Chapter 1. Introduction
The trouble with doing something right the first time is that nobody appreciates how difficult it was.
There is a general perception that Java programs are slow. Part of this perception is pure assumption: many people assume that if a program is not compiled, it must be slow. Part of this perception is based in reality: many early applets and applications were slow, because of nonoptimal coding, initially unoptimized Java Virtual Machines (VMs), and the overhead of the language.
In earlier versions of Java, you had to struggle hard and compromise a lot to make a Java application run quickly. More recently, there have been fewer reasons why an application should be slow. The VM technology and Java development tools have progressed to the point where a Java application (or applet, servlet, etc.) is not particularly handicapped. With good designs and by following good coding practices and avoiding bottlenecks, applications usually run fast enough. However, the truth is that the first (and even several subsequent) versions of a program written in any language are often slower than expected, and the reasons for this lack of performance are not always clear to the developer.
This book shows you why a particular Java application might be running slower than expected, and suggests ways to avoid or overcome these pitfalls and improve the performance of your application. In this book I’ve gathered several years of tuning experiences in one place. I hope you will find it useful in making your Java application, applet, servlet, and component run as fast as you need.
Throughout the book I use the generic words “application” and “program” to cover Java applications, applets, servlets, beans, libraries, and really any use of Java code. Where a technique can be applied only to some subset of these various types of Java programs, I say so. Otherwise, the technique applies across all types of Java programs.
Why Is It Slow?
This question is always asked as soon as the first tests are timed: “Where is the time going? I did not expect it to take this long.” Well, the short answer is that it’s slow because it has not been performance-tuned. In the same way the first version of the code is likely to have bugs that need fixing, it is also rarely as fast as it can be. Fortunately, performance tuning is usually easier than debugging. When debugging, you have to fix bugs throughout the code; in performance tuning, you can focus your effort on the few parts of the application that are the bottlenecks.
The longer answer? Well, it’s true that there is overhead in the Java runtime system, mainly due to its virtual machine layer that abstracts Java away from the underlying hardware. It’s also true that there is overhead from Java’s dynamic nature. These overhead s can cause a Java application to run slower than an equivalent application written in a lower-level language (just as a C program is generally slower than the equivalent program written in assembler). Java’s advantages—namely, its platform-independence, memory management, powerful exception checking, built-in multithreading, dynamic resource loading, and security checks—add costs in terms of an interpreter, garbage collector, thread monitors, repeated disk and network accessing, and extra runtime checks.
For example, hierarchical method invocation requires an extra computation for every method call because the runtime system has to work out which of the possible methods in the hierarchy is the actual target of the call. Most modern CPU s are designed to be optimized for fixed call and branch targets and do not perform as well when a significant percentage of calls need to be computed on the fly. On the other hand, good object-oriented design actually encourages many small methods and significant polymorphism in the method hierarchy. Compiler inlining is another frequently used technique that can significantly improve compiled code. However, this technique cannot be applied when it is too difficult to determine method calls at compile time, as is the case for many Java methods.
Of course, the same Java language features that cause these overheads may be the features that persuaded you to use Java in the first place. The important thing is that none of them slows the system down too much. Naturally, “too much” differs depending on the application, and the users of the application usually make this choice. But the key point with Java is that a good round of performance tuning normally makes your application run as fast as you need it to run. There are already plenty of nontrivial Java applications, applets, and servlets that run fast enough to show that Java itself is not too slow. So if your application is not running fast enough, chances are that it just needs tuning.
The Tuning Game
Performance tuning is similar to playing a strategy game (but happily, you are usually paid to do it!). Your target is to get a better score (lower time) than the last score after each attempt. You are playing with, not against, the computer, the programmer, the design and architecture, the compiler, and the flow of control. Your opponents are time, competing applications, budgetary restrictions, etc. (You can complete this list better than I can for your particular situation.)
I once worked with a customer who wanted to know if there was a “go faster” switch somewhere that he could just turn on and make the whole application go faster. Of course, he was not really expecting one, but checked just in case he had missed a basic option somewhere.
There is no such switch, but very simple techniques sometimes provide the equivalent. Techniques include switching compilers, turning on optimizations, using a different runtime VM, finding two or three bottlenecks in the code or architecture that have simple fixes, and so on. I have seen all of these yield huge improvements to applications, sometimes a 20-fold speedup. Order-of-magnitude speedups are typical for the first round of performance tuning.
