diff --git a/README.md b/README.md index 37d2d46..a56c16b 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,670 @@ # Distributed Systems Practice Notes from learning about distributed systems in [GW CS 6421](https://gwdistsys18.github.io/) with [Prof. Wood](https://faculty.cs.gwu.edu/timwood/) -## Area 1 -> Include notes here about each of the links +## Area 1 Docker +### Beginner +1. Why Docker - 10 min + - This video discussed the trend of advancements in computer science + it acknowledged that Docker and containerization in general is a + trend that many people are moving towards due to it's ease in setup + and the speed associated with spinning up a new container. -## Area 2 -> Include notes here about each of the links +2. DevOps Docker Beginners Guide - 45 min + - This tutorial started with a few simple commands and explained what + docker is doing when those commands are run. + - This tutorial then uses a lightweight version of linux to run some + more basic commands in while continueing to explain Docker infrastructure + further. + - The tutorial helps to further solidify my understanding of the + differences between VMs and containers. Primarily the focus is on + the speed with which a contianer can be spun up and killed. Fast + enough to run a single command and get an output within a reasonable + amount of time. + - Security is inforced through the separate instances of contianers. + although n contiars can be run using the same instance they all have + isolation ensure because they are executing in different contexts + while sharing the same image. + - Docker speed and isolation can be beneficial for app testing. This + is an interesting idea, using a container to test changes to an + application while preserving a running context. + +### Intermediate +1. What are Containers? - 18 min + - At a base level containers are isolation mechanisms for processes + to give them their own namespace and address space. + - Containers are typically one process per container + - The container's lifecycle is coupled with the process lifecycle + - A container image is a binary representation of a container + - Container images can have a hierarchy with parent child relationships + - Containers can share images which allows for consolidation of binaries + - Nodes that are high up on the image tree can be updated and after + pushing that change it is inherited to all of the children of that node + - Dockerfiles inherit from image files, to create a container image + - Images can be created from a container, setup can happen and then + be committed to an image + - All docker dependencies are packaged within the container, this is + why it's called a container because it's entirely contained. + - Docker uses an image tree to track what's needed to be fetched + from the docker store + - Volumes persist data beyond the lifecycle of the container + +2. VMs Versus Containers - 8 min + - Hypervisor has to be extremely complex to support level of + abstraction the VM expects + - Dependencies are backed into containers, in a VM this has to be + handled at the operating system level. Containers are entirely + user level execution, and VMs need the OS/kernel to make smart + decisions about how to abstract the hw. + - VMware employees like to defend virtualisation + +3. Docker intro - 30 min + - Containers share a kernel so it needs to be the same on a system + that's running multiple containers + - Containers have multiple use cases, running a single command, + running something in the background and existing as an interactive + shell + - When configuring a container that will eventually be exported as + an image interactive containers can be useful + - Docker files are sort of like the spec for how to build the image + and eventually the container + - Building and launching a website feels pretty cool inside of a demo + - Container deployability and speed are clearly demonstrated in this + +4. Doing More With Docker Images - 45 min + - Docker images can be created from an existing machine that has any + amount of customization on it + - Saving the state of a docker container in a image is as easy as + writing the command "commit". This can then be used to create any + number of additional images with the same packages and config + - A Dockerfile seems like the best way to build a container + - Dockerfiles track changes really well while images are just a + bunch of binary data representing some state. + +5. VMs Versus Containers Deep Dive - 9 min + - Containers while they contain all of the dependencies needed to + run an application are far smaller than an equivalent VM + - Containers are less secure than a VM because containers are as + secure as the containr sandbox and the kernel + - VMs are far slower to boot up an application than a container + * A VM needs POST->kernel->init->process + * A container needs sandbox->process + +6. Docker Networking - 45 min + - Docker networking setup seems to be entirely through the Docker + network command. + - Networks attached to Docker containers have drivers