1. Antes de começar
Neste codelab, você vai integrar o Firebase SQL Connect a um banco de dados do Cloud SQL para criar o Friendly Exchange, um app da Web de mercado de ações de emojis em tempo real.
O app concluído mostra recursos avançados do SQL Connect, incluindo:
- SQL nativo:execute instruções complexas da linguagem de manipulação de dados (DML) e expressões de tabela comuns (CTEs) com segurança usando
_executee_select. - Visualizações SQL:crie objetos GraphQL estritos e com segurança de tipos com suporte de consultas dinâmicas do Postgres usando a diretiva
@view. - Assinaturas em tempo real:mantenha a interface do usuário do front-end sincronizada usando gatilhos
@refresh. - Transações atômicas:encadeie várias operações e valide o estado usando
@transactione@check. - (Opcional) Pesquisa geoespacial e vetorial:use o PostGIS e o pgvector para encontrar recursos em alta perto das coordenadas de um usuário e fazer pesquisas semânticas.
- (Opcional) Resolvers personalizados:conecte a lógica personalizada do Cloud Run ao seu esquema do GraphQL para gerar manchetes de negociação de IA.
Pré-requisitos
Você precisa ter um bom conhecimento de JavaScript/TypeScript, React e sintaxe básica de SQL.
O que você aprenderá
- Como usar o SQL nativo para reduzir a diferença entre o GraphQL declarativo e a lógica bruta do PostgreSQL.
- Como integrar extensões do Postgres, como o PostGIS, diretamente às consultas do banco de dados.
- Como aplicar uma lógica complexa usando blocos atômicos
@transaction. - Como criar
@viewscom segurança de tipo para quadros de classificação e estatísticas. - Como configurar assinaturas em tempo real usando
@refresh.
O que é necessário
- Git
- Visual Studio Code
- Instale o Node.js.
- Um projeto do Firebase no plano de preços Blaze de pagamento por uso (necessário para resoluções personalizadas e Vertex AI).
2. Configurar o ambiente de desenvolvimento
Esta etapa orienta você na configuração do front-end e da instância do Cloud SQL para recursos avançados.
- Clone o repositório do projeto e instale as dependências necessárias para o app:
git clone https://github.com/firebaseextended/codelab-dataconnect-web cd codelab-dataconnect-web git switch emoji-init npm install
- Abra a pasta clonada usando o Visual Studio Code e instale a extensão do Firebase SQL Connect para Visual Studio.
- No terminal, verifique se a CLI do Firebase está totalmente atualizada. Isso é necessário para novos recursos, como
@refreshe SQL nativo:
npm uninstall -g firebase-tools npm install -g firebase-tools firebase login firebase use your-project-id firebase init
Selecione "Hospedagem", "Autenticação" e "Conexão SQL".
Gere os SDKs do SQL Connect: execute o comando:
firebase dataconnect:sdk:generate
- Conecte seu app da Web ao projeto do Firebase:registre o app da Web no projeto do Firebase usando o Console do Firebase:
- Abra seu projeto e clique em Adicionar app (selecione o ícone da Web).
- Ignore a configuração do SDK por enquanto, mas copie o objeto
firebaseConfiggerado. - Abra
lib/firebase.tsxno editor de código e substitua o marcador de posição atual pela configuração que você acabou de copiar:
const firebaseConfig = {
apiKey: "API_KEY",
authDomain: "PROJECT_ID.firebaseapp.com",
projectId: "PROJECT_ID",
storageBucket: "PROJECT_ID.firebasestorage.app",
messagingSenderId: "SENDER_ID",
appId: "APP_ID"
};
- Execute o servidor de desenvolvimento:
npm run dev
3. Revisar a base de código inicial
Nesta seção, você vai conhecer as principais áreas da base de código inicial do app. Embora você escreva o esquema e as consultas do zero, é útil entender como o front-end está conectado para interagir com o SQL Connect.
