Today we continue discussing the best practice from storage engineering:
674) There is no limit to the number of operators run during deploy. It is preferred to run them sequentially one after the other. The more granular the operators the better they are for maintenance.
675) The diagnosability of operators improves with each operator being small and transparent.
676) The upgrade operator can be rolling upgrades. Then the operator does not require scale up or down.
677) The operators can invoke methods defined in other operators as long as they are accessible.
678) Frequently storage applications are written with dependencies on other applications. Those applications may also support operators. Operators can be chained one way or the other.
679) Requirements on operator logic change whether the product is an analysis package or a storage product.
680) There is a lot in common between installer packages on any other desktop platform and the containerization deployment operators.
681) The setup of service accounts is part of the operator actions for deployment and it remains one of the core requirements for the deployment of the product.
682) The dependencies of the software deployment may include such things as identity provider. The identity provider facilitates the registrations of service accounts
683) Identity providers issue token for accounts and these can be interpreted for user and roles
684) Identity providers do not mandate the use of one protocol such as Oauth or JWT and it is easy to switch from one to the other with http requests.
685) The accounts can be made suitable for use with any Identity provider.

#codingexercise

// topN
    for (int k = 0; k < N; k++) {
      Double value;
      if (first[i] > second[j]) {
          value = first[i];
          i++;
      } else {
          value = second[j];
          j++;
      }
    }

Today we continue discussing the best practice from storage engineering :

670) There is no special requirement for performance or security from the containerization framework than what the application wants from the host because this is internal and not visible to the user.

671) Operators like update involve scaling down the nodes, then performing the update and then scaling back up. There is no restriction to reuse logic between operators.

672) Operators generally work one at a time on the same cluster. This prevents states from being mixed and allowing each reconcile between state and deploy to happen sequentially.

673) Operators do not have retain any information between invocations. Anything that needs to be persisted has to be part of state.

674) There is no limit to the number of operators run during deploy. It is preferred to run them sequentially one after the other. The more granular the operators the better they are for maintenance.

675) The diagnosability of operators improves with each operator being small and transparent.

676) The upgrade operator can be rolling upgrades. Then the operator does not require scale up or down.

677) The operators can invoke methods defined in other operators as long as they are accessible.

678) Frequently storage applications are written with dependencies on other applications. Those applications may also support operators. Operators can be chained one way or the other.

679) Requirements on operator logic change whether the product is an analysis package or a storage product.

680) There is a lot in common between installer packages on any other desktop platform and the containerization deployment operators.

Today we continue discussing the best practice from storage engineering:

667) The type of features from the operator sdk used by the product depend on the automation within the container-specific operator specified by the product. If the product wants to restrict its usage of the container, it can take advantage of just the minimum.

668) One of these features is metering and it works the same way as in the public cloud. Containers are still catching up with the public cloud on this feature.

669) The operators can be written in a variety of languages depending on the sdk however in many cases a barebone application without heavy interpreters or compilers is preferred. Go language is used for these purposes and particularly in devOps.

670) There is no special requirement for performance or security from the containerization framework than what the application wants from the host because this is internal and not visible to the user.

671) Operators like update involve scaling down the nodes, then performing the update and then scaling back up. There is no restriction to reuse logic between operators.

672) Operators generally work one at a time on the same cluster. This prevents states from being mixed and allowing each reconcile between state and deploy to happen sequentially.

673) Operators do not have retain any information between invocations. Anything that needs to be persisted has to be part of state.

674) There is no limit to the number of operators run during deploy. It is preferred to run them sequentially one after the other. The more granular the operators the better they are for maintenance.

675) The diagnosability of operators improves with each operator being small and transparent.

Today we continue discussing the best practice from storage engineering:

661) The operator used to build and deploy the storage server can take care of all the human oriented administrative tasks such as upgrade, scaling, and backups.

662) The logic of these administrative tasks remains the same across versions, size and nature.

