Membangun Aplikasi Trading Real-time dengan Firebase SQL Connect (web)

1. Sebelum memulai

Dalam codelab ini, Anda akan mengintegrasikan Firebase SQL Connect dengan database Cloud SQL untuk mem-build Friendly Exchange, aplikasi web pasar saham emoji real-time.

Aplikasi yang telah selesai menunjukkan fitur SQL Connect lanjutan, termasuk:

  • SQL Native: Jalankan pernyataan Bahasa Pengolahan Data (DML) dan Ekspresi Tabel Umum (CTE) yang kompleks secara aman menggunakan _execute dan _select.
  • Tampilan SQL: Buat objek GraphQL yang ketat dan aman jenisnya yang didukung oleh kueri Postgres dinamis menggunakan direktif @view.
  • Langganan Real-time: Menjaga sinkronisasi UI frontend Anda menggunakan pemicu @refresh.
  • Transaksi Atomik: Rantai beberapa operasi dan validasi status menggunakan @transaction dan @check.
  • (Opsional) Penelusuran Geospasial & Vektor: Manfaatkan PostGIS dan pgvector untuk menemukan aset trending di dekat koordinat pengguna dan melakukan penelusuran semantik.
  • (Opsional) Pemecah Masalah Kustom: Hubungkan logika Cloud Run kustom ke skema GraphQL Anda untuk membuat judul perdagangan AI.

Prasyarat

Anda memerlukan pemahaman yang mendalam tentang JavaScript/TypeScript, React, dan sintaksis SQL dasar.

Hal yang akan Anda pelajari

  • Cara menggunakan SQL Native untuk menjembatani kesenjangan antara GraphQL deklaratif dan logika PostgreSQL mentah.
  • Cara mengintegrasikan ekstensi Postgres seperti PostGIS langsung ke dalam kueri database Anda.
  • Cara menerapkan logika kompleks menggunakan blok @transaction atomik.
  • Cara membuat @views yang aman untuk papan peringkat dan statistik.
  • Cara menyiapkan langganan real-time menggunakan @refresh.

Hal yang akan Anda perlukan

  • Git
  • Visual Studio Code
  • Instal Node.js
  • Project Firebase dengan paket harga Blaze bayar sesuai penggunaan (diperlukan untuk Pemecah Masalah Kustom dan Vertex AI).

2. Menyiapkan lingkungan pengembangan

Tahap ini memandu Anda menyiapkan frontend dan mengonfigurasi instance Cloud SQL untuk fitur lanjutan.

  1. Clone repositori project dan instal dependensi yang diperlukan untuk aplikasi:
git clone https://github.com/firebaseextended/codelab-dataconnect-web
cd codelab-dataconnect-web
git switch emoji-init
npm install
  1. Buka folder yang di-clone menggunakan Visual Studio Code dan instal Ekstensi Visual Studio Firebase SQL Connect.
  2. Di terminal Anda, pastikan Firebase CLI Anda sudah sepenuhnya diupdate (ini diperlukan untuk fitur baru seperti @refresh dan Native SQL):
npm uninstall -g firebase-tools
npm install -g firebase-tools
firebase login
firebase use your-project-id
firebase init

(Pilih Hosting, Autentikasi, dan Koneksi SQL).

Buat SDK Koneksi SQL: Jalankan perintah:

firebase dataconnect:sdk:generate
  1. Hubungkan aplikasi web Anda ke project Firebase: Daftarkan aplikasi web Anda di project Firebase menggunakan Firebase console:
    1. Buka project Anda, lalu klik Add App (pilih ikon Web).
    2. Abaikan penyiapan SDK dan konfigurasi penyiapan untuk saat ini, tetapi pastikan untuk menyalin objek firebaseConfig yang dihasilkan.
    3. Buka lib/firebase.tsx di editor kode Anda dan ganti placeholder yang ada dengan konfigurasi yang baru saja Anda salin:
const firebaseConfig = {
  apiKey: "API_KEY",
  authDomain: "PROJECT_ID.firebaseapp.com",
  projectId: "PROJECT_ID",
  storageBucket: "PROJECT_ID.firebasestorage.app",
  messagingSenderId: "SENDER_ID",
  appId: "APP_ID"
};
  1. Jalankan server pengembangan:
npm run dev

3. Meninjau codebase awal

Di bagian ini, Anda akan menjelajahi area utama codebase awal aplikasi. Meskipun Anda akan menulis skema dan kueri dari awal, sebaiknya pahami cara frontend terhubung untuk berinteraksi dengan SQL Connect.

Struktur folder dan file

Direktori dataconnect/

Folder ini berisi definisi backend Anda—mulai dari struktur database hingga kueri SQL tertentu yang diizinkan untuk dijalankan oleh aplikasi Anda.

  • schema/schema.gql: Tempat Anda akan menentukan tabel Postgres dasar menggunakan jenis GraphQL standar.
  • schema/views.gql: Tempat Anda akan menentukan tampilan SQL hanya baca yang kompleks (seperti papan peringkat) menggunakan direktif @view.
  • friendly-exchange/queries.gql & mutations.gql: "Penghubung" Anda. Di sinilah Anda akan menentukan kueri persisnya dan SQL Native (_execute, _select) yang diizinkan oleh aplikasi Anda.
  • dataconnect.yaml: File konfigurasi yang menentukan setelan pembuatan SDK dan deployment Cloud SQL.

Direktori lib/

Berisi logika aplikasi, autentikasi, dan interaksi dengan Firebase SQL Connect SDK.

  • firebase.tsx: Menangani inisialisasi aplikasi Firebase, Auth, dan instance SQL Connect Anda.
  • ExchangeService.tsx: Ini adalah jembatan antara komponen React dan database Anda. Wrapper ini menggabungkan fungsi SDK yang dihasilkan (seperti buyStock atau sellStock) dalam fungsi asinkron standar untuk menangani penangkapan error, logika bisnis, dan notifikasi toast.

SDK yang Dibuat

Saat Anda menulis kueri atau mutasi di SQL Connect, ekstensi VS Code akan otomatis membuat SDK yang memiliki jenis yang kuat. Dalam project ini, frontend mengimpor fungsi ini langsung dari @dataconnect/generated.

4. Menentukan skema untuk pertukaran emoji Anda

Di bagian ini, Anda akan menentukan struktur dan hubungan antara entitas utama dalam aplikasi perdagangan. Entitas seperti User, Emoji, StockOwnership, Event, dan PriceHistory dipetakan ke tabel database, dengan hubungan yang dibuat menggunakan direktif skema Firebase SQL Connect dan GraphQL.

Setelah skema ini diterapkan, aplikasi Anda akan siap menangani semuanya, mulai dari menjalankan transaksi jual/beli dan memperbarui papan peringkat global hingga memetakan tren geospasial lokal.

Entitas dan hubungan inti

  • Emoji: Berisi detail utama seperti simbol, nama, harga, dan tren, yang digunakan aplikasi untuk menampilkan pasar.
  • Pengguna: Melacak profil pedagang, poin yang tersedia (mata uang), dan koordinat geografis untuk pemindaian radar lokal.
  • Hubungan: Tabel gabungan StockOwnership melacak secara tepat berapa banyak berbagi yang dimiliki pengguna tertentu dari emoji tertentu. Jenis Event dan PriceHistory berfungsi sebagai buku besar yang tidak dapat diubah, mencatat dampak pasar dan titik harga historis dari waktu ke waktu.

Menyiapkan tabel Pengguna

Jenis User menentukan pedagang dalam sistem, melacak saldo, peran, dan lokasi fisiknya untuk kueri geospasial.

Salin dan tempel cuplikan kode berikut ke dalam file dataconnect/schema/schema.gql Anda:

# Users
# user-stockOwnership is a one-to-many relationship, user-events is a one-to-many relationship
# Utilizes the Firebase Auth uid expression as the primary key
type User @table {
  id: String! @default(expr: "auth.uid")
  username: String!
  profileImage: String
  role: String! @default(value: "USER")
  points: Float! @default(value: 100.0)
  city: String @default(value: "Las Vegas")
  latitude: Float @default(value: 36.1699)
  longitude: Float @default(value: -115.1398)
}

Poin-Poin Penting:

  • id: Langsung terikat ke Firebase Authentication menggunakan @default(expr: "auth.uid"). Hal ini memastikan identitas database dan identitas Auth aman 1:1, sehingga mencegah pengguna memalsukan ID.
  • points: Mata uang virtual yang digunakan untuk berdagang, secara default ditetapkan ke 100.0 untuk pengguna baru.

Menyiapkan tabel Emoji

Jenis Emoji menentukan aset utama yang diperdagangkan, termasuk kolom untuk penelusuran teks standar.

Salin dan tempel cuplikan kode ini ke dalam file dataconnect/schema/schema.gql Anda:

# Emojis
# emoji-stockOwnership is a one-to-many relationship, emoji-priceHistory is a one-to-many relationship
# Implements @searchable directives for full-text search
type Emoji @table {
  id: UUID! @default(expr: "uuidV4()")
  symbol: String!
  name: String! @searchable
  tags: [String!]
  description: String! @searchable
  currentPrice: Float! @default(value: 10.0)
  trend: Float! @default(value: 0.0)
}

Poin-Poin Penting:

  • name & description: Menggunakan direktif @searchable untuk mengoptimalkan kolom ini untuk penelusuran teks lengkap standar.