Estrutura de pastas e arquivos
O diretório dataconnect/
Essa pasta contém a definição do back-end, desde a estrutura do banco de dados até as consultas SQL específicas que o app pode executar.
schema/schema.gql: onde você vai definir suas tabelas base do Postgres usando tipos padrão do GraphQL.schema/views.gql: onde você vai definir visualizações SQL complexas e somente leitura (como rankings) usando a diretiva@view.friendly-exchange/queries.gqlemutations.gql: seus "conectores". É aqui que você vai definir as consultas exatas e o SQL nativo (_execute,_select) permitidos pelo seu app.dataconnect.yaml: o arquivo de configuração que determina as configurações de geração do SDK e implantação do Cloud SQL.
O diretório lib/
Contém a lógica do aplicativo, a autenticação e a interação com o SDK do Firebase SQL Connect.
firebase.tsx: processa a inicialização do App do Firebase, do Auth e da instância do SQL Connect.ExchangeService.tsx: é a ponte entre seus componentes do React e o banco de dados. Ele encapsula as funções geradas do SDK (comobuyStockousellStock) em funções assíncronas padrão para processar captura de erros, lógica de negócios e notificações de toast.
O SDK gerado
Quando você escreve uma consulta ou mutação no SQL Connect, a extensão do VS Code gera automaticamente um SDK fortemente tipado. Neste projeto, o front-end importa essas funções diretamente de @dataconnect/generated.
4. Definir um esquema para sua troca de emojis
Nesta seção, você vai definir a estrutura e as relações entre as principais entidades no aplicativo de negociação. Entidades como User, Emoji, StockOwnership, Event e PriceHistory são mapeadas para tabelas de banco de dados, com relacionamentos estabelecidos usando o Firebase SQL Connect e as diretivas de esquema do GraphQL.
Depois que esse esquema estiver implementado, seu app estará pronto para lidar com tudo, desde a execução de transações de compra/venda e a atualização de rankings globais até o mapeamento de tendências geoespaciais locais.
Entidades e relações principais
- Emoji:contém detalhes importantes, como o símbolo, o nome, o preço e a tendência, que o app usa para mostrar o mercado.
- Usuário:rastreia o perfil do trader, os pontos disponíveis (moeda) e as coordenadas geográficas para a verificação do radar local.
- Relacionamentos:a tabela de junção
StockOwnershiprastreia exatamente quantas ações um usuário específico tem de um determinado emoji. Os tiposEventePriceHistoryservem como livros contábeis imutáveis, registrando impactos no mercado e faixas de preço históricas ao longo do tempo.
Configurar a tabela "Usuário"
O tipo User define um trader no sistema, acompanhando o saldo, a função e a localização física para consultas geoespaciais.
Copie e cole o seguinte snippet de código no arquivo 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)
}
Aprendizados importantes:
id: vincula-se diretamente ao Firebase Authentication usando@default(expr: "auth.uid"). Isso garante que a identidade do banco de dados e a identidade do Auth sejam 1:1 com segurança, impedindo que os usuários façam spoofing de IDs.points: a moeda virtual usada para negociação, que é100.0por padrão para novos usuários.
Configurar a tabela de emojis
O tipo Emoji define o principal recurso negociado, incluindo campos para pesquisa de texto padrão.
Copie e cole este snippet de código no arquivo 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)
}
Aprendizados importantes:
nameedescription: usa a diretiva@searchablepara otimizar essas colunas para a pesquisa de texto completo padrão.
Configurar a tabela StockOwnership
O tipo StockOwnership é uma tabela de junção que processa relações de muitos para muitos entre usuários e os emojis que eles têm. Copie e cole este snippet no seu arquivo 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)
}
Aprendizados importantes:
key: ["user", "emoji"]: cria uma chave primária composta. Um usuário não pode ter dois registros separados para o mesmo emoji. Isso garante a exclusividade por par.- Referências implícitas:ao referenciar diretamente os tipos
UsereEmoji, o SQL Connect gera automaticamente as chaves estrangeirasuserId: String!eemojiId: UUID!nos bastidores.
