bucket | Basic di-container for php | Dependency Injection library

Terraform AWS Provider is upgraded to version 4.0.0 which is published on 10 February 2022.

Major changes in the release include:

• Version 4.0.0 of the AWS Provider introduces significant changes to the aws_s3_bucket resource.
• Version 4.0.0 of the AWS Provider will be the last major version to support EC2-Classic resources as AWS plans to fully retire EC2-Classic Networking. See the AWS News Blog for additional details.
• Version 4.0.0 and 4.x.x versions of the AWS Provider will be the last versions compatible with Terraform 0.12-0.15.

The reason for this change by Terraform is as follows: To help distribute the management of S3 bucket settings via independent resources, various arguments and attributes in the aws_s3_bucket resource have become read-only. Configurations dependent on these arguments should be updated to use the corresponding aws_s3_bucket_* resource. Once updated, new aws_s3_bucket_* resources should be imported into Terraform state.

So, I updated my code accordingly by following the guide here: Terraform AWS Provider Version 4 Upgrade Guide | S3 Bucket Refactor

The new working code looks like this:

This answer is accurate as of 19th January 2022.

Upon manual debug of this dataflow, I found that the issue is due to the dataflow job is looking for a schema with the exact same name as the value passed for the parameter databaseName and there was no other input parameter for the job using which we could pass a schema name. Therefore for this job to work, the tables will have to be created/imported into a schema with the same name as the database.

However, as @Iñigo González said this dataflow is currently in Beta and seems to have some bugs as I ran into another issue as soon as this was resolved which required me having to change the source code of the dataflow template job itself and build a custom docker image for it.

Use array.every() to check all the elements of the array.

To the best of my knowledge, you can only override particular values of YAML anchors. Attempts to 'pass arguments' won't work.

Instead, Bitbucket Pipelines provide Deployments - an ad-hoc way to assign different values to your variables depending on the environment. You'll need to create two deployments (say, dev and uat), and use them when referring to a step:

Using getFirestore from lite library will not work with onSnapshot. You are importing getFirestore from lite version:

I think the performance issue is because of expandToNeighbors method generates 4 points all the time. It becomes crucial on the border, where you'd better generate 3 (or even 2 on corner) points, so extra point is current position again. So first border point doubles following points count, second one doubles it again (now it's x4) and so on.

If I'm right, you saw not the slow method work, but it was called too often.

You need to upgrade the python to 3.9 and Pywin32 to Build 302. In addition, you need to install the OpenOPC-Python3x 1.3.1.

Following the steps in this post fixed my issue:

This is due to a missing permission:

The TLDR is that loads which miss all levels of the TLB (and so require a page walk) and which are separated by address unknown stores can't execute in parallel, i.e., the loads are serialized and the memory level parallelism (MLP) factor is capped at 1. Effectively, the stores fence the loads, much as lfence would.

The slow version of your insert function results in this scenario, while the other two don't (the store address is known). For large region sizes the memory access pattern dominates, and the performance is almost directly related to the MLP: the fast versions can overlap load misses and get an MLP of about 3, resulting in a 3x speedup (and the narrower reproduction case we discuss below can show more than a 10x difference on Skylake).

The underlying reason seems to be that the Skylake processor tries to maintain page-table coherence, which is not required by the specification but can work around bugs in software.

For those who are interested, we'll dig into the details of what's going on.

I could reproduce the problem immediately on my Skylake i7-6700HQ machine, and by stripping out extraneous parts we can reduce the original hash insert benchmark to this simple loop, which exhibits the same issue: