IoP | Internet of Plants

Convert the object to an array of [key, values] entries using Object.entries(), and reduce the entries a new array. Map the current array of values, and create a string for each val with the current key.

If the accumulator (acc) is empty, return the create an array of strings. If the acc is not empty, iterate it with Array.flatMap(), map the array of values of the current entry (arr), and combine the strings.

The subquery produces one row that contains an array.

If you use = ANY (SELECT ...), the result set is converted to an array, so you end up with

Your elected regular expression (.+?) which assumes a presence at least one symbol in title tag - what leads regex to capturing from this place skipping empty tag until next closing tag, definitely not what you intended to achieve.

Perhaps following code is self explanatory

Before sharding or partitioning, first analyze your queries to make sure they are as optimized as you can make them. This usually means designing indexes specifically to support the queries you run. You might like my presentation How to Design Indexes, Really (video).

Partitioning isn't as much a solution as people think. It has many restrictions, including the foreign key issue you found. Besides that, it only improves queries that can take advantage of partition pruning.

Also, I've done a lot of benchmarking of Amazon RDS for my current job and also a previous job. RDS is slow. It's really slow. It uses remote EBS storage, so it's bound to incur overhead for every read from storage or write to storage. RDS is just not suitable for any application that needs high performance.

Amazon Aurora is significantly better on latency and throughput. But it's also very expensive. The more you use it, the more you use I/O requests, and they charge extra for that. For a busy app, you end up spending as much as you did for RDS with high provisioned IOPS.

The only way I found to get high performance in the cloud is to forget about managed databases like RDS and Aurora, and instead install and run your own instance of MySQL on an ec2 instance with locally-attached NVMe storage. This means the i3 family of ec2 instances. But local storage is ephemeral instance storage, so if the instance restarts, you lose your data. So you must add one or more replicas and have a failover plan.

If you need an OLTP database in the cloud, and you also need top-tier performance, you either have to spend $$$ for a managed database, or else you need to hire full-time DevOps and DBA staff to run it.

Sorry to give you the bad news, but the TANSTAAFL adage remains true.

You have two choices if you don't want to limit usage of your db:

1. Increase size of your storage, as for gp2, the bigger the size the more IOPS you get.
2. Change from gp2 to Provisioned IOPS SSD where you can specify fixed IOPS that you require. Provisioned IOPS SSD will be probably also more expensive.

Try to use EXPLAIN to analyze your SELECT DISTINCT query. I bet it will include "Using temporary" and/or "Using filesort". With a large enough result set, these queries will use temporary disk space. But the more frequently you run these queries, the more disk space it uses.

I don't know why you use SELECT DISTINCT * if the rows are already distinct. This may cause the use of a temporary table unnecessarily.

Ideally your query should be:

The issue with AWS provider, which didn't have much options, So I have upgraded to terraform-provider-aws_3.24.0_linux_amd64.zip and now can be added specific tags for each EBS volume

I fix this problem by adding the ignore_changes block to the aws_instance resource:

You have a couple of indentation issues.

1. You need to indent everything below ElasticsearchDomain:
2. Your arrays need to be indented.