System Limitations and What to Tune
Three resources limit all applications:
When tuning an application, the first step is to determine which of these is causing your application to run too slowly.
If your application is CPU-bound, you need to concentrate your efforts on the code, looking for bottlenecks, inefficient algorithms, too many short-lived objects (object creation and garbage collection are CPU-intensive operations), and other problems, which I will cover in this book.
If your application is hitting system-memory limits, it may be paging sections in and out of main memory. In this case, the problem may be caused by too many objects, or even just a few large objects, being erroneously held in memory; by too many large arrays being allocated (frequently used in buffered applications); or by the design of the application, which may need to be reexamined to reduce its running memory footprint.
On the other hand, external data access or writing to the disk can be slowing your application. In this case, you need to look at exactly what you are doing to the disks that is slowing the application: first identify the operations, then determine the problems, and finally eliminate or change these to improve the situation.
For example, one program I know of went through web server logs and did reverse lookups on the IP addresses. The first version of this program was very slow. A simple analysis of the activity being performed determined that the major time component of the reverse lookup operation was a network query. These network queries do not have to be done sequentially. Consequently, the second version of the program simply multithreaded the lookups to work in parallel, making multiple network queries simultaneously, and was much, much faster.
In this book we look at the causes of bad performance. Identifying the causes of your performance problems is an essential first step to solving those problems. There is no point in extensively tuning the disk-accessing component of an application because we all know that “disk access is much slower than memory access” when, in fact, the application is CPU-bound.
Once you have tuned the application’s first bottleneck, there may be (and typically is) another problem, causing another bottleneck. This process often continues over several tuning iterations. It is not uncommon for an application to have its initial “memory hog” problems solved, only to become disk-bound, and then in turn CPU-bound when the disk-access problem is fixed. After all, the application has to be limited by something, or it would take no time at all to run.
Because this bottleneck-switching sequence is normal—once you’ve solved the existing bottleneck, a previously hidden or less important one appears—you should attempt to solve only the main bottlenecks in an application at any one time. This may seem obvious, but I frequently encounter teams that tackle the main identified problem, and then instead of finding the next real problem, start applying the same fix everywhere they can in the application.
One application I know of had a severe disk I/O problem caused by using unbuffered streams (all disk I/O was done byte by byte, which led to awful performance). After fixing this, some members of the programming team decided to start applying buffering everywhere they could, instead of establishing where the next bottleneck was. In fact, the next bottleneck was in a data-conversion section of the application that was using inefficient conversion methods, causing too many temporary objects and hogging the CPU. Rather than addressing and solving this bottleneck, they instead created a large memory-allocation problem by throwing an excessive number of buffers into the application.
A Tuning Strategy
Here’s a strategy I have found works well when attacking performance problems:
Identify the main bottlenecks (look for about the top five bottlenecks, but go higher or lower if you prefer).
Choose the quickest and easiest one to fix, and address it (except for distributed applications where the top bottleneck is usually the one to attack: see the following paragraph).
Repeat from Step 1.
This procedure gets your application tuned the quickest. The advantage of choosing the “quickest to fix” of the top few bottlenecks rather than the absolute topmost problem is that once a bottleneck has been eliminated, the characteristics of the application change, and the topmost bottleneck may not need to be addressed any longer. However, in distributed applications I advise you target the topmost bottleneck. The characteristics of distributed applications are such that the main bottleneck is almost always the best to fix and, once fixed, the next main bottleneck is usually in a completely different component of the system.
Although this strategy is simple and actually quite obvious, I nevertheless find that I have to repeat it again and again: once programmers get the bit between their teeth, they just love to apply themselves to the interesting parts of the problems. After all, who wants to unroll loop after boring loop when there’s a nice juicy caching technique you’re eager to apply?
You should always treat the actual identification of the cause of the performance bottleneck as a science, not an art. The general procedure is straightforward:
Measure the performance by using profilers and benchmark suites and by instrumenting code.
Identify the locations of any bottlenecks.
Think of a hypothesis for the cause of the bottleneck.
Consider any factors that may refute your hypothesis.
Create a test to isolate the factor identified by the hypothesis.
Test the hypothesis.
Alter the application to reduce the bottleneck.
Test that the alteration improves performance, and measure the improvement (include regression-testing the affected code).
