Learning Angular.js and beyond

I'm Sean Lee. Cloud Architect in Toronto.

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What is Container Runtime?

Container runtime is a software that runs and manages the components required to run containers. These tools make it easier to securely execute and efficiently deploy containers, and are a key component of container management. As containers themselves have evolved and changed, so have their runtimes.

Open Container Initiative (OCI) Runtimes - Native Runtimes - runC - Railcar - Crun - rkt Open Container Initiative (OCI) Runtimes - Sandboxed and Virtualized Runtimes - gviso - nabla-containers - runV - clearcontainers - kata-containers

Container Runtime Interface - containerd - cri-o

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Container vs hypervisor

Containers and hypervisors are both involved in making applications faster and more efficient, but they achieve this in different ways.

Hypervisors:

Allow an operating system to run independently from the underlying hardware through the use of virtual machines.

Share virtual computing, storage and memory resources.

Can run multiple operating systems on top of one server (bare-metal hypervisor) or installed on top of one standard operating system and isolated from it (hosted hypervisor).

Containers:

Allow applications to run independently of an operating system.

Can run on any operating system—all they need is a container engine to run.

Are extremely portable since in a container, an application has everything it needs to run.

Hypervisors and containers are used for different purposes. Hypervisors are used to create and run virtual machines (VMs), which each have their own complete operating systems, securely isolated from the others. In contrast to VMs, containers package up just an app and its related services. This makes them more lightweight and portable than VMs, so they are often used for fast and flexible application development and movement.

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What is Hypervisor?

A hypervisor, also known as a virtual machine monitor or VMM, is software that creates and runs virtual machines (VMs). A hypervisor allows one host computer to support multiple guest VMs by virtually sharing its resources, such as memory and processing.

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What is Full Load Strategy?

During a full load task, AWS DMS loads tables individually. By default, eight tables are loaded at a time. AWS DMS captures ongoing changes to the source during a full load task so the changes can be applied later on the target endpoint. The changes are cached in memory; if available memory is exhausted, changes are cached to disk. When a full load task completes for a table, AWS DMS immediately applies the cached changes to the target table.

You can create an AWS DMS task that captures ongoing changes to the source data store. You can do this capture while you are migrating your data. You can also create a task that captures ongoing changes after you complete your initial (full-load) migration to a supported target data store. This process is called ongoing replication or change data capture (CDC). AWS DMS uses this process when replicating ongoing changes from a source data store. This process works by collecting changes to the database logs using the database engine's native API.

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What is AWS Database Migration Service?

AWS Database Migration Service (AWS DMS) is a cloud service that makes it easy to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use AWS DMS to migrate your data into the AWS Cloud, between on-premises instances (through an AWS Cloud setup), or between combinations of cloud and on-premises setups.

With AWS DMS, you can perform one-time migrations, and you can replicate ongoing changes to keep sources and targets in sync. If you want to change database engines, you can use the AWS Schema Conversion Tool (AWS SCT) to translate your database schema to the new platform. You then use AWS DMS to migrate the data. Because AWS DMS is a part of the AWS Cloud, you get the cost efficiency, speed to market, security, and flexibility that AWS services offer.

AWS DMS supports nearly all of today's most popular DBMS engines as data sources, including Oracle, Microsoft SQL Server, MySQL, MariaDB, PostgreSQL, Db2 LUW, SAP, MongoDB, and Amazon Aurora.

AWS DMS provides a broad coverage of available target engines including Oracle, Microsoft SQL Server, PostgreSQL, MySQL, Amazon Redshift, SAP ASE, Amazon S3, and Amazon DynamoDB.

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What is AWS WAF?

AWS WAF is a web application firewall that helps protect web applications from attacks by allowing you to configure rules that allow, block, or monitor (count) web requests based on conditions that you define. These conditions include IP addresses, HTTP headers, HTTP body, URI strings, SQL injection and cross-site scripting.

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What is AWS Shield?

AWS Shield is a managed service that provides protection against Distributed Denial of Service (DDoS) attacks for applications running on AWS. AWS Shield Standard is automatically enabled to all AWS customers at no additional cost. AWS Shield Advanced is an optional paid service. AWS Shield Advanced provides additional protections against more sophisticated and larger attacks for your applications running on Amazon EC2, Elastic Load Balancing (ELB), Amazon CloudFront, AWS Global Accelerator, and Route 53.

