1. Prima di iniziare
In questo codelab, integrerai Firebase SQL Connect con un database Cloud SQL per creare Friendly Exchange, un'app web di mercato azionario di emoji in tempo reale.
L'app completata mostra le funzionalità avanzate di SQL Connect, tra cui:
- SQL nativo:esegui istruzioni DML (Data Manipulation Language) ed espressioni di tabella comuni (CTE) complesse in modo sicuro utilizzando
_executee_select. - SQL Views: crea oggetti GraphQL rigorosi e type-safe supportati da query Postgres dinamiche utilizzando la direttiva
@view. - Abbonamenti in tempo reale:mantieni sincronizzata la UI frontend utilizzando i trigger
@refresh. - Transazioni atomiche:concatenare più operazioni e convalidare lo stato utilizzando
@transactione@check. - (Facoltativo) Ricerca geospaziale e vettoriale: utilizza PostGIS e pgvector per trovare asset di tendenza vicino alle coordinate di un utente ed eseguire ricerche semantiche.
- (Facoltativo) Resolver personalizzati: collega la logica personalizzata di Cloud Run allo schema GraphQL per generare titoli di scambi di AI.
Prerequisiti
Devi avere una solida conoscenza di JavaScript/TypeScript, React e della sintassi SQL di base.
Cosa imparerai a fare
- Come utilizzare SQL nativo per colmare il divario tra GraphQL dichiarativo e la logica PostgreSQL non elaborata.
- Come integrare le estensioni Postgres come PostGIS direttamente nelle query del database.
- Come applicare una logica complessa utilizzando i blocchi
@transactionatomici. - Come creare
@viewstype-safe per classifiche e statistiche. - Come configurare gli abbonamenti in tempo reale utilizzando
@refresh.
Che cosa ti serve
- Git
- Visual Studio Code
- Installa Node.js.
- Un progetto Firebase con il piano tariffario Blaze con pagamento a consumo (obbligatorio per i resolver personalizzati e Vertex AI).
2. Configurazione dell'ambiente di sviluppo
Questa fase ti guida nella configurazione del frontend e dell'istanza Cloud SQL per le funzionalità avanzate.
- Clona il repository del progetto e installa le dipendenze richieste per l'app:
git clone https://github.com/firebaseextended/codelab-dataconnect-web cd codelab-dataconnect-web git switch emoji-init npm install
- Apri la cartella clonata utilizzando Visual Studio Code e installa l'estensione Visual Studio per Firebase SQL Connect.
- Nel terminale, assicurati che l'interfaccia a riga di comando di Firebase sia completamente aggiornata (è necessario per le nuove funzionalità come
@refreshe SQL nativo):
npm uninstall -g firebase-tools npm install -g firebase-tools firebase login firebase use your-project-id firebase init
(Seleziona Hosting, Autenticazione e Connessione SQL).
Genera gli SDK SQL Connect: esegui il comando:
firebase dataconnect:sdk:generate
- Collega la tua app web al tuo progetto Firebase:registra la tua app web nel tuo progetto Firebase utilizzando la Console Firebase:
- Apri il progetto e fai clic su Aggiungi app (seleziona l'icona Web).
- Per ora ignora la configurazione dell'SDK, ma assicurati di copiare l'oggetto
firebaseConfiggenerato. - Apri
lib/firebase.tsxnell'editor di codice e sostituisci il segnaposto esistente con la configurazione che hai appena copiato:
const firebaseConfig = {
apiKey: "API_KEY",
authDomain: "PROJECT_ID.firebaseapp.com",
projectId: "PROJECT_ID",
storageBucket: "PROJECT_ID.firebasestorage.app",
messagingSenderId: "SENDER_ID",
appId: "APP_ID"
};
- Esegui il server di sviluppo:
npm run dev
3. Esamina la codebase iniziale
In questa sezione esplorerai le aree chiave del codebase iniziale dell'app. Anche se scriverai lo schema e le query da zero, è utile capire come il frontend è collegato per interagire con SQL Connect.