Configurar as tabelas "Event" e "PriceHistory"
Esses tipos representam o livro razão do aplicativo, registrando exatamente o que aconteceu e como os preços mudaram. Copie e cole os snippets finais no arquivo 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")
}
Aprendizados importantes:
createdAterecordedAt: definidos automaticamente para o horário exato em que a transação do banco de dados ocorre usando@default(expr: "request.time"). Isso impede que os clientes manipulem os carimbos de data/hora.
Campos e padrões gerados automaticamente
O esquema usa expressões como @default(expr: "uuidV4()") e @default(expr: "auth.uid") para gerar automaticamente IDs exclusivos e aplicar a propriedade sem exigir que o aplicativo cliente os forneça.
5. Recuperar dados de mercado e dados do usuário
Nesta seção, você vai inserir dados de mercado simulados no banco de dados e implementar os conectores (consultas) e o código TypeScript para chamar esses conectores no aplicativo da Web. No final, seu app poderá buscar e mostrar dinamicamente o mercado de emojis ao vivo, os perfis de usuários e os rankings diretamente do banco de dados.
Inserir dados simulados de mercado e de usuário
- No VSCode, abra
dataconnect/seed.gql. - Verifique se os emuladores na extensão do Firebase SQL Connect estão em execução ou se a instância do Cloud SQL está conectada.
- Você vai encontrar um botão Executar (local) ou Executar (produção) do CodeLens na parte de cima do arquivo. Clique aqui para inserir os dados de emoji simulados e os históricos de preços iniciais no seu banco de dados.
- Verifique o terminal de execução do SQL Connect para confirmar se os dados foram adicionados corretamente.
Implementar consultas básicas
Primeiro, vamos consultar as tabelas padrão definidas no esquema.
- Abra
dataconnect/friendly-exchange/queries.gql. - Adicione as seguintes consultas para recuperar os dados do painel, os perfis de usuário e os históricos de preços básicos:
# 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
}
}
Aprendizados importantes:
emojis()/events():campos de consulta GraphQL gerados automaticamente para buscar dados diretamente das suas tabelas.id_expr: "auth.uid":protege o acesso buscando o perfil do usuário que corresponde ao token do usuário autenticado atual do Firebase._on_:permite acesso direto a campos de um tipo associado que tem uma relação de chave estrangeira. OstockOwnerships_on_userbusca todo o portfólio do usuário em uma consulta.insecureReason:obrigatório ao expor operações paraPUBLIC. Ele documenta explicitamente por que esses dados podem ser expostos sem autenticação.
Criar visualizações SQL com segurança de tipo
Antes de escrever SQL personalizado, é importante entender as diferentes maneiras como o Firebase SQL Connect processa consultas:
- GraphQL padrão:ideal para CRUD básico e relações simples com segurança de tipo estrita de ponta a ponta.
- Visualizações SQL (
@view): melhor para SQL somente leitura e complexo (como rankings usando funções de janela) em que você ainda quer um objeto GraphQL estrito e com segurança de tipo retornado ao cliente. - SQL nativo (
_execute/_select): ideal para executar DML, CTEs ou extensões PostGIS diretamente. Você troca a tipagem estrita no tempo de compilação pela máxima flexibilidade no tempo de execução (retorna JSON dinâmico).
Para criar nossos rankings e gráficos de linhas, precisamos calcular médias móveis e classificar os usuários. Este é um caso de uso para @view.
- Abra
dataconnect/schema/views.gql. - Adicione as seguintes visualizações para calcular as estatísticas necessárias no servidor:
# 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
}
Agora, abra dataconnect/friendly-exchange/queries.gql e substitua os TODOs para buscar dados das suas novas visualizações:
# 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: $emojiId } }
orderBy: [{ recordedAt: ASC }]
limit: 50
) {
price
movingAverage
recordedAt
}
}
# List top traders ordered by rank from topTraders view
query GetTopTraders
@auth(
level: PUBLIC
insecureReason: "This operation is safe to expose to the public."
