Cloudflow vs Docker Swarm

Overview

Docker Swarm

Stacks779

Followers990

Votes282

Cloudflow

Stacks5

Followers13

Votes0

GitHub Stars323

Forks89

Docker Swarm vs Cloudflow: What are the differences?

Docker Swarm: Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host. Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself; Cloudflow: *Streaming Data Pipeline on Kubernetes *. It enables you to quickly develop, orchestrate, and operate distributed streaming applications on Kubernetes. With Cloudflow, streaming applications are comprised of small composable components wired together with schema-based contracts. It can dramatically accelerate streaming application development—reducing the time required to create, package, and deploy—from weeks to hours.

Docker Swarm can be classified as a tool in the "Container Tools" category, while Cloudflow is grouped under "Big Data Tools".

Docker Swarm and Cloudflow are both open source tools. It seems that Docker Swarm with 5.84K GitHub stars and 1.13K forks on GitHub has more adoption than Cloudflow with 172 GitHub stars and 50 GitHub forks.

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Advice on Docker Swarm, Cloudflow

Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

Decidedon

GitHub

GitHub Pages

Markdown

Our whole DevOps stack consists of the following tools:

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
Scalability: All-in-one framework for distributed systems.
Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

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Detailed Comparison

Docker Swarm	Cloudflow
Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.	It enables you to quickly develop, orchestrate, and operate distributed streaming applications on Kubernetes. With Cloudflow, streaming applications are comprised of small composable components wired together with schema-based contracts. It can dramatically accelerate streaming application development—reducing the time required to create, package, and deploy—from weeks to hours.
-	Apache Spark, Apache Flink, and Akka Streams; Focus only on business logic, leave the boilerplate to us; We provide all the tooling for going from business logic to a deployable Docker image; We provide Kubernetes tooling to deploy your distributed system with a single command, and manage durable connections between processing stages; With a Lightbend subscription, you get all the tools you need to provide insights, observability, and lifecycle management for evolving your distributed streaming application
Statistics
GitHub Stars -	GitHub Stars 323
GitHub Forks -	GitHub Forks 89
Stacks 779	Stacks 5
Followers 990	Followers 13
Votes 282	Votes 0
Pros & Cons
Pros 55 Docker friendly 46 Easy to setup 40 Standard Docker API 38 Easy to use 23 Native Cons 9 Low adoption	No community feedback yet
Integrations
Docker	Kubernetes Apache Spark Akka Apache Flink

What are some alternatives to Docker Swarm, Cloudflow?

Kubernetes

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

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

Distributed SQL Query Engine for Big Data

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.

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

Apache Flink

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.

Related Comparisons

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
Scalability: All-in-one framework for distributed systems.
Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

Cloudflow vs Docker Swarm