Struttura di cartelle e file
La directory dataconnect/
Questa cartella contiene la definizione del backend, ovvero tutto ciò che riguarda la struttura del database e le query SQL specifiche che la tua app può eseguire.
schema/schema.gql: dove definirai le tabelle Postgres di base utilizzando i tipi GraphQL standard.schema/views.gql: dove definirai viste SQL complesse e di sola lettura (come le classifiche) utilizzando l'istruzione@view.friendly-exchange/queries.gqlemutations.gql: i tuoi "connettori". Qui definirai le query esatte e l'SQL nativo (_execute,_select) consentiti dalla tua app.dataconnect.yaml: il file di configurazione che determina le impostazioni di generazione dell'SDK e di deployment di Cloud SQL.
La directory lib/
Contiene la logica dell'applicazione, l'autenticazione e l'interazione con l'SDK Firebase SQL Connect.
firebase.tsx: gestisce l'inizializzazione dell'app Firebase, di Auth e dell'istanza SQL Connect.ExchangeService.tsx: questo è il ponte tra i componenti React e il database. Esegue il wrapping delle funzioni SDK generate (comebuyStockosellStock) in funzioni asincrone standard per gestire l'intercettazione degli errori, la logica di business e le notifiche di tipo avviso popup.
L'SDK generato
Quando scrivi una query o una mutazione in SQL Connect, l'estensione VS Code genera automaticamente un SDK fortemente tipizzato. In questo progetto, il frontend importa queste funzioni direttamente da @dataconnect/generated.
4. Definisci uno schema per lo scambio di emoji
In questa sezione definirai la struttura e le relazioni tra le entità chiave nell'applicazione di trading. Entità come User, Emoji, StockOwnership, Event e PriceHistory vengono mappate alle tabelle del database, con relazioni stabilite utilizzando le direttive dello schema Firebase SQL Connect e GraphQL.
Una volta implementato questo schema, la tua app sarà pronta a gestire qualsiasi attività, dall'esecuzione di transazioni di acquisto/vendita e l'aggiornamento delle classifiche globali alla mappatura delle tendenze geospaziali locali.
Entità e relazioni principali
- Emoji:contiene dettagli chiave come simbolo, nome, prezzo e tendenza, che l'app utilizza per visualizzare il mercato.
- Utente:monitora il profilo del commerciante, i punti disponibili (valuta) e le coordinate geografiche per la scansione radar locale.
- Relazioni: la tabella di unione
StockOwnershiptiene traccia del numero esatto di condivisioni di una determinata emoji di proprietà di un utente specifico. I tipiEventePriceHistoryfungono da registri immutabili, registrando gli impatti sul mercato e i punti di prezzo storici nel tempo.
Configurare la tabella Utente
Il tipo User definisce un commerciante nel sistema, tenendo traccia del suo saldo, del suo ruolo e della sua posizione fisica per le query geospaziali.
Copia e incolla il seguente snippet di codice nel file dataconnect/schema/schema.gql:
# 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)
}
Concetti chiave:
id: si associa direttamente a Firebase Authentication utilizzando@default(expr: "auth.uid"). In questo modo, l'identità del database e l'identità di Auth sono in modo sicuro 1:1, impedendo agli utenti di falsificare gli ID.points: la valuta virtuale utilizzata per il trading, impostata per impostazione predefinita su100.0per i nuovi utenti.
Configurare la tabella Emoji
Il tipo Emoji definisce l'asset principale oggetto di scambio, inclusi i campi per la ricerca di testo standard.
Copia e incolla questo snippet di codice nel file dataconnect/schema/schema.gql:
# 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)
}
Concetti chiave:
nameedescription: utilizza la direttiva@searchableper ottimizzare queste colonne per la ricerca full-text standard.
Configurare la tabella StockOwnership
Il tipo StockOwnership è una tabella di unione che gestisce le relazioni molti-a-molti tra gli utenti e le emoji di loro proprietà. Copia e incolla questo snippet nel file dataconnect/schema/schema.gql:
# 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)
}
Concetti chiave:
key: ["user", "emoji"]: crea una chiave primaria composita. Un utente non può avere due record separati per la stessa emoji; viene applicata l'unicità per coppia.- Riferimenti impliciti:facendo riferimento direttamente ai tipi
UsereEmoji, SQL Connect genera automaticamente le chiavi esterneuserId: String!eemojiId: UUID!in background.