and a per + network scope + - Setting a container to spin and then networking into it to then + interact and then make changes + - I'm not sure I see the point on the same machine but I guess if + you're running your contianer in someone else server this is useful + - Using swarm to orchestrate networking seems useful + - Initializing a swarm is startlingly easy + - Adding nodes to a swarm is as simple as pasting in a generated + join command. This clearly demonstrates the ease of using an integrated + orchestration tool + - Building a testing services in this swarm is very simple and continues + to display how useful it is to have swarm built in + - The only disadvantages this shows to kubernetes is that I think + you can do more with kubernetes it just takes longer to setup + +7. Swarm Mode Instruction - 30 min + - Again this demonstrates how convenient it is to have swarm be built + buy docker and integrated + - A stack in the context of Docker Swarm is a collection of services + that are deployed together. Like a development stack + - Both Docker Swarm and kubernetes use .YAML files to define the + structure of their system management + +8. Kubernetes VS. Swarm - 4 min + - Comparison of orchestration systems + - Swarm is Dockers version of kubernetes and since its build in + it is simpler than kubernetes + +9. Kubernetes - 5 min + - Cluster services helps to manage a cluster + - Workers are container hosts with a "kublet" process + - Yaml spec for config about cluster + * pod config - group of containers running the same image + * replica - how many of this pod + - Cluster services manages scheduling the right number of pods + according to the spec + - This is across any number of workers this is because workers + can have multiple containers + - Cluster services also handles when workers go down + +10. Kubernetes++ - 60 min + - Kubernetes clusters can now be launched using a voice command to + the google assistant + - Still so new to the point that we don't have a set way of doing things + - You can use kubernetes build triggers to build an instance of something + when a commit is made to master on a git repo + - This is crazy for development, pushing to a repo builds and pushes to a + website that reflects changes + - If build passes and test passes changes are reflected + - The yaml file is checked out automatically from the infrastructure repo + and is updated to reflect the new updates + - + - + +11. Docker in Cluster of EC2 Instances using Kubernetes to Orchestrate - 60 min + - Amazon provides a service called Elastic Container Service(ECS) this is + what eventually is fed a docker image and serves as the docker container + - ECS is typically setup as a cluster, the process is straightforward + if default configuration is assumed + - ECS instances use a task definition from amazon that defines which + docker image to use for container configuration + - Using ECS amazon provides elastic load balancing which isn't needed + for this project but very useful in real world applications + - The basics of interacting with a cluster are verified using a key pair + similar to EC2 generation and access + - KOPS is a somewhat simple to use open source tool for installing + kubernetes on a cluster of EC2 instances + - + + +### Bring it all together +1. This module goes through putting a monolith application in a container + then deploying that container + - This module then breaks the monolith into microservices without any + downtime + - Each of the pieces of this process are addressed and explained in + short. Starting with containers the tutorial explains why they're + useful in this situation + - This tutorial requires more access than a standard amazon educate + account provides so I needed to make a personal account for this module + - This module is multistep and hands off compared to the others + - Setting up a containerized version of an app requires first building + a docker image to use for the container + - The first large step is to deploy a large application inside of a + container + * First step is to build a cluster using the image created earlier + * Build a stack(not the data structure) + * Setup and orchestration .yaml file + - Next once the cluster is deployed it must have it's task set to + the application to be run + - Setting up a load balancer is the next important step + * Setting a target group is needed to route traffic to containers + in the cluster + * Setting the listener is needed to handle requests to the load + balancer + - The next portion of the module focusses on pulling the application + a piece at a time + - After building a docker image and tagging it in the repo contianers + can be built to the spec + - Setting up a cluster using the spec provided was also a simple task + * This involved building a new stack and using the provided .yaml + file to orchestrate at a basic level + - Deploying the monolith definitely took some doing, breaking into + microservices shouldn't be too bad because the way this application + was built it allows for it to be broken