) {
topTraders(orderBy: [{ rank: ASC }]) {
id
username
profileImage
netWorth
rank
}
}
# Get chronological market ticker feed of recent events from tickerFeeds view
query GetChronologicalTicker
@auth(
level: PUBLIC
insecureReason: "This operation is safe to expose to the public."
) {
tickerFeeds(orderBy: [{ eventTime: DESC }], limit: 30) {
type
symbol
name
currentPrice
trend
description
eventTime
}
}
# Get simple price points for rendering emoji sparkline charts from emojiSparklines view
query GetEmojiSparklines
@auth(
level: PUBLIC
insecureReason: "This operation is safe to expose to the public."
) {
emojiSparklines {
emojiId
price
recordedAt
}
}
pontos principais
@view:encapsula a lógica complexa do banco de dados no servidor, mantendo o código do lado do cliente estritamente tipado. O SQL Connect mapeia os campos do GraphQL no tipo@viewpara as colunas retornadas pela instruçãoSELECT.- Somente leitura:as visualizações não têm chaves primárias e não podem ser modificadas diretamente.
- Geração de consultas:observe como
topTraders()eemojiSparklines()funcionam exatamente da mesma forma que a consulta de uma tabela padrão.
Implementar consultas de pesquisa
O SQL Connect gera automaticamente consultas de pesquisa padrão para todos os campos marcados com a diretiva @searchable no seu esquema.
Adicione a seguinte consulta a dataconnect/friendly-exchange/queries.gql para ativar a pesquisa de texto completo:
# Search emojis using full-text search query
query SearchEmojis($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
}
}
pontos principais
emojis_search:um campo de consulta gerado automaticamente criado porque você aplicou@searchableaos camposnameedescriptionno esquemaEmoji.
Gerar o SDK
Como você definiu novas consultas e visualizações nos arquivos GraphQL, é necessário executar o gerador de SDK para que o front-end TypeScript possa usá-las com segurança.
Abra o terminal e execute:
firebase dataconnect:sdk:generate
Integrar consultas no web app
O compilador do Firebase SQL Connect gera SDKs com base nos seus arquivos .gql. Como ele foi projetado para ser um app em tempo real, você vai usar o método subscribe com as referências de consulta geradas em vários componentes.
Substitua os blocos useEffect vazios nos seguintes arquivos pela lógica abaixo:
1. Página inicial (
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. Componentes do perfil do usuário
app/profile/page.tsx
, update os hooks:
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();
}, []);
Para components/FloatingMenu.tsx, substitua também o objeto const { data } manual pelo hook gerado:
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]);
pontos principais
getUserProfileRef/getDashboardDataRef:funções geradas automaticamente que preparam as consultas GraphQL para execução, preservando os tipos estritos definidos pelas tabelas e visualizações.subscribe:um método do SDK do SQL Connect que escuta a consulta. No momento, ele apenas busca dados quando o componente é montado, mas, em uma etapa posterior, vamos atualizar o back-end para acionar essa função automaticamente sempre que o banco de dados mudar.
- Painel de mercado (
components/MarketPanel.tsx): da mesma forma, no componente MarketPanel (components/MarketPanel.tsx), é possível substituir osTODOs para chamar várias consultas simultaneamente e criar a barra lateral.
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();
}, []);
- Página do ranking (
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();
}, []);
- Modal de emojis (
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();
}, []);
Veja isso na prática
Atualize o app da Web para ver as consultas em ação. A página inicial e a barra lateral agora mostram a lista de emojis, buscando dados diretamente do seu banco de dados PostgreSQL.
6. Processar atualizações de usuários e transações de mercado
Nesta seção, você vai implementar a funcionalidade de login do usuário usando o Firebase Authentication para fazer upsert de perfis de usuário (como nome de exibição e local físico) no Firebase SQL Connect. Você também vai usar as diretivas @transaction e @check do SQL Connect para executar com segurança um evento de mercado atômico de várias etapas.
