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Comment by coldtea | Hacker News Reader

It's impressive how well PHP holds up with many queries per request (which is the most common CRUD/webapp scenario).

While for no or just one query it's slower than a lot of the other frameworks (due to PHP being slow to parse, startup etc), as soon as we have a lot of DB queries, the C interface to MySQL leaves the other frameworks in the dust.

The well known PHP shortcomings aside, that's a nice example of optimizing for the things that matter most, especially for it's common use cases (Wordpress, Drupal, etc).

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In really scalable sites, you need sharding. Unless your database itself is doing the scaling (such as with Riak), you're going to sometimes hit multiple shards. With PHP and other languages that can't do async, you're going to have to query the DB sequentially, increasing latency proportionally to the number of shards you have to hit. With Node.js and other asynchronous apps, you don't.

Disclaimer: mysqli does have async capabilities, but most people such as myself use PDO for its other benefits. And mysqli only works with MySQL.

What you need for sharded queries is concurrency, not necessarily asynchronous requests. Async callbacks (ala Node.js, Twisted Python, Event Machine, etc.) give you a kind of cooperative multitasking, which is one way to have concurrent I/O-bound tasks going; multithreaded programs are another. (Ruby and Python threads are kind of in-between, due to their respective GIL limitations.)

That being said, above a certain scale and complexity level, you probably want the topology of your persistent data store hidden from your web request handlers anyway. For one thing, making requests to N backend shards from M frontend web workers starts to get bad when N and M are both large; for another, introducing really complex scatter-gather query logic into your request-handling pipeline can be a maintenance and debugging nightmare.

Introducing a proxy or data-abstraction service in between cuts down on the number of open connections and lets you change the data storage topology without updating frontend code.

Some of the fastest implementations you see in these tests are not asynchronous.

With Servlet for example, a worker thread is chosen from Resin's thread pool and used to handle a request. The Servlet then executes 20 queries sequentially and returns the resulting list data structure. This is Servlet 3.0 but not using Servlet 3.0 async.

Async isn't making the top performers fast. Being fast is making them fast.

I agree, but what about the special case of hitting multiple shards and aggregating the results? Shouldn't the non-blocking win over the blocking?

Well depends exactly on the implementation.

Some may issue queries in parallel and aggregate the results, blocking until everything is done. Others may run them sequentially, which is the simplest but slowest way.

Or if your data supports it, you shard based on an algorithm that is repeatable and cheap, and the client can compute where to look for data, if it exists.

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