Configurare le tabelle Event e PriceHistory
Questi tipi rappresentano il registro dell'applicazione, registrando esattamente cosa è successo e come sono cambiati i prezzi. Copia e incolla gli snippet finali nel file dataconnect/schema/schema.gql:
# 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")
}
Concetti chiave:
createdAterecordedAt: impostati automaticamente sull'ora esatta in cui si verifica la transazione del database utilizzando@default(expr: "request.time"). In questo modo, i client non possono manipolare i timestamp.
Campi e valori predefiniti generati automaticamente
Lo schema si basa su espressioni come @default(expr: "uuidV4()") e @default(expr: "auth.uid") per generare automaticamente ID univoci e applicare la proprietà senza richiedere all'applicazione client di fornirli.
5. Recuperare i dati di mercato e degli utenti
In questa sezione, inserirai dati di mercato simulati nel database, quindi implementerai i connettori (query) e il codice TypeScript per chiamarli nell'applicazione web. Al termine, la tua app sarà in grado di recuperare e visualizzare dinamicamente il mercato delle emoji live, i profili utente e le classifiche direttamente dal database.
Inserire dati utente e di mercato simulati
- In VSCode, apri
dataconnect/seed.gql. - Assicurati che gli emulatori nell'estensione Firebase SQL Connect siano in esecuzione (o che l'istanza Cloud SQL sia connessa).
- Nella parte superiore del file dovresti vedere un pulsante CodeLens Esegui (locale) o Esegui (produzione). Fai clic qui per inserire i dati delle emoji simulati e le cronologie iniziali dei prezzi nel database.
- Controlla il terminale di esecuzione di SQL Connect per verificare che i dati siano stati aggiunti correttamente.
Implementare query di base
Innanzitutto, eseguiamo una query sulle tabelle standard che hai definito nello schema.
- Apri
dataconnect/friendly-exchange/queries.gql. - Aggiungi le seguenti query per recuperare i dati della dashboard, i profili utente e le cronologie dei prezzi di base:
# 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
}
}
Concetti chiave:
emojis()/events(): campi di query GraphQL generati automaticamente per recuperare i dati direttamente dalle tabelle.id_expr: "auth.uid": protegge l'accesso recuperando il profilo utente che corrisponde al token dell'utente Firebase autenticato corrente._on_: consente l'accesso diretto ai campi di un tipo associato che ha una relazione di chiave esterna.stockOwnerships_on_userrecupera l'intero portafoglio dell'utente in una sola query.insecureReason: obbligatorio quando le operazioni vengono esposte aPUBLIC. Documenta esplicitamente perché questi dati possono essere esposti in modo sicuro senza autenticazione.
Crea viste SQL type-safe
Prima di scrivere SQL personalizzato, è importante comprendere i diversi modi in cui Firebase SQL Connect gestisce le query:
- GraphQL standard:ideale per operazioni CRUD di base e relazioni semplici con sicurezza dei tipi end-to-end rigorosa.
- Viste SQL (
@view): ideali per SQL complessi di sola lettura (come le classifiche che utilizzano le funzioni finestra) in cui vuoi comunque che al client venga restituito un oggetto GraphQL rigoroso e type-safe. - SQL nativo (
_execute/_select): ideale per eseguire direttamente DML, CTE o estensioni PostGIS. Scambi la digitazione rigorosa in fase di compilazione con la massima flessibilità in fase di esecuzione (restituisce JSON dinamico).
Per creare le nostre classifiche e i grafici sparkline, dobbiamo calcolare le medie mobili e classificare gli utenti. Questo è un caso d'uso per @view.