Implementar conectores de usuário e local
Abra dataconnect/friendly-exchange/mutations.gql. Substitua os TODOs adicionando as seguintes mutações para processar a criação, atualização e localização de usuários:
# 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
}
)
}
pontos principais
id_expr: "auth.uid":usaauth.uid, que é fornecido diretamente pelo token do Firebase Authentication. Ao avaliar isso do lado do servidor, você garante que um usuário só possa atualizar os próprios dados do perfil, adicionando uma camada de segurança inquebrável.
Lógica de encadeamento com @transaction
Em seguida, você vai implementar um "Market Maker" que um administrador pode acionar para simular uma atividade aleatória do mercado. Como isso exige atualizar o preço de um emoji, registrar um evento e atualizar a propriedade de ações do sistema de uma só vez, precisamos de uma transação atômica.
Adicione esta mutação ao arquivo 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 })
}
pontos principais
@transaction:garante que todas as operações de banco de dados (inserção/atualização de ações, atualização do preço de emojis, registro do evento) sejam bem-sucedidas ou falhem juntas.@check:uma diretiva que avalia uma condição antes de prosseguir. Aqui, ele verifica se oroledo usuário autenticado é'ADMIN'. Se o usuário for apenas um'USER'padrão, toda a transação será rejeitada e revertida.@redact:impede que os resultados da consulta (como a verificação de função do usuário) sejam retornados ao cliente no payload da resposta, mantendo a resposta da transação limpa.
Gerar o SDK
Como você definiu novas mutações nos arquivos GraphQL, é necessário executar o gerador do SDK para que o front-end TypeScript possa chamá-lo.
Abra o terminal e execute:
firebase dataconnect:sdk:generate
Integrar mutações no web app
No seu web app, você vai encapsular essas mutações geradas do SDK em funções assíncronas padrão para processar a captura de erros e as notificações da interface.
Abra lib/ExchangeService.tsx e revise as funções de wrapper. Substitua os blocos TODO pelas seguintes implementações:
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);
Aprendizados importantes:
onGraphRequest: um wrapper especializado do Firebase Functions que mapeia uma função do Cloud para um esquema de resolvedor personalizado do SQL Connect.args: os argumentos transmitidos da mutação GraphQL são automaticamente digitados e extraídos aqui para serem injetados no comando do Gemini.
Adicionar a mutação ao conector
Agora que a lógica do resolvedor personalizado existe, exponha-a pelo conector do aplicativo para que o front-end possa chamá-la.
Abra dataconnect/friendly-exchange/mutations.gql e adicione a mutação:
# 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
)
}
Implantar e gerar o SDK
Como os resolvedores personalizados são executados pelo Cloud Functions, é necessário implantar as funções no Google Cloud para que o endpoint seja ativado.
Abra o terminal e implante a função:
firebase deploy --only functions
Depois da implantação, gere o SDK do front-end para incluir sua nova mutação de IA:
firebase dataconnect:sdk:generate
Integrar o AI Resolver ao app da Web
Vamos configurar para que qualquer negociação de 10 ou mais ações acione um alerta de notícias de última hora.
Abra lib/ExchangeService.tsx. Primeiro, importe generateTradeHeadline e triggerEvent na parte de cima:
import {
buyStock,
sellStock,
generateTradeHeadline,
triggerEvent
} from "@dataconnect/generated";
Em seguida, role a tela para baixo até a parte de baixo da função executeBuyStock e substitua o TODO pelo bloco de acionamento de IA logo antes do fim da função:
// ... (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(),
});
}
};
Faça exatamente a mesma coisa na parte de baixo da função 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(),
});
}
};
Veja isso na prática
- Atualize o app da Web.
- Confira se você fez login e tem moeda suficiente.
- Selecione um emoji e compre 10 ou mais ações de uma só vez.
- Confira o ticker do mercado global no lado direito do painel. Em alguns segundos, você vai ver uma manchete de notícias satírica personalizada gerada pelo Gemini.