Repeat from Step 1.
Here’s the procedure for a particular example:
You run the application through your standard profiler (measurement).
You find that the code spends a huge 11% of its time in one method (identification of bottleneck).
Looking at the code, you find a complex loop and guess this is the problem (hypothesis).
You see that it is not iterating that many times, so possibly the bottleneck could be outside the loop (confounding factor).
You could vary the loop iteration as a test to see if that identifies the loop as the bottleneck. However, you instead try to optimize the loop by reducing the number of method calls it makes: this provides a test to identify the loop as the bottleneck and at the same time provides a possible solution. In doing this, you are combining two steps, Steps 5 and 7. Although this is frequently the way tuning actually goes, be aware that this can make the tuning process longer: if there is no speedup, it may be because your optimization did not actually make things faster, in which case you have neither confirmed nor eliminated the loop as the cause of the bottleneck.
Rerunning the profile on the altered application finds that this method has shifted its percentage time down to just 4%. This method may still be a candidate for further optimization, but nevertheless it’s confirmed as the bottleneck and your change has improved performance.
(Already done, combined with Step 5.)
(Already done, combined with Step 6.)
Perceived Performance
It is important to understand that the user has a particular view of performance that allows you to cut some corners. The user of an application sees changes as part of the performance. A browser that gives a running countdown of the amount left to be downloaded from a server is seen to be faster than one that just sits there, apparently hung, until all the data is downloaded. People expect to see something happening, and a good rule of thumb is that if an application is unresponsive for more than three seconds, it is seen as slow. Some Human Computer Interface authorities put the user patience limit at just two seconds; an IBM study from the early ’70s suggested people’s attention began to wander after waiting for more than just one second. For performance improvements, it is also useful to know that users are not generally aware of response time improvements of less than 20%. This means that when tuning for user perception, you should not deliver any changes to the users until you have made improvements that add more than a 20% speedup.
A few long response times make a bigger impression on the memory than many shorter ones. According to Arnold Allen,[1] the perceived value of the average response time is not the average, but the 90th percentile value: the value that is greater than 90% of all observed response times. With a typical exponential distribution, the 90th percentile value is 2.3 times the average value. Consequently, as long as you reduce the variation in response times so that the 90th percentile value is smaller than before, you can actually increase the average response time, and the user will still perceive the application as faster. For this reason, you may want to target variation in response times as a primary goal. Unfortunately, this is one of the more complex targets in performance tuning: it can be difficult to determine exactly why response times are varying.
If the interface provides feedback and allows the user to carry on other tasks or abort and start another function (preferably both), the user sees this as a responsive interface and doesn’t consider the application as slow as he might otherwise. If you give users an expectancy of how long a particular task might take and why, they often accept this and adjust their expectations. Modern web browsers provide an excellent example of this strategy in practice. People realize that the browser is limited by the bandwidth of their connection to the Internet and that downloading cannot happen faster than a given speed. Good browsers always try to show the parts they have already received so that the user is not blocked, and they also allow the user to terminate downloading or go off to another page at any time, even while a page is partly downloaded. Generally, it is not the browser that is seen to be slow, but rather the Internet or the server site. In fact, browser creators have made a number of tradeoffs so that their browsers appear to run faster in a slow environment. I have measured browser display of identical pages under identical conditions and found browsers that are actually faster at full page display but seem slower because they do not display partial pages, download embedded links concurrently, and so on. Modern web browsers provide a good example of how to manage user expectations and perceptions of performance.
However, one area in which some web browsers have misjudged user expectation is when they give users a momentary false expectation that operations have finished when in fact another is to start immediately. This false expectation is perceived as slow performance. For example, when downloading a page with embedded links such as images, the browser status bar often shows reports like “20% of 34K,” which moves up to “56% of 34K,” etc., until it reaches 100% and indicates that the page has finished downloading. However, at this point, when the user expects that all the downloading has finished, the status bar starts displaying “26% of 28K” and so on, as the browser reports separately on each embedded graphic as it downloads them. This frustrates users who initially expected the completion time from the first download report and had geared themselves up to do something, only to have to wait again (often repeatedly). A better practice would be to report on how many elements need to be downloaded as well as the current download status, giving the user a clearer expectation of the full download time.