- Apri
dataconnect/schema/views.gql. - Aggiungi le seguenti visualizzazioni per calcolare le statistiche necessarie sul 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
}
Ora apri dataconnect/friendly-exchange/queries.gql e sostituisci i TODO per recuperare i dati dalle nuove visualizzazioni:
# 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: $emojiId } }
orderBy: [{ recordedAt: ASC }]
limit: 50
) {
price
movingAverage
recordedAt
}
}
# List top traders ordered by rank from topTraders view
query GetTopTraders
@auth(
level: PUBLIC
insecureReason: "This operation is safe to expose to the public."
) {
topTraders(orderBy: [{ rank: ASC }]) {
id
username
profileImage
netWorth
rank
}
}
# Get chronological market ticker feed of recent events from tickerFeeds view
query GetChronologicalTicker
@auth(
level: PUBLIC
insecureReason: "This operation is safe to expose to the public."
) {
tickerFeeds(orderBy: [{ eventTime: DESC }], limit: 30) {
type
symbol
name
currentPrice
trend
description
eventTime
}
}
# Get simple price points for rendering emoji sparkline charts from emojiSparklines view
query GetEmojiSparklines
@auth(
level: PUBLIC
insecureReason: "This operation is safe to expose to the public."
) {
emojiSparklines {
emojiId
price
recordedAt
}
}
Concetti chiave
@view: incapsula la logica complessa del database sul server mantenendo il codice lato client strettamente tipizzato. SQL Connect mappa i campi GraphQL del tipo@viewalle colonne restituite dall'istruzioneSELECT.- Sola lettura: le viste non hanno chiavi primarie e non possono essere mutate direttamente.
- Generazione di query:nota come
topTraders()eemojiSparklines()funzionano esattamente come l'esecuzione di query su una tabella standard.
Implementare le query di ricerca
SQL Connect genera automaticamente query di ricerca standard per tutti i campi contrassegnati con la direttiva @searchable nello schema.
Aggiungi la seguente query a dataconnect/friendly-exchange/queries.gql per attivare la ricerca a testo intero:
# Search emojis using full-text search query
query SearchEmojis($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
}
}
Concetti chiave
emojis_search: un campo di query generato automaticamente creato perché hai applicato@searchableai campinameedescriptionnello schemaEmoji.
Genera l'SDK
Poiché hai definito nuove query e viste nei file GraphQL, devi eseguire il generatore di SDK in modo che il frontend TypeScript possa utilizzarli in modo sicuro.
Apri il terminale ed esegui:
firebase dataconnect:sdk:generate
Integrare le query nell'app web
Il compilatore Firebase SQL Connect genera SDK basati sui tuoi file .gql. Poiché è progettata per essere un'app in tempo reale, utilizzerai il metodo subscribe insieme ai riferimenti alle query generati in più componenti.
Sostituisci i blocchi useEffect vuoti nei seguenti file con la logica riportata di seguito:
1. Home page (
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. Componenti del profilo utente
app/profile/page.tsx
, aggiorna gli 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();
}, []);
Per components/FloatingMenu.tsx, sostituisci anche l'oggetto const { data } manuale con l'hook generato:
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]);
Concetti chiave
getUserProfileRef/getDashboardDataRef: funzioni generate automaticamente che preparano le query GraphQL per l'esecuzione, preservando i tipi rigorosi definiti dalle tabelle e dalle viste.subscribe: un metodo dell'SDK SQL Connect che ascolta la query. Al momento recupera semplicemente i dati quando viene montato il componente, ma in un passaggio successivo eseguiremo l'upgrade del backend per attivare automaticamente questa funzione ogni volta che il database cambia.
- Pannello del mercato (
components/MarketPanel.tsx): allo stesso modo, nel componente MarketPanel (components/MarketPanel.tsx), puoi sostituire iTODOper chiamare più query contemporaneamente per creare la barra laterale.
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();
}, []);
- Pagina della classifica (
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();
}, []);
- Modale delle 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();
}, []);
Guarda come funziona
Ricarica l'app web per vedere le query in azione. La home page e la barra laterale ora mostrano l'elenco delle emoji, recuperando i dati direttamente dal tuo database PostgreSQL.
