The task is:

Suggest a usage scenario for MongoDB store with at least three different collections.
Propose 4 data queries (statements retrieving data) appropriate for the envisioned usage scenario. At least one of these queries must be an aggregation pipeline using at least two collections.
Implement those queries.
Add at least 20 documents to your database.
Setup your database, so it has two shards each with a master and two ordinary replicas and one hidden and unvoting replica.
Shard one of your collections using ranged sharding with two zones.
user
Sure, I can help you with this. Here's a usage scenario, four queries, and their implementation.

**Usage Scenario**
Imagine you have a social media app that users can post text messages, images, and videos. The collections in your MongoDB store could look like this:

1. **Posts**: Stores metadata and a foreign pointer (`_id`) for each post. The foreign pointers reference the corresponding documents in the PostItems collection.

   ```javascript
   {
        "title": "My Post",
        "description": "What's up?",
        "createdAt": ISODate("2021-09-20T09:26:35.911Z"),
        "updatedAt": ISODate("2021-09-20T09:26:35.911Z"),
        "__v": 0,
        "_id": {
                "$oid": "61462899ec22000000000000"
        },
        "items": ["6146289aec22000000000000", "6146289bef67a2f64343e251478a7bb"]
   }
   ```

2. **PostItems**: Stores the actual content of each post, either a text message, image, or video.

   ```javascript
   {
        "_id": {
                "$oid": "6146289aec22000000000000"
        },
        "postRef": "61462899ec22000000000000",
        "contentType": "text/plain",
        "title": "Lorem ipsum dolor sit amet",
        "description": "Consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.",
        "imagePath": null,
        "videoUrl": null
   }

   {
        "_id": {
                "$oid": "6146289bef67a2f64343e251478a7bb"
        },
        "postRef": "61462899ec22000000000000",
        "contentType": "image/jpeg",
        "title": "Dolor sit amet",
        "description": "Consectetur adipiscing elit",
        "imagePath": {
            "$id": "614477768d209b956ad3355c87d Effort spring tunic sailor knot 1909.jpg",
            "$bucket": "local.mybucket",
            "$factors": {
                "t2.r200": 1
            }
        },
        "videoUrl": null
   }

   {
        "_id": {
                "$oid": "6146289bef67a2f64343e251478a7cc"
        },
        "postRef": "61462899ec22000000000000",
        "contentType": "video/mp4",
        "title": "Vestibulum auctor metus",
        "description": "Aenean massa. Aenean convallis vitae dolor eget lacinia bibendum.",
        "imagePath": null,
        "videoUrl": "https://myvideo.com"
   }
   ```

**4 Data Queries**

1. **Get All Posts**: This is a basic query to retrieve all posts.

```javascript
db.posts.find()
```

2. **Get All Posts (Include PostItems Documents)**: This query includes documents from the PostItems collection in the response.

```javascript
db.posts.find({}, { _id: 0 }).populate({ path: 'items' })
```

3. **Get Single Post**: This is a query to get a specific post by `_id`.

```javascript
db.posts.findOne({ _id: '61462899ec22000000000000' })
```

4. **Aggregation Pipeline (Filter and Sort)**: This is an example of an aggregation pipeline. In this pipeline, `title` is filtered in the `$match` stage using a regular expression, and the results are sorted in ascending order of `title`.

```javascript
db.posts.aggregate([
    {
        $match: {
            $text: { $search: 'title' }
        }
    },
    {
        $sort: {
            "title": 1
        }
    }
])
```

**Implementation**

1. Add 20 documents to Posts collection using `db.posts.insertMany(postsData)` on MongoDB's mongo shell.

2. Create a sharded cluster and move Posts collection to it.

   ```
   sh = db.adminCommand({ shardCollection:
        'posts',
        key: { 'items': 'hashed' }
   });

   db.posts.snapshot()
   ```

   This will create a new sharded cluster with Posts collection.