Where there are varying possibilities for performance tradeoffs (e.g., resolution versus frame rate for animation, compression size versus speed of compression for compression utilities, etc.), the best strategy is to put the user in control. It is better to provide the option to choose between faster performance and better functionality. When users have made the choice themselves, they are often more willing to put up with actions taking longer in return for better functionality. When users do not have this control, their response is usually less tolerant.
This strategy also allows those users who have strong performance requirements to be provided for at their own cost. But it is always important to provide a reasonable default in the absence of any choice from the user. Where there are many different parameters, consider providing various levels of user-controlled tuning parameters, e.g., an easy set of just a few main parameters, a middle level, and an expert level with access to all parameters. This must, of course, be well documented to be really useful.
Threading to Appear Quicker
A lot of time (in CPU cycles) passes while the user is reacting to the application interface. This time can be used to anticipate what the user wants to do (using a background low-priority thread), so that precalculated results are ready to assist the user immediately. This makes an application appear blazingly fast.
Similarly, ensuring that your application remains responsive to the user, even while it is executing some other function, makes it seem fast and responsive. For example, I always find that when starting up an application, applications that draw themselves on screen quickly and respond to repaint requests even while still initializing (you can test this by putting the window in the background and then bringing it to the foreground) give the impression of being much faster than applications that seem to be chugging away unresponsively. Starting different word-processing applications with a large file to open can be instructive, especially if the file is on the network or a slow (removable) disk. Some act very nicely, responding almost immediately while the file is still loading; others just hang unresponsively with windows only partially refreshed until the file is loaded; others don’t even fully paint themselves until the file has finished loading. This illustrates what can happen if you do not use threads appropriately.
In Java, the key to making an application responsive is multithreading. Use threads to ensure that any particular service is available and unblocked when needed. Of course, this can be difficult to program correctly and manage. Handling interthread communication with maximal responsiveness (and minimal bugs) is a complex task, but it does tend to make for a very snappily built application.
Streaming to Appear Quicker
When you display the results of some activity on the screen, there is often more information than can fit on a single screen. For example, a request to list all the details on all the files in a particular large directory may not fit on one display screen. The usual way to display this is to show as much as will fit on a single screen and indicate that there are more items available with a scrollbar. Other applications or other information may use a “more” button or have other ways of indicating how to display or move on to the extra information.
In these cases, you initially need to display only a partial result of the activity. This tactic can work very much in your favor. For activities that take too long and for which some of the results can be returned more quickly than others, it is certainly possible to show just the first set of results while continuing to compile more results in the background. This gives the user an apparently much quicker response than if you were to wait for all the results to be available before displaying them.
This situation is often the case for distributed applications. A well-known example is (again!) found in web browsers that display the initial screenful of a page as soon as it is available, without waiting for the whole page to be downloaded. The general case is when you have a long activity that can provide results in a stream so that the results can be accessed a few at a time. For distributed applications, sending all the data is often what takes a long time; in this case, you can build streaming into the application by sending one screenful of data at a time. Also, bear in mind that when there is a really large amount of data to display, the user often views only some of it and aborts, so be sure to build in the ability to stop the stream and restore its resources at any time.
Caching to Appear Quicker
This section briefly covers the general principles of caching. Caching is an optimization technique I return to in several different sections of this book when appropriate to the problem under discussion. For example, in the area of disk access, there are several caches that apply: from the lowest-level hardware cache up through the operating-system disk read and write caches, cached filesystems, and file reading and writing classes that provide buffered I/O. Some caches cannot be tuned at all; others are tuneable at the operating-system level or in Java. Where it is possible for a developer to take advantage of or tune a particular cache, I provide suggestions and approaches that cover the caching technique appropriate to that area of the application. In cases where caches are not directly tuneable, it is still worth knowing the effect of using the cache in different ways and how this can affect performance. For example, disk hardware caches almost always apply a read-ahead algorithm: the cache is filled with the next block of data after the one just read. This means that reading backward through a file (in chunks) is not as fast as reading forward through the file.
Caches are effective because it is expensive to move data from one place to another or to calculate results. If you need to do this more than once to the same piece of data, it is best to hang onto it the first time and refer to the local copy in the future. This applies, for example, to remote access of files such as browser downloads. The browser caches the downloaded file locally on disk to ensure that a subsequent access does not have to reach across the network to reread the file, thus making it much quicker to access a second time. It also applies, in a different way, to reading bytes from the disk. Here, the cost of reading one byte for operating systems is the same as reading a page (usually 4 or 8 KB), as data is read into memory a page at a time by the operating system. If you are going to read more than one byte from a particular disk area, it is better to read in a whole page (or all the data if it fits on one page) and access bytes through your local copy of the data.
