Pragmatic Dictator

Updated to include additional commentary on speed of persistence

I often get asked the question “What kind of performance can I get with a JavaSpace?”

For anyone who knows me, you won’t be surprised to hear that I respond with “it depends“. But if you’re reading this entry you’ll want more than that right?

There are a multitude of factors involved - some of them are user related and some of them are implementation related. This sounds complicated but we can essentially derive all we need to know from examining the user factors and relating them to the underlying implementation constraints:

• How big is the typical Entry?
• How many Entry’s do you plan to hold?
• Typical search patterns
• How many concurrent access do you expect?
• How much use do you make of notify?
• Do you need persistence?
• Use of transactions

Before we start lets define a couple of terms. JavaSpaces implementations tend to support two basic modes of operation (some support others as well which lie in between these two extremes). Persistent mode means that as soon as an Entry is written it is guarenteed to be available after a crash. Transient mode is the opposite, Entry’s are typically only held in memory and will be lost after a crash.

The size and constituents of an Entry have a direct relationship to the number of network packets you’re likely to incur transporting them back and forth. In many cases you can tune your TCP/IP stack accordingly. I haven’t mentioned RMI yet have I? That’s because owing to the power of Jini and the use of smart proxies, it is possible for a JavaSpace to use a custom protocol for communication with the server rather than RMI. JERI (Jini’s RMI replacement) allows for some serious customization at the invocation layers. How about serialization costs? Well, they are there but there are various techniques for accelerating this which some JavaSpaces implementations exploit.

The number of Entry’s and number of types you have in your application stresses several aspects of a JavaSpaces engine. First and foremost, large numbers of Entry’s may not fit in memory so you’ll be hitting on the caching algorithm the engine uses if any (some implementations can’t swap and either always hit a database for access or attempt to retain everything in memory blowing up if you overfill them). Basically, just like databases, the more memory you can give a JavaSpace the better. The number of different types determines how easily the engine can partition Entry’s. It can also affect the number of database tables and size of database footprint if any (this is particularly relevant to persistent JavaSpaces but also relevant for transient JavaSpaces that swap to disk if memory is exhausted). Finally, the number of Entry’s stresses the indexing algorithms the engine uses and this leads us into the discussion about search patterns.

Some JavaSpaces applications adopt a flow or streaming type approach where only a few Entry’s are present at any one time and are rapidly taken by some process. Often these processes use general templates which are wholly wildcard matches (all their fields are null). Such behaviours can’t really be accelerated by indexes and the optimal Entry storage mechanism here is a linked list which is linearly scanned. However, linked lists are slow for random access and this brings us to the other behaviour where an application typically searches for Entry’s using specific values in fields of a template (think primary key if you like). Under this circumstance, large numbers of Entry’s will penalize searches along linked lists and various of the JavaSpaces implementations use some variant of hash-based searching to accelerate these searches.

No discussion of access patterns is complete without some discussion of FIFO. Basically, FIFO has all sorts of nasty side effects - it tends to penalize indexing, renders various concurrency optimizations useless and tends to incur more disk searching in cases where swapping is required because the number of Entry’s is larger than available memory - it’s basically cache defeating (you actually want the least recently accessed Entry and that’s the first element to be swapped to disk by most decent caching algorithms). One can change cache policies etc but this can slow down other forms of search in cases where you want random access on top of the FIFO behaviour.

The amount of concurrent access stresses the transport. nio is the basic solution adopted here and those JavaSpaces running a custom socket protocol or using JERI (which has an nio option) are at an advantage. Concurrency also exaggerates issues associated with swapping particularly disk thrashing and the effectiveness of the cache policy. It can cause lock contention on caches (particularly in the presence of swapping) and other areas of the engine particularly in the implementation of the behaviour where a blocking take/read is awoken by the arrival of a new Entry from a write.

The number of notify requests dictates the amount of latent template matching the engine has to do for newly written Entry’s. High rates of Entry arrival can overflow queues and stack up behind delivery of the events to remote clients. JavaSpaces implementations typically combat this by applying throttling and using multiple threads to process new Entry’s and deliver events. There’s a certain amount of advantage in using indexing/hashing but it’s mostly a brute force exercise based around thread pools. More threads and more CPU’s means faster notify processing but the network and transport will also be a potential bottleneck. Essentially, notify is a push mechanism whilst take and read are pull mechanisms. Notify requires extra CPU effort because it has to deliver the payload to the client as well as determine matching whilst take and read take some of the delivery load off the engine.

The performance of persistent mode is largely dictated by the performance of the disk subsystem. Specifically it’s governed by the speed with which the OS and disk subsystem can work together to get a forced sync of some data onto the disk platters. The forced sync is essential to guarentee persistence post crash. We can improve performance here with battery-backed write buffers etc which come as part of the more advanced disk infrastructures from the likes of EMC (or something like this). There are various techniques for improving throughput to the disk which amount to reducing the amount of head activity (some JavaSpaces implementations support options in this area, others don’t). If your application is going to require the JavaSpaces implementation to swap to disk (assuming it supports that mode of operation) it’s best to place the database component of the JavaSpaces engine on one disk and the logs on another as these two components tend to have conflicting disk access patterns which can slow things down substantially.

Anyone used to tuning databases will recognise much of what I say above but there’s one other thing I need to mention. Many databases have some aggressive optimizations which trade some level of recovery guarentee (i.e. they may lose the last few updates) for a boost in performance. They still use logs but they buffer updates to the logs in memory for some period of time before flushing them to disk. Some JavaSpaces implementations support this option (this is the intermediate point between full persistence and transient modes) and may even use it as the default. They can give substantial performance improvement but you can lose Entry’s. If you absolutely cannot afford to lose Entry’s make sure you’ve disabled this option if it’s present.

Note that there are some more exotic methods of doing logging which involve passing log entry’s over the network (typically in batches) for holding on another machine. If one sends the log entry’s to several machines, they can each keep copies in memory and not touch disk giving a reliable but fast log assuming your network can cope with the concurrent load of logging and traffic for takes, writes etc.

Transactions need to be co-ordinated by a transaction manager and typically use a two-phase commit protocol, even with a single participant (one JavaSpace) you incur a number of additional network roundtrips. It’s worth knowing that the default settings for Mahalo are not optimal for many high throughput situations. Certain JavaSpaces implementations also optimize the common case of one transaction against one JavaSpace to reduce the roundtrips (there are several ways to do this).

Okay, so the above is the why’s and the wherefore’s, I’ll leave you with a few figures:

1. On 100 Mbit/sec ethernet, an RMI call with a reasonable sized payload (2k or so when I last tested) will take about 2ms
2. Blitz’s core engine is well capable of sub-millisecond writes and takes even in persistent mode where most of the cost is in the disk logging activity).

If you still have questions, feel free to post a comment.

This entry was posted on Tuesday, January 31st, 2006 at 7:54 pm and is filed under Blitz JavaSpaces, JavaSpaces, Jini. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.