Architecture and Hardware Practice

The API Performance Contract

How can the expected interactions between caller and implementation be guaranteed?
  1. Introduction
  2. A Performance Taxonomy
  3. Segmenting APIs By Performance
  4. Why Does the Performance Contract Matter?
  5. Making Composition Work
  6. Acknowledgments
  7. References
  8. Authors
  9. Sidebar: 1. Forming Your Own Model of Performance
  10. Sidebar: 2. Function Creep
  11. Sidebar: 3. When the Performance Contract Breaks
  12. Sidebar: 4. The Long Story of malloc and Dynamic Memory Allocation
performance illustration

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When you call functions in an API, you expect them to work correctly; sometimes this expectation is called a contract between the caller and the implementation. Callers also have performance expectations about these functions, and often the success of a software system depends on the API meeting these expectations.

So there is a performance contract as well as a correctness contract. The performance contract is usually implicit, often vague, and sometimes breached (by caller or implementation). How can this aspect of API design and documentation be improved?

Any significant software system today depends on the work of others: sure, you write some code, but you call functions in the operating system and in a variety of software packages through APIs that cut down on the amount of code you must write. In some cases you even outsource work to distant servers hooked to you by cranky networks. You depend on these functions and services for correct operation, but you also depend on them for performing well enough so the overall system performs well. There is bound to be performance variation in complex systems that involve paging, network delays, sharing resources such as disks, and so on. Even in simple setups such as a stand-alone computer with all program and data in memory, however, you can be surprised when an API or operating system does not meet performance expectations.

People are accustomed to speaking of the contract between an application and the implementation of an API to describe correct behavior when an API function is invoked. The caller must meet certain initial requirements, and the function is then required to perform as specified. (These dual responsibilities are like the pre- and post-conditions that appear in Floyd-Hoare logic for proving program correctness.) While today’s API specifications do not make correctness criteria explicit in a way that leads to correctness proofs, the type declarations and textual documentation for an API function strive to be unambiguous about its logical behavior. If a function is described as “add the element e to the end of list l” where e and l are described by types in the calling sequence, then the caller knows what behavior to expect.

There is more to an API function than correctness, however. What resources does it consume, and how fast is it? People often make assumptions based on their own judgments of what the implementation of the function being performed should be. Adding an element to the end of a list should be “cheap.” Reversing a list might be “a linear function of list length.” For many simple functions, these intuitions suffice to avoid trouble—though not always, as described later in this article. For functions of any complexity, however, such as “draw string s in font f at (x,y) in window w,” or “find the average of the values stored on a remote file,” there is room for surprise if not frustration at the performance. Further, the API documentation provides no hints about which functions are cheap and which are costly.

To complicate matters, after you have tuned your application to the performance characteristics of an API, a new version of the API implementation arrives or a new remote server stores the data file, and rather than overall performance increasing (the eternal hope for something new), it tanks. In short, the performance contracts that accompany the composition of software components in systems deserve more attention.

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A Performance Taxonomy

Programmers begin building intuitive API performance models early on (see Sidebar 1). To be useful, the model need not be very accurate. Here is a simple taxonomy:

Always cheap (examples: toupper, isdigit, java.util.HashMap.get). The first two functions are always cheap, usually inlined table lookups. Lookup in a properly sized hash table is expected to be fast, but a hash collision may slow down an occasional access.

Usually cheap (examples: fgetc, java.util.HashMap.put). Many functions are designed to be fast most of the time but require occasionally invoking more complex code; fgetc must occasionally read a new character buffer. Storing a new item in a hash table may make the table so full the implementation enlarges it and rehashes all its entries.

Some libraries offer more than one way of performing a function, usually because the alternatives have quite different performance.