General aspects of caching are covered in more detail in Section 11.7. Caching is an important performance-tuning technique that trades space for time, and it should be used whenever extra memory space is available to the application.
Starting to Tune
Before diving into the actual tuning, there are a number of considerations that will make your tuning phase run more smoothly and result in clearly achieved objectives.
User Agreements
Any application must meet the needs and expectations of its users, and a large part of those needs and expectations is performance. Before you start tuning, it is crucial to identify the target response times for as much of the system as possible. At the outset, you should agree with your users (directly if you have access to them, or otherwise through representative user profiles, market information, etc.) what the performance of the application is expected to be.
The performance should be specified for as many aspects of the system as possible, including:
Multiuser response times depending on the number of users (if applicable)
Systemwide throughput (e.g., number of transactions per minute for the system as a whole, or response times on a saturated network, again if applicable)
The maximum number of users, data, files, file sizes, objects, etc., the application supports
Any acceptable and expected degradation in performance between minimal, average, and extreme values of supported resources
Agree on target values and acceptable variances with the customer or potential users of the application (or whoever is responsible for performance) before starting to tune. Otherwise, you will not know where to target your effort, how far you need to go, whether particular performance targets are achievable at all, and how much tuning effort those targets may require. But most importantly, without agreed targets, whatever you achieve will tend to become the starting point.
The following scenario is not unusual: a manager sees horrendous performance, perhaps a function that was expected to be quick, but takes 100 seconds. His immediate response is, “Good grief, I expected this to take no more than 10 seconds.” Then, after a quick round of tuning that identifies and removes a huge bottleneck, function time is down to 10 seconds. The manager’s response is now, “Ah, that’s more reasonable, but of course I actually meant to specify 3 seconds—I just never believed you could get down so far after seeing it take 100 seconds. Now you can start tuning.” You do not want your initial achievement to go unrecognized (especially if money depends on it), and it is better to know at the outset what you need to reach. Agreeing on targets before tuning makes everything clear to everyone.
Setting Benchmarks
After establishing targets with the users, you need to set benchmarks. These are precise specifications stating what part of the code needs to run in what amount of time. Without first specifying benchmarks, your tuning effort is driven only by the target, “It’s gotta run faster,” which is a recipe for a wasted return. You must ask, “How much faster and in which parts, and for how much effort?” Your benchmarks should target a number of specific functions of the application, preferably from the user perspective (e.g., from the user pressing a button until the reply is returned or the function being executed is completed).
You must specify target times for each benchmark. You should specify ranges: for example, best times, acceptable times, etc. These times are often specified in frequencies of achieving the targets. For example, you might specify that function A take not more than 3 seconds to execute from user click to response received for 80% of executions, with another 15% of response times allowed to fall in the 3- to 5-second range, and 5% in the 5- to 10-second range. Note that the earlier section on user perceptions indicates that the user will see this function as having a 5-second response time (the 90th percentile value) if you achieve the specified ranges.
You should also have a range of benchmarks that reflect the contributions of different components of the application. If possible, it is better to start with simple tests so that the system can be understood at its basic levels, and then work up from these tests. In a complex application, this helps to determine the relative costs of subsystems and which components are most in need of performance-tuning.
The following point is critical: Without clear performance objectives, tuning will never be completed. This is a common syndrome on single or small group projects, where code keeps being tweaked as better implementations or cleverer code is thought up.
Your general benchmark suite should be based on real functions used in the end application, but at the same time should not rely on user input, as this can make measurements difficult. Any variability in input times or any other part of the application should either be eliminated from the benchmarks or precisely identified and specified within the performance targets. There may be variability, but it must be controlled and reproducible.
The Benchmark Harness
There are tools for testing applications in various ways.[2] These tools focus mostly on testing the robustness of the application, but as long as they measure and report times, they can also be used for performance testing. However, because their focus tends to be on robustness testing, many tools interfere with the application’s performance, and you may not find a tool you can use adequately or cost-effectively. If you cannot find an acceptable tool, the alternative is to build your own harness.