Menyiapkan tabel StockOwnership

Jenis StockOwnership adalah tabel gabungan yang menangani hubungan many-to-many antara pengguna dan emoji yang mereka miliki. Salin dan tempel cuplikan ini ke dalam file dataconnect/schema/schema.gql Anda:

# Join table for many-to-many relationship between users and emojis
# The 'key' param signifies the primary key(s) of this table
# In this case, the keys are [user, emoji], the generated fields of the reference types
type StockOwnership @table(key: ["user", "emoji"]) {
  user: User!
  emoji: Emoji!
  shares: Int! @default(value: 0)
}

Poin-Poin Penting:

  • key: ["user", "emoji"]: Membuat kunci utama komposit. Pengguna tidak dapat memiliki dua catatan terpisah untuk emoji yang sama; hal ini memberlakukan keunikan per pasangan.
  • Referensi Implisit: Dengan mereferensikan jenis User dan Emoji secara langsung, SQL Connect akan otomatis membuat kunci asing userId: String! dan emojiId: UUID! di balik layar.

Menyiapkan tabel Event dan PriceHistory

Jenis ini merepresentasikan buku besar aplikasi, mencatat secara persis apa yang terjadi dan bagaimana harga berubah. Salin dan tempel cuplikan akhir ke dalam file dataconnect/schema/schema.gql Anda:

# Events
# Event-User is a many-to-one relationship, Event-Emoji is a many-to-one relationship
# Evaluates the createdAt timestamp purely on the server side using the request.time expression
type Event @table {
  id: UUID! @default(expr: "uuidV4()")
  user: User!
  emoji: Emoji!
  impact: Float!
  description: String!
  createdAt: Timestamp! @default(expr: "request.time")
}

# Price History
# PriceHistory-Emoji is a many-to-one relationship
type PriceHistory @table {
  id: UUID! @default(expr: "uuidV4()")
  emoji: Emoji!
  price: Float!
  recordedAt: Timestamp! @default(expr: "request.time")
}

Poin-Poin Penting:

  • createdAt & recordedAt: Otomatis disetel ke waktu yang tepat saat transaksi database terjadi menggunakan @default(expr: "request.time"). Tindakan ini mencegah klien memanipulasi stempel waktu.

Kolom dan setelan default yang dibuat otomatis

Skema ini mengandalkan ekspresi seperti @default(expr: "uuidV4()") dan @default(expr: "auth.uid") untuk otomatis membuat ID unik dan menerapkan kepemilikan tanpa mengharuskan aplikasi klien menyediakannya.

5. Mengambil data pasar dan pengguna

Di bagian ini, Anda akan memasukkan data pasar tiruan ke dalam database, lalu menerapkan konektor (kueri) dan kode TypeScript untuk memanggil konektor ini di seluruh aplikasi web. Pada akhirnya, aplikasi Anda akan dapat mengambil dan menampilkan pasar emoji live, profil pengguna, dan papan peringkat secara dinamis langsung dari database.

Menyisipkan data pengguna dan pasar tiruan

  1. Di VSCode, buka dataconnect/seed.gql.
  2. Pastikan emulator di ekstensi Firebase SQL Connect sedang berjalan (atau instance Cloud SQL Anda terhubung).
  3. Anda akan melihat tombol CodeLens Run (local) atau Run (Production) di bagian atas file. Klik ini untuk menyisipkan data emoji tiruan dan histori harga awal ke dalam database Anda.
  4. Periksa terminal SQL Connect Execution untuk mengonfirmasi bahwa data berhasil ditambahkan.

Menerapkan kueri dasar

Pertama, mari kueri tabel standar yang Anda tentukan dalam skema.

  1. Buka dataconnect/friendly-exchange/queries.gql.
  2. Tambahkan kueri berikut untuk mengambil data dasbor, profil pengguna, dan histori harga dasar:
# Get dashboard data including top emojis by price and recent market events
query GetDashboardData
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  emojis(orderBy: [{ currentPrice: DESC }]) {
    id
    symbol
    name
    description
    currentPrice
    trend
  }
  events(orderBy: [{ createdAt: DESC }], limit: 15) {
    id
    description
    impact
    createdAt
    user {
      username
      profileImage
    }
    emoji {
      symbol
    }
  }
}

# Get current authenticated user profile and their stock ownership using auth.uid
query GetUserProfile @auth(level: USER) {
  user(id_expr: "auth.uid") {
    points
    username
    profileImage
    role
    stockOwnerships_on_user {
      shares
      emoji {
        id
        symbol
        currentPrice
        name
      }
    }
    city
    latitude
    longitude
  }
}

# Get price history for a specific emoji ordered by time
query GetPriceHistory($emojiId: UUID!, $limit: Int)
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  priceHistories(
    where: { emojiId: { eq: $emojiId } }
    orderBy: [{ recordedAt: ASC }]
    limit: $limit
  ) {
    price
    recordedAt
  }
}

Poin-Poin Penting:

  • emojis() / events(): Kolom kueri GraphQL yang dibuat secara otomatis untuk mengambil data langsung dari tabel Anda.
  • id_expr: "auth.uid": Mengamankan akses dengan mengambil profil pengguna yang cocok dengan token pengguna Firebase yang diautentikasi saat ini.
  • _on_: Memungkinkan akses langsung ke kolom dari jenis terkait yang memiliki hubungan kunci asing. stockOwnerships_on_user mengambil seluruh portofolio pengguna dalam satu kueri.
  • insecureReason: Wajib diisi saat mengekspos operasi ke PUBLIC. Secara eksplisit mendokumentasikan alasan data ini aman untuk diekspos tanpa autentikasi.

Membuat Tampilan SQL yang aman untuk jenis

Sebelum menulis SQL kustom, penting untuk memahami berbagai cara Firebase SQL Connect menangani kueri:

  • GraphQL Standar: Terbaik untuk CRUD dasar dan relasi sederhana dengan keamanan jenis end-to-end yang ketat.
  • Tampilan SQL (@view): Paling cocok untuk SQL kompleks hanya baca (seperti papan peringkat menggunakan fungsi jendela) yang tetap menginginkan objek GraphQL yang ketat dan aman untuk jenis yang ditampilkan ke klien.
  • SQL Native (_execute / _select): Terbaik untuk menjalankan DML, CTE, atau ekstensi PostGIS secara langsung. Anda menukar pengetikan waktu kompilasi yang ketat dengan fleksibilitas waktu eksekusi maksimum (menampilkan JSON dinamis).

Untuk membuat papan peringkat dan diagram sparkline, kita perlu menghitung rata-rata bergerak dan memberi peringkat pengguna. Ini adalah kasus penggunaan untuk @view.

  1. Buka dataconnect/schema/views.gql.
  2. Tambahkan tampilan berikut untuk menghitung statistik yang diperlukan di server:
# Rank users on a leaderboard based on their total net worth
type TopTrader
@view(
  sql: """
  SELECT
    u.id,
    u.username,
    u.profile_image,
    (u.points + COALESCE(SUM(so.shares * e.current_price), 0)) AS net_worth,
    RANK() OVER (ORDER BY (u.points + COALESCE(SUM(so.shares * e.current_price), 0)) DESC) AS rank
  FROM "user" u
  LEFT JOIN stock_ownership so ON u.id = so.user_id
  LEFT JOIN emoji e ON so.emoji_id = e.id
  WHERE u.id != 'system_market_maker'
  GROUP BY u.id, u.username, u.profile_image, u.points
  """
) {
  id: String
  username: String
  profileImage: String
  netWorth: Float
  rank: Int
}

# Identify the top shareholder (whale) for each emoji and their total ownership percentage
type EmojiWhaleStat
  @view(
    sql: """
    WITH total_shares AS (
      SELECT emoji_id, SUM(shares) AS total_supply
      FROM stock_ownership WHERE shares > 0 GROUP BY emoji_id
    ),
    ranked_holders AS (
      SELECT
        so.emoji_id, u.username AS whale_username, u.profile_image AS whale_profile_image,
        so.shares AS whale_shares, ts.total_supply,
        ROUND((so.shares::DECIMAL / NULLIF(ts.total_supply, 0)) * 100, 2) AS whale_percentage,
        RANK() OVER (PARTITION BY so.emoji_id ORDER BY so.shares DESC) AS holder_rank
      FROM stock_ownership so
      JOIN "user" u ON u.id = so.user_id
      JOIN total_shares ts ON ts.emoji_id = so.emoji_id
      WHERE so.shares > 0
    )
    SELECT emoji_id, whale_username, whale_profile_image, whale_shares, total_supply, whale_percentage
    FROM ranked_holders WHERE holder_rank = 1
    """
  ) {
  emojiId: UUID
  whaleUsername: String
  whaleProfileImage: String
  whaleShares: Int
  totalSupply: Int
  whalePercentage: Float
}

# Calculate the moving average of historical prices for each emoji
type EmojiHistoryStat
  @view(
    sql: """
    SELECT
      emoji_id, price, recorded_at,
      AVG(price) OVER (PARTITION BY emoji_id ORDER BY recorded_at ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) as moving_average
    FROM price_history
    """
  ) {
  emojiId: UUID
  price: Float
  recordedAt: Timestamp
  movingAverage: Float
}

