[FLINK-21926][doc] Add docs for fine-grained resource management

[KarmaGYZ]

What is the purpose of the change

(For example: This pull request makes task deployment go through the blob server, rather than through RPC. That way we avoid re-transferring them on each deployment (during recovery).)

Brief change log

(for example:)

• The TaskInfo is stored in the blob store on job creation time as a persistent artifact
• Deployments RPC transmits only the blob storage reference
• TaskManagers retrieve the TaskInfo from the blob cache

Verifying this change

(Please pick either of the following options)

This change is a trivial rework / code cleanup without any test coverage.

(or)

This change is already covered by existing tests, such as (please describe tests).

(or)

This change added tests and can be verified as follows:

(example:)

• Added integration tests for end-to-end deployment with large payloads (100MB)
• Extended integration test for recovery after master (JobManager) failure
• Added test that validates that TaskInfo is transferred only once across recoveries
• Manually verified the change by running a 4 node cluser with 2 JobManagers and 4 TaskManagers, a stateful streaming program, and killing one JobManager and two TaskManagers during the execution, verifying that recovery happens correctly.

Does this pull request potentially affect one of the following parts:

• Dependencies (does it add or upgrade a dependency): (yes / no)
• The public API, i.e., is any changed class annotated with @Public(Evolving): (yes / no)
• The serializers: (yes / no / don't know)
• The runtime per-record code paths (performance sensitive): (yes / no / don't know)
• Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: (yes / no / don't know)
• The S3 file system connector: (yes / no / don't know)

Documentation

• Does this pull request introduce a new feature? (yes / no)
• If yes, how is the feature documented? (not applicable / docs / JavaDocs / not documented)

[flinkbot]

Thanks a lot for your contribution to the Apache Flink project. I'm the @flinkbot. I help the community
to review your pull request. We will use this comment to track the progress of the review.

Automated Checks

Last check on commit 8cd1586 (Thu Jul 22 03:37:50 UTC 2021)

✅no warnings

_{Mention the bot in a comment to re-run the automated checks.}

Review Progress

• ❓ 1. The [description] looks good.
• ❓ 2. There is [consensus] that the contribution should go into to Flink.
• ❓ 3. Needs [attention] from.
• ❓ 4. The change fits into the overall [architecture].
• ❓ 5. Overall code [quality] is good.

Please see the Pull Request Review Guide for a full explanation of the review process.

The Bot is tracking the review progress through labels. Labels are applied according to the order of the review items. For consensus, approval by a Flink committer of PMC member is required

Bot commands

The @flinkbot bot supports the following commands:

• @flinkbot approve description to approve one or more aspects (aspects: description, consensus, architecture and quality)
• @flinkbot approve all to approve all aspects
• @flinkbot approve-until architecture to approve everything until architecture
• @flinkbot attention @username1 [@username2 ..] to require somebody's attention
• @flinkbot disapprove architecture to remove an approval you gave earlier

[flinkbot]

CI report:

• b3eac93 Azure: FAILURE

Bot commands

The @flinkbot bot supports the following commands:

• @flinkbot run travis re-run the last Travis build
• @flinkbot run azure re-run the last Azure build

[sjwiesman]

I've made a first pass

[sjwiesman]

Suggested change

	the resource for task execution in TaskManager is split into a bunch of slots, where job tasks are scheduled.
	task execution resources in a TaskManager are split into many slots.

[sjwiesman]

Suggested change

	The slot is the basic unit of both resource scheduling and resource requirement in Flink runtime.
	The slot is the basic unit of both resource scheduling and resource requirement in Flinks runtime.

[KarmaGYZ]

Maybe Flink's?

[sjwiesman]

Suggested change

	In fine-grained resource management, the slots are requested with specific resource profiles, which can be specified by users.
	Flink will respect those user-specified resource requirements and dynamically cut an exactly-matched slot out of the TaskManager’s available
	resources. As shown above, there is a requirement for a slot with 0.25 Core and 1GB memory and Flink allocates *Slot 1* for it.
	With fine-grained resource management, the slots requests contain specific resource profiles, which users can specify.
	Flink will respect those user-specified resource requirements and dynamically cut an exactly-matched slot out of the TaskManager’s available
	resources. As shown above, there is a requirement for a slot with 0.25 Core and 1GB memory, and Flink allocates *Slot 1* for it.

[sjwiesman]

Suggested change

	Previously in Flink, the resource requirement only contained the number of the required slots, without fine-grained resource
	profiles, namely **coarse-grained resource management**. The TaskManager contained a fixed number of identical slots to fulfill those requirements.

I moved this down

[sjwiesman]

Suggested change

	{{< hint info >}}
	Previously in Flink, the resource requirement only contained the number of the required slots, without fine-grained resource
	profiles, namely **coarse-grained resource management**. The TaskManager contained a fixed number of identical slots to fulfill those requirements.
	{{< /hint >}}

[sjwiesman]

Suggested change

	<div class="alert alert-info">
	<strong>Note:</strong> Each slot sharing group can only attach to one specified resource, any conflict will fail the compiling of your job.
	</div>
	{{< hint warning >}}
	**Note:** Each slot sharing group can only attach to one specified resource, any conflict will fail the compiling of your job.
	{{< /hint >}}

[sjwiesman]

Suggested change

	For the resource requirement without a specified resource profile, Flink will automatically decide the resource profile of it.
	For the resource requirement without a specified resource profile, Flink will automatically decide a resource profile.

[sjwiesman]

Suggested change

	management. In the future, there might be various strategies that can be selected for different scenarios.
	management. In the future, there might be various strategies that users can select for different scenarios.

[sjwiesman]

Suggested change

	Apache Flink allows you to control the resource consumption of your workload in a finer granularity, namely **fine-grained resource management**.
	It provides means for users to further improve Flink’s resource efficiency with knowledge of their specific scenarios.
	Apache Flink works hard to auto-derive sensible default resource requirements for all applications out of the box.
	For users who wish to fine-tune their resource consumption, based on knowledge of their specific scenarios, Flink offers **fine-grained resource management**.

I want to make it clear to new users that you don't have to do this.

[KarmaGYZ]

Thanks for the valuable comments! @sjwiesman PR updated.

[KarmaGYZ]

@sjwiesman Hi, would you like to give it another pass?