Your benchmark harness can be as simple as a class that sets some
values and then starts the main( ) method of your
application. A slightly more sophisticated harness might turn on
logging and timestamp all output for later analysis. GUI-run
applications need a more complex harness and require either an
alternative way to execute the graphical functionality without going
through the GUI (which may depend on whether your design can support
this), or a screen event capture and playback tool (several such
tools exist[3]). In any case, the most important
requirement is that your harness correctly reproduce user activity
and data input and output. Normally, whatever regression-testing
apparatus you have (and presumably are already using) can be adapted
to form a benchmark harness.
The benchmark harness should not test the quality or robustness of the system. Operations should be normal: startup, shutdown, and uninterrupted functionality. The harness should support the different configurations your application operates under, and any randomized inputs should be controlled, but note that the random sequence used in tests should be reproducible. You should use a realistic amount of randomized data and input. It is helpful if the benchmark harness includes support for logging statistics and easily allows new tests to be added. The harness should be able to reproduce and simulate all user input, including GUI input, and should test the system across all scales of intended use up to the maximum numbers of users, objects, throughputs, etc. You should also validate your benchmarks, checking some of the values against actual clock time to ensure that no systematic or random bias has crept into the benchmark harness.
For the multiuser case, the benchmark harness must be able to simulate multiple users working, including variations in user access and execution patterns. Without this support for variations in activity, the multiuser tests inevitably miss many bottlenecks encountered in actual deployment and, conversely, do encounter artificial bottlenecks that are never encountered in deployment, wasting time and resources. It is critical in multiuser and distributed applications that the benchmark harness correctly reproduce user-activity variations, delays, and data flows.
Taking Measurements
Each run of your benchmarks needs to be under conditions that are as identical as possible; otherwise, it becomes difficult to pinpoint why something is running faster (or slower) than in another test. The benchmarks should be run multiple times, and the full list of results retained, not just the average and deviation or the ranged percentages. Also note the time of day that benchmarks are being run and any special conditions that apply, e.g., weekend or after hours in the office. Sometimes the variation can give you useful information. It is essential that you always run an initial benchmark to precisely determine the initial times. This is important because, together with your targets, the initial benchmarks specify how far you need to go and highlight how much you have achieved when you finish tuning.
It is more important to run all benchmarks under the same conditions than to achieve the end-user environment for those benchmarks, though you should try to target the expected environment. It is possible to switch environments by running all benchmarks on an identical implementation of the application in two environments, thus rebasing your measurements. But this can be problematic: it requires detailed analysis because different environments usually have different relative performance between functions (thus your initial benchmarks could be skewed compared with the current measurements).
Each set of changes (and preferably each individual change) should be followed by a run of benchmarks to precisely identify improvements (or degradations) in the performance across all functions. A particular optimization may improve the performance of some functions while at the same time degrading the performance of others, and obviously you need to know this. Each set of changes should be driven by identifying exactly which bottleneck is to be improved and how much of a speedup is expected. Rigorously using this methodology provides a precise target for your effort.
You need to verify that any particular change does improve performance. It is tempting to change something small that you are sure will give an “obvious” improvement, without bothering to measure the performance change for that modification (because “it’s too much trouble to keep running tests”). But you could easily be wrong. Jon Bentley once discovered that eliminating code from some simple loops can actually slow them down.[4] If a change does not improve performance, you should revert to the previous version.
The benchmark suite should not interfere with the application. Be on the lookout for artificial performance problems caused by the benchmarks themselves. This is very common if no thought is given to normal variation in usage. A typical situation might be benchmarking multiuser systems with lack of user simulation (e.g., user delays not simulated, causing much higher throughput than would ever be seen; user data variation not simulated, causing all tests to try to use the same data at the same time; activities artificially synchronized, giving bursts of activity and inactivity; etc.). Be careful not to measure artificial situations, such as full caches with exactly the data needed for the test (e.g., running the test multiple times sequentially without clearing caches between runs). There is little point in performing tests that hit only the cache, unless this is the type of work the users will always perform.
When tuning, you need to alter any benchmarks that are quick (under five seconds) so that the code applicable to the benchmark is tested repeatedly in a loop to get a more consistent measure of where any problems lie. By comparing timings of the looped version with a single-run test, you can sometimes identify whether caches and startup effects are altering times in any significant way.
Optimizing code can introduce new bugs, so the application should be tested during the optimization phase. A particular optimization should not be considered valid until the application using that optimization’s code path has passed quality assessment.
Optimizations should also be completely documented. It is often useful to retain the previous code in comments for maintenance purposes, especially as some kinds of optimized code can be more difficult to understand (and therefore to maintain).
It is typically better (and easier) to tune multiuser applications in single-user mode first. Many multiuser applications can obtain 90% of their final tuned performance if you tune in single-user mode, and then identify and tune just a few major multiuser bottlenecks (which are typically a sort of give-and-take between single-user performance and general system throughput). Occasionally, though, there will be serious conflicts that are revealed only during multiuser testing, such as transaction conflicts that can slow an application to a crawl. These may require a redesign or rearchitecting of the application. For this reason, some basic multiuser tests should be run as early as possible to flush out potential multiuser-specific performance problems.
Tuning distributed applications requires access to the data being transferred across the various parts of the application. At the lowest level, this can be a packet sniffer on the network or server machine. One step up from this is to wrap all the external communication points of the application so that you can record all data transfers. Relay servers are also useful. These are small applications that just reroute data between two communication points. Most useful of all is a trace or debug mode in the communications layer that allows you to examine the higher-level calls and communication between distributed parts.
What to Measure
The main measurement is always wall-clock time. You should use this measurement to specify almost all benchmarks, as it’s the real-time interval that is most appreciated by the user. (There are certain situations, however, in which system throughput might be considered more important than the wall-clock time, e.g., for servers, enterprise transaction systems, and batch or background systems.)
The obvious way to measure wall-clock time is to get a timestamp
using System.currentTimeMillis(
)
and then
subtract this from a later timestamp to determine the elapsed time.
This works well for elapsed time measurements that are
not short.[5] Other types of
measurements have to be
system-specific and
often application-specific. You can measure:
CPU time (the time allocated on the CPU for a particular procedure)
The number of runnable processes waiting for the CPU (this gives you an idea of CPU contention)
Paging of processes
Memory sizes
Disk throughput
Disk scanning times
Network traffic, throughput, and latency
Transaction rates
Other system values
However, Java doesn’t provide mechanisms for measuring these values directly, and measuring them requires at least some system knowledge, and usually some application-specific knowledge (e.g., what is a transaction for your application?).
You need to be
careful when running tests with small differences in timings. The
first test is usually slightly slower than any other tests. Try
doubling the test run so that each test is run twice within the VM
(e.g., rename main( ) to maintest(
), and call maintest( ) twice from a new
main( )).
There are almost always small variations between test runs, so always use averages to measure differences and consider whether those differences are relevant by calculating the variance in the results.
For distributed applications , you need to break down measurements into times spent on each component, times spent preparing data for transfer and from transfer (e.g., marshalling and unmarshalling objects and writing to and reading from a buffer), and times spent in network transfer. Each separate machine used on the networked system needs to be monitored during the test if any system parameters are to be included in the measurements. Timestamps must be synchronized across the system (this can be done by measuring offsets from one reference machine at the beginning of tests). Taking measurements consistently from distributed systems can be challenging, and it is often easier to focus on one machine, or one communication layer, at a time. This is usually sufficient for most tuning.
Don’t Tune What You Don’t Need to Tune
The most efficient tuning you can do is not to alter what works well. As they say, “If it ain’t broke, don’t fix it.” This may seem obvious, but the temptation to tweak something just because you have thought of an improvement has a tendency to override this obvious statement.
The second most efficient tuning is to discard work that doesn’t need doing. It is not at all uncommon for an application to be started with one set of specifications and to have some of the specifications change over time. Many times the initial specifications are much more generic than the final product. However, the earlier generic specifications often still have their stamps in the application. I frequently find routines, variables, objects, and subsystems that are still being maintained but are never used and never will be used because some critical aspect is no longer supported. These redundant parts of the application can usually be chopped without any bad consequences, often resulting in a performance gain.
In general, you need to ask yourself exactly what the application is doing and why. Then question whether it needs to do it in that way, or even if it needs to do it at all. If you have third-party products and tools being used by the application, consider exactly what they are doing. Try to be aware of the main resources they use (from their documentation). For example, a zippy DLL (shared library) that is speeding up all your network transfers is using some resources to achieve that speedup. You should know that it is allocating larger and larger buffers before you start trying to hunt down the source of your mysteriously disappearing memory. Then you can realize that you need to use the more complicated interface to the DLL that restricts resource usage rather than a simple and convenient interface. And you will have realized this before doing extensive (and useless) object profiling because you would have been trying to determine why your application is being a memory hog.