# Combine recent price updates and major news events into a single chronological feed
type TickerFeed
  @view(
    sql: """
    WITH latest_prices AS (
      SELECT emoji_id, MAX(recorded_at) as last_trade_time
      FROM price_history GROUP BY emoji_id
    )
    SELECT
      'PRICE' as type, e.symbol, e.name, e.current_price, e.trend,
      '' as description, lp.last_trade_time as event_time
    FROM emoji e JOIN latest_prices lp ON e.id = lp.emoji_id
    UNION ALL
    SELECT
      'NEWS' as type, e.symbol, '' as name, 0 as current_price, 0 as trend,
      ev.description, ev.created_at as event_time
    FROM event ev JOIN emoji e ON ev.emoji_id = e.id
    """
  ) {
  type: String
  symbol: String
  name: String
  currentPrice: Float
  trend: Float
  description: String
  eventTime: Timestamp
}

# Retrieve the 15 most recent price points for each emoji to render sparkline charts
type EmojiSparkline
  @view(
    sql: """
    WITH RankedPrices AS (
      SELECT
        emoji_id, price, recorded_at,
        ROW_NUMBER() OVER(PARTITION BY emoji_id ORDER BY recorded_at DESC) as rn
      FROM price_history
    )
    SELECT emoji_id, price, recorded_at
    FROM RankedPrices WHERE rn <= 15 ORDER BY recorded_at ASC
    """
  ) {
  emojiId: UUID
  price: Float
  recordedAt: Timestamp
}

Sekarang, buka dataconnect/friendly-exchange/queries.gql dan ganti TODO untuk mengambil data dari tampilan baru Anda:

# Get emoji whale statistics to identify top shareholders from emojiWhaleStats view
query GetEmojiWhaleStats
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  emojiWhaleStats {
    emojiId
    whaleUsername
    whaleProfileImage
    whaleShares
    totalSupply
    whalePercentage
  }
}

# Get historical price and moving average stats for a specific emoji from emojiHistoryStats view
query GetEmojiHistoryStats($emojiId: UUID!)
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  emojiHistoryStats(
    where: { emojiId: { eq: $emojiId } }
    orderBy: [{ recordedAt: ASC }]
    limit: 50
  ) {
    price
    movingAverage
    recordedAt
  }
}

# List top traders ordered by rank from topTraders view
query GetTopTraders
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  topTraders(orderBy: [{ rank: ASC }]) {
    id
    username
    profileImage
    netWorth
    rank
  }
}

# Get chronological market ticker feed of recent events from tickerFeeds view
query GetChronologicalTicker
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  tickerFeeds(orderBy: [{ eventTime: DESC }], limit: 30) {
    type
    symbol
    name
    currentPrice
    trend
    description
    eventTime
  }
}

# Get simple price points for rendering emoji sparkline charts from emojiSparklines view
query GetEmojiSparklines
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  emojiSparklines {
    emojiId
    price
    recordedAt
  }
}

Poin-Poin Penting

  • @view: Merangkum logika database yang kompleks di server sambil menjaga kode sisi klien Anda diketik secara ketat. SQL Connect memetakan kolom GraphQL pada jenis @view ke kolom yang ditampilkan oleh pernyataan SELECT Anda.
  • Hanya baca: Tampilan tidak memiliki kunci utama dan tidak dapat dimutasi secara langsung.
  • Pembuatan Kueri: Perhatikan cara kerja topTraders() dan emojiSparklines() yang sama persis dengan membuat kueri tabel standar.

Menerapkan kueri penelusuran

SQL Connect otomatis membuat kueri penelusuran standar untuk kolom apa pun yang ditandai dengan direktif @searchable dalam skema Anda.

Tambahkan kueri berikut ke dataconnect/friendly-exchange/queries.gql untuk mengaktifkan penelusuran teks lengkap:

# Search emojis using full-text search query
query SearchEmojis($query: String)
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  emojis_search(query: $query) {
    id
    symbol
    name
    description
    currentPrice
    trend
  }
}

Poin-Poin Penting

  • emojis_search: Kolom kueri yang dibuat otomatis karena Anda menerapkan @searchable ke kolom name dan description dalam skema Emoji.

Buat SDK

Karena Anda telah menentukan kueri dan tampilan baru dalam file GraphQL, Anda harus menjalankan generator SDK agar frontend TypeScript dapat menggunakannya dengan aman.

Buka terminal Anda dan jalankan:

firebase dataconnect:sdk:generate

Mengintegrasikan kueri di aplikasi web

Compiler Firebase SQL Connect menghasilkan SDK berdasarkan file .gql Anda. Karena aplikasi ini didesain sebagai aplikasi real-time, Anda akan menggunakan metode subscribe bersama dengan referensi kueri yang dihasilkan di beberapa komponen.

Ganti blok useEffect yang kosong di file berikut dengan logika di bawah:

1. Halaman Beranda (

app/page.tsx

)

import { subscribe } from "@firebase/data-connect";
import {
  getDashboardDataRef,
  searchEmojisRef,
  getChronologicalTickerRef,
  getUserProfileRef,
} from "@dataconnect/generated";

// Inside the Home component:
  useEffect(() => {
    // Subscribe to realtime updates for the main market dashboard data including top emojis and recent events
    const unsubscribe = subscribe(
      getDashboardDataRef(),
      (res) => {
        if (res.data) setDashboardData(res.data);
        setIsDashboardLoading(false);
      },
      (err) => {
        console.error("Dashboard Realtime Error:", err);
        setIsDashboardLoading(false);
      },
    );
    return () => unsubscribe();
  }, [user]);

  useEffect(() => {
    // Subscribe to a realtime chronological ticker feed combining recent price updates and major news events
    const unsubscribe = subscribe(
      getChronologicalTickerRef(),
      (res) => {
        if (res.data) setTickerData(res.data);
      },
      (err) => console.error("Ticker Realtime Error:", err),
    );
    return () => unsubscribe();
  }, []);

  useEffect(() => {
    if (loading || !user) return;
    // Subscribe to realtime updates for the authenticated user's profile and stock ownership
    const unsubscribe = subscribe(
      getUserProfileRef(),
      (res) => {
        if (res.data) setProfileData(res.data);
      },
      (err) => console.error("Profile Error:", err),
    );
    return () => unsubscribe();
  }, [user, loading]);

  useEffect(() => {
    if (!debouncedSearch) {
      setSearchData(null);
      return;
    }
    
    // Subscribe to realtime full-text search results for emojis based on user input
    const unsubscribe = subscribe(
      searchEmojisRef({ query: debouncedSearch }),
      (res) => {
        if (res.data) setSearchData(res.data.emojis_search);
        setIsSearchLoading(false);
      },
      (err) => {
        console.error("Text Search Error:", err);
        setIsSearchLoading(false);
      },
    );

    return () => unsubscribe();
  }, [debouncedSearch]);

2. Komponen Profil Pengguna

app/profile/page.tsx

, update hook:

import { subscribe } from "@firebase/data-connect";
import { getUserProfileRef } from "@dataconnect/generated";

useEffect(() => {
  // Subscribe to realtime updates for the authenticated user's profile and stock ownership
  const unsubscribe = subscribe(
    getUserProfileRef(),
    (res) => {
      if (res.data) {
        setData(res.data);
      }
      setIsLoading(false);
    },
    (err) => {
      console.error("Profile Realtime Error:", err);
      setIsLoading(false);
    },
  );
  return () => unsubscribe();
}, []);

components/NavBar.tsx

:

 useEffect(() => {
    // Subscribe to realtime updates for the authenticated user's profile and stock ownership
    const unsub = subscribe(
      getUserProfileRef(),
      (res) => {
        if (res.data) setData(res.data);
      },
      (err) => console.error("Navbar Balance Realtime Error:", err),
    );
    return () => unsub();
  }, []);

Untuk components/FloatingMenu.tsx, ganti juga objek const { data } manual dengan hook yang dihasilkan:

const { data, refetch: refetchDashboard } = useGetDashboardData();

 useEffect(() => {
    if (!user) return;
    // Subscribe to realtime updates for the authenticated user's profile
    const unsub = subscribe(getUserProfileRef(), (res) => {
      if (res.data) {
        setProfileData(res.data);
        setOptimisticRole(null);
      }
    });
    return () => unsub();
  }, [user]);

Poin-Poin Penting

  • getUserProfileRef / getDashboardDataRef: Fungsi yang dibuat otomatis yang menyiapkan kueri GraphQL untuk dieksekusi, dengan mempertahankan jenis ketat yang ditentukan oleh tabel dan tampilan Anda.
  • subscribe: Metode SQL Connect SDK yang memproses kueri. Saat ini, fungsi tersebut hanya mengambil data saat komponen di-mount, tetapi pada langkah selanjutnya, kita akan mengupgrade backend untuk memicu fungsi ini secara otomatis setiap kali database berubah.
  1. Panel Pasar (components/MarketPanel.tsx): Demikian pula, di komponen MarketPanel (components/MarketPanel.tsx), Anda dapat mengganti TODO untuk memanggil beberapa kueri secara bersamaan guna membuat sidebar.
import { subscribe } from "@firebase/data-connect";
import { getDashboardDataRef, getEmojiSparklinesRef } from "@dataconnect/generated";

// Inside the MarketPanel component:
  useEffect(() => {
    // Subscribe to realtime updates for the main market dashboard data including top emojis and recent events
    const unsub = subscribe(
      getDashboardDataRef(),
      (res) => {
        if (res.data) setData(res.data);
      },
      (err) => console.error("Market Panel Realtime Error:", err)
    );
    return () => unsub();
  }, []);

  useEffect(() => {
    // Subscribe to realtime price history updates to render emoji sparkline charts
    const unsub = subscribe(
      getEmojiSparklinesRef(),
      (res) => {
        if (res.data?.emojiSparklines) {
          setSparklineRawData(res.data.emojiSparklines);
        }
      },
      (err) => console.error("Global Sparklines Error:", err)
    );
    return () => unsub();
  }, []);


