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L1 AWS Best Practices

1、AWS Design Principles

1-1 Scalability

While AWS provides virtually unlimited on-demand capacity, the architecture should be designed to take advantage of those resources

There are two ways to scale an IT architecture

Vertical Scaling

  • takes place through increasing specifications of an individual resource for e.g. updating EC2 instance type with increasing RAM, CPU, IOPS, or networking capabilities
  • will eventually hit a limit, and is not always a cost effective or highly available approach

Horizontal Scaling

  • takes place through increasing number of resources for e.g. adding more EC2 instances or EBS volumes
  • can help leverage the elasticity of cloud computing
  • not all the architectures can be designed to distribute their workload to multiple resources

  • applications designed should be stateless,

    • that needs no knowledge of previous interactions and stores no session information
    • capacity can be increased and decreased, after running tasks have been drained

  • State, if needed, can be implemented using

    • Low latency external store, for e.g. DynamoDB, Redis, to maintain state information
    • Session affinity, for e.g. ELB sticky sessions, to bind all the transactions of a session to a specific compute resource. However, it cannot be guaranteed or take advantage of newly added resources for existing sessions

  • Load can be distributed across multiple resources using

    • Push model, for e.g. through ELB where it distributes the load across multiple EC2 instances
    • Pull model, for e.g. through SQS or Kinesis where multiple consumers subscribe and consume

  • Distributed processing, for e.g. using EMR or Kinesis, helps process large amounts of data by dividing task and its data into many small fragments of works

2、Disposable Resources Instead of Fixed Servers

Resources need to be treated as temporary disposable resources rather than fixed permanent on-premises resources before

AWS focuses on the concept of Immutable infrastructure

  • servers once launched, is never updated throughout its lifetime.
  • updates can be performed on a new server with latest configurations,
  • this ensures resources are always in a consistent (and tested) state and easier rollbacks

AWS provides multiple ways to instantiate compute resources in an automated and repeatable way

2-1 Bootstraping

scripts to configure and setup for e.g. using data scripts and cloud-init to install software or copy resources and code

2-2 Golden Images

  • a snapshot of a particular state of that resource,
  • faster start times and removes dependencies to configuration services or third-party repositories

2-3 Containers

  • AWS support for docker images through Elastic Beanstalk and ECS
  • Docker allows packaging a piece of software in a Docker Image, which is a standardized unit for software development, containing everything the software needs to run: code, runtime, system tools, system libraries, etc

2-4 Infrastructure as Code

  • AWS assets are programmable, techniques, practices, and tools from software development can be applied to make the whole infrastructure reusable, maintainable, extensible, and testable.
  • AWS provides services like CloudFormation, OpsWorks for deployment

3、Automation

AWS provides various automation tools and services which help improve system’s stability, efficiency and time to market.

Elastic Beanstalk

a PaaS that allows quick application deployment while handling resource provisioning, load balancing, auto scaling, monitoring etc

EC2 Auto Recovery

  • creates CloudWatch alarm that monitors an EC2 instance and automatically recovers it if it becomes impaired.
  • A recovered instance is identical to the original instance, including the instance ID, private & Elastic IP addresses, and all instance metadata.
  • Instance is migrated through reboot, in memory contents are lost.

Auto Scaling

  • allows maintain application availability and scale the capacity up or down automatically as per defined conditions

CloudWatch Alarms

  • allows SNS triggers to be configured when a particular metric goes beyond a specified threshold for a specified number of periods

CloudWatch Events

  • allows real-time stream of system events that describe changes in AWS resources

OpsWorks

  • allows continuous configuration through lifecycle events that automatically update the instances’ configuration to adapt to environment changes.
  • Events can be used to trigger Chef recipes on each instance to perform specific configuration tasks

Lambda Scheduled Events

  • allows Lambda function creation and direct AWS Lambda to execute it on a regular schedule.