When benchmarking third-party components, you need to apply a good simulation of exactly how you will use those products. Determine characteristics from your benchmarks and put the numbers into your overall model to determine if performance can be reached. Be aware that vendor benchmarks are typically useless for a particular application. Break your application down into a hugely simplified version for a preliminary benchmark implementation to test third-party components. You should make a strong attempt to include all the scaling necessary so that you are benchmarking a fully scaled usage of the components, not some reduced version that reveals little about the components in full use.
Performance Checklist
Specify the required performance.
Ensure performance objectives are clear.
Specify target response times for as much of the system as possible.
Specify all variations in benchmarks, including expected response ranges (e.g., 80% of responses for X must fall within 3 seconds).
Include benchmarks for the full range of scaling expected (e.g., low to high numbers of users, data, files, file sizes, objects, etc.).
Specify and use a benchmark suite based on real user behavior. This is particularly important for multiuser benchmarks.
Agree on all target times with users, customers, managers, etc., before tuning.
Make your benchmarks long enough: over five seconds is a good target.
Use elapsed time (wall-clock time) for the primary time measurements.
Ensure the benchmark harness does not interfere with the performance of the application.
Run benchmarks before starting tuning, and again after each tuning exercise.
Take care that you are not measuring artificial situations, such as full caches containing exactly the data needed for the test.
Break down distributed application measurements into components, transfer layers, and network transfer times.
Tune systematically: understand what affects the performance; define targets; tune; monitor and redefine targets when necessary.
Approach tuning scientifically: measure performance; identify bottlenecks; hypothesize on causes; test hypothesis; make changes; measure improved performance.
Determine which resources are limiting performance: CPU, memory, or I/O.
Accurately identify the causes of the performance problems before trying to tune them.
Use the strategy of identifying the main bottlenecks, fixing the easiest, then repeating.
Don’t tune what does not need tuning. Avoid “fixing” nonbottlenecked parts of the application.
Measure that the tuning exercise has improved speed.
Target one bottleneck at a time. The application running characteristics can change after each alteration.
Improve a CPU limitation with faster code, better algorithms, and fewer short-lived objects.
Improve a system-memory limitation by using fewer objects or smaller long-lived objects.
Improve I/O limitations by targeted redesigns or speeding up I/O, perhaps by multithreading the I/O.
Work with user expectations to provide the appearance of better performance.
Hold back releasing tuning improvements until there is at least a 20% improvement in response times.
Avoid giving users a false expectation that a task will be finished sooner than it will.
Reduce the variation in response times. Bear in mind that users perceive the mean response time as the actual 90th percentile value of the response times.
Keep the user interface responsive at all times.
Aim to always give user feedback. The interface should not be dead for more than two seconds when carrying out tasks.
Provide the ability to abort or carry on alternative tasks.
Provide user-selectable tuning parameters where this makes sense.
Use threads to separate potentially blocking functions.
Calculate “look-ahead” possibilities while the user response is awaited.
Provide partial data for viewing as soon as possible, without waiting for all requested data to be received.
Cache locally items that may be looked at again or recalculated.
Quality-test the application after any optimizations have been made.
Document optimizations fully in the code. Retain old code in comments.
[1] Introduction to Computer Performance Analysis with Mathematica (Academic Press).
[2] You can search the Web for “java+perf+test” to find performance-testing tools. In addition, some Java profilers are listed in Chapter 19.
[3] JDK 1.3 introduced a
java.awt.Robot
class, which provides for generating
native system-input events, primarily to support automated testing of
Java GUIs.
[4] Jon Bentley, “Code Tuning in Context,” Dr. Dobb’s Journal, May 1999. An empty loop in C ran slower than one that contained an integer increment operation.
[5]
System.currentTimeMillis(
) can take up to half a millisecond to execute. Any
measurement including the two calls needed to measure the time
difference should be over an interval greater than 100 milliseconds
to ensure that the cost of the System.currentTimeMillis(
) calls are less than 1% of the total measurement. I
generally recommend that you do not make more than one time
measurement (i.e., two calls to System.currentTimeMillis(
)) per second.
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