如何从Observable无痛切换到Flowable？海量Player数据查询异常答疑

Hey there, let's break this down step by step to address your concerns:

1. Feasibility of the conversion #

First off: yes, using players.getPlayers().toFlowable(BackpressureStrategy.BUFFER) is technically feasible to convert your Observable<Player> to a Flowable. But there's a crucial caveat you need to keep in mind:

The BUFFER strategy will cache all elements emitted by the upstream Observable in memory until the downstream can process them. If you're dealing with truly massive numbers of Player objects (like 10k+), this could still put you at risk of an OutOfMemoryError (OOME)—just like the original Observable would, because you're still holding all elements in memory at once.

Since you can't modify the DAO's return type, this conversion is a valid workaround, but it doesn't solve the root memory pressure issue on its own. If you need to avoid OOME, consider alternative backpressure strategies based on your business needs:

• BackpressureStrategy.DROP: Discards new elements if the downstream can't keep up (only use this if losing data is acceptable for your use case).
• BackpressureStrategy.LATEST: Keeps only the most recent element, dropping older ones (again, only suitable if stale data is okay).
• If you need to retain all data, pair the BUFFER strategy with downstream operators that process elements in batches or stream them to disk temporarily to reduce in-memory load.

2. Why you're seeing the NoHostAvailableException #

It's important to note that the NoHostAvailableException you're hitting might not be directly caused by using Observable instead of Flowable. When querying massive datasets from Cassandra, this error often stems from:

• A long-running query timing out: Cassandra nodes might drop the connection if the query takes too long to return all results.
• Lack of pagination: If your DAO's query isn't using Cassandra's built-in pagination, it's trying to return all Player records in a single response, which can overwhelm both the driver and the Cassandra nodes.
• Overloaded Cassandra cluster: Large queries can consume significant memory and CPU on your Cassandra nodes, leading them to become unresponsive.

Even after switching to Flowable, you'll need to address these Cassandra-specific issues to prevent the exception from recurring. Here are some fixes to try:

• Check if your DAO's query is using pagination (the DataStax driver defaults to 5000 rows per page, but you can adjust this with setFetchSize()).
• Increase the driver's connection timeout settings to account for longer-running queries.
• Verify your Cassandra cluster's health: check node statuses, load metrics, and ensure there are no network issues between your app and the cluster.

3. Bonus: Handling UndeliverableException #

The UndeliverableException wraps the underlying Cassandra error because RxJava can't deliver the error to a downstream that might have already unsubscribed. To handle this cleanly, add error-handling operators to your Flowable chain, like:

players.getPlayers()
    .toFlowable(BackpressureStrategy.BUFFER)
    .onErrorResumeNext(error -> {
        // Log the error and return a fallback or empty stream
        LOGGER.error("Failed to fetch players", error);
        return Flowable.empty();
    })
    .subscribe(...);

This ensures errors are properly caught and don't bubble up as undeliverable exceptions.

内容的提问来源于stack exchange，提问作者Alex Kokorin