up. Some apps aren't architected + to support containerization and distribution + - Advantages of isolation into microservices inside of containers + * Crashes are isolated, the entire system doesn't have to go down + * If a container becomes compromised it cannot affect the rest of + the system + * If a portion of the system needs to scale to account for new needs + it can easily do so without constraining the rest of the system + * This one is not intuitive, development speed is greatly increased + dependencies across services are almost entirely abstracted away from + the developer. A dev can work and and test their service entirely + seperate from the rest of the system + - The work of breaking an application into microservices is not difficult + a lot of steps but all fairly simple + - After all of the tasks are generated linking them all together takes + a bit of work but AWS makes this easier with listener generation wizards + + +## Area 2 Cloud Web Apps +### Beginner +1. AWS: Launch A VM - 15 min + - Starting a EC2 instance seems fairly straight forward. + - Download a key pair and use the public url to ssh in. + +2. Lab: Intro to S3 - 30 min + - This lab was slightly more involved than the first. + - Creating an S3 instance and interacting with it is + faily intuitive but this lab made it very explicit. + - Setting up the instance to view the image was simple, + I anticipate creating a bucket policy will be more involved. + - As I expected creating a bucket policy required a little more + leg work, although the policy generator that amazon provides + is quite useful. + +### Intermediate +1. Video: Virtualization - 10 min + - Virtualization is what allows to processing time to be bought and + soled like in cloud computing and distributed computing. + - IBM created a virtual machine to support more OSes on their desktops + - VMWare: Software Virtualizaiton that helps to isolate crashes in apps + to provide redundancy in the case of a crash. + - The idea of rings are used to separate the level of privilege in an os. + - When an OS running in user space attempts to execute a privileged operation + this is cought by some sort of VM management portion of the base os that + can serve to emulate the operation and serve what the OS in user space + accepts as a response to the call that was made and cought. + - Some ISAs are designed to be virtualized but x86 is not which makes + the ability to virtualize x86 machines even more impressive. + - The Xen hypervisior is smaller than a standard VM monitor. + - An OS operating in domain 0 executes privileged instruction for the + guest OS but is not technically a part of the hypervisor which is what + allows the HV to be so small. + - Software emulation of instructions is not fast and has an overhead + for the protection switches that happen. + - Virtualization support now exists in Intel processors so HW can take + some of the work away from the software helping to improve performance + of the virtual machine. + - This helps companies that can't or won't update the OS that they use + but want to use new hardware or unsupported software. + +2. Install a LAMP Web Server on Amazon Linux 2 - 30 min + - LAMP stands for Linux, Apache, MySQL, and PHP + - Installing and running is a simple as setting up an AWS Linux2 instance + and installing a LAMP Maria DB + - By default MariaDB comes installed with some features that are great + for testing but should be removed to ensure a secure systems when this + eventually moves to production. + +3. S3 VS. EC2 VM - 30 min + - S3 advantages are that it was built for storage. The instance exists + to host and deliver web content. This means in terms of complexity it + would take more effort to turn an EC2 instance into an acceptable server. + - Setting up an S3 is comparable to setting up an EC2 but when an S3 + is done being setup it is ready to provide fast media hosting, the same + cannot be said about the EC2 more work is required to have even close + to the support and speed of the S3. + - This comparison comes down to the complexity of setup, the serving + of web content that is media heavy an S3 is gonna be the ideal choice. + - If the website that you want to setup requires some involvement in + the configuration of the VM or additional packages to be installed + then maybe the EC2 is an easier choice, it's much more flexible than + the S3. + +4. Introduction to DynamoDB - 20 min + - DynamoDB is a fully managed noSQL database that can be used for a + variety of applications. + - This service from Amazon is managed at the level of tables not + entire instances like S3 or EC2 + - Creating a table is as simple as entering fields and selecting + a primary key which is crucial to identifying a tuple in a relational + database. + - In dynamoDB tuples are referred to as items and tables can have + any number of items in them + - The concept of a sortkey exists in DynamoDB and if an item has + a sortkey is must be provided upon insert of that item. + - The sortkey is a field separate from the primary key used