 2020

Needs adviceonMicrosoft AzureMicrosoft Azure

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!

133k views133k
Comments
Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

AWS Lambda
AWS Lambda
Apache Flink
Apache Flink

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.

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

Extend other AWS services with custom logic;Build custom back-end services;Completely Automated Administration;Built-in Fault Tolerance;Automatic Scaling;Integrated Security Model;Bring Your Own Code;Pay Per Use;Flexible Resource Model
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars
-
GitHub Stars
25.4K
GitHub Forks
-
GitHub Forks
13.7K
Stacks
26.0K
Stacks
534
Followers
18.8K
Followers
879
Votes
432
Votes
38
Pros & Cons
Pros
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 47
    No deploy, no server, great sleep
Cons
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort
Pros
  • 16
    Unified batch and stream processing
  • 8
    Easy to use streaming apis
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 4
    Open Source
  • 2
    Low latency
Integrations
No integrations available
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka

What are some alternatives to AWS Lambda, Apache Flink?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Azure Functions

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.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Serverless

Serverless

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.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

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