  1. Halaman Papan Peringkat (app/leaderboard/page.tsx)
import { subscribe } from "@firebase/data-connect";
import { getTopTradersRef } from "@dataconnect/generated";

// Inside the Leaderboard component:
  useEffect(() => {
    // Subscribe to realtime updates for the global leaderboard ranking top traders by net worth
    const unsubscribe = subscribe(
      getTopTradersRef(),
      (res) => {
        if (res.data) setData(res.data);
        setIsLoading(false);
      },
      (err) => {
        console.error("Leaderboard Realtime Error:", err);
        setIsLoading(false);
      },
    );
    return () => unsubscribe();
  }, []);

  1. Modal Emoji (components/EmojiModal.tsx)
import { subscribe } from "@firebase/data-connect";
import {
  getEmojiHistoryStatsRef,
  getEmojiWhaleStatsRef,
} from "@dataconnect/generated";

// Inside the EmojiModal component:
  useEffect(() => {
    if (!emoji?.id) return;

    setStatsLoading(true);
    // Subscribe to realtime historical price and moving average statistics for the selected emoji
    const unsub = subscribe(
      getEmojiHistoryStatsRef({ emojiId: emoji.id }),
      (res) => {
        if (res.data) setStatsData(res.data);
        setStatsLoading(false);
      },
      (err) => {
        console.error("History Realtime Error:", err);
        setStatsLoading(false);
      },
    );
    return () => unsub();
  }, [emoji?.id]);

  useEffect(() => {
    // Subscribe to realtime whale statistics to identify the top shareholder for the selected emoji
    const unsub = subscribe(
      getEmojiWhaleStatsRef(),
      (res) => {
        if (res.data) setWhaleData(res.data);
      },
      (err) => console.error("Whale Realtime Error:", err),
    );
    return () => unsub();
  }, []);

Lihat penerapannya

Muat ulang aplikasi web Anda untuk melihat cara kerja kueri. Halaman beranda dan sidebar kini menampilkan daftar emoji, yang mengambil data langsung dari database PostgreSQL Anda.

6. Menangani pembaruan pengguna dan transaksi pasar

Di bagian ini, Anda akan menerapkan fungsi login pengguna menggunakan Firebase Authentication untuk meng-update atau menyisipkan profil pengguna (seperti nama tampilan dan lokasi fisik mereka) di Firebase SQL Connect. Anda juga akan menggunakan direktif @transaction dan @check SQL Connect untuk mengeksekusi peristiwa pasar multi-langkah yang bersifat atomik secara aman.

Menerapkan konektor pengguna dan lokasi

Buka dataconnect/friendly-exchange/mutations.gql. Ganti TODO dengan menambahkan mutasi berikut untuk menangani pembuatan, pembaruan, dan penemuan pengguna:

# Upserts a user record using the Firebase Auth uid expression as the primary key
# Upsert (update or insert) a user's profile information
mutation UpsertUser($username: String!, $profileImage: String!)
@auth(level: USER) {
  user_upsert(
    data: {
      id_expr: "auth.uid"
      username: $username
      profileImage: $profileImage
    }
  )
}

# Update a user's role
mutation UpdateUserRole($role: String!) @auth(level: USER) {
  user_update(key: { id_expr: "auth.uid" }, data: { role: $role })
}

# Update a user's location
mutation UpdateUserLocation(
  $city: String!
  $latitude: Float!
  $longitude: Float!
) @auth(level: USER) {
  user_update(
    key: { id_expr: "auth.uid" }
    data: { city: $city, latitude: $latitude, longitude: $longitude }
  )
}

# Trigger a new market event for an emoji
mutation TriggerEvent(
  $emojiId: UUID!
  $impact: Float!
  $description: String!
  $now: Timestamp!
) @auth(level: USER) {
  event_insert(
    data: {
      userId_expr: "auth.uid"
      emojiId: $emojiId
      impact: $impact
      description: $description
      createdAt: $now
    }
  )
}

Poin-Poin Penting

  • id_expr: "auth.uid": Ini menggunakan auth.uid, yang disediakan langsung oleh token Firebase Authentication. Dengan mengevaluasi sisi server ini, Anda memastikan bahwa pengguna hanya dapat memperbarui data profilnya sendiri, sehingga menambahkan lapisan keamanan yang tidak dapat ditembus.

Logika berantai dengan @transaction

Selanjutnya, Anda akan menerapkan "Market Maker" yang dapat dipicu admin untuk menyimulasikan aktivitas pasar acak. Karena hal ini memerlukan pembaruan harga emoji, pencatatan peristiwa, dan pembaruan kepemilikan stok sistem secara bersamaan, kita memerlukan transaksi atomik.

Tambahkan mutasi ini ke file mutations.gql Anda:

# Execute a market maker trade to adjust emoji price and shares
mutation MarketMakerTrade(
  $emojiId: UUID!
  $priceImpact: Float!
  $shareDelta: Int!
  $eventDesc: String!
  $newPrice: Float!
)
@auth(
  level: USER
  insecureReason: "This operation is safe to expose to any user."
)
@transaction {
  query @redact {
    user(key: { id_expr: "auth.uid" })
      @check(
        expr: "this != null && this.role == 'ADMIN'",
        message: "Access Denied: You must have the ADMIN role to deploy the Market Maker bot."
      ) {
      role
    }
  }
  stockOwnership_upsert(
    data: {
      userId: "system_market_maker"
      emojiId: $emojiId
      shares_update: { inc: $shareDelta }
    }
  )
  emoji_update(
    id: $emojiId
    data: { currentPrice_update: { inc: $priceImpact }, trend: $priceImpact }
  )
  event_insert(
    data: {
      userId: "system_market_maker"
      emojiId: $emojiId
      impact: $priceImpact
      description: $eventDesc
    }
  )
  priceHistory_insert(data: { emojiId: $emojiId, price: $newPrice })
}

Poin-Poin Penting

  • @transaction: Memastikan semua operasi database (meng-upsert stok, memperbarui harga emoji, mencatat peristiwa) berhasil bersama-sama atau gagal bersama-sama.
  • @check: Arahan yang mengevaluasi kondisi sebelum melanjutkan. Di sini, kode akan memeriksa apakah role pengguna terautentikasi adalah 'ADMIN'. Jika pengguna hanya merupakan 'USER' standar, seluruh transaksi akan ditolak dan di-roll back.
  • @redact: Mencegah hasil kueri (seperti pemeriksaan peran pengguna) ditampilkan kepada klien dalam payload respons, sehingga respons transaksi tetap bersih.

Buat SDK

Karena Anda telah menentukan mutasi baru dalam file GraphQL, Anda harus menjalankan generator SDK agar frontend TypeScript dapat memanggilnya.

Buka terminal Anda dan jalankan:

firebase dataconnect:sdk:generate

Mengintegrasikan mutasi di aplikasi web

Di aplikasi web, Anda akan membungkus mutasi SDK yang dihasilkan ini dalam fungsi asinkron standar untuk menangani penangkapan error dan notifikasi UI.

Buka lib/ExchangeService.tsx dan tinjau fungsi wrapper. Ganti blok TODO dengan implementasi berikut:

import {
  upsertUser,
  updateUserLocation,
  marketMakerTrade,
  updateUserRole,
  triggerMarketCrash,
} from "@dataconnect/generated";

// Upsert (update or insert) a user's profile information and log the event
export const executeUpsertUser = async (
  username: string,
  profileImage: string,
  logEvent: (key: LogEventKey, params?: any) => void,
): Promise<void> => {
  logEvent("UPSERT_USER_MUTATION", { username });
  await upsertUser({ username, profileImage });
};

// Update a user's role and log the event
export const executeUpdateRole = async (
  role: string,
  logEvent: (key: LogEventKey, params?: any) => void
): Promise<void> => {
  logEvent("UPDATE_USER_ROLE_MUTATION", { role });
  await updateUserRole({ role });
};

// Update a user's city and geographic coordinates
export const executeUpdateLocation = async (
  city: string,
  latitude: number,
  longitude: number,
): Promise<void> => {
  await updateUserLocation({ city, latitude, longitude });
};

// Execute a random market maker trade and adjust an emoji's stock price
export const executeManualBotTrade = async (
  randomEmoji: any,
  username: string,
  logEvent: (key: LogEventKey, params?: any) => void,
): Promise<{ isBuy: boolean; tradeAmount: number }> => {
  logEvent("MARKET_MAKER_TRADE");
  const isBuy = Math.random() > 0.5;
  const tradeAmount = Number((Math.random() * (10 - 2) + 2).toFixed(2));

  await marketMakerTrade({
    emojiId: randomEmoji.id,
    priceImpact: isBuy ? tradeAmount : -tradeAmount,
    shareDelta: isBuy ? 10 : -10,
    eventDesc: `Admin ${username} triggered market event: ${randomEmoji.symbol} went ${isBuy ? "up" : "down"} by $${tradeAmount.toFixed(2)}.`,
    newPrice: Math.max(0.01, randomEmoji.currentPrice + (isBuy ? tradeAmount : -tradeAmount)),
  });

  return { isBuy, tradeAmount };
};

Triggering upsert on login: In app/src/components/Navbar.tsx, you can see how executeUpsertUser is called immediately after Firebase Authentication successfully signs a user in via Google Popup. This guarantees the SQL Connect database is synced with Firebase Auth.