663) The parameters for the task are best described in the declarative syntax associated with the so-called custom resource required for these deployment operators

664) The number of tasks or their type depend from application to application and can became sophisticated and customized. There is no restriction to this kind of automation

665) The containerized image built has to be registered in the hub so that it is made available everywhere.

666) There are many features on the container framework that the storage product can leverage. Some of these features are available via the SDK. However, container technologies continue to evolve in terms of following the Platform as a service layer and the public cloud example.

667) The type of features from the operator sdk used by the product depend on the automation within the container-specific operator specified by the product. If the product wants to restrict its usage of the container, it can take advantage of just the minimum.

668) One of these features is metering and it works the same way as in the public cloud. Containers are still catching up with the public cloud on this feature.

669) The operators can be written in a variety of languages depending on the sdk however in many cases a barebone application without heavy interpreters or compilers is preferred. Go language is used for these purposes and particularly in devOps.

670) There is no special requirement for performance or security from the containerization framework than what the application wants from the host because this is internal and not visible to the user.

Today we continue discussing the best practice from storage engineering:

656) Different containerization technologies have proprietary format and semantics for the storage application to use. Of these the most critical aspect of the storage server has been networking because the nodes often scale out and the nodes need to be declared to the containers for proper connectivity. NAS storage is not going away as long as hard drives continue to be nearline storage.

657) These same containerization technologies allow the storage services to be deployed as images. This is very helpful to run the same image across a variety of hosts without having to recompile and build the package.

658) Such an image needs to be built with the help of declared dependencies for the storage service often called custom resources in the containerization parlance and a logic to reconcile the state of the deployment with the intended declarations. This process is often aided by the sdk from the containerization technology.

659) The sdk from containers can also help with the scaffolding code generation for use with the storage service to build and deploy the service as an image. This code is called an operator

660) The operator used to build and deploy the storage server as an image for the container is usually one per product. The flavors of the product and its releases can be detailed in the definitions for the same operator.

Today we continue discussing the best practice from storage engineering:

649) If the entries vary widely affecting the overall results at a high rate, it is easier to take on the changes on the compute side but allow the storage of the listings to be progressive. This way tasks can communicate the changes to their respective sortings to the scheduler which can then adjust the overall sort order

650) If the listing is a stream, processing on a stream works the same was as cursor on a database adjusting the rankings gathered so far for each and every entry as they are encountered.

651) The processing of stream is facilitated with compute packages from Microsoft and Apache for example. These  kind of packages highlight the stream processing techniques that can be applied to stream from a variety of storage.

652) About the query and the algorithm be it mining or machine learning can be externalized. This can work effectively across storage just as much as it is applicable to specific data.

653) The algorithms vary widely in their duration and convergence even for the same data. There is usually no specific rule to follow when comparing algorithms in the same category

654) The usual technique in the above case is to use a strategy pattern that interchanges algorithms and evaluated them on trial and error basis.

655) Storage services can take advantage of containerization just like other applications. Provisioning the service over the containers while allowing the nodes to remain part of the cluster is both scale-able and portable.

Today we continue discussing the best practice from storage engineering

648) If the listing is distributed, it helps to have a map-reduce on the listing

649) If the entries vary widely affecting the overall results at a high rate, it is easier to take on the changes on the compute side but allow the storage of the listings to be progressive. This way tasks can communicate the changes to their respective sortings to the scheduler which can then adjust the overall sort order

650) If the listing is a stream, processing on a stream works the same was as cursor on a database adjusting the rankings gathered so far for each and every entry as they are encountered.

651) The processing of stream is facilitated with compute packages from Microsoft and Apache for example. These  kind of packages highlight the stream processing techniques that can be applied to stream from a variety of storage.

652) About the query and the algorithm be it mining or machine learning can be externalized. This can work effectively across storage just as much as it is applicable to specific data.

653) The algorithms vary widely in their duration and convergence even for the same data. There is usually no specific rule to follow when comparing algorithms in the same category

654) The usual technique in the above case is to use a strategy pattern that interchanges algorithms and evaluated them on trial and error basis.