Pragmatic Dictator

I’m currently working on getting 2.0-alpha3 out the door which means all the usual release procedures and in particular a lot of thumb-twiddling whilst various tests run. People often ask about the testing that goes into a Blitz release and so, given that I’m suffering from test-induced thumb-twiddling it seems like this would be a good time to write something down.

Blitz is basically a layered Jini service with remote wrappers cleanly separated from the core JavaSpace implementation. Thus we can thrash the core without needing to do a full remote deployment which provides many benefits such as eliminating a lot of network I/O that can hide race conditions and deadlocks.

My test machine is a dual processor dual core machine (2 x 2.6Ghz Xeons with HyperThreading, 2Gb, 2×70Gb SCSI Disks) and it’s main duties are to run the long term soak tests for long periods of time (unit tests get run as part of day-to-day development on my PowerMac Dual G5). There are two basic types of soak test that get run regularly they are:

1. TxnStress - a multi-threaded test which fills a Blitz instance with a fixed number of entry’s and then randomly takes and re-writes one of those Entry’s with each take/write pair done under a separate transaction.
2. Stream - a multi-threaded test which writes a sequence of Entry’s into Blitz whilst another thread takes them. Writers and Takers are run in pairs up to a configured number.

Whilst these tests run I typically have jconsole hooked up plotting memory consumption so leaks can be detected and fixed. In addition, Blitz’s in-built statistics API is used to check queue sizes, number of entry’s etc (this is the same API used by the remote dashboard). Finally, each test has code that ensures we are not leaking Entry’s or missing them. Blitz also has a debug-mode where one can simply connect to a pre-configured socket and trigger the dumping of statistics to the console at any time.

These tests get run against Blitz in both transient and persistent mode (which is a mere flip of a configuration variable).

Stream is just about done, so I’ll be shipping a release to SourceForge shortly.

Technorati Tags: javaSpaces, jini, Blitz

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.

For a number of applications, the standard JavaSpace interface’s single Entry operations are sufficient to construct a scalable and simple solution.

However, there are certain cases where perhaps we’d like to write or take a large batch of Entry’s. With the existing interface this is possible but would incur a significant number of network round trips which can hurt performance. Fortunately, with the release of JINI 2.1 we gained an additional interface JavaSpace05 which provides bulk write/take and some other goodies (which I’ll mention later).

Not only do these bulk operations permit transfer of multiple Entry’s but they also have a provision for the use of multiple templates using an OR-based matching strategy. i.e. take an entry if it matches template A or template B or template C. As with the original operations you get timeouts, leasing and transactions.

Other additions include a second notify method (registerForAvailabilityEvent) which provides more detail concerning the lifetime of an Entry. For example, one can now receive an event to indicate the availability of an Entry as the result of an aborted take.

Lastly we have contents which allows for a form of multiple read against multiple templates. This is treated slightly differently from multiple take in recognition of it’s non-destructive nature. Some developers like to view this feature as an iterator but there are some key differences such as the ability for iteration to never end (due to a constant stream of new writes) and remoteness.

Currently Outrigger (the example JavaSpaces implementation in the Jini Starter Kit) and Blitz provide complete implementations of JavaSpace05. GigaSpaces provide their own equivalents but as yet haven’t announced compatible support for this new standard extension.

Still working away at this one. Blitz now has an integrated experimental version of my nio based transport which can be configured on or off. It currently only boosts, write, take and read performance (roughly 100% faster than normal). I’ve not tested it hard enough to be confident of heavyweight concurrent loads and once you start mixing in transactions, you only get maybe 20% performance boost. I figure I’ll have to extend the transport to support the TransactionParticipant interface as well.

I’ve had some chats with Peter Jones over at SUN and he’s given me some notes and some pointers as to where he sees issues in the current JERI code. He’s not been able to do much in that direction owing to commitments to JDK RMI which is a shame (IMHO) as JERI is way better and ought to be getting the attention. Ah well, maybe one day.

Anyway, the plan is still to go digging through JERI and find the problem - I have a few hunches but I need to run some more tests. My preference is definitely to fix up JERI and then junk the experimental transport (which won’t ever provide much in the way of security etc unless someone else wants to do that work).

Recently, Blitz has been getting some commercial interest which has kept me busy especially when one of my customers hit a deadlock. I’ve written that same customer a remote bootstrapping application that allows them to remotely load their entire JINI application and get it up and running. The real killer is that each downloader can be given a different profile as required by the system admin - this has many uses including testing new builds, value add for specific customers etc.

In between that work, I’ve been developing a JavaSpaces/JINI application rather than an implementation. It makes a nice change to work above the JavaSpaces API rather than under the covers. It allows me to spend time looking at Blitz from a users’ perspective and tweaking things accordingly. Of course, I’m rather an advanced user so it’s only so useful. Thus far, it’s meant a couple of additional configuration variables and a few fixes for the experimental nio transport.

So what is the application? Sorry, it’s remaining cloaked for now. What I will say is that it’s not going to be a pure JavaSpaces/JINI play - you can certainly access it that way but it’s likely going to have a REST-based web interface as well. There’s lots of things you could do with it but I have some specific ideas which will shape it’s direction for a while (no, I’m not telling). At least initially, it won’t be opensource which is something of a departure from the norm, instead it’ll be provided via Lone Crusader in some manner. I’ve already developed a couple of new patterns, not sure how to/if I should make those public. Bit of an experiment, should be interesting……

All the details are at SourceForge

If there are no last minute complaints, this release will become 1.13 (once I’ve updated the documentation) and become the first fully supported JINI 2.1 release.

Hmmm, been looking at the network layer in Blitz and am seeing some odd behaviour in respect of JERI under concurrent load. Worse, enabling nio seems to make no odds which means the bottleneck is elsewhere. So I did some profiling and we seem to be spending all our time waiting for socket io.

So I wrote my own bare bones transport with nio which is way faster than JERI and doesn’t seem to have the same bottleneck (whatever it is!). Strange cos I can’t see any obvious reason for the large difference, both use serialization, both have to transport about the same amount of data, both can use nio and with a single thread handling use of Selector. Bit more work to do on my transport and then I’ll do a full comparison.

Whilst my transport might be a lot quicker, I’d rather use JERI with in-built support for security etc. So I really want to try and solve the problem but if I can’t, then maybe I’ll make my transport a “fast I/O if you don’t need security option”.

Up now on the SourceForge site in the form of Blitz Pure Java 1.13-pre7.

You can find it here.

Haven’t done the installer yet - I’m most likely to make the installer JINI 2.1 only - preferences anyone?

Update: -pre8 is now available which bundles a JINI 2.1 compatible installer.