See it in action

Now, click the Sign In button in the navbar. You can sign in using Firebase Authentication. After signing in:

  1. Navigate to your Profile and test out the Auto-Locate button. When you click Update Coordinates, the UpdateUserLocation mutation will execute.
  2. Open the Floating Control Panel (the purple icon in the bottom right corner).
  3. Click USER and switch your authorization level to ADMIN.
  4. Click Trigger random market activity. Because your role is now 'ADMIN', the @check directive passes, the @transaction executes, and you will instantly see the market prices update across your application!

7. Advanced operations with Native SQL

In this section, you will use Native SQL to execute complex Data Manipulation Language (DML) statements and leverage PostgreSQL-specific extensions.

While standard GraphQL and @views are ideal for strictly-typed CRUD and read-only operations, Native SQL provides execution-time flexibility. It allows you to use Common Table Expressions (CTEs) to chain multiple updates in a single database round-trip, and lets you query native PostgreSQL extensions directly.

Enable the PostGIS extension

Before we write geospatial queries, you need to enable the PostGIS extension on your Cloud SQL database.

  1. Navigate to the Google Cloud Console.
  2. Go to Cloud SQL -> select your provisioned instance -> click Cloud SQL Studio.
  3. Log into your database and execute the following command:
CREATE EXTENSION IF NOT EXISTS postgis;

Implement Native SQL Queries

Let's use Native SQL to find trending emojis near the user's physical location, and to calculate the top emojis per city using complex ranking.

  1. Open dataconnect/friendly-exchange/queries.gql.
  2. Add the following Native SQL queries using the _select field:
# Get top trending emojis partitioned by user city using native SQL
query GetTopEmojisByCity
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  cityTrends: _select(
    sql: """
    WITH city_shares AS (
      SELECT
        u.city,
        AVG(u.latitude) as latitude,
        AVG(u.longitude) as longitude,
        e.id as emoji_id,
        e.symbol,
        e.name,
        SUM(so.shares) as total_shares,
        RANK() OVER (PARTITION BY u.city ORDER BY SUM(so.shares) DESC) as rank
      FROM stock_ownership so
      JOIN "user" u ON so.user_id = u.id
      JOIN emoji e ON so.emoji_id = e.id
      WHERE u.city IS NOT NULL AND u.latitude IS NOT NULL AND so.shares > 0
      GROUP BY u.city, e.id, e.symbol, e.name
    )
    SELECT city, latitude, longitude, emoji_id, symbol, name, total_shares
    FROM city_shares
    WHERE rank = 1
    ORDER BY city ASC
    """
    params: []
  )
}

# Get trending emojis within a geographic radius using native SQL and PostGIS extension
query GetTrendingEmojisNearMe(
  $userLng: Float!
  $userLat: Float!
  $radiusMeters: Float!
)
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
) {
  regionalTrends: _select(
    sql: """
    SELECT
      e.id,
      e.symbol,
      e.name,
      e.current_price,
      e.trend,
      COUNT(so.shares)   AS regional_holders,
      SUM(so.shares)     AS regional_shares
    FROM emoji e
    JOIN stock_ownership so ON so.emoji_id = e.id
    JOIN "user" u            ON u.id = so.user_id
    WHERE u.latitude  IS NOT NULL
      AND u.longitude IS NOT NULL
      AND so.shares > 0
      AND ST_DWithin(
        ST_MakePoint(u.longitude, u.latitude)::geography,
        ST_MakePoint($1, $2)::geography,
        $3
      )
    GROUP BY e.id, e.symbol, e.name, e.current_price, e.trend
    ORDER BY regional_shares DESC
    LIMIT 10
    """
    params: [$userLng, $userLat, $radiusMeters]
  )
}

Key Takeaways

  • _select: Executes a Data Query Language (DQL) statement returning a JSON array ([Any]).
  • ST_DWithin: A native PostGIS function that calculates distances on a sphere. Native SQL allows you to use this without mapping complex geometry types into your GraphQL schema.
  • params: Variables like $userLng are bound to the SQL string via positional parameters ($1, $2, $3), preventing SQL injection.

Implement Native SQL Mutations

When a user buys or sells a stock, the system must validate their funds, deduct the cost, add the shares, update the global emoji price, and log the history. Doing this across multiple standard mutations could lead to race conditions. Instead, we can use a CTE (WITH) to do this atomically in one Native SQL execution.

Open dataconnect/friendly-exchange/mutations.gql and replace the TODOs with the following Native SQL mutations:

# Buy shares of an emoji stock
mutation BuyStock($emojiId: UUID!, $amount: Int!, $isDiscounted: Boolean!)
@auth(level: USER) {
  buyStock: _execute(
    sql: """
    WITH validated_params AS (
      SELECT
        $1::uuid AS emoji_id,
        $2::int AS amount,
        $3::boolean AS is_discounted,
        $4::text AS user_id
    ),
    target_emoji AS (
      SELECT
        e.id,
        (e.current_price * (CASE WHEN vp.is_discounted THEN 0.5 ELSE 1.0 END) * vp.amount) AS total_cost
      FROM emoji e
      CROSS JOIN validated_params vp
      WHERE e.id = vp.emoji_id
        AND vp.amount > 0
        AND vp.amount <= 100
    ),
    deduct_funds AS (
      UPDATE "user" u
      SET points = u.points - te.total_cost
      FROM target_emoji te, validated_params vp
      WHERE u.id = vp.user_id AND u.points >= te.total_cost
      RETURNING u.id
    ),
    upsert_ownership AS (
      INSERT INTO stock_ownership (user_id, emoji_id, shares)
      SELECT vp.user_id, vp.emoji_id, vp.amount
      FROM validated_params vp
      WHERE EXISTS (SELECT 1 FROM deduct_funds)
      ON CONFLICT (user_id, emoji_id) DO UPDATE
      SET shares = stock_ownership.shares + EXCLUDED.shares
      RETURNING stock_ownership.emoji_id
    ),
    update_emoji AS (
      UPDATE emoji e
      SET
        current_price = GREATEST(0.01, e.current_price + (e.current_price * 0.01 * vp.amount)),
        trend = GREATEST(0.01, e.current_price + (e.current_price * 0.01 * vp.amount)) - e.current_price
      FROM validated_params vp
      WHERE e.id = vp.emoji_id AND EXISTS (SELECT 1 FROM deduct_funds)
      RETURNING e.id, e.current_price, e.trend
    )
    INSERT INTO price_history (id, emoji_id, price, recorded_at)
    SELECT gen_random_uuid(), ue.id, ue.current_price, NOW()
    FROM update_emoji ue;
    """
    params: [$emojiId, $amount, $isDiscounted, { _expr: "auth.uid" }]
  )
}

# Sell shares of an emoji stock
mutation SellStock($emojiId: UUID!, $amount: Int!) @auth(level: USER) {
  sellStock: _execute(
    sql: """
    WITH validated_params AS (
      SELECT
        $1::uuid AS emoji_id,
        $2::int AS amount,
        $3::text AS user_id
    ),
    target_emoji AS (
      SELECT
        e.id,
        (e.current_price * vp.amount) AS total_revenue,
        GREATEST(0.01, e.current_price * POWER(0.99, vp.amount)) AS new_price
      FROM emoji e
      CROSS JOIN validated_params vp
      WHERE e.id = vp.emoji_id
        AND vp.amount > 0
        AND vp.amount <= 100
    ),
    check_shares AS (
      SELECT so.user_id
      FROM stock_ownership so
      CROSS JOIN validated_params vp
      WHERE so.user_id = vp.user_id
        AND so.emoji_id = vp.emoji_id
        AND so.shares >= vp.amount
    ),
    add_funds AS (
      UPDATE "user" u
      SET points = u.points + te.total_revenue
      FROM target_emoji te, validated_params vp
      WHERE u.id = vp.user_id AND EXISTS (SELECT 1 FROM check_shares)
      RETURNING u.id
    ),
    update_ownership AS (
      UPDATE stock_ownership so
      SET shares = so.shares - vp.amount
      FROM validated_params vp
      WHERE so.user_id = vp.user_id
        AND so.emoji_id = vp.emoji_id
        AND EXISTS (SELECT 1 FROM check_shares)
        AND EXISTS (SELECT 1 FROM add_funds)
    ),
    update_emoji AS (
      UPDATE emoji e
      SET
        current_price = te.new_price,
        trend = te.new_price - e.current_price
      FROM target_emoji te, validated_params vp
      WHERE e.id = vp.emoji_id
        AND EXISTS (SELECT 1 FROM check_shares)
        AND EXISTS (SELECT 1 FROM add_funds)
      RETURNING e.id, e.current_price, e.trend
    )
    INSERT INTO price_history (id, emoji_id, price, recorded_at)
    SELECT gen_random_uuid(), ue.id, ue.current_price, NOW()
    FROM update_emoji ue;
    """
    params: [$emojiId, $amount, { _expr: "auth.uid" }]
  )
}

Key Takeaways

  • _execute: Executes a Data Manipulation Language (DML) statement, such as UPDATE, INSERT, or DELETE.
  • Common Table Expressions (WITH): Each block in the CTE depends on the previous one. For example, add_funds will only execute if check_shares returns a result. This handles the complex conditions completely within Postgres.
  • Context Injection: { _expr: "auth.uid" } injects the authenticated user's ID into the query directly on the server, enforcing security.

Generate the SDK

Because you have defined new queries and mutations in your GraphQL files, you must run the SDK generator so your TypeScript frontend can call it.