Standard

AWS Shield Standard provides protection for all AWS customers against common and most frequently occurring infrastructure (layer 3 and 4) attacks like SYN/UDP floods, reflection attacks, and others to support high availability of your applications on AWS.

Advanced

AWS Shield Advanced provides enhanced protections for your applications running on protected Amazon EC2, Elastic Load Balancing (ELB), Amazon CloudFront, AWS Global Accelerator, and Route 53 resources against more sophisticated and larger attacks. AWS Shield Advanced protection provides always-on, flow-based monitoring of network traffic and active application monitoring to provide near real-time notifications of suspected DDoS incidents. AWS Shield Advanced also employs advanced attack mitigation and routing techniques for automatically mitigating attacks. Customers, with Business or Enterprise support, can also engage the DDoS Response Team (DRT) 24x7 to manage and mitigate their application layer DDoS attacks. The DDoS cost protection for scaling protects your AWS bill against higher fees due to usage spikes from protected Amazon EC2, Elastic Load Balancing (ELB), Amazon CloudFront, AWS Global Accelerator, and Amazon Route 53 during a DDoS attack.

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What Is Amazon Kinesis Data Firehose?

Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES), Splunk, and any custom HTTP endpoint or HTTP endpoints owned by supported third-party service providers, including Datadog, MongoDB, and New Relic. 

Kinesis Data Firehose is part of the Kinesis streaming data platform, along with Kinesis Data Streams, Kinesis Video Streams, and Amazon Kinesis Data Analytics. With Kinesis Data Firehose, you don't need to write applications or manage resources. You configure your data producers to send data to Kinesis Data Firehose, and it automatically delivers the data to the destination that you specified. You can also configure Kinesis Data Firehose to transform your data before delivering it.

Compared to Kinesis Data Stream, Kinesis Firehose is a bit simpler where you create the delivery stream and send the data to S3, Redshift or ElasticSearch (using the Kinesis Agent or API) directly and storing it in those services.

With Kinesis Streams, you build applications using the Kinesis Producer Library put the data into a stream and then process it with an application that uses the Kinesis Client Library and with Kinesis Connector Library send the processed data to S3, Redshift, DynamoDB etc.

Kinesis Streams on the other hand can store the data for up to 7 days. Which is why is leans towards to Domain 2.0: Storage.

You may use Kinesis Streams if you want to do some custom processing with streaming data. With Kinesis Firehose you are simply ingesting it into S3, Redshift or ElasticSearch.

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What is IPsec VPN?

IPsec VPN that enables you to create an encrypted connection over the public Internet between your Amazon VPC and your private IT infrastructure. The VPN connection lets you extend your existing security and management policies to your VPC as if they were running within your own infrastructure.

VPN is a great connectivity option for businesses that are just getting started with AWS. It is quick and easy to setup. Keep in mind, however, that VPN connectivity utilizes the public Internet, which can have unpredictable performance and despite being encrypted, can present security concerns.

To answer those concerns, you can establish an AWS VPN over an AWS Direct Connect connection. It’s like getting the best of both.

An AWS VPN over a DX connection to your VPC is likely faster and more secure than a VPN over the internet. An AWS VPN connection over a DX connection provides consistent levels of throughput and encryption algorithms that protect your data.

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What is Virtual Interface?

You must create one of the following virtual interfaces to begin using your AWS Direct Connect connection.

Private virtual interface: A private virtual interface should be used to access an Amazon VPC using private IP addresses.

Public virtual interface: A public virtual interface can access all AWS public services using public IP addresses.

Transit virtual interface: A transit virtual interface should be used to access one or more Amazon VPC Transit Gateways associated with Direct Connect gateways. You can use transit virtual interfaces with 1/2/5/10 Gbps AWS Direct Connect connections. 

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What is Strongly Consistent Read?

A GetItem request performs an eventually consistent read by default. You can use the ConsistentRead parameter to request a strongly consistent read instead. (This consumes additional read capacity units, but it returns the most up-to-date version of the item.)

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What is Conditional Writes?

By default, the DynamoDB write operations (PutItem, UpdateItem, DeleteItem) are unconditional: Each operation overwrites an existing item that has the specified primary key.