6. Gestire gli aggiornamenti degli utenti e le transazioni di mercato
In questa sezione, implementerai la funzionalità di accesso degli utenti utilizzando Firebase Authentication per eseguire l'upsert dei profili utente (come il nome visualizzato e la posizione fisica) in Firebase SQL Connect. Utilizzerai anche le direttive @transaction e @check di SQL Connect per eseguire in modo sicuro un evento di mercato atomico in più passaggi.
Implementare i connettori per utenti e località
Apri dataconnect/friendly-exchange/mutations.gql. Sostituisci i TODO aggiungendo le seguenti mutazioni per gestire la creazione, l'aggiornamento e l'individuazione degli utenti:
# 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
}
)
}
Concetti chiave
id_expr: "auth.uid": utilizzaauth.uid, fornito direttamente dal token Firebase Authentication. Valutando questo aspetto lato server, ti assicuri che un utente possa aggiornare solo i propri dati del profilo, aggiungendo un livello di sicurezza inviolabile.
Logica della catena con @transaction
Successivamente, implementerai un "Market Maker " che un amministratore può attivare per simulare un'attività di mercato casuale. Poiché questa operazione richiede l'aggiornamento del prezzo di un'emoji, la registrazione di un evento e l'aggiornamento della proprietà delle scorte del sistema contemporaneamente, abbiamo bisogno di una transazione atomica.
Aggiungi questa mutazione al tuo file mutations.gql:
# 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 })
}
Concetti chiave
@transaction: garantisce che tutte le operazioni del database (inserimento di scorte, aggiornamento del prezzo delle emoji, registrazione dell'evento) vengano eseguite correttamente o non vengano eseguite.@check: un'istruzione che valuta una condizione prima di procedere. Qui viene verificato seroledell'utente autenticato è'ADMIN'. Se l'utente è solo un'USER'standard, l'intera transazione viene rifiutata e viene eseguito il rollback.@redact: impedisce che i risultati della query (come il controllo del ruolo dell'utente) vengano restituiti al client nel payload della risposta, mantenendo pulita la risposta della transazione.
Genera l'SDK
Poiché hai definito nuove mutazioni nei file GraphQL, devi eseguire il generatore di SDK in modo che il frontend TypeScript possa chiamarlo.
Apri il terminale ed esegui:
firebase dataconnect:sdk:generate
Integrare le mutazioni nell'app web
Nella tua app web, racchiuderai queste mutazioni dell'SDK generate in funzioni asincrone standard per gestire l'intercettazione degli errori e le notifiche dell'UI.
Apri lib/ExchangeService.tsx e rivedi le funzioni wrapper. Sostituisci i blocchi TODO con le seguenti implementazioni:
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:
- Navigate to your Profile and test out the Auto-Locate button. When you click Update Coordinates, the
UpdateUserLocationmutation will execute. - Open the Floating Control Panel (the purple icon in the bottom right corner).
- Click USER and switch your authorization level to ADMIN.
- Click Trigger random market activity. Because your role is now
'ADMIN', the@checkdirective passes, the@transactionexecutes, 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.
- Navigate to the Google Cloud Console.
- Go to Cloud SQL -> select your provisioned instance -> click Cloud SQL Studio.
- 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.
- Open
dataconnect/friendly-exchange/queries.gql. - Add the following Native SQL queries using the
_selectfield:
# 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$userLngare 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 asUPDATE,INSERT, orDELETE.- Common Table Expressions (
WITH): Each block in the CTE depends on the previous one. For example,add_fundswill only execute ifcheck_sharesreturns 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
- 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.
- Execute Trades: In
lib/ExchangeService.tsx, we wrap the generatedbuyStockandsellStockSDKs. Notice how the return typesbuyResultandsellResultmust be manually validated as arrays, because_executereturns dynamic JSON data based on your specificRETURNINGclauses in the SQL strings. - Replace the empty
executeBuyStockandexecuteSellStockfunctions 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
- In your browser, navigate to the Geo page from the top navigation bar.