Open your terminal and run:

firebase dataconnect:sdk:generate

Integrate Native SQL in the web app

  1. Native SQL returns a flexible JSON payload rather than a strictly typed object. Because of this, it's essential to manually validate the returned data shape in your client code to handle the dynamic response.
  2. Execute Trades: In lib/ExchangeService.tsx, we wrap the generated buyStock and sellStock SDKs. Notice how the return types buyResult and sellResult must be manually validated as arrays, because _execute returns dynamic JSON data based on your specific RETURNING clauses in the SQL strings.
  3. Replace the empty executeBuyStock and executeSellStock functions with your original complete code:
import { buyStock, sellStock, generateTradeHeadline, triggerEvent } from "@dataconnect/generated";
import { LogEventKey } from "./InspectorContext";

// Execute a stock purchase, validating limits and potentially generating an AI news headline for large trades
export const executeBuyStock = async (
  emoji: any,
  amount: number,
  isDiscounted: boolean,
  user: any,
  logEvent: (key: LogEventKey, params?: any) => void,
): Promise<void> => {
  const MAX_AMOUNT = 100;

  if (!Number.isInteger(amount) || amount <= 0 || amount > MAX_AMOUNT) {
    throw new Error(`Amount must be an integer between 1 and ${MAX_AMOUNT}.`);
  }
  const singleSharePrice = isDiscounted
    ? emoji.currentPrice * 0.5
    : emoji.currentPrice;
  const estimatedCost = singleSharePrice * amount;
  const estimatedImpact = emoji.currentPrice * 0.05 * amount;

  logEvent("BUY_STOCK_TRANSACTION", { amount, symbol: emoji.symbol });

  const response = await buyStock({
    emojiId: emoji.id,
    amount: amount,
    isDiscounted: isDiscounted,
  });

  const buyResult = response.data?.buyStock as any;

  if (
    !buyResult ||
    buyResult === 0 ||
    (Array.isArray(buyResult) && buyResult.length === 0)
  ) {
    throw new Error(
      "Transaction denied: Insufficient funds or price mismatch.",
    );
  }

  const actualCost = Array.isArray(buyResult)
    ? buyResult[0].actual_cost
    : estimatedCost;
  const actualImpact = Array.isArray(buyResult)
    ? buyResult[0].actual_impact
    : estimatedImpact;

// TODO: Optionally add a custom resolver to call AI to generate headline for this purchase
};

// Execute a stock sale, validating ownership and potentially generating an AI news headline for large trades
export const executeSellStock = async (
  emoji: any,
  amount: number,
  ownedShares: number,
  user: any,
  logEvent: (key: LogEventKey, params?: any) => void,
): Promise<void> => {
  const MAX_AMOUNT = 100;

  if (!Number.isInteger(amount) || amount <= 0 || amount > MAX_AMOUNT) {
    throw new Error(`Amount must be an integer between 1 and ${MAX_AMOUNT}.`);
  }
  if (amount > ownedShares) {
    throw new Error(
      "INSUFFICIENT SHARES: You cannot sell more shares than you own.",
    );
  }

  const estimatedRevenue = emoji.currentPrice * amount;
  const dropRatePerShare = 0.05;
  const targetPrice =
    emoji.currentPrice * Math.pow(1 - dropRatePerShare, amount);
  const estimatedImpact = Math.max(0.01, targetPrice) - emoji.currentPrice;

  logEvent("SELL_STOCK_TRANSACTION", { amount, symbol: emoji.symbol });

  const response = await sellStock({
    emojiId: emoji.id,
    amount: amount,
  });

  const sellResult = response.data?.sellStock as any;

  if (
    !sellResult ||
    sellResult === 0 ||
    (Array.isArray(sellResult) && sellResult.length === 0)
  ) {
    throw new Error("Transaction denied: Insufficient shares.");
  }

  const actualRevenue = Array.isArray(sellResult)
    ? sellResult[0].actual_revenue
    : estimatedRevenue;
  const actualImpact = Array.isArray(sellResult)
    ? sellResult[0].actual_impact
    : estimatedImpact;
// TODO: Optionally add a custom resolver to call AI to generate headline for this sale

};

Query Geospatial Data (Local Radar): In app/src/components/LocalRadar.tsx, we subscribe to the getTrendingEmojisNearMeRef query. The dynamic JSON array from the _select execution maps directly to the UI list, utilizing PostGIS's distance calculations.

import { subscribe } from "@firebase/data-connect";
import { getTrendingEmojisNearMeRef } from "@dataconnect/generated";

// ... inside the component
  useEffect(() => {
    if (!location) return;

    setIsLoadingTrends(true);

    // Subscribe to realtime updates for trending emojis within a 50km radius
    const unsub = subscribe(
      getTrendingEmojisNearMeRef({
        userLat: location.lat,
        userLng: location.lng,
        radiusMeters: 50000, // 50km
      }),
      (res) => {
        if (res.data) setLocalData(res.data);
        setIsLoadingTrends(false);
      },
      (err) => {
        console.error("Local Radar Realtime Error:", err);
        setIsLoadingTrends(false);
      },
    );

    return () => unsub();
  }, [location?.lat, location?.lng]);

Query Geospatial Data (Global Assets Map): In app/src/app/map/page.tsx (the Insights Page), we use Native SQL's complex window functions (RANK() OVER) to find the single most popular emoji for every city in the database.

import { subscribe } from "@firebase/data-connect";
import { getTopEmojisByCityRef, getTrendingEmojisNearMeRef, getUserProfileRef } from "@dataconnect/generated";

// ... inside the component
  useEffect(() => {
    // Subscribe to realtime updates for the authenticated user's profile and stock ownership
    const unsub = subscribe(getUserProfileRef(), (res) => {
      if (res.data) setProfileData(res.data);
    });
    return () => unsub();
  }, []);

  useEffect(() => {
    // Subscribe to realtime updates for top trending emojis partitioned by user city
    const unsub = subscribe(getTopEmojisByCityRef(), (res) => {
      if (res.data) setCityData(res.data);
    });
    return () => unsub();
  }, []);

  useEffect(() => {
    setRadarLoading(true);
    // Subscribe to realtime updates for trending emojis within a specified geographic radius
    const unsub = subscribe(
      getTrendingEmojisNearMeRef({
        userLat: coords.lat,
        userLng: coords.lng,
        radiusMeters: radiusKm * 1000,
      }),
      (res) => {
        if (res.data) setRadarData(res.data);
        setRadarLoading(false);
      },
    );
    return () => unsub();
  }, [coords.lat, coords.lng, radiusKm]);

See it in action

  1. In your browser, navigate to the Geo page from the top navigation bar.
  2. If your location is correctly set in your Profile, the Global Top Assets map will ping the GetTopEmojisByCity native query to drop pins on cities with high trade volumes.
  3. Click Scan Local Network. The Local Radar Scanner will ask for your browser's location and ping the GetTrendingEmojisNearMe native query, utilizing PostGIS to find the top assets specifically traded within 50km of your coordinates!
  4. Navigate to the Home page or Profile page and purchase some assets to see your balance deduct and the emoji price update automatically via your atomic _execute queries.

8. Realtime subscriptions and caching

In the previous section, we used the subscribe() method in our React components to fetch data. While that successfully retrieved the initial state, a true stock exchange needs to feel alive. If another user buys a massive amount of emoji stock, your screen should update instantly.

This is where Firebase SQL Connect's Realtime features come in.

What is Realtime and how does it work?

Realtime support allows your application to receive proactive notifications from the server whenever data your app is using has been updated.

Here is the underlying mechanism:

  1. Trigger (@refresh): You tell the SQL Connect backend which specific mutations should trigger a data refresh for a given query.
  2. Broadcast: When one of those mutations executes (e.g., someone runs BuyStock), the server proactively broadcasts a realtime notification to any connected clients listening to that query.
  3. Cache Update: When the notification arrives, the JS SDK treats it just like an ad-hoc query execution. The local cache is instantly updated with the new data.
  4. UI Reactivity: The SDK automatically fires the onNext callbacks for all active subscribers, causing your React state to update and your UI to re-render "in real time".

Add @refresh triggers to your queries

To enable this on the backend, we need to add the @refresh directive to our queries.

  1. Open dataconnect/friendly-exchange/queries.gql.
  2. Update your existing queries by attaching @refresh directives for every market-altering mutation. For example, update GetDashboardData and GetUserProfile:
# Get dashboard data including top emojis by price and recent market events
query GetDashboardData
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
)
@refresh(onMutationExecuted: { operation: "BuyStock" })
@refresh(onMutationExecuted: { operation: "SellStock" })
@refresh(onMutationExecuted: { operation: "TriggerEvent" })
@refresh(onMutationExecuted: { operation: "MarketMakerTrade" }) {
  emojis(orderBy: [{ currentPrice: DESC }]) {
    id
    symbol
    name
    description
    currentPrice
    trend
  }
  events(orderBy: [{ createdAt: DESC }], limit: 15) {
    id
    description
    impact
    createdAt
    user {
      username
      profileImage
    }
    emoji {
      symbol
    }
  }
}

# Get current authenticated user profile and their stock ownership using auth.uid
query GetUserProfile
@auth(level: USER)
@refresh(onMutationExecuted: { operation: "BuyStock" })
@refresh(onMutationExecuted: { operation: "SellStock" })
@refresh(onMutationExecuted: { operation: "UpdateUserLocation" })
@refresh(onMutationExecuted: { operation: "UpdateUserRole" }) {
  user(id_expr: "auth.uid") {
    points
    username
    profileImage
    role
    stockOwnerships_on_user {
      shares
      emoji {
        id
        symbol
        currentPrice
        name
      }
    }
    city
    latitude
    longitude
  }
}

Key Takeaways

  • @refresh(onMutationExecuted: ...): Instructs the server to re-evaluate this query and push new data to subscribers whenever the specified mutation occurs.