The documentation for java.util. HashMap makes a commendable start at exposing a performance contract: “This implementation provides constant-time performance for the basic operations (get and put), assuming the hash function disperses the elements properly among the buckets. Iteration over collection views requires time proportional to the “capacity” of the HashMap…”3

The performance of fgetc depends on properties of the underlying stream. If it is a disk file, then the function will normally read from a user-memory buffer without requiring an operating-system call, but it must occasionally invoke the operating system to read a new buffer. If it is reading input from a keyboard, then the implementation probably invokes the operating system for each character read.

Predictable (examples: qsort, regexec). Performance of these functions varies with properties of their arguments, (for example, the size of an array to sort or the length of a string to search). These functions are often data-structure or common-algorithm utilities that use well-known algorithms and do not require system calls. You can usually judge performance based on expectations for the underlying algorithms (for example, that sort will take n log n time). When using complex data structures (for example, B-trees) or generic collections (where it may be difficult to identify the underlying concrete implementation), it may be more difficult to estimate performance. It is important to understand the predictability may be only probable; regexec is generally predictable based on its input, but there are pathological expressions that will cause exponential time explosions.

Unknown (examples: fopen, fseek, pthread_create, many “initialization” functions, and any call that traverses a network). These functions are not cheap and often have high variance in their performance. They allocate resources from pools (threads, memory, disk, operating system objects), often requiring exclusive access to shared operating system or I/O resources. There is usually substantial initialization required. Calling across a network is always expensive (relative to local access), but the variation in the expense can be larger still, making the formation of a reasonable performance model much more difficult.

Threads libraries are easy marks for performance issues. The Posix standard took many years to settle down, and implementations are still plagued with problems.6 Portability of threaded applications remains dicey. Some of the reasons for difficulties with threads are: the need for tight integration with operating systems, almost all of which (including Unix and Linux) were not designed originally with threads in mind; interaction with other libraries, especially making functions thread-safe and dealing with performance problems thereby induced; and several different design points for threads implementations, loosely characterized as lightweight and heavyweight.

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Segmenting APIs By Performance

Some libraries offer more than one way of performing a function, usually because the alternatives have quite different performance.

Returning to sidebar 1, note that most programmers are told that using a library function to fetch each character is not the fastest way (even if the code for the function is inlined to avoid the function-call overhead). The more performance-conscious will read a very large array of characters and extract each one using array or pointer operations in the programming language. In extreme cases the application can map pages of the file into memory pages to avoid copying data into an array. In return for performance, these functions put a larger burden on the caller (for example, to get buffer arithmetic correct and to make the implementation consistent with other library calls such as a call to fseek that would require adjusting buffer pointers and perhaps contents).

Programmers are always counseled to avoid premature optimization in their programs, thus delaying extreme revisions until simpler ones have proven inadequate. The only way to be sure about performance is to measure it. Programmers usually write the entire program before confronting mismatches between performance expectations (or estimates) and the reality delivered by an implementation.

Performance variation. The performance of “predictable” functions can be estimated from properties of their arguments. The “unknown” functions may also vary greatly depending on what they are asked to do. The time required to open a stream on a storage device will certainly depend on access times, and perhaps data-transfer rates, of the underlying device. Storage accessed via network protocols may be especially costly; certainly it will be variable.

Many cheap functions are cheap only most of the time, or they have an expected cheap cost. A “get a character” routine must occasionally refill a buffer using an operating system call that always takes much longer than fetching a character from a full buffer—and might occasionally take a very long time indeed (for example, reading a file on a heavily loaded file server or from a disk that is dying and will succeed only after multiple read retries).

The functions with “unknown” performance are likely to exhibit wide performance variations for a variety of reasons. One reason is function creep (see sidebar 2), where a generic function becomes more powerful over time. I/O streams are a good example: a call to open a stream invokes very different code in libraries and operating systems depending on the type of stream being opened (local disk file, network-served file, pipe, network stream, string in memory, and so on). As the range of I/O devices and types of files expands, the variance of the performance can only increase. The common life cycle of most APIs—to add functionality incrementally over time—inevitably increases performance variations.

