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Merge remote-tracking branch 'upstream/main' into dev-1.24

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Nate W
2022-04-04 09:40:29 -07:00
parent 0b4f78f671 016148039b
commit 22643121ff
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content/en/docs/concepts/configuration/configmap.md
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@@ -15,7 +15,6 @@ If the data you want to store are confidential, use a
or use additional (third party) tools to keep your data private.
{{< /caution >}}
<!-- body -->
## Motivation
@@ -282,5 +281,3 @@ to the deleted ConfigMap, it is recommended to recreate these pods.
* Read [Configure a Pod to Use a ConfigMap](/docs/tasks/configure-pod-container/configure-pod-configmap/).
* Read [The Twelve-Factor App](https://12factor.net/) to understand the motivation for
separating code from configuration.
content/en/docs/concepts/overview/what-is-kubernetes.md
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@@ -4,7 +4,7 @@ reviewers:
- mikedanese
title: What is Kubernetes?
description: >
Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.
Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.
content_type: concept
weight: 10
card:
@@ -19,7 +19,7 @@ This page is an overview of Kubernetes.
<!-- body -->
Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.
Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.
The name Kubernetes originates from Greek, meaning helmsman or pilot. K8s as an abbreviation results from counting the eight letters between the "K" and the "s". Google open-sourced the Kubernetes project in 2014. Kubernetes combines [over 15 years of Google's experience](/blog/2015/04/borg-predecessor-to-kubernetes/) running production workloads at scale with best-of-breed ideas and practices from the community.
content/en/docs/concepts/services-networking/service.md
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@@ -158,9 +158,9 @@ Each port definition can have the same `protocol`, or a different one.
### Services without selectors
Services most commonly abstract access to Kubernetes Pods, but they can also
abstract other kinds of backends.
For example:
Services most commonly abstract access to Kubernetes Pods thanks to the selector,
but when used with a corresponding Endpoints object and without a selector, the Service can abstract other kinds of backends,
including ones that run outside the cluster. For example:
* You want to have an external database cluster in production, but in your
test environment you use your own databases.
content/en/docs/reference/command-line-tools-reference/feature-gates.md
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@@ -450,6 +450,7 @@ different Kubernetes components.
| `ServerSideApply` | `false` | Alpha | 1.14 | 1.15 |
| `ServerSideApply` | `true` | Beta | 1.16 | 1.21 |
| `ServerSideApply` | `true` | GA | 1.22 | - |
| `ServerSideFieldValidation` | `false` | Alpha | 1.23 | - |
| `ServiceAccountIssuerDiscovery` | `false` | Alpha | 1.18 | 1.19 |
| `ServiceAccountIssuerDiscovery` | `true` | Beta | 1.20 | 1.20 |
| `ServiceAccountIssuerDiscovery` | `true` | GA | 1.21 | - |
@@ -1088,6 +1089,9 @@ Each feature gate is designed for enabling/disabling a specific feature:
cache to accelerate list operations.
- `ServerSideApply`: Enables the [Sever Side Apply (SSA)](/docs/reference/using-api/server-side-apply/)
feature on the API Server.
- `ServerSideFieldValidation`: Enables server-side field validation. This means the validation
of resource schema is performed at the API server side rather than the client side
(for example, the `kubectl create` or `kubectl apply` command line).
- `ServiceAccountIssuerDiscovery`: Enable OIDC discovery endpoints (issuer and
JWKS URLs) for the service account issuer in the API server. See
[Configure Service Accounts for Pods](/docs/tasks/configure-pod-container/configure-service-account/#service-account-issuer-discovery)
content/en/docs/reference/glossary/shuffle-sharding.md
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@@ -1,5 +1,5 @@
---
title: shuffle sharding
title: Shuffle-sharding
id: shuffle-sharding
date: 2020-03-04
full_link:
@@ -18,28 +18,28 @@ We are often concerned with insulating different flows of requests
from each other, so that a high-intensity flow does not crowd out low-intensity flows.
A simple way to put requests into queues is to hash some
characteristics of the request, modulo the number of queues, to get
the index of the queue to use. The hash function uses as input
characteristics of the request that align with flows. For example, in
the index of the queue to use. The hash function uses as input
characteristics of the request that align with flows. For example, in
the Internet this is often the 5-tuple of source and destination
address, protocol, and source and destination port.
That simple hash-based scheme has the property that any high-intensity flow
will crowd out all the low-intensity flows that hash to the same queue.
Providing good insulation for a large number of flows requires a large
number of queues, which is problematic. Shuffle sharding is a more
number of queues, which is problematic. Shuffle-sharding is a more
nimble technique that can do a better job of insulating the low-intensity
flows from the high-intensity flows. The terminology of shuffle sharding uses
flows from the high-intensity flows. The terminology of shuffle-sharding uses
the metaphor of dealing a hand from a deck of cards; each queue is a
metaphorical card. The shuffle sharding technique starts with hashing
metaphorical card. The shuffle-sharding technique starts with hashing
the flow-identifying characteristics of the request, to produce a hash
value with dozens or more of bits. Then the hash value is used as a
value with dozens or more of bits. Then the hash value is used as a
source of entropy to shuffle the deck and deal a hand of cards
(queues). All the dealt queues are examined, and the request is put
into one of the examined queues with the shortest length. With a
(queues). All the dealt queues are examined, and the request is put
into one of the examined queues with the shortest length. With a
modest hand size, it does not cost much to examine all the dealt cards
and a given low-intensity flow has a good chance to dodge the effects of a
given high-intensity flow. With a large hand size it is expensive to examine
given high-intensity flow. With a large hand size it is expensive to examine
the dealt queues and more difficult for the low-intensity flows to dodge the
collective effects of a set of high-intensity flows. Thus, the hand size
collective effects of a set of high-intensity flows. Thus, the hand size
should be chosen judiciously.
content/en/docs/tasks/tls/managing-tls-in-a-cluster.md
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@@ -308,7 +308,7 @@ kubectl create secret tls server --cert server.crt --key server-key.pem
secret/server created
```
Finally, you can populate `ca.pem` into a {< glossary_tooltip text="ConfigMap" term_id="configmap" >}}
Finally, you can populate `ca.pem` into a {{< glossary_tooltip text="ConfigMap" term_id="configmap" >}}
and use it as the trust root to verify the serving certificate:
```shell