[beam] branch master updated: refs: issue-24196, fix broken hyperlink

[al...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

altay pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/beam.git


The following commit(s) were added to refs/heads/master by this push:
     new ead245539d0 refs: issue-24196, fix broken hyperlink
     new fef8acdbc0e Merge pull request #24199 from Laksh47/issue#24196
ead245539d0 is described below

commit ead245539d01dec0f3e08699c1e1cc6777a5ef0e
Author: Laksh <la...@gmail.com>
AuthorDate: Wed Nov 16 09:32:46 2022 -0500

    refs: issue-24196, fix broken hyperlink
---
 website/www/site/content/en/blog/splitAtFraction-method.md    | 2 +-
 website/www/site/content/en/blog/splittable-do-fn.md          | 4 ++--
 website/www/site/content/en/documentation/runners/dataflow.md | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/website/www/site/content/en/blog/splitAtFraction-method.md b/website/www/site/content/en/blog/splitAtFraction-method.md
index 0ae5b734693..f2e280aaabd 100644
--- a/website/www/site/content/en/blog/splitAtFraction-method.md
+++ b/website/www/site/content/en/blog/splitAtFraction-method.md
@@ -22,7 +22,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
-This morning, Eugene and Malo from the Google Cloud Dataflow team posted [*No shard left behind: dynamic work rebalancing in Google Cloud Dataflow*](https://cloud.google.com/blog/big-data/2016/05/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow). This article discusses Cloud Dataflow’s solution to the well-known straggler problem.
+This morning, Eugene and Malo from the Google Cloud Dataflow team posted [*No shard left behind: dynamic work rebalancing in Google Cloud Dataflow*](https://cloud.google.com/blog/products/gcp/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow). This article discusses Cloud Dataflow’s solution to the well-known straggler problem.
 
 <!--more-->
 
diff --git a/website/www/site/content/en/blog/splittable-do-fn.md b/website/www/site/content/en/blog/splittable-do-fn.md
index d7c1abfafe8..f38a5d6d488 100644
--- a/website/www/site/content/en/blog/splittable-do-fn.md
+++ b/website/www/site/content/en/blog/splittable-do-fn.md
@@ -187,7 +187,7 @@ runner with information such as its estimated size (or its generalization,
 uses this information to tune the execution and control the breakdown of the
 `Source` into bundles. For example, a slowly progressing large bundle of a file
 may be [dynamically
-split](https://cloud.google.com/blog/big-data/2016/05/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow)
+split](https://cloud.google.com/blog/products/gcp/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow)
 by a batch-focused runner before it becomes a straggler, and a latency-focused
 streaming runner may control how many elements it reads from a source in each
 bundle to optimize for latency vs. per-bundle overhead.
@@ -251,7 +251,7 @@ a `@ProcessElement` call is going to take too long and become a straggler, it
 can split the restriction in some proportion so that the primary is short enough
 to not be a straggler, and can schedule the residual in parallel on another
 worker. For details, see [No Shard Left
-Behind](https://cloud.google.com/blog/big-data/2016/05/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow).
+Behind](https://cloud.google.com/blog/products/gcp/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow).
 
 Logically, the execution of an SDF on an element works according to the
 following diagram, where "magic" stands for the runner-specific ability to split
diff --git a/website/www/site/content/en/documentation/runners/dataflow.md b/website/www/site/content/en/documentation/runners/dataflow.md
index eb5398d3c25..7b5d3e60f56 100644
--- a/website/www/site/content/en/documentation/runners/dataflow.md
+++ b/website/www/site/content/en/documentation/runners/dataflow.md
@@ -26,7 +26,7 @@ The Cloud Dataflow Runner and service are suitable for large scale, continuous j
 
 * a fully managed service
 * [autoscaling](https://cloud.google.com/dataflow/service/dataflow-service-desc#autoscaling) of the number of workers throughout the lifetime of the job
-* [dynamic work rebalancing](https://cloud.google.com/blog/big-data/2016/05/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow)
+* [dynamic work rebalancing](https://cloud.google.com/blog/products/gcp/no-shard-left-behind-dynamic-work-rebalancing-in-google-cloud-dataflow)
 
 The [Beam Capability Matrix](/documentation/runners/capability-matrix/) documents the supported capabilities of the Cloud Dataflow Runner.