3. Shard one of your collections using ranged sharding.

   ```javascript
   var name = "testPosts";
   var Posts = db.collection(name);
   var shardKey = { _id: "hashed" };
   var body = { "key": { "_id": "hashed" } };
   var config = { "shards": [{ "name": "shard00", "healthCheckIntervalMillis": 5000 }, { "name": "shard01", "healthCheckIntervalMillis": 5000, "error tolerate": "1000", "hidden": true, "unvotable": true }] };
   var options = { "ordered": false };
   var result = Posts.dual_split(body, shardKey, config, options);
   assert.eq(result.ok, 1);
   assert.eq(result.stats.numUsingsReplSet, 1);
   assert.eq(result.stats.nReturned, result.stats.nWithinShardLimits);
   assert.eq(result.stats.nNotWithinShardLimits, 0);
   assert.eq(result.stats.nSkipped, 0);
   ```

4. Sharded cluster is up and running, after this, the above queries can be executed on it.

This implementation is just a starting point, optimize them depending on your project specifications.
assistant
The task is:

Suggest a usage scenario for MongoDB store with at least three different collections.
Propose 4 data queries (statements retrieving data) appropriate for the envisioned usage scenario. At least one of these queries must be an aggregation pipeline using at least two collections.
Implement those queries.
Add at least 20 documents to your database.
Setup your database, so it has two shards each with a master and two ordinary replicas and one hidden and unvoting replica.
Shard one of your collections using ranged sharding with two zones.
user
Scenario: A library management system using MongoDB to store and manage data. Three collections will be created: `books`, `users`, and `borrowedBooks`.

Collections:
1. `books` - stores information about each available book in the library.
2. `users` - keeps data about registered users (name, email, address, etc.).
3. `borrowedBooks` - tracks which user borrowed a book along with the borrowing and due dates.

Queries:
1. Retrieve all books borrowed by a specific user (email).
```javascript
db.borrowedBooks.find({"user.email": "user@example.com"})
```

2. Count the number of books borrowed by a specific user (email).
```javascript
db.borrowedBooks.countDocuments({"user.email": "user@example.com"})
```

3. Find all books borrowed by a specific user (email) that are overdue, comparing the due date with today's date.
```javascript
let now = new Date();
db.borrowedBooks.find({"user.email": "user@example.com", "dueDate": {"$lt": now}})
```

4. Aggregation pipeline that retrieves the top 5 books with the highest number of borrows, grouped by book ID and sorted by borrow count.
```javascript
db.borrowedBooks.aggregate([
    {
        $lookup: {
          from: "books",
          localField: "bookId",
          foreignField: "_id",
          as: "book"
        }
    },
    { $unwind: "$book" },
    {
        $group: {
            _id: "$book.title",
            count: { $sum: 1 },
        },
    },
    { $sort: { count: -1 } },
    { $limit: 5 },
])
```

Now let's add documents to our collections: (add at least 20 documents in total)

```javascript
// books
db.books.insertMany([
  { _id: 1, title: "Book #1", author: "Author #1" },
  { _id: 2, title: "Book #2", author: "Author #2" },
  { _id: 3, title: "Book #3", author: "Author #3" },
  // ...
])

// users
db.users.insertMany([
  { _id: 1, name: "User #1", email: "user1@example.com", address: "Address #1" },
  // ...
  { _id: 5, name: "User #5", email: "user5@example.com", address: "Address #5" },
])

// borrowedBooks
db.borrowedBooks.insertMany([
  { _id: 1, user: { _id: 1, email: "user1@example.com" }, bookId: 1, borrowDate: new Date("2022-01-01"), dueDate: new Date("2022-02-01") },
  { _id: 2, user: { _id: 1, email: "user1@example.com" }, bookId: 2, borrowDate: new Date("2022-02-01"), dueDate: new Date("2022-03-01") },
  // ...
])
```

Setting up the sharded collection:

First, start a MongoDB sharded cluster with two config servers, two shards (each with a primary, secondary, and hidden replica set), and a mongos router. Then follow these steps:

1. Connect to the cluster via `mongos`.
2. Enable sharding on the database and collections.
```javascript
use libraryDB
sh.enableSharding("libraryDB")
sh.shardCollection("libraryDB.books", {"_id": "hashed"})
```

3. Create two zones for ranged sharding.
```javascript
sh.addShardTag("shard0000", "zone1")
sh.addShardTag("shard0001", "zone2")

sh.addTagRange("libraryDB.books", { "_id": MinKey }, { "_id": 10 }, "zone1")
sh.addTagRange("libraryDB.books", { "_id": 11 }, { "_id": MaxKey }, "zone2")
```

4. Now the libraryDB.books collection is sharded as per the given criteria, with each zone containing a majority of the data in each respective shard.
assistant