You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


# Setting-up basic DB mappings

## Overview

In this section we will configure the DB<=>Haskell mapping for the following table:

* `tenants` - the master table of "tenants" in a typical multi-tenant SaaS app. You can think of a tenant as a "company account", where no two company accounts share any data.

At the end of the mapping process, we would like to have a schema as close to the following, as possible.

```
--
-- Tenants
--

create table tenants(
       id serial primary key
       ,created_at timestamp with time zone not null default current_timestamp
       ,updated_at timestamp with time zone not null default current_timestamp
       ,name text not null
       ,first_name text not null
       ,last_name text not null
       ,email text not null
       ,phone text not null
       ,status text not null default 'inactive'
       ,owner_id integer
       ,backoffice_domain text not null
       constraint ensure_not_null_owner_id check (status!='active' or owner_id is not null)
);
create unique index idx_index_owner_id on tenants(owner_id);
create index idx_status on tenants(status);
create index idx_tenants_created_at on tenants(created_at);
create index idx_tenants_updated_at on tenants(updated_at);
create unique index idx_unique_tenants_backoffice_domain on tenants(lower(backoffice_domain));
```

Further, we will see how each DB library deals with the following four cases:

* Non-nullable columns without DB-specified defaults
* Non-nullable columns with DB-specified defaults
* Nullable columns without DB-specified defaults
* Nullable columns with DB-specified defaults - TODO: What's a good use-case for such a column?

## Opaleye

### Creating the DB

Since Opaleye does not have any support for migrations, setting up the DB schema is done by simply issuing SQL statement directly.

```
$ createdb vacationlabs
$ psql vacationlabs < includes/db-mappings/schema.sql
```

Now, to setup the DB<=>Haskell mapping for the `tenants` table, we'll walk down the following code:

```
module DB where

import Opalaye
import Data.Text
import Data.Time (UTCTime)

data TenantPoly key createdAt updatedAt name status ownerId backofficeDomain = Tenant {
  tenantKey :: key
  ,tenantCreatedAt :: createdAt
  ,tenantUpdatedAt :: updatedAt
  ,tenantName :: name
  ,tenantStatus :: status
  ,tenantOwnerId :: ownerId
  ,tenantBackofficeDomain :: backofficeDomain
  } deriving Show

type TenantPGWrite = TenantPoly
  (Maybe (Column PGInt8)) -- key
  (Maybe (Column PGTimestamptz)) -- createdAt
  (Column PGTimestamptz) -- updatedAt
  (Column PGText) -- name
  (Column PGText) -- status
  (Column (Nullable PGInt8)) -- ownerId
  (Column PGText) -- backofficeDomain

type TenantPGRead = TenantPoly
  (Column PGInt8) -- key
  (Column PGTimestamptz) -- createdAt
  (Column PGTimestamptz) -- updatedAt
  (Column PGText) -- name
  (Column PGText) -- status
  (Column (Nullable PGInt8)) -- ownerId
  (Column PGText) -- backofficeDomain

type Tenant = TenantPoly
  Integer -- key
  UTCTime -- createdAt
  UTCTime -- updatedAt
  Text -- name
  Text -- status
  (Maybe Integer) -- ownerId
  Text -- backofficeDomain

$(makeAdaptorAndInstance "pTenant" ''TenantPoly)
$(makeLensesWith abbreviatedFields ''TenantPoly)


tenantTable :: Table TenantPGWrite TenantPGRead
tenantTable = Table "tenants" (pTenant Tenant{
                                  tenantKey = optional "id"
                                  ,tenantCreatedAt = optional "created_at"
                                  ,tenantUpdatedAt = required "updated_at"
                                  ,tenantName = required "name"
                                  ,tenantStatus = required "status"
                                  ,tenantOwnerId = required "owner_id"
                                  ,tenantBackofficeDomain = required "backoffice_domain"
                                  })
```

That's quite a **lot of code** to setup mappings for just one table! Most of it is just boilerplate that can easily be abstracted away using typefamilies or some TemplateHaskell. In fact there are libraries, such as, SilkOpaleye and dbrecord-opaleye which try to give Opaleye an easier-to-use API.