Generate the SDK

Because you have defined new queries and mutations in your GraphQL files, you must run the SDK generator so your TypeScript frontend can call it.

Open your terminal and run:

firebase dataconnect:sdk:generate

Handle Realtime Subscriptions in the Web App

We already laid the groundwork for this in the previous section by using the subscribe method. Let's look closer at how the generated SDK handles this in React.

If you open app/src/app/page.tsx (the Home page), you will see the useEffect hook managing the dashboard data:

import { subscribe } from "@firebase/data-connect";
import { getDashboardDataRef } from "@dataconnect/generated";

// ... inside the component
  useEffect(() => {
    const queryRef = getDashboardDataRef();

    // The subscribe function registers the QueryRef and callbacks
    const unsubscribe = subscribe(
      queryRef,
      (res) => {
        // onNext: Fires initially, AND whenever a @refresh trigger occurs
        if (res.data) setDashboardData(res.data);
        setIsDashboardLoading(false);
      },
      (err) => {
        // onError: Handles any server or permission errors
        console.error("Dashboard Realtime Error:", err);
        setIsDashboardLoading(false);
      }
    );

    // onComplete/Cleanup: Unregisters the callbacks when the component unmounts
    return () => unsubscribe();
  }, [user]);

Key Takeaways

  • subscribe(queryRef, onNext, onError): Enables Realtime notifications for the specific QueryRef.
  • unsubscribe(): Calling subscribe returns a cleanup function. It is critical to return this in your useEffect so that when the component unmounts (e.g., the user navigates away), the subscription is canceled and memory leaks are prevented.
  • Caching Efficiency: If multiple components subscribe to the same query (like GetDashboardData), the SDK shares the cached result. When a Realtime notification arrives, the cache updates once, and all active subscribers are notified automatically.

See it in action

Because you've added @refresh to your backend and subscribe to your frontend, your app is now fully reactive.

  1. Open your web app in two separate browser windows side-by-side.
  2. In one window, purchase a few shares of an emoji.
  3. Watch the second window—without refreshing the page, you will instantly see the emoji's price increase!

9. Conclusion

Congratulations, you've successfully built and deployed a realtime, highly complex trading platform directly on top of PostgreSQL using Firebase SQL Connect!

By utilizing SQL Connect, you were able to:

  • Define a strictly-typed GraphQL schema that maps directly to PostgreSQL.
  • Enforce granular, row-level security using Firebase Authentication and @auth directives.
  • Leverage advanced Native SQL to query geospatial data with PostGIS and write atomic market transactions via CTEs.
  • Make your entire application reactive using the @refresh directive for realtime subscriptions.
  • Seamlessly generate frontend SDKs to keep your client code synced with your database.

If you want to play with your own market data, feel free to insert your own mock emojis, locations, and pricing histories using the Firebase SQL Connect extension by mimicking the .gql seed files, or add them through the SQL Connect execution pane in VS Code.

10. Deploy to Cloud

Now that you've worked through the local development iteration, it's time to deploy your schema, data, and queries to the server. This can be done using the Firebase SQL Connect VS Code extension or the Firebase CLI.

Set up Firebase Authentication in your Firebase project

  1. Set up Firebase Authentication with Google Sign-In.
  2. (Optional) Allow domains for Firebase Authentication using the Firebase console (for example, http://127.0.0.1).
    1. In the Authentication settings, go to Authorized Domains.
    2. Click "Add Domain" and include your local domain in the list.

Enable required PostgreSQL Extensions

Because this app utilizes PostgreSQL extensions for vector search and location tracking, you must manually enable them on your provisioned Cloud SQL instance before deploying your schema.

  1. Navigate to the Google Cloud Console.
  2. Go to Cloud SQL -> select your provisioned instance -> click Cloud SQL Studio.
  3. Log into your database and execute the following commands:
# Required for the Geo Map page
CREATE EXTENSION IF NOT EXISTS postgis;
# Required for Vector Search
CREATE EXTENSION IF NOT EXISTS "vector";
# Required for automatic Vector Search embedding generation
CREATE EXTENSION IF NOT EXISTS "google_ml_integration";

Build your web app for hosting

Back in VS Code, ensure you have placed your firebaseConfig variables in lib/firebase.tsx (as done in the setup section).

Next, guarantee that your frontend is using the latest generated hooks by running:

firebase dataconnect:sdk:generate

Then, build the React web app for hosting deployment:

npm run build

Deploy with the Firebase CLI

In dataconnect/dataconnect.yaml, ensure that your instance ID, database, and service ID match your actual Google Cloud project identifiers, and use the v1 specification:

specVersion: v1
serviceId: your-project-id-service
location: us-west4
schemas:
  - source: ./schema
    datasource:
      postgresql:
        database: your-project-id-database
        cloudSql:
          instanceId: your-project-id-instance
connectorDirs:
  - ./friendly-exchange

In your terminal, run the following command to deploy:

firebase deploy --only dataconnect,hosting

For updates or refactors, run this command to compare your schema changes:

firebase dataconnect:sql:diff

If the changes are acceptable, apply them with:

firebase dataconnect:sql:migrate

Your Cloud SQL for PostgreSQL instance will be updated with the final deployed schema and data. You should now be able to see your app live at your-project.web.app/.

Learn more

11. Optional: Vector search with Firebase SQL Connect (billing required)

In this section, you'll enable vector search in your emoji exchange using Firebase SQL Connect. This feature allows for semantic, content-based searches, such as finding emojis that match a vibe or concept using vector embeddings.

This step requires that you completed the last step of this codelab to deploy to Google Cloud.

Update the schema to include embeddings for a field

In dataconnect/schema/schema.gql, add the descriptionEmbedding field to your Emoji table. Replace your existing Emoji type with this updated version:

# Emojis
# emoji-stockOwnership is a one-to-many relationship, emoji-priceHistory is a one-to-many relationship
# Implements @searchable directives for full-text search
# Optional: implements Vector type for semantic search
type Emoji @table {
  id: UUID! @default(expr: "uuidV4()")
  symbol: String!
  name: String! @searchable
  tags: [String!]
  description: String! @searchable
  descriptionEmbedding: Vector @col(size: 768)
  currentPrice: Float! @default(value: 10.0)
  trend: Float! @default(value: 0.0)
}

Key Takeaways

  • descriptionEmbedding: Vector @col(size: 768): This field stores the semantic embeddings of your emoji descriptions, enabling vector-based content search in your app.

Add a vector search query

In dataconnect/friendly-exchange/queries.gql, add the following query to perform vector searches:

# Search emoji descriptions using Vertex AI embeddings
query VectorSearchEmojis($query: String!)
@auth(
  level: PUBLIC
  insecureReason: "This operation is safe to expose to the public."
)
@refresh(onMutationExecuted: { operation: "BuyStock" })
@refresh(onMutationExecuted: { operation: "SellStock" })
@refresh(onMutationExecuted: { operation: "TriggerEvent" })
@refresh(onMutationExecuted: { operation: "MarketMakerTrade" }) {
  emojis_descriptionEmbedding_similarity(
    compare_embed: { model: "text-multilingual-embedding-002", text: $query }
    method: COSINE
    within: 2
    limit: 15
  ) {
    id
    symbol
    name
    description
    currentPrice
    trend
    _metadata {
      distance
    }
  }
}

Key Takeaways:

  • compare_embed: Specifies the embedding model (text-multilingual-embedding-002) and the input text ($query) for comparison.
  • method: Specifies the similarity method (COSINE), measuring the cosine similarity between the vectors.
  • within: Limits the search to emojis with a distance of 2 or less, focusing on close content matches.

Generate the SDK

Because you have defined new queries and mutations in your GraphQL files, you must run the SDK generator so your TypeScript frontend can call it.

Open your terminal and run:

firebase dataconnect:sdk:generate

Activate Vertex AI and re-deploy

  1. Follow the prerequisites guide to set up Vertex AI APIs from Google Cloud. This step is essential to support the embedding generation.
  2. Re-deploy your schema to activate pgvector and vector search by running firebase deploy --only dataconnect or clicking "Deploy to Production" using the Firebase SQL Connect VS Code extension.

Populate the database with embeddings

  1. Open the dataconnect folder in VS Code.
  2. Click Run (Production) in optional_vector_seed.gql to populate your deployed database with the 768-dimensional embeddings for the emojis.

Implement the vector search function in your app

Now that the schema and query are set up, integrate the vector search into your app's frontend.

