Thread safety analysis.
- C/C++/ObjC: Yes
- Java: Yes
RacerD finds data races in your C++ and Java code. This page gives a more in-depth explanation of how the analysis works for Java code, but may be less complete than the Thread Safety Violation bug description page.
To run the analysis, you can use plain
infer (to run RacerD along with other
analyses that are run by default) or
infer --racerd-only (to run only RacerD).
For example, the command
infer --racerd-only -- javac File.java will run
RacerD on File.java.
RacerD statically analyzes Java code to detect potential concurrency bugs. This analysis does not attempt to prove the absence of concurrency issues, rather, it searches for a high-confidence class of data races. At the moment RacerD concentrates on race conditions between methods in a class that is itself intended to be thread safe. A race condition occurs when there are two concurrent accesses to a class member variable that are not separated by mutual exclusion, and at least one of the accesses is a write. Mutual exclusion can be ensured by synchronization primitives such as locks, or by knowledge that both accesses occur on the same thread.
Triggering the analysis
RacerD doesn't try to check all code for concurrency issues; it only looks at
code that it believes can run in a concurrent context. There are two signals
that RacerD looks for: (1) Explicitly annotating a class/method with
@ThreadSafe and (2) using a lock via the
synchronized keyword. In both
cases, RacerD will look for concurrency issues in the code containing the signal
and all of its dependencies. In particular, it will report races between any
private methods of the same class that can peform conflicting accesses.
Annotating a class/interface with
@ThreadSafe also triggers checking for all
of the subclasses of the class/implementations of the interface.
Let's take a look at the different types of concurrency issues that RacerD
flags. Two of the warning types are data races (
Unprotected write and
Read/write race), and the third warning type encourages adding
annotations to interfaces to trigger additional checking.
RacerD will report an unprotected write when one or more writes can run in parallel without synchronization. These come in two flavors: (1) a self-race (a write-write race that occurs due to a method running in parallel with itself) and (2) two conflicting writes to the same location. Here's an example of the self-race flavor:
Dinner will generate the following report on the public method
There may be a Thread Safety Violation: makeDinner() indirectly writes to mTemperature outside of synchronization.
This warning can be fixed by synchronizing the access to
suppressing the warning by annotating the
Dinner class or
@ThreadSafe(enableChecks = false).
We sometimes need to protect read accesses as well as writes. Consider the following class with unsynchronized methods.
If you run the
withdraw() method in parallel with itself or with
you can get unexpected results here. For instance, if the stored balance is 11
and you run
withdraw(10) in parallel with itself you can get a negative
balance. Furthermore, if you synchronize only the write statement
mBalance -= amount, then you can still get this bad result. The reason is that
there is a read/write race between the boolean condition
mBalance - amount >= 0 and the writes. RacerD will duly warn
Read/Write race. Public method int Account.withdraw(int) reads from field Account.mBalance. Potentially races with writes in methods void Account.deposit(int), int Account.withdraw(int)
on the line with this boolean condition.
A solution to the threading problem here is to make both methods
to wrap both read and write accesses, or to use an
mBalance rather than an ordinary
Interface not thread-safe
In the following code, RacerD will report an
Interface not thread-safe warning
on the call to
The way to fix this warning is to add a
@ThreadSafe annotation to the
I, which will enforce the thread-safety of each of the
You might wonder why it's necessary to annotate
I -- can't RacerD just look at
all the implementations of
i at the call site for
bar? Although this is a
fine idea idea in principle, it's a bad idea in practice due to a (a) separate
compilation and (b) our diff-based deployment model. In the example above, the
compiler doesn't have to know about all implementations (or indeed, any
I at the time it compiles this code, so there's no
guarantee that RacerD will know about or be able to check all implementations of
I. That's (a). For (b), say that we check that all implementations of
thread-safe at the time this code is written, but we don't add the annotation.
If someone else comes along and adds a new implementation of
I that is not
thread-safe, RacerD will have no way of knowing that this will cause a potential
foo. But if
I is annotated, RacerD will enforce that all new
I are thread-safe, and
foo will remain bug-free.
Annotations to help RacerD understand your code
Getting started with RacerD doesn't require any annotations at all -- RacerD
will look at your usage of locks and figure out what data is not guarded
consistently. But increasing the coverage and signal-to-noise ratio may require
@ThreadSafe annotations along with some of the other annotations
described below. Most of annotations described below can be used via the Maven
Central package available
The intuitive idea of thread-safety is that a class is impervious to concurrency
issues for all concurrent contexts, even those that have not been written yet
(it is future-proof). RacerD implements this by naively assuming that any method
can potentially be called on any thread. You may determine, however, that an
object, method, or field is only ever accessed on a single thread during program
execution. Annotating such elements with
@ThreadConfined informs RacerD of
this restriction. Note that a thread-confined method cannot race with itself but
it can still race with other methods.