4、Loose Coupling

AWS helps loose coupled architecture that reduces interdependencies, a change or failure in a component does not cascade to other components

4-1 Asynchronous Integration

  • does not involve direct point-to-point interaction but usually through an intermediate durable storage layer for e.g. SQS, Kinesis
  • decouples the components and introduces additional resiliency
  • suitable for any interaction that doesn’t need an immediate response and an ack that a request has been registered will suffice

4-2 Service Discovery

  • allows new resources to be launched or terminated at any point in time and discovered as well for e.g. using ELB as a single point of contact with hiding the underlying instance details or Route 53 zones to abstract load balancer’s endpoint

4-3 Well-Defined Interfaces

  • allows various components to interact with each other through specific, technology agnostic interfaces for e.g. RESTful apis with API Gateway

5、Databases

AWS provides different categories of database technologies

5-1 Relational Databases (RDS)

  • normalizes data into well-defined tabular structures known as tables, which consist of rows and columns
  • provide a powerful query language, flexible indexing capabilities, strong integrity controls, and the ability to combine data from multiple tables in a fast and efficient manner
  • allows vertical scalability by increasing resources and horizontal scalability using Read Replicas for read capacity and sharding or data partitioning for write capacity
  • provides High Availability using Multi-AZ deployment, where data is synchronously replicated

5-2 NoSQL Databases (DynamoDB)

  • provides databases that trade some of the query and transaction capabilities of relational databases for a more flexible data model that seamlessly scales horizontally
  • perform data partitioning and replication to scale both the reads and writes in a horizontal fashion
  • DynamoDB service synchronously replicates data across three facilities in an AWS region to provide fault tolerance in the event of a server failure or Availability Zone disruption

5-3 Data Warehouse (Redshift)

  • Specialized type of relational database, optimized for analysis and reporting of large amounts of data
  • Redshift achieves efficient storage and optimum query performance through a combination of massively parallel processing (MPP), columnar data storage, and targeted data compression encoding schemes
  • Redshift achieves efficient storage and optimum query performance through a combination of massively parallel processing (MPP), columnar data storage, and targeted data compression encoding schemes
  • Redshift MPP architecture enables increasing performance by increasing the number of nodes in the data warehouse cluster

6、Removing Single Points of Failure

AWS provides ways to implement redundancy, automate recovery and reduce disruption at every layer of the architecture

AWS supports redundancy in the following ways

6-1 Standby Redundancy

  • When a resource fails, functionality is recovered on a secondary resource using a process called failover.
  • Failover will typically require some time before it completes, and during that period the resource remains unavailable.
  • Secondary resource can either be launched automatically only when needed (to reduce cost), or it can be already running idle (to accelerate failover and minimize disruption).
  • Standby redundancy is often used for stateful components such as relational databases.

6-2 Active Redundancy

  • requests are distributed to multiple redundant compute resources, if one fails, the rest can simply absorb a larger share of the workload.
  • Compared to standby redundancy, it can achieve better utilization and affect a smaller population when there is a failure.

AWS supports replication

6-3 Synchronous replication

  • acknowledges a transaction after it has been durably stored in both the primary location and its replicas.
  • protects data integrity from the event of a primary node failure
  • used to scale read capacity for queries that require the most up-to-date data (strong consistency).
  • compromises performance and availability

6-4 Asynchronous replication

  • decouples the primary node from its replicas at the expense of introducing replication lag
  • used to horizontally scale the system’s read capacity for queries that can tolerate that replication lag.

6-5 Quorum-based replication

  • combines synchronous and asynchronous replication to overcome the challenges of large-scale distributed database systems
  • Replication to multiple nodes can be managed by defining a minimum number of nodes that must participate in a successful write operation

6-6 AWS provide services to reduce or remove single point of failure

  • Regions, Availability Zones with multiple data centers
  • ELB or Route 53 to configure health checks and mask failure by routing traffic to healthy endpoints
  • Auto Scaling to automatically replace unhealthy nodes
  • EC2 auto-recovery to recover unhealthy impaired nodes
  • S3, DynamoDB with data redundantly stored across multiple facilities
  • Multi-AZ RDS and Read Replicas
  • ElastiCache Redis engine supports replication with automatic failover