to + sort the items in a table. + - DynamoDB has two way to find items in a table scan and query: + * Query: Uses a primary key and occasionally the sortkey to + find a tuple. This is usually very fast. + +5. Deploy a Scalable Node.js Web App - 60 min + - AWS has it's own JavaScript Node.js SDK this allows for easy + integration to existing AWS services like a DynamoDB + - An application running on AWS elastic beanstalk can use either + a DynamoDB table within it's runtime environment or an external + table also hosted by AWS. + - Amazon's Elastic Beanstalk environment can be configured to + support application that aren't Node.js + - An Elastic Beanstalk Environment is comprised of the following: + * A standard EC2 with configuration to run a specific kind of + web application + * Instance Security Group this limits access to port 80 + * Load Balancer this is used to distribute incoming requests + * Loab Balancer Security Group, this is used to limit load + balancer access to port 80 + * Auto Scaling Group, this monitors the need for rebuilding + or adding additional instances to the EC2 + * S3 bucket used for storing application related data + * CloudWatch Alarms, monitor load on instances and notify + the manager if load is too high or low on any give instance + * CloudFormation stack, management tool for distributing + configuration changes amongst instances related to the elastic + beanstalk. + - The above mentioned auto scaling group and the CloudWatch alarms + work together to scale the application only when the CloudWatch + alarms detect the need for scaling. + - Updating the application to link it to a DynamoDB table is as + simple as updating the application configuration file and letting + the CloudFormation stack to the rest. + +6. Intro to AWS Lambda - 30 min + - Lambdas are event driven meaning they only execute code when + an event has been recieved. + - Using the simple example of a serverless image thumbnail application + * First an image is uploaded to an S3 bucket + * The bucket then serves an object created event to the lambda + * The lambda then fetches the object using the event to id it + * The image is finall rendered by the lambda function + - Lambdas have extensive logging support varying from duration of + execution to the numbers of throttles that occured. + +7. Introduction to Amazon API Gateway - 40 min + - What is a microservices architecture + * Developing an application as a suite of small services that + all have their own processing and communicate through light + weight mechanisms + * These managable lightweight services are easy to improve + and scale as needed. Instead of scaling the entire application + scale as needed. + - API stands for Application Programming Interface + * A set of defined ways to interact with a system using + things that resemble function calls. + - RESTful APIs + * Allows for a stateless server. This lends itself really + well to the idea of a lambda function using API calls as events + - Amazon API Gateway, allows us to map GET, PUT, and POST functions + to a backend within AWS in this case Lambda functions. + +8. Build a Serverless Web Application - 120 min + - In this tutorial we use an S3 bucket to host our web app. The + data hosted on the S3 will eventually be available upon requests + made to the Amazon API Gateway. + - Amazon Cognito is a tool to manage acces to an application or + specific media or pages on an AWS instance. This is what we use + to allow uses to create a login with a password. + - The S3 bucket configuration needs to be updated to support + the user group created by all of the prebaked sign up sheet + from Amazon Cognito + - Next the backend is built to be serverless using AWS Lambda + functions to service all calls made to the system. In this case + all calls to the API will serve as events for the lambdas + - The first thing a Lambda does upon receiving an event is + store it in the related table for events in DynamoDB + - Next after testing Lambda function making sure they spin + when asked and do what was requested the API needs to link + the calls made to the backend itself. + - The Amazon API Gateway helps us to map the pre-existing + components to eachother, calls to the API will trigger + lambda function execution as well as S3 page delivery + and media changes. + +### Bring it all Together + Build a Modern Web Application +1. Build a Static Website + - This website will use an AWS S3 bucket to host it's static content + - Amazon Cloud 9 is an in browser IDE helpful for writing and debugging + code in the browser. + - Amazon S3 buckets are configured to hold data but not specifically + website data. This means before you can host a website on an S3 instance + it must be configured. + - The configuration for an S3 includes setting up the registered DNS + name of the bucket as well as establish the appropriate bucket policy + - Bucket policies are a form of access control. In the case of a website + the idea of access control comes down to which users can see what content +2. Host Application on a