The article mentions that with AOF persistence there is a problem about fsync. I'll try to go in further details here.
Basically when using Redis AOF you can select among three levels of fsync: fsync always that will call fsync after every command, before returning the OK message to the client. Bank-alike security that data was written on disk, but very very slow. Not what most users want.
Then there is 'fsync everysec' that just calls fsync every second. This is what most users want. And finally 'fsync never' that will let the OS decide when to flush things on disk. With Linux default config writing buffers on disk can be delayed up to 30 seconds.
So with fsync none, there are no problems, everything will be super fast, but durability is not great.
With fsync everysec, there is the problem that form time to time we need to fsync. Guess what? Even if we fsync in a different thread, write(2) will block anyway.
Usually this does not happen, as the disk is spare. But once you start compacting the log with the BGREWRITEAOF command, the disk I/O increases as there is a Redis child trying to perform the compaction, so the fsync() will start to be slow.
How to fix that? For now we introduced in Redis 2.2 an option that will not fsync the AOF file while writing IF there is a compaction in progress.
In the future we'll try to find even Linux-specific ways to fsync without blocking. We just want to say the kernel: please flush the current buffers, but even if you are doing so, new writes should go inside the write buffer, so don't try to delay new writes if the fsync in progress is not yet completed. This way we can just fsycn every second in another thread.
Another option is to write+fsync the AOF log in a different process, talking with the main process via a pipe. Guess what? The current setup at Bump is somewhat doing this already with the master->slave setup. But there should be no need to do this.
So surely things will improve.
About diskstore, this is I think better suited for a different use case, that is: big data, much bigger than RAM, but mostly reads, and need to restart the server without loading everything in memory. So I think Bump is already using Redis in the best way, just we need to improve the fsync() process.
> With fsync everysec, there is the problem that form time to time we need to fsync. Guess what? Even if we fsync in a different thread, write(2) will block anyway
Yep, but this could be avoided if a thread was devoted to all I/O incl. write() (and then line-level buffering really would be possible as well). Communication with this thread would be on a thread-safe queue--the main thread would never block on disk I/O, and only two threads would mean mutex contention for the queue lock would be low. This would be one solution, correct? This is a variation of your "two processes + pipe" suggestion.
> How to fix that? For now we introduced in Redis 2.2 an option that will not fsync the AOF file while writing IF there is a compaction in progress.
Well, we enabled that.. but, we found that it's still a problem in a couple of circumstances:
1. Something other than the AOF recompaction makes the disk busy. Like, say, even a moderate amount of disk activity by another process.
2. Redis's own logging to stdout, if redirected to a file, itself can cause the redis main thread to block if stdout is being flushed onto a busy disk.
Basically, if any I/O which may hit a disk (AOF record/flush or even logging) is being done on the single epoll-driven thread redis uses to process incoming requests, the system must make very good guarantees that those I/O calls will not block. We have found these guarantees practically impossible to make on a very busy master, so we've given on up having the master do AOF work altogether.
Exactly the logging process can well be a thread for better performances, thanks for the hint!
About the other scenarios where fsync will perform poorly, indeed every other I/O is going to be a problem.
I guess the "all the AOF business in a different thread" is the most sensible approach to follow probably, unless there is an (even Linux specific syscall) that is able to avoid blocking but just to force commit of old data.
I can say, empirically, none of the many, many challenges we've had building and scaling Bump, have been related to Redis's capabilities as a messaging bus. So "good enough" wins again.
I've often thought it would be useful to have a redis equivalent of MongoDB's capped collections, specifically to make things like recent activity logs easier to implement. At the moment you can simulate it with an rpush followed by an ltrim, but it would be nice if using two commands wasn't necessary.
sending LPUSH+LTRIM in a pipeline is the same as having a special command for this. But having a special command for this, and for other use cases, makes Redis somewhat less general. What I mean is that if we consider every added feature a cost (complexity cost, not development cost), why don't instead add a feature that allows for a use case currently not covered?
Btw there is an interesting pattern so you actually need to rarely send the LTRIM. Imagine this: you want a list to save the user timeline, you are interested only in the latest 100 messages. So for every entry you can LPUSH+LTRIM. But after all you can just LTRIM 10% of the times. Your list will fluctuate in length between 100 and 110, but as you access things using LRANGE the additional elements wil not create any problem. So the cost of the LTRIM, while already very very small, can be made 90% smaller with this simple trick.
I'd be interested in hearing if they tried to use Scribe for the same task and found it wanting, or if there's some other story.
To answer your question, simply put, no one here had heard about Scribe.
I do have mongodb replicated across two other machines, but could you briefly shed light on what the problems between redis and mongo on a single box were?