### Strange polymorphic records

Firstly, let's tackle the strangely polymorphic `TenantPoly`.

```
data TenantPoly key createdAt updatedAt name status ownerId backofficeDomain = Tenant {
  tenantKey :: key
  ,tenantCreatedAt :: createdAt
  ,tenantUpdatedAt :: updatedAt
  ,tenantName :: name
  ,tenantStatus :: status
  ,tenantOwnerId :: ownerId
  ,tenantBackofficeDomain :: backofficeDomain
  } deriving Show
```

This is a **base type** which defines the **shape** of a set of related record-types (namely `TenantPGRead`, `TenantPGWrite`, and `Tenant`). `TenantPoly` is polymorphic over every single field of the record. This allows us to easily change the type of each field, while ensuring that the *shape* of all these related records is always the same. (*Why* would we want records with similar shapes, but different types, will get clearer in a moment - hang in there!) Generally, `TenantPoly` is never used directly in any Opaleye operation. The concrete types - `TenantPGRead` `TenantPGWrite` and `Tenant` - are used instead.

Now, it seems that Opalaye does **not do any reflection** on the DB schema whatsoever. This is a completely different approach compared to Rails (in the Ruby world) and HRR (in the Haskell world) which generate the DB<=>Haskell classes/record-types completely on the basis of schema reflection). So, Opaleye does not know what data-types to expect for each column when talking to the DB. Therefore, we have to teach it by duplicating the column definitions in Haskell. This is precisely what `TenantPGRead`,  `TenantPGWrite`, `makeAdaptorAndInstance` and `tenantTable` do, and this is what we absolutely hate about Opaleye!

```
type TenantPGWrite = TenantPoly
  (Maybe (Column PGInt8)) -- key
  (Maybe (Column PGTimestamptz)) -- createdAt
  (Column PGTimestamptz) -- updatedAt
  (Column PGText) -- name
  (Column PGText) -- status
  (Column (Nullable PGInt8)) -- ownerId
  (Column PGText) -- backofficeDomain

type TenantPGRead = TenantPoly
  (Column PGInt8) -- key
  (Column PGTimestamptz) -- createdAt
  (Column PGTimestamptz) -- updatedAt
  (Column PGText) -- name
  (Column PGText) -- status
  (Column (Nullable PGInt8)) -- ownerId
  (Column PGText) -- backofficeDomain

$(makeAdaptorAndInstance "pTenant" ''TenantPoly)

tenantTable :: Table TenantPGWrite TenantPGRead
tenantTable = Table "tenants" (pTenant Tenant{
                                  tenantKey = optional "id"
                                  ,tenantCreatedAt = optional "created_at"
                                  ,tenantUpdatedAt = optional "updated_at"
                                  ,tenantName = required "name"
                                  ,tenantStatus = required "status"
                                  ,tenantOwnerId = required "owner_id"
                                  ,tenantBackofficeDomain = required "backoffice_domain"
                                  })
```

### Different types for read & write

With this, we witness another quirk (and power) of Opaleye. It allows us to define different types for the read (SELECT) and write (INSERT/UPDATE) operations. In fact, our guess is that, to achieve type-safety, it is forced to do this. Let us explain. If you're using standard auto-increment integers for the primary key (which most people do), you essentially end-up having two different types for the `INSERT` and `SELECT` operations. In the `INSERT` operation, you *should not* be specifying the `id` field/column. Whereas, in the `SELECT` operation, you will always be reading it. (Look at Persistent if you want to see another approach of solving this problem.)

One way to omit the primary key during `INSERT` is by defining only a single type `TenantPG`, letting the `id` (or `key`) field be lazy, and depending on it being `undefined` for new records. We haven't tried this approach yet, but we're very sure it would require us to teach Opalaye how to map `undefined` values to SQL. Nevertheless, depending upon partially defined records for something as common as `INSERT` operations does not bode too well for a language that prides itself on type-safety and correctness.

```
-- NOT RECOMMENDED. NOT TESTED.
type TenantPG = TenantPoly
  (Column PGInt8) -- key
  (Column PGTimestamptz) -- createdAt
  (Column PGTimestamptz) -- updatedAt
  (Column PGText) -- name
  (Column PGText) -- status
  (Column (Nullable PGInt8)) -- ownerId
  (Column PGText) -- backofficeDomain
```

Therefore, the need for two separate types: `TenantPGRead` and `TenantPGWrite`, with subtle differences. But, before we discuss the differences, we need to understand how Opaleye deals with `NULL` values and "omitted columns".