Web Server + - This website will have a modern microservices backend this means + that instead of a monolithic application it will be distributed to + various nodes in a cluster probably with a load balancer to hand out + the work to be done. + - AWS Fargate is a deployment option of Amazon ECS which stands for + Elastic Container Service. + - Fargate abstracts the details of managing a cluster by providing + a load balancer and a cluster of nodes. + - Fargate is ideal of PaaS, mobile and web platforms because it is + optimized to service long running processes like microservices + - A Fargate ECS will scale automatically and completely abstracts + cluster management and configuration. This allows the application + developer not to get cought up in the weeds of configuration + - A serverless architecture can be delivered in a variety of ways + one of which being Fargate another popular method is using Amazon + Lambda functions. These serve better to act as microservices for + a data or event driver architecture. + - Amazon VPC stands for Virtual Private Cloud this is fully + customizable isolated portion of the cloud to host services in. + - Amazon CloudFormation Templates serve to collect network and + security in a bundle to be deployed and synchronized together. + * Amazon VPC + * Two NAT Gateways + * DynamoDB VPC Endpoint + * Security Groups + * IAM Roles + - Listed above are the core parts of a CloudFormation Template + these serve as the networking backbone of the application as + well as provide some security inherint in AWS systems. + - Flask is a micro web framework written in Python and used in + this example to help provide the API definitions for the app. + - A CI/CD pipeline allows for Continuous Integrating and + Continuous Delivery. + - This means that when updates are made to the code base in this + case the Flask app they are push and distributed to the rest of + the system without having to manually update every piece. +3. Store System Information + - This step requires building an Amazon DynamoDB. This will be + easier to do now that the CI/CD pipeline is established. It will + allow us to make changes to the system and have them distributed + automatically throughout instead of having to manually update + each component. + - Originally the content was populated base on data in a static + JSON file. Now after establish a new DynamoDB table we can update + the Flask application to read from this table instead of the static + JSON. + - Now that the application is not accessing a file from the S3 + bucket it will need to make a request to receive the data that + it will serve. + - This request to the DynamoDB page is made through an Amazon + SDK called boto3. It's basically a python library that can access + AWS services. +4. User Registration + - This component of the web application will rely heavily on + Amazon's cognito service and user pool access control. + - In order to allow users to create accounts the outward API + of the Flask app needs to be exteneded. This is still a RESTful + API and will leverage the Amazon API Gateway to handle calls + made to it. + - Swagger will be helpful in this scenario. It is a framework + that will aid in describing the API in the form of a JSON. + - Updating these security roles in the main website cannot be + done auto matically and require some code to be changed. This + requires some small updates to the app in the S3 bucket. +5. Capture User Behavior + - This component will help to learn more about user interaction + with the application but does not require any down time or updates + to the application itself. It can serve as a microservice that + remains completely decoupled from the primary application. + - This service is only really useful when a user is actively + interacting with a site. These interactions translate really + well into events. + - Lambdas are the perfect tool to utilize when addressing an + events based system or serivce. + - The Lambda can help to analyze and parse the data being + received from the user but it can't record these actions or + create the events that trigger it to begin processing. + - The tool we use to track events and trigger lambdas is + called Kinesis Firehose. + - Kinesis Firehose is an analytics streaming service available + on a majority of AWS provided services like S3 and Amazon + Redshift Data Warehouse clusters. + - The data streaming from the Kinesis Firehose connected to + the S3 bucket we're using to host our website will trigger the + lambda functions to collect and parse the interaction data + from the user. + +## Area 3 Big Data and Machine Learning +### Beginner +1. Hadoop Intro - 20 min + - Data is growing so rapidly that our previous solutions for handling + more data no longer work. + - The idea of distributed systems helps to improve the solution from + buying more machines to using a group of machines in an intelligent + way. + - When using a distributed system to help process large amounts of + data throughput increases greatly but at the tradeoff of having a + higher