A large source of variation is differences between ports of libraries to different platforms. Of course, the underlying speed of the platform—both hardware and operating system—will vary, but library ports can lead to changes in the relative performance of functions within an API or performance across APIs. It’s not uncommon for a quick-and-dirty initial port to have many performance problems, which are gradually fixed. Some libraries, such as those for handling threads, have notoriously wide porting-performance variation. Thread anomalies can surface as extreme behaviors—an impossibly slow application or even deadlock.

These variations are one reason why constructing precise performance contracts is difficult. It is usually not necessary to know performance with great precision, but extreme variations from expected behavior can cause problems.

Failure performance. The specifications for an API include details of behavior when a call fails. Returning an error code and throwing an exception are common ways of telling the caller the function did not succeed. As with specifications for normal behavior, however, the performance of the failure is not specified. Here are three important cases:

  • Fail fast. A call fails quickly—as fast or faster than its normal behavior. Calling sqrt(-1) fails fast. Even when a malloc call fails because no more memory is available, the call should return about as fast as any malloc call that must request more memory from the operating system. A call to open a stream for reading a nonexistent disk file is likely to return about as fast as a successful call.
  • Fail slow. Sometimes a call fails very slowly—so slowly the application program might have wanted to proceed in other ways. For example, a request to open a network connection to another computer may fail only after several long time-outs expire.
  • Fail forever. Sometimes a call simply stalls and does not allow the application program to proceed at all. For example, a call whose implementation waits on a synchronization lock that is never released may never return.

Intuition about failure performance is rarely as good as that for normal performance. One reason is simply that writing, debugging, and tuning programs provides far less experience with failure events than with normal events. Another is that a function call can fail in many, many ways, some of them fatal, and not all described in the API’s specs. Even exception mechanisms, which are intended to describe more precisely the handling of errors, do not make all possible exceptions visible. Moreover, as library functionality increases, so do the opportunities for failure. For example, APIs that wrap network services (ODBC, JDBC, UPnP, …) intrinsically subscribe to the vast array of network-failure mechanisms.

A diligent application programmer uses massive artillery to deal with unlikely failures. A common technique is to surround rather large parts of a program with try...catch blocks that can retry whole sections that fail. Interactive programs can try to save a user’s work using a giant try... catch around the entire program, the effect of which is to mitigate failure of the main program by saving, in a disk file, key logs, or data structures that record the effects of the work done by the user before the failure.

The only way to deal with stalls or deadlocks is to set up a watchdog thread, which expects a properly running application program to check in periodically with the watchdog, saying, in effect, “I’m still running properly.” If too much time elapses between check-ins, the watchdog takes action—for example, saving state, aborting the main thread, and restarting the entire application. If an interactive program responds to a user’s command by calling functions that may fail slowly, it may use a watchdog to abort the entire command and return to a known state that allows the user to proceed with other commands. This gives rise to a defensive programming style that plans for the possible abortion of every command.

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Why Does the Performance Contract Matter?

Why must an API adhere to a performance contract? Because major structures of an application program may depend on adherence to such a contract. A programmer chooses APIs, data structures, and overall program structures based in part on expectations of API performance. If the expectations or the performance are grossly wrong, then the programmer cannot recover merely by tuning API calls but must rewrite large, possibly major, portions of the program. The defensive structure of the interactive program previously mentioned is another example.

In effect, a serious violation of the performance contract leads to a composition failure: a program written to the contract cannot be mated to (composed with) an implementation that fails to uphold the contract.

Of course, there are many programs whose structure and performance are influenced very little by library performance (scientific computations and large simulations are often in this category); however, much of today’s “routine IT,” especially the software that pervades Web-based services, makes extensive use of libraries whose performance is vital to overall performance.

Many programmers expect computers and their software to get faster with time—uniformly.