In app/src/app/page.tsx (your Home component), review the useEffect that listens to the search input and swaps dynamically between full-text search and vector search based on the user's selected searchMode:

import { subscribe } from "@firebase/data-connect";
import {
  getDashboardDataRef,
  searchEmojisRef,
  vectorSearchEmojisRef, // <-- Add this!
  getChronologicalTickerRef,
  getUserProfileRef,
} from "@dataconnect/generated";

// Inside Home component, find the search useeffect
useEffect(() => {
    if (!debouncedSearch) {
      setSearchData(null);
      return;
    }

    let unsubscribe: () => void;

    if (searchMode === "TEXT") {
      // Subscribe to realtime full-text search results for emojis based on user input
      unsubscribe = subscribe(
        searchEmojisRef({ query: debouncedSearch }),
        (res) => {
          if (res.data) setSearchData(res.data.emojis_search);
          setIsSearchLoading(false);
        },
        (err) => {
          console.error("Text Search Error:", err);
          setIsSearchLoading(false);
        },
      );
    } else {
      // Subscribe to realtime vector search results using semantic similarity for emojis based on user input
      unsubscribe = subscribe(
        vectorSearchEmojisRef({ query: debouncedSearch }),
        (res) => {
          if (res.data)
            setSearchData(res.data.emojis_descriptionEmbedding_similarity);
          setIsSearchLoading(false);
        },
        (err) => {
          console.error("Vector Search Error:", err);
          setIsSearchLoading(false);
        },
      );
    }

    return () => {
      if (unsubscribe) unsubscribe();
    };
  }, [debouncedSearch, searchMode]);

See it in action

Navigate to the search bar on your app's homepage. Type in abstract phrases like "happy", "nature", or "technology". Toggle the search mode from TEXT to VECTOR and notice how the results shift from exact string matches to contextual, semantic matches returned directly from Vertex AI and PostgreSQL!

12. Optional: Custom Resolvers with Vertex AI (billing required)

10:00

By writing Custom Resolvers, you can extend Firebase SQL Connect to support other data sources and combine them into your unified GraphQL schema. In this section, you'll write a Firebase Cloud Function that uses Vertex AI (Gemini) to generate a satirical financial news headline whenever a user makes a large trade, and expose that function through SQL Connect.

Initialize the custom resolver

Instead of creating all the boilerplate files manually, the Firebase CLI has a built-in generator for custom resolvers.

Open your terminal in the root of your project and run:

firebase init dataconnect:resolver

When prompted by the CLI:

  1. Enter generateTradeHeadline as the name for your custom resolver.
  2. Select TypeScript to generate the example implementation.

The CLI will automatically create a new dataconnect/schema_generateTradeHeadline/schema.gql file, initialize a functions directory with sample code, and link the resolver in your dataconnect.yaml configuration!

Define the custom resolver schema

Next, you need to define the exact shape of your custom endpoint using a GraphQL schema.

Open the newly generated dataconnect/schema_generateTradeHeadline/schema.gql file and replace its contents with the following code:

# Custom resolver fields can be defined on root Query and Mutation types.
type Mutation {
  # This field will be backed by your Cloud Function.
  generateTradeHeadline(
    emojiSymbol: String!
    emojiName: String!
    username: String!
    tradeAmount: Int!
    tradeCost: Float!
    tradeType: String!
  ): String!
}

Key Takeaways:

  • By placing this inside the root type Mutation, you are telling SQL Connect that this operation might have side-effects (like calling an AI API) rather than just reading data.

Implement the custom resolver logic

Next, implement your resolver using Cloud Functions. Under the hood, you are creating a GraphQL server; however, Cloud Functions provides a helper method, onGraphRequest, that handles the boilerplate so you only need to write the core logic.

Open your Firebase Functions file (functions/src/index.ts), which the CLI generated for you. Replace the entire file with the Gemini API implementation:

import { setGlobalOptions } from "firebase-functions";
import {
  FirebaseContext,
  onGraphRequest,
} from "firebase-functions/dataconnect/graphql";
import { initializeApp, getApps } from "firebase-admin/app";
import { GoogleGenAI } from "@google/genai";

setGlobalOptions({
  maxInstances: 10,
  region: "us-west4",
});

if (getApps().length === 0) {
  initializeApp();
}

const ai = new GoogleGenAI({
  vertexai: true,
  project: process.env.GCLOUD_PROJECT || "your-project-id",
  location: process.env.GCLOUD_LOCATION || "us-west4",
});

const headlineOpts = {
  // Points to the schema you defined earlier
  schemaFilePath: "dataconnect/schema_generateTradeHeadline/schema.gql",
  resolvers: {
    mutation: {
      // Generate a satirical financial news headline for a stock trade using Vertex AI
      async generateTradeHeadline(
        _parent: unknown,
        args: Record<string, unknown>,
        _contextValue: FirebaseContext,
        _info: unknown,
      ): Promise<string> {
        const {
          emojiSymbol,
          emojiName,
          username,
          tradeAmount,
          tradeCost,
          tradeType,
        } = args;

        try {
          const prompt = `You are a hype-driven, satirical financial news bot. 
          A user named '${username}' just executed a massive ${tradeType} of ${tradeAmount} shares of ${emojiSymbol} (${emojiName}) for $${tradeCost}. 
          Write a single, punchy, dramatic news headline (under 12 words) about this market move, use puns wherever possible, but don't round or exagerate the numbers. Include the asset symbol.`;
          
          const response = await ai.models.generateContent({
            model: "gemini-2.5-flash-lite",
            contents: prompt,
          });

          if (!response.text) {
            throw new Error("No text returned from Vertex AI");
          }

          return response.text.trim();
        } catch (error) {
          console.error("Vertex AI generation failed:", error);
          return `BREAKING: Massive ${tradeType} detected on ${emojiSymbol}! Market reacting.`;
        }
      },
    },
  },
};

export const generateTradeHeadline = onGraphRequest(headlineOpts);

Poin-Poin Penting:

  • onGraphRequest: Wrapper Firebase Functions khusus yang memetakan Cloud Function ke skema SQL Connect Custom Resolver.
  • args: Argumen yang diteruskan dari mutasi GraphQL diketik dan diekstrak secara otomatis di sini untuk disisipkan ke dalam perintah Gemini.

Menambahkan mutasi ke konektor

Setelah logika resolver kustom ada, ekspos melalui konektor aplikasi Anda sehingga frontend dapat memanggilnya.

Buka dataconnect/friendly-exchange/mutations.gql dan tambahkan mutasi:

# Generate an AI headline for a stock trade
mutation GenerateTradeHeadline(
  $emojiSymbol: String!
  $emojiName: String!
  $username: String!
  $tradeAmount: Int!
  $tradeCost: Float!
  $tradeType: String!
)
@auth(
  level: USER
  insecureReason: "This operation is safe to expose to any authenticated user."
) {
  aiHeadline: generateTradeHeadline(
    emojiSymbol: $emojiSymbol
    emojiName: $emojiName
    username: $username
    tradeAmount: $tradeAmount
    tradeCost: $tradeCost
    tradeType: $tradeType
  )
}

Men-deploy dan Membuat SDK

Karena Custom Resolver berjalan melalui Cloud Functions, Anda harus men-deploy fungsi ke Google Cloud agar endpoint menjadi aktif.

Buka terminal Anda dan deploy fungsi:

firebase deploy --only functions

Setelah di-deploy, buat SDK frontend untuk menyertakan mutasi AI baru Anda:

firebase dataconnect:sdk:generate

Mengintegrasikan AI Resolver di aplikasi web

Mari kita siapkan agar setiap perdagangan 10 saham atau lebih memicu pemberitahuan berita penting.

Buka lib/ExchangeService.tsx. Pertama, pastikan Anda mengimpor generateTradeHeadline dan triggerEvent di bagian atas:

import { 
  buyStock, 
  sellStock, 
  generateTradeHeadline, 
  triggerEvent 
} from "@dataconnect/generated";

Selanjutnya, scroll ke bawah hingga ke bagian bawah fungsi executeBuyStock, lalu ganti TODO dengan blok pemicu AI tepat sebelum fungsi berakhir:

// ... (existing executeBuyStock code)

  const actualImpact = Array.isArray(buyResult)
    ? buyResult[0].actual_impact
    : estimatedImpact;

  if (amount >= 10 && user) {
    setTimeout(() => {
      logEvent("GENERATE_HEADLINE_RESOLVER");
    }, 2000);

    const headlineResult = await generateTradeHeadline({
      emojiSymbol: emoji.symbol,
      emojiName: emoji.name,
      username: user.displayName || "Anonymous Whale",
      tradeAmount: amount,
      tradeCost: actualCost.toFixed(2),
      tradeType: "BUY",
    });

    await triggerEvent({
      emojiId: emoji.id,
      impact: actualImpact.toFixed(2),
      description: `GEMINI REPORT: ${headlineResult.data?.aiHeadline}`,
      now: new Date().toISOString(),
    });
  }
};

Lakukan hal yang sama persis di bagian bawah fungsi executeSellStock:

// ... (existing executeSellStock code)

  const actualImpact = Array.isArray(sellResult)
    ? sellResult[0].actual_impact
    : estimatedImpact;

  if (amount >= 10 && user) {
    const headlineResult = await generateTradeHeadline({
      emojiSymbol: emoji.symbol,
      emojiName: emoji.name,
      username: user.displayName || "Anonymous Whale",
      tradeAmount: amount,
      tradeCost: actualRevenue.toFixed(2),
      tradeType: "SELL",
    });

    await triggerEvent({
      emojiId: emoji.id,
      impact: actualImpact.toFixed(2),
      description: `GEMINI REPORT: ${headlineResult.data?.aiHeadline}`,
      now: new Date().toISOString(),
    });
  }
};

Lihat penerapannya

  1. Muat ulang aplikasi web Anda.
  2. Pastikan Anda login dan memiliki cukup mata uang.
  3. Pilih emoji dan beli 10 atau lebih banyak pembagian sekaligus.
  4. Tonton Ticker Pasar Global di sisi kanan dasbor Anda. Dalam beberapa detik, Anda akan melihat judul berita satir kustom yang dibuat Gemini muncul.