### Handling `NULL` and database defaults

Let's look at `TenantPGWrite` again:

| Column | Data type | Meaning|
| --- | --- | --- |
| `key` |  `(Maybe (Column PGInt8))` | A PG column of type PGInt8, which may be omitted from the INSERT/UPDATE, thus leaving its fate to the DB. If the DB has a default-value for this column (which it does, it's an auto-increment primary key), it will be used, else it will be `NULL`. |
| `createdAt` | `(Maybe (Column PGTimestamptz))` | A PG column of type PGTimestamptz (`TIME WITH TIME ZONE`), which may be omitted from the INSERT/UPDATE, thus leaving its fate to the DB. If the DB has a default-value for this column (which it does, it is `current_timestamp`), it will be used, else it will be `NULL`. |
| `updatedAt` | `(Column PGTimestamptz)` | A PG column of type PGTimestamptz, which can NOT be omitted from the INSERT/UPDATE statement AND its value must be `NOT NULL` |
| `name` | `(Column PGText)` | A PG column of type PGText (`TEXT`), which can NOT be omitted from the INSERT/UPDATE statement AND its value must be `NOT NULL` |
| `status` | `(Column PGText)`| A PG column of type PGText (`TEXT`), which can NOT be omitted from the INSERT/UPDATE statement AND its value must be `NOT NULL` |
| `ownerId` | `(Column (Nullable PGInt8))` | A PG column of type PGInt8, which can NOT be omitted from the INSERT/UPDATE statement, HOWEVER its value CAN be `NULL`. While the column can hold `NULL` values, it cannot be omitted from the INSERT/UPDATE statement. This means, that even if you want to set it to `NULL` you need to do so explicitly. Moreover, actual `NULL` values are represented as [`null`](https://hackage.haskell.org/package/opaleye-0.5.1.0/docs/Opaleye-Column.html#v:null) instead of `Nothing` in Opaleye. |
| `backofficeDomain` | `(Column PGText)` | A PG column of type PGText (`TEXT`), which can NOT be omitted from the INSERT/UPDATE statement AND its value must be `NOT NULL` |

### Different types for read & write - again

Now, coming back to the subtle differences in `TenantPGWrite` and `TenantPGRead`:

* While writing, we may **omit** the `key` and `createdAt` columns (because their type is `(Maybe (Column x))` in `TenantPGWrite`)
* However, we are telling Opaleye, that while reading from the DB, we guarantee that `key` and `createdAt` will be of type `NUMERIC NOT NULL` and `TIME WITH TIME ZONE NOT NULL`, respectively. This is because in `TenantPGRead` their types are `(Column x)`

**Here's a small exercise:** What if `ownerId` had the following types. What would it mean?

* `TenantPGWrite`: (Maybe (Column (Nullable PGInt8)))
* `TenantPGRead`: (Column (Nullable PGInt8))

**Here's another small exercise:** What if `ownerId` had the following types. What would it mean?

* `TenantPGWrite`: (Maybe (Column PGInt8))
* `TenantPGRead`: (Column (Nullable PGInt8))

**Here's more to think about:** What if `ownerId` had the following types. What would it mean? What does having a `(Maybe (Column x))` during `SELECT` operations really mean? Does it mean anything in regular `SELECT` operations? What about `LEFT JOIN` operations?

* `TenantPGWrite`: (Maybe (Column PGInt8))
* `TenantPGRead`: (Maybe (Column PGInt8))

**Making things even more typesafe:** If you notice, `TenantPGWrite` has the `key` field as `(Maybe (Column PGInt8))`, which makes it *omittable*, but it also makes it *definable*. Is there really any use of sending across the primary-key value from Haskell to the DB? In most cases, we think not. So, if we want to make this interface ultra typesafe, Opaleye allows us to do the following as well (notice the type of `key`):

```
type TenantPGWrite = TenantPoly
  () -- key
  (Maybe (Column PGTimestamptz)) -- createdAt
  (Column PGTimestamptz) -- updatedAt
  (Column PGText) -- name
  (Column PGText) -- status
  (Column (Nullable PGInt8)) -- ownerId
  (Column PGText) -- backofficeDomain
```