DynamoDB optionally supports conditional writes for these operations. A conditional write succeeds only if the item attributes meet one or more expected conditions. Otherwise, it returns an error. Conditional writes are helpful in many situations. For example, you might want a PutItem operation to succeed only if there is not already an item with the same primary key. Or you could prevent an UpdateItem operation from modifying an item if one of its attributes has a certain value.

Conditional writes are helpful in cases where multiple users attempt to modify the same item. 

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What is Placement Group?

When you launch a new EC2 instance, the EC2 service attempts to place the instance in such a way that all of your instances are spread out across underlying hardware to minimize correlated failures. You can use placement groups to influence the placement of a group of interdependent instances to meet the needs of your workload. Depending on the type of workload, you can create a placement group using one of the following placement strategies:

Cluster – packs instances close together inside an Availability Zone. This strategy enables workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication that is typical of HPC applications.

Partition – Each rack has its own network and power source. No two partitions within a placement group share the same racks, allowing you to isolate the impact of hardware failure within your application.

spreads your instances across logical partitions such that groups of instances in one partition do not share the underlying hardware with groups of instances in different partitions. This strategy is typically used by large distributed and replicated workloads, such as Hadoop, Cassandra, and Kafka.

Spread – A spread placement group is a group of instances that are each placed on distinct racks, with each rack having its own network and power source. strictly places a small group of instances across distinct underlying hardware to reduce correlated failures.

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What is Elastic Fabric Adapter?

An Elastic Fabric Adapter (EFA) is a network device that you can attach to your Amazon EC2 instance to accelerate High Performance Computing (HPC) and machine learning applications. EFA enables you to achieve the application performance of an on-premises HPC cluster, with the scalability, flexibility, and elasticity provided by the AWS Cloud.

EFA provides lower and more consistent latency and higher throughput than the TCP transport traditionally used in cloud-based HPC systems. It enhances the performance of inter-instance communication that is critical for scaling HPC and machine learning applications. It is optimized to work on the existing AWS network infrastructure and it can scale depending on application requirements.

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What is Elastic Network Adapter?

Elastic Network Adapter (ENA) is a module can be installed on EC2 instances that support ENA. It will allow hig

While delivering high throughput and great packet per second (PPS) performance, ENA minimizes the load on the host processor in a number of ways, and also does a better job of distributing the packet processing workload across multiple vCPUs. 

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What is Jumbo Frames?

The maximum transmission unit (MTU) of a network connection is the size, in bytes, of the largest permissible packet that can be passed over the connection. The larger the MTU of a connection, the more data that can be passed in a single packet. Ethernet packets consist of the frame, or the actual data you are sending, and the network overhead information that surrounds it.

Ethernet frames can come in different formats, and the most common format is the standard Ethernet v2 frame format. It supports 1500 MTU, which is the largest Ethernet packet size supported over most of the internet. The maximum supported MTU for an instance depends on its instance type. All Amazon EC2 instance types support 1500 MTU, and many current instance sizes support 9001 MTU, or jumbo frames.

Jumbo frames allow more than 1500 bytes of data by increasing the payload size per packet, and thus increasing the percentage of the packet that is not packet overhead. Fewer packets are needed to send the same amount of usable data. 

However, outside of a given AWS Region (EC2-Classic), a single VPC, or a VPC peering connection, you will experience a maximum path of 1500 MTU. VPN connections and traffic sent over an internet gateway are limited to 1500 MTU. If packets are over 1500 bytes, they are fragmented, or they are dropped if the Don't Fragment flag is set in the IP header.

Jumbo frames should be used with caution for internet-bound traffic or any traffic that leaves a VPC. Packets are fragmented by intermediate systems, which slows down this traffic. To use jumbo frames inside a VPC and not slow traffic that's bound for outside the VPC, you can configure the MTU size by route, or use multiple elastic network interfaces with different MTU sizes and different routes.

For instances that are collocated inside a cluster placement group, jumbo frames help to achieve the maximum network throughput possible, and they are recommended in this case. For more information, see Placement groups.

You can use jumbo frames for traffic between your VPCs and your on-premises networks over AWS Direct Connect. For more information, and for how to verify Jumbo Frame capability, see Setting Network MTU in the AWS Direct Connect User Guide.

All current generation instances support jumbo frames. The following previous generation instances support jumbo frames: C3, G2, I2, M3, and R3.

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What is AWS Batch?

AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. 

With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2 and Spot Instances.

It can be used for processing jobs such as Video effects rendering, Drug screening and Post-trade analytics.