chance of failure by the introduction of more machines into + the system. Another problem introduced by distrubuted sytems is the + need to synchronization and the inability to guarantee that at any + give time data is not consistent accross multiple machines. + - Hadoop claims to offer solutions to the challenges faced by distributed + systems. Being able to handle these challenges means that a person + can have the benefits of more computing power without the inherint + drawbacks of distribution. + - Hadoop runs on commodity hardware and uses multiple copies of data + to ensure redundancy. + - Hadoop completely reverses the way data is processed. A central + program distributes the data amongst the system and performs + processing on the data where it is store instead of having the + data sent to the program it brings the program to the data. + - The Hadoop ecosystem: + * HDFS: Storage layer, streaming access to fs data and authentication + * HBASE: Stores data in HDFS non-relational DB, high throughput + * Sqoop: Imports and Exports data to and from relationals DBs + * Flume: Collects event data and transfers it to HDFS + * Spark: Data processing component, cluster computing processing + * Map Reduce: Original Hadoop processing engine, fault tolerant + * Pig: Open source high level data flow system + * Impala: SQL Engine, interactice analysis with low latency + * Hive: Abstraction layer similar to impala but better for ETL ops + * Cloudera Search: Near-real-time access no skills required + * Oozie: Coordinating map-reduce workflows from other apps + * HUE: User level interaction with previousls mentioned components + - These tools help to abstract the map reduce writing portion of development + away from the user. One of the problems with distributed systems is that + they require complex code to preform well. This helps to solve that + problem by not making the user write the code. + - A lot of the problems introduced by distribution are solved by these + components. They make writing the code for these systems easier and + provide levels of fault tolerance otherwise lost on large distributed + systems. + - The four steps of big data processing: + * Ingest: Scoop and Flume transfer data to Hadoop + * Processing: HDFS->Hbase->Map Reduce||Spark + * Analyze: Pig, Hive and Impala + * Access: Hue or Cloudera Search + +2. Analyze Big Data with Hadoop - 45 min + - Amazon EMR: Is a platform to help support big data processing + services like Hadoop. + * Consist of EC2 instances organized in a cluster + * There are master nodes, core nodes, and task noes. + * The master node is responsible for distributing work to + be done by the the worker nodes. + * Core nodes store data using HDFS + * Task Nodes do not use HDFS and are optional + - Creating an Amazon EMR Cluster + * EMR is designed to serve as a platform for distributed big + data processing. + * EMR Clusters are reliable and scalable, leveraging AWS easily + scalable architecure. + * Using EMR you can select multiple FS backends including HDFS + as well as Amazon's own distributed FS + * The available Data Processing frameworks are the same the + two available for Hadoop like map reduce and spark + +## Blog Post +### Containers: Why do we have them? What can I put in them? How do I make one? +1. What is Virtualization? + - Virtualization is emulating hardware for an operating system. + Originally it was designed to allow OSes to run on HW they wouldn't + typically support. Virtualization is done at both the hardware and + software levels. An operating running in a virtual machine is + typically referred to as a guest os. When the guest OS makes calls + down to the hardware instead of allowing the hardware to answer + the call is caught and handled buy some sort of manager. In many + cases this is referred to as the VMM or VM manager. The VMM is + responsible for fielding these calls made by the guest os and + responding to them in a way that the guest OS expects. +2. How do containers do this differently? + - Containers are slightly different than virtual machines in that + they all need to run on the same kernel. A kernel in this case is + similar to the VMM it will handle the interaction with the hardware + abstracting away all of the system calls. Using a virtual machine + the kernel being run on is almost irrelevant. A linux kernel can + run a windows vm and a windows kernel can support a linux vm. This + interoperability is why VMs are used in some cases. The flexibility + provided by a VM is ideal for many companies and applications. + There are some drawbacks to this flexibility and ability to have + an extremely custom system and the most prevalent is the time to + boot. A VM running on a non native environment like linux running + on windows will be slow to boot and in some cases slow to run. + This is where docker comes in. Docker provides the ability to + spin up containers far faster than any other VM. This comes with + the tradeoff of less customization. A docker container can only + run on a kernel of the same type. A type