Streaming partial results

A long-running task can publish intermediate results as it works; a consumer polls them as they arrive — useful for ETL, ML training steps, or batch progress. In Java, partials ride on task logs: durable, per-job log rows in shared storage.

Publish

From inside the handler, write a log row against the job (get the id across with the middleware pattern):

for (int epoch = 1; epoch <= epochs; epoch++) {
    double loss = trainEpoch(epoch);
    flexiq.writeTaskLog(jobId, "train", "result",
            "{\"epoch\": " + epoch + ", \"loss\": " + loss + "}");
}

writeTaskLog(jobId, taskName, level, message) takes any level string; an overload adds an extra payload. By the cross-SDK contract, published partials use level "result" — workers in other processes that publish partials store them the same way, so they are readable here.

Consume

getTaskLogs(jobId) returns every row for the job, oldest first; poll it until the job is terminal:

String id = flexiq.enqueue(train, config);

int seen = 0;
while (flexiq.getJob(id).map(j -> j.status == JobStatus.PENDING || j.status == JobStatus.RUNNING).orElse(false)) {
    List<TaskLog> logs = flexiq.getTaskLogs(id);
    for (TaskLog log : logs.subList(seen, logs.size())) {
        if ("result".equals(log.level)) {
            render(log.message);
        }
    }
    seen = logs.size();
    Thread.sleep(200);
}

int result = flexiq.getResult(id, Integer.class).orElseThrow();

It works from any process sharing the storage, and a late consumer still reads every value already published. Each TaskLog carries jobId, taskName, level, message, extra, and loggedAt (Unix ms).

Watch partial results arrive

The consumer sees each chunk as the task publishes it, rather than waiting for the whole job to finish.