Even small variations in performance can cause major changes in the perception of the program by its users. This is especially true in programs dealing with various kinds of media. Dropping occasional frames of a video stream might be acceptable (indeed, more acceptable than allowing the frame rate to lag other media), but humans have evolved to detect even slight dropouts in audio, so minor changes in the performance of that type of media may have major impacts on the acceptability of the overall program. Such worries have led to considerable interest in notions of quality of service, which in many ways is the attempt to ensure performance at a high level.

How can contract violations be avoided? Although performance-contract violations are rare and rarely catastrophic, paying attention to the role of performance when using a software library can help lead to more robust software. Here are some precautions and strategies to use:

  1. Choose APIs and program structures carefully. If you have the luxury of writing a program from scratch, ponder performance-contract implications as you begin. If the program starts out as a prototype and then remains in service for a while, it will undoubtedly be rewritten at least once; a rewrite is an opportunity to rethink API and structure choices.
  2. API implementers have an obligation to present a consistent performance contract as new versions and ports are released. Even a new experimental API will garner users who will begin to derive a performance model of the API. Thereafter, changing the performance contract will surely irritate developers and may cause them to rewrite their programs.
      Once an API is mature, it is essential the performance contract not change. In fact, the most universal APIs (for example, libc) arguably have become so in part because their performance contracts have been stable during the evolution of the API. The same goes for ports of APIs.
      One could hope that API implementers might routinely test new versions to verify they have introduced no performance quirks. Unfortunately, such testing is rarely done. This does not mean, however, that you cannot have your own tests for the parts of an API you depend on. With a profiler, a program can often be found to rely on a small number of APIs. Writing a performance test suite that will compare new versions of a library against the recorded performance of earlier versions can give programmers an early warning the performance of their own code is going to change with the release of the new library.
      Many programmers expect computers and their software to get faster with time—uniformly. That is, they expect each new release of a library or a computer system to scale up performance of all API functions equally, especially the “cheap” ones. This is in fact very difficult for vendors to guarantee, but it so closely describes actual practice that customers believe it. Many workstation customers expected newer versions of graphics libraries, drivers, and hardware to increase performance of all graphics applications, but they were equally keen on functionality improvements of many kinds, which usually reduce performance of older functions, even if only slightly.
      One could also hope that API specifications would make the performance contract explicit, so that those using, modifying, or porting the code would honor the contract. Note that a function’s use of dynamic memory allocation, whether implicit or automatic, should be part of this documentation.
  3. Defensive programming can help. A programmer can use special care when invoking API functions with unknown or highly variable performance; this is especially true for considerations of failure performance. You can move initializations outside performance-critical areas and try to warm up any cached data an API may use (for example, fonts). APIs that exhibit large performance variance or have a lot of internally cached data could help by providing functions for passing “hints” from the application to the API about how to allocate or initialize these structures. Occasional pings to servers that are known to be contacted can establish a list of those that might be unavailable, allowing some long failure pauses to be avoided.
      One technique sometimes used in graphics applications is to do a dry run, rendering a window of graphics into an off-screen (not visible) window, merely to warm up caches of fonts and other graphics data structures.
  4. Tune parameters exposed by the API. Some libraries offer explicit ways of influencing performance (for example, controlling the size of buffers allocated for files, the initial sizes of tables, or the size of a cache). Operating systems also provide tuning options. Adjusting these parameters can improve performance within the confines of a performance contract; tuning does not remedy gross problems but can mitigate otherwise fixed choices embedded in libraries that heavily influence performance.
      Some libraries provide alternative implementations of functions with identical semantics, usually in the form of concrete implementations of generic APIs. Tuning by choosing the best concrete implementation is usually very easy. The Java Collections package is a good example of this structure.
      Increasingly, APIs are designed to adapt to usage on the fly, freeing the programmer from choosing the best parameter settings. If a hash table gets too full, it is automatically expanded and rehashed (a virtue balanced by, alas, the performance hit of the occasional expansions). If a file is being read sequentially, then more buffers can be allocated so that it is read in larger chunks.
  5. Measure performance to verify assumptions. Common advice to programmers is to instrument key data structures to determine whether each structure is being used correctly. For example, you might measure how full a hash table is or how often hash collisions occur. Or you might verify that a structure designed to be fast for reading at the expense of write performance is actually being read more than written.
      Adding sufficient instrumentation to measure the performance of many API calls accurately is difficult, a large amount of work, and probably not worth the information it yields. Adding instrumentation on those API calls that are central to the performance of an application (assuming you can identify them and your identification is correct), however, can save a lot of time when problems arise. Note that much of this code can be reused as part of the performance monitor for the next release of the library, as mentioned earlier.
      None of this is meant to discourage dreamers from working on tools that would automate such instrumentation and measurement, or on ways to specify performance contracts so that performance measurements can establish compliance with the contract. These are not easy goals, and the return may not be huge.
      It is often possible to make performance measurements without having instrumented the software in advance (for example, by using profilers or tools such as DTrace5). These have the advantage of not requiring any work until there is a problem to track down. They can also help diagnose problems that creep in when modifications to your code or to libraries upset performance. As Programming Pearls author Jon Bentley recommends, “Profile routinely; measure performance drift from a trusted base.”1
  6. Use logs: Detect and record anomalies. Increasingly, violations of performance contracts appear in the field when distributed services are composed to form a complex system. (Note that major services offered via network interfaces sometimes have SLAs [service-level agreements] that specify acceptable performance. In many configurations, a measurement process occasionally issues service requests to check the SLA is met.) Since these services are invoked over a network connection using methods akin to API function calls (for example, remote procedure call or its variants such as XML-RPC, SOAP, or REST), the expectation of a performance contract applies. Applications detect failure of these services and often adapt gracefully. Slow response, however, especially when there are dozens of such services that depend on each other, can destroy system performance very quickly. Professionally managed services environments, such as those that provide Web services at major Internet sites, have elaborate instrumentation and tooling to monitor Web-service performance and to tackle problems that arise. The far more modest computer collections in homes also depend on such services—many that are part of the operating system on each laptop computer and some that are embedded in appliances on the network (for example, a printer, network file system, or file backup service)—but there are no aids to help detect and deal with performance problems.