like windows or linux. + Windows containers are capable of running on a windows kernel + and linux containers are capable of running on a linux kernel. + Docker claims to provide support for linux containers on a + Windows kernel but this is just a wrapper around preexistting + kernel features in linux. Docker sacrifices some customization + options and configurations for fast boot and fast use. + + - Below are some diagrams comparing VMs to Containers: + +![alt text][logo] + +[logo]: ./vm_vs_container.png "Virtual Machine's VS. Container" + +This diagram outlines a crucial difference between containers and VMs +and that's what's actually spun up and running as part of the instance. +A docker image contains only the libraries and the applications. A +Virtual Machine Image contains the entire guest os as well as the +libraries and the applications. This difference in size is what leads +to the slow down faced by VMs on boot. + +3. What can you use a container for? + - Containers are used for a variety of reasons but many companies + have adopted them and now rely on them for everyday use. The + examples on Dockers website help to highlight some of the reasons + to switch to docker and provide some examles of companies that + have made the switch. + * ADP: This company supports some old technology and has + access to various cloud architecture but wanted a unified + platoform to run their business. Docker containers can + support some outdate OSes as well as scale to whatever size + cloud a company can throw at it. + * GSK: This Company leverages the speed of Docker to help + improve it's speed of development of new drugs and cures. + * PayPal: This company values security highly so Dockers + isolation provides a fast enough platform to interact with + users that's also extremely secure. +4. How Do I Setup a Basic Docker Container? + - For this short tutorial the goal will be to setup a docker + contianer and to run a simple interactive linux distro. This + introductory tutorial is similar to Dockers hosted one but + you can do it from your own machine. + - Prerequisites: + * Have a Docker username + * Have Docker installed + * Understand how to run commands on linux + - Step 1: + The first step is to check if our connection to Docker is + up and running. + Run the command: + ``` + docker container run alpine hostname + ``` + The Docker command perfix `docker container run` are used + to execute a single command in the specified container. + The `alpine` specifies that we want to use the special + docker verison of alpine linux. The last portion of the + command is the actual command to be run on the container + try it again but with `ls -a` the output should look + similar to when you run ls -a on your machine(maybe with + less files). + - Step 2: + Next run the command: + ``` + docker container run --interactive --tty --rm ubuntu bash + ``` + This command starts an interactive container, any commands + that you run now will be inside of the contianer. + * `--interactive` makes the command interactive + * `--tty` allocates pseudo-tty + * `--rm` cleans up the container after it's done executing + +![alt text][logo1] + +[logo1]: ./ops-basics-hello-world.svg "Docker Diagram Tutorial: First Alpine Linux Container" + +This diagram and the suggested commands are from two docker +tutorials: +[Docker for Beginners](https://training.play-with-docker.com/ops-s1-hello/) +[First Alpine Linux Containers](https://training.play-with-docker.com/beginner-linux/#Task_1) + + + + + + + + + diff --git a/Screenshot from 2018-10-28 23-15-23.png b/Screenshot from 2018-10-28 23-15-23.png new file mode 100644 index 0000000..bacd606 Binary files /dev/null and b/Screenshot from 2018-10-28 23-15-23.png differ diff --git a/Screenshot from 2018-10-28 23-21-05.png b/Screenshot from 2018-10-28 23-21-05.png new file mode 100644 index 0000000..b038884 Binary files /dev/null and b/Screenshot from 2018-10-28 23-21-05.png differ diff --git a/Screenshot from 2018-10-29 13-32-51.png b/Screenshot from 2018-10-29 13-32-51.png new file mode 100644 index 0000000..99d11e4 Binary files /dev/null and b/Screenshot from 2018-10-29 13-32-51.png differ diff --git a/Screenshot from 2018-10-29 13-32-55.png b/Screenshot from 2018-10-29 13-32-55.png new file mode 100644 index 0000000..85f3907 Binary files /dev/null and b/Screenshot from 2018-10-29 13-32-55.png differ diff --git a/ops-basics-hello-world.svg b/ops-basics-hello-world.svg new file mode 100644 index 0000000..ac6457b --- /dev/null +++ b/ops-basics-hello-world.svg @@ -0,0 +1 @@ +HelloWorld:WhatHappened?$ docker run hello-worldUnable to find image locallylatest: Pulling fromlibrary/hello-worldStatus: Downloaded newer image forhello-world:latestHello from Docker!...DockerEngineLinuxplay-with-docker.com❹❷❶❸hello-world:latestImageRegistryhttps://store.docker.comRepository:libraryImage:hello-worldTag:latest \ No newline at end of file diff --git a/vm_vs_container.png b/vm_vs_container.png new file mode 100644 index 0000000..3934f60 Binary files /dev/null and b/vm_vs_container.png differ