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WIP: Add initial version of the how-to guide
Signed-off-by: Matheus Cruz <matheuscruz.dev@gmail.com>
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‎core/integration-tests/src/test/java/io/quarkiverse/flow/it/Call4PapersTest.java‎

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package io.quarkiverse.flow.it;
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import io.quarkiverse.flow.Flow;
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import io.quarkus.test.junit.QuarkusTest;
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import io.serverlessworkflow.api.types.Workflow;
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import io.serverlessworkflow.fluent.func.FuncWorkflowBuilder;
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import io.serverlessworkflow.impl.WorkflowContextData;
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import io.serverlessworkflow.impl.WorkflowModel;
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import io.smallrye.mutiny.Uni;
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import jakarta.enterprise.context.ApplicationScoped;
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import jakarta.inject.Inject;
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import org.junit.jupiter.api.Assertions;
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import org.junit.jupiter.api.Test;
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import java.net.URI;
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import java.util.Objects;
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import java.util.function.Function;
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import static io.serverlessworkflow.fluent.func.dsl.FuncDSL.function;
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import static io.serverlessworkflow.fluent.func.dsl.FuncDSL.http;
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@QuarkusTest
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public class Call4PapersTest {
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@Inject
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Call4PapersFlow flow;
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@Test
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void should_execute_correctly() {
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var submission = new Call4PapersFlow.ProposalSubmission(
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"Reactive Workflows with Quarkus",
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"This paper explores reactive workflow patterns...",
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"Jane Developer"
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);
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WorkflowModel workflowModel = flow.startInstance(submission).await().indefinitely();
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Assertions.assertNotNull(workflowModel);
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}
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@ApplicationScoped
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public static class Call4PapersFlow extends Flow {
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@Override
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public Workflow descriptor() {
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return FuncWorkflowBuilder.workflow("call4papers")
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.tasks(
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// Step 1: Validate proposal with inputFrom transformation
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function("validateProposal", (Proposal input) -> {
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System.out.println("Validating proposal: " + input.title());
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return input;
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}, Proposal.class)
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.inputFrom((ProposalSubmission submission) -> new Proposal(
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submission.title(),
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submission.proposal(), // Maps to abstractText
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submission.author()), ProposalSubmission.class),
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// Step 2: Score proposal with outputAs transformation
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function("scoreProposal", (Proposal input) -> {
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Integer score = calculateScore(input.abstractText());
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System.out.println("Score calculated having the result as: " + score);
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return score;
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}, Proposal.class)
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.outputAs((Long score) -> {
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return new ProposalScore(score, score >= 7);
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}, Long.class),
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// Step 3: Prepare notification with exportAs using workflow context
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function("prepareNotification", proposalScore -> new ProposalScore(proposalScore.score, proposalScore.accepted), ProposalScore.class)
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.exportAs((ProposalScore proposalScore, WorkflowContextData workflowContext) -> {
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// Access original workflow input to get title and author
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WorkflowModel originalInput = workflowContext.instanceData().input();
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ProposalSubmission submission = originalInput.as(ProposalSubmission.class).orElseThrow();
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// Create enriched payload combining original input with score
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return new NotificationPayload(
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submission.title(),
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submission.author(),
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proposalScore.score(),
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proposalScore.accepted());
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}, ProposalScore.class).inputFrom(Function.identity(), ProposalScore.class),
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// Step 4: Send notification via HTTP
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http("sendNotification")
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.POST()
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.body("${.}") // Uses the NotificationPayload from exportAs
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.header("Content-Type", "application/json")
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.uri(URI.create("http://localhost:9999/notifications")))
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.build();
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}
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/**
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* Calculate a score for the proposal based on its abstract.
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* In a real implementation, this might use NLP, keyword analysis, etc.
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*/
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private Integer calculateScore(String abstractText) {
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// Simple scoring: longer abstracts get higher scores
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int length = abstractText.length();
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if (length > 500)
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return 9;
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if (length > 300)
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return 7;
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if (length > 150)
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return 5;
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return 3;
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}
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// Data types representing different stages of the workflow
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/**
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* External DTO received from the API
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*/
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public record ProposalSubmission(String title, String proposal, String author) {
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}
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/**
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* Internal domain model used for processing
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*/
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public record Proposal(String title, String abstractText, String author) {
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}
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/**
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* Scoring result persisted in workflow data
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*/
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public record ProposalScore(long score, boolean accepted) {
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}
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/**
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* Final notification payload enriched with data from multiple sources
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*/
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public record NotificationPayload(String title, String author, long score, boolean accepted) {
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}
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}
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}