It would be helpful if clients of these services made note of the performance they expect and produce log entries that help diagnose problems (this is what syslog is for). When your file backup seems unreasonably slow (one hour to back up 200MB), is it slower than yesterday? Slower than it was before the latest operating system software update? Given that several computers may be sharing the backup device, is it slower than you should expect? Or is there some logical explanation (for example, the backup system finds a damaged data structure and embarks on a long procedure to rebuild it)?

Diagnosing performance problems in compositions of opaque software (where no source code is available, and there are no details of the modules and APIs that form the composition) requires the software play a role in reporting performance and detecting problems. While you cannot address performance problems within the software itself (it is opaque), you can make adjustments or repairs to operating systems and the network. If the backup device is slow because its disk is almost full, then you can surely add more disk space. Good logs and associated tools would help; sadly, logs are an undervalued and neglected area of computer system evolution.

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Making Composition Work

Today’s software systems depend on composing independently developed components in such a way that they work—meaning they perform the desired computations at acceptable speed. The dream of statically checking a composition to guarantee the composition will be correct (“correct by composition”) remains elusive. Instead, software-engineering practice has developed methods for testing components and compositions that work pretty well. Every time an application binds to a dynamic library or launches atop an operating-system interface, the correctness of the composition is required.

Despite its importance, the performance of a composition—whether the client and the provider of an interface adhere to the performance contract between them—has been given short shrift. True, this contract is not as important as the correctness contract, but the full power of composition depends on it.

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Thanks to Butler Lampson, who coined the term cheap to describe functions whose performance is fast enough to dispel all attempts to optimize them away; it has a wonderful ring of “economical yet serviceable,” Eric Allman, for his helpful comments, and Jon Bentley, an inveterate performance debugger.

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