### Wrapping-up

Coming to the last part of setting up DB<=>Haskell mapping with Opaleye, we need to issue these magic incantations:

```
$(makeAdaptorAndInstance "pTenant" ''TenantPoly)

tenantTable :: Table TenantPGWrite TenantPGRead
tenantTable = Table "tenants" (pTenant Tenant{
                                  tenantKey = optional "id"
                                  ,tenantCreatedAt = optional "created_at"
                                  ,tenantUpdatedAt = optional "updated_at"
                                  ,tenantName = required "name"
                                  ,tenantStatus = required "status"
                                  ,tenantOwnerId = required "owner_id"
                                  ,tenantBackofficeDomain = required "backoffice_domain"
                                  })
```

The TH splice - `makeAdaptorAndInstance` - does two very important things:

* Defines the `pTenant` function, which is subsequently used in `tenantTable`
* Defines the `Default` instance for `TenantPoly` (this is not `Data.Default`, but the [poorly named `Data.Profunctor.Product.Default`](https://github.com/tomjaguarpaw/haskell-opaleye/issues/225#issuecomment-258441089))

Right now, we don't need to be bothered with the internals of `pTenant` and `Default`, but we *will* need them when we want to do some advanced DB<=>Haskell mapping. Right now, what we need to be bothered about is `tenantTable`. That is what we've been waiting for! This is what represents the `tenants` table in the Haskell land. Every SQL operation on the `tenants` table will need to reference `tenantsTable`. And while defining `tenantsTable` we've finally assembled the last piece of the puzzle: field-name <=> column-name mappings AND the name of the table! (did you happen to forget about them?)

**Note:** We're not really clear why we need to specify `optional` and `required` in the table definition when `TenantPGWrite` has already defined which columns are optional and which are required.

And, one last thing. We've been talking about `PGText`, `PGTimestamptz`, and `PGInt8` till now. These aren't the regular Haskell types that we generally deal with! These are representations of native PG types in Haskell. You would generally not build your app with these types. Instead, you would use something like `Tenant`, defined below:

```
type Tenant = TenantPoly
  Integer -- key
  UTCTime -- createdAt
  UTCTime -- updatedAt
  Text -- name
  Text -- status
  (Maybe Integer) -- ownerId
  Text -- backofficeDomain
```

**Remember these three types and their purpose. We will need them when we're inserting, udpating, and selecting rows.**

* `TenantPGWrite` defines the record-type that can be written to the DB in terms of PG types.
* `TenantPGRead` defines the record-type that can be read from the DB in terms of PG types.
* `Tenant` defines the records that represents rows of the `tenants` table, in terms of Haskell types. We haven't yet split this into separate read and write types.

### Template Haskell expansion

If you're curious, this is what the TH splice expands to (not literally, but conceptually). You might also want to read the [documentation of Data.Profunctor.Product.TH](https://hackage.haskell.org/package/product-profunctors-0.7.1.0/docs/Data-Profunctor-Product-TH.html) to understand what's going on here.

```
    pTenant :: ProductProfunctor p =>
      TenantPoly 
        (p key0 key1)
        (p createdAt0 createdAt1) 
        (p updatedAt0 updatedAt1)
        (p name0 name1)
        (p status0 status1)
        (p ownerId0 ownerId1)
        (p backofficeDomain0 backofficeDomain1)
      -> p  (TenantPoly key0 createdAt0 updatedAt0 name0 status0 ownerId0 backofficeDomain0) 
            (TenantPoly key1 createdAt1 updatedAt1 name1 status ownerId1 backofficeDomain1)
    pTenant = (((dimap toTuple fromTuple) . Data.Profunctor.Product.p7). toTuple)
      where
          toTuple (Tenant key createdAt updatedAt name status ownerId backofficeDomain)
            = (key, createdAt, updatedAt, name, status, ownerId, backofficeDomain)
          fromTuple (key, createdAt, updatedAt, name, status, ownerId, backofficeDomain)
            = Tenant key createdAt updatedAt name status ownerId backofficeDomain


    instance (ProductProfunctor p,
              Default p key0 key1,
              Default p createdAt0 createdAt1,
              Default p updatedAt0 updatedAt1,
              Default p name0 name1,
              Default p status0 status,
              Default p ownerId0 ownerId1,
              Default p backofficeDomain0 backofficeDomain1) =>
             Default p (TenantPoly key0 createdAt0 updatedAt0 name0 status0 ownerId0 backofficeDomain0) 
                       (TenantPoly key1 createdAt1 updatedAt1 name1 status ownerId1 backofficeDomain1) where
      def = pTenant (Tenant def def def def def def def)
```

## HRR

## Persistent