- If your location is correctly set in your Profile, the Global Top Assets map will ping the
GetTopEmojisByCitynative query to drop pins on cities with high trade volumes. - Click Scan Local Network. The
Local Radar Scannerwill ask for your browser's location and ping theGetTrendingEmojisNearMenative query, utilizing PostGIS to find the top assets specifically traded within 50km of your coordinates! - 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
_executequeries.
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:
- Trigger (
@refresh): You tell the SQL Connect backend which specific mutations should trigger a data refresh for a given query. - 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. - 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.
- UI Reactivity: The SDK automatically fires the
onNextcallbacks 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.
- Open
dataconnect/friendly-exchange/queries.gql. - Update your existing queries by attaching
@refreshdirectives for every market-altering mutation. For example, updateGetDashboardDataandGetUserProfile:
# 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 specificQueryRef.unsubscribe(): Callingsubscribereturns a cleanup function. It is critical to return this in youruseEffectso 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.
- Open your web app in two separate browser windows side-by-side.
- In one window, purchase a few shares of an emoji.
- 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
- Set up Firebase Authentication with Google Sign-In.
- (Optional) Allow domains for Firebase Authentication using the Firebase console (for example,
http://127.0.0.1).- In the Authentication settings, go to Authorized Domains.
- 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.
- Navigate to the Google Cloud Console.
- Go to Cloud SQL -> select your provisioned instance -> click Cloud SQL Studio.
- 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
- Follow the prerequisites guide to set up Vertex AI APIs from Google Cloud. This step is essential to support the embedding generation.
- Re-deploy your schema to activate
pgvectorand vector search by runningfirebase deploy --only dataconnector clicking "Deploy to Production" using the Firebase SQL Connect VS Code extension.
Populate the database with embeddings
- Open the
dataconnectfolder in VS Code. - Click Run (Production) in
optional_vector_seed.gqlto 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:
- Enter
generateTradeHeadlineas the name for your custom resolver. - 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);
Concetti chiave:
onGraphRequest: un wrapper specializzato di Firebase Functions che mappa una Cloud Function a uno schema di resolver personalizzato di SQL Connect.args: gli argomenti passati dalla mutazione GraphQL vengono digitati ed estratti automaticamente qui per essere inseriti nel prompt di Gemini.
Aggiungere la mutazione al connettore
Ora che la logica del resolver personalizzato esiste, esponila tramite il connettore dell'applicazione in modo che il frontend possa chiamarla.
Apri dataconnect/friendly-exchange/mutations.gql e aggiungi la mutazione:
# 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
)
}
Esegui il deployment e genera l'SDK
Poiché i resolver personalizzati vengono eseguiti tramite Cloud Functions, devi eseguire il deployment delle funzioni su Google Cloud affinché l'endpoint diventi attivo.
Apri il terminale ed esegui il deployment della funzione:
firebase deploy --only functions
Una volta eseguito il deployment, genera l'SDK frontend per includere la nuova mutazione dell'AI:
firebase dataconnect:sdk:generate
Integrare AI Resolver nell'app web
Configuriamo il sistema in modo che qualsiasi scambio di 10 o più azioni attivi un avviso di ultime notizie.
Apri lib/ExchangeService.tsx. Innanzitutto, assicurati di importare generateTradeHeadline e triggerEvent nella parte superiore:
import {
buyStock,
sellStock,
generateTradeHeadline,
triggerEvent
} from "@dataconnect/generated";
Poi, scorri fino in fondo alla funzione executeBuyStock e sostituisci TODO con il blocco trigger AI subito prima della fine della funzione:
// ... (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(),
});
}
};
Fai esattamente la stessa cosa in fondo alla funzione 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(),
});
}
};
Guarda come funziona
- Ricarica l'app web.
- Assicurati di aver eseguito l'accesso e di avere valuta sufficiente.
- Seleziona un'emoji e acquista 10 o più condivisioni contemporaneamente.
- Guarda il ticker del mercato globale sul lato destro della dashboard. Dopo pochi secondi, vedrai un titolo di notizie satiriche personalizzato creato da Gemini.