‎docs/modules/ROOT/examples/org/acme/dataflow/Call4PapersFlow.java‎

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package org.acme.dataflow;
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import static io.serverlessworkflow.fluent.func.dsl.FuncDSL.function;
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import static io.serverlessworkflow.fluent.func.dsl.FuncDSL.http;
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import java.net.URI;
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import jakarta.enterprise.context.ApplicationScoped;
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import io.quarkiverse.flow.Flow;
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import io.serverlessworkflow.api.types.Workflow;
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import io.serverlessworkflow.fluent.func.FuncWorkflowBuilder;
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import io.serverlessworkflow.impl.WorkflowContextData;
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import io.serverlessworkflow.impl.WorkflowModel;
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@ApplicationScoped
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public class Call4PapersFlow extends Flow {
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@Override
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public Workflow descriptor() {
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return FuncWorkflowBuilder.workflow("call4papers")
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.tasks(
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// Step 1: Validate proposal with inputFrom transformation
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function("validateProposal", (Proposal input) -> {
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System.out.println("Validating proposal: " + input.title());
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return input;
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}, Proposal.class)
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.inputFrom((ProposalSubmission submission) -> new Proposal(
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submission.title(),
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submission.proposal(), // Maps to abstractText
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submission.author()), ProposalSubmission.class),
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// Step 2: Score proposal with outputAs transformation
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function("scoreProposal", (Proposal input) -> {
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int score = calculateScore(input.abstractText());
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return score;
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}, Proposal.class)
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.outputAs((Integer score) -> new ProposalScore(score, score >= 7),
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Integer.class),
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// Step 3: Prepare notification with exportAs using workflow context
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function("prepareNotification", (ProposalScore proposalScore) -> {
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return proposalScore; // Pass through
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}, ProposalScore.class)
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.exportAs((ProposalScore proposalScore, WorkflowContextData workflowContext) -> {
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// Access original workflow input to get title and author
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WorkflowModel originalInput = workflowContext.instanceData().input();
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ProposalSubmission submission = originalInput.as(ProposalSubmission.class).orElseThrow();
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// Create enriched payload combining original input with score
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return new NotificationPayload(
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submission.title(),
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submission.author(),
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proposalScore.score(),
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proposalScore.accepted());
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}, ProposalScore.class),
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// Step 4: Send notification via HTTP
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http("sendNotification")
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.POST()
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.body("${.}") // Uses the NotificationPayload from exportAs
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.header("Content-Type", "application/json")
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.uri(URI.create("http://localhost:9999/notifications")))
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.build();
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}
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/**
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* Calculate a score for the proposal based on its abstract.
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* In a real implementation, this might use NLP, keyword analysis, etc.
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*/
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private int calculateScore(String abstractText) {
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// Simple scoring: longer abstracts get higher scores
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int length = abstractText.length();
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if (length > 500)
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return 9;
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if (length > 300)
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return 7;
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if (length > 150)
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return 5;
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return 3;
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}
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// Data types representing different stages of the workflow
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/**
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* External DTO received from the API
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*/
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public record ProposalSubmission(String title, String proposal, String author) {
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}
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/**
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* Internal domain model used for processing
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*/
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public record Proposal(String title, String abstractText, String author) {
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}
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/**
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* Scoring result persisted in workflow data
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*/
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public record ProposalScore(int score, boolean accepted) {
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}
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/**
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* Final notification payload enriched with data from multiple sources
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*/
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public record NotificationPayload(String title, String author, int score, boolean accepted) {
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}
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}

‎docs/modules/ROOT/pages/data-flow.adoc‎

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This is handy for logging, debugging, or adding metadata without polluting your
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business types.
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[[export-as]]
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=== 2.2 `exportAs` – pass data to the next step
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`exportAs` controls what the task **exports** for downstream consumers (the next
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You can freely mix JQ and Java across tasks in the same workflow.
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[[quick-reference]]
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== 5. Quick reference
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* `inputFrom(String jq)` – slice workflow data using JQ.

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