In this example, both
mCache. But there's no
possibility of a race between the two methods because both of them will run
sequentially on the UI thread. Adding a
annotation to these methods will stop it from warning that there is a race on
mCache. We could also choose to add a
@ThreadConfined annotation to
Not all races are bugs; a race can be benign. Consider the following:
This code caches the result of an expensive network call that checks whether the
current user should be shown an experimental feature. This code looks racy, and
indeed it is: if two threads execute
shouldShowFeature() at the same time, one
mShouldShowFeature at the same time the other is writing it.
However, this is actually a benign race that the programmer intentionally
allows for performance reasons. The reason this code is safe is that the
programmer knows that
askNetworkIfShouldShowFeature() will always return the
same value in the same run of the app. Adding synchronization would remove the
race, but acquiring/releasing locks and lock contention would potentially slow
down every call to
shouldShowFeature(). The benign race approach makes every
call after the first fast without changing the safety of the code.
RacerD will report a race on this code by default, but adding the
@Functional annotation to askNetworkIfShouldShowFeature() informs RacerD that
the function is always expected to return the same value. This assumption allows
RacerD to understand that this particular code is safe, though it will still
(correctly) warn if
mShouldShowFeature is read/written elsewhere.
Be sure not to use the
@Functional pattern for singleton instantiation, as
it's possible the "singleton" can be constructed more than once.
RacerD does not warn on unprotected writes to owned objects. An object is
owned if it has been freshly allocated in the current thread and has not escaped
to another thread. RacerDf automatically tracks ownership in most cases, but it
needs help with
interface methods that return ownership:
RacerD reports races between any two non
-private methods of a class that may
run in a concurrent context. Sometimes, a RacerD report may be false because one
of the methods cannot actually be called from outside the current class. One fix
is making the method
private to enforce this, but this might break unit tests
that need to call the method in order to test it. In this case, the
@VisibleForTesting annotation will allow RacerD to consider the method as
private will still allowing it to be called from the unit test:
Unlike the other annotations shown here, this one lives in Android.
An important feature of RacerD is that it finds races by analyzing not just one file or class, but by looking at memory accesses that occur after going through several procedure calls. It handles this even between classes and between files.
Here is a very basic example
B is not annotated
@ThreadSafe and does not have any locks, so RacerD
does not directly look for threading issues there. However, method
A has a potential self-race, if it is run in parallel with itself and
the same argument for each call. RacerD discovers this.
RacerD does this sort of reasoning using what is known as a compositional
inteprocedural analysis. There, each method is analyzed independently of its
context to produce a summary of the behaviour of the procedure. In this case the
m1()' andmeth()' include information as follows.
The descriptions here are cryptic and do not include all the information in the summaries, but the main point is that you can use RacerD to look for races in codebases where the mutations done by threads might occur only after a chain of procedure calls.
Reasoning about concurrency divides into bug detection and proving absence of bugs. RacerD is on the detection side of reasoning.
The rapid growth in the number of interleavings is problematic for tools that attempt exhaustive exploration. With just 150 instructions for two threads, the number 10^88 of interleavings is more that the estimated number of atoms in the known universe. There has been important work which uses various techniques to attempt to reduce the number of interleavings while still in principle covering all possibilities, but scale is still a challenge. Note that RacerD is not exhaustive: it has false negatives (missed bugs). But in compensation it is fast, and effective (it finds bugs in practice).
Static analysis for concurrency has attracted a lot of attention from researchers, but difficulties with scalability and precision have meant that previous techniques have had little industrial impact. Automatic static race detection itself has seen significant work. The most advanced approaches, exemplified by the Chord tool, often use a whole-program analysis paired with a sophisticated alias analysis, two features we have consciously avoided. Generally speaking, the leading research tools can be more precise, but RacerD is faster and can operate without the whole program: we have opted to go for speed in a way that enables industrial deployment on a large, rapidly changing codebase, while trying to use as simple techniques as possible to cover many (not all) of the patterns covered by slower but precise research tools.
An industrial static analysis tool from Contemplate also targets @ThreadSafe annotations, but limits the amount of inter-procedural reasoning: “This analysis is interprocedural, but to keep the overall analysis scalable, only calls to private and protected methods on the same class are followed”. RacerD does deep, cross-file and cross-class inter-procedural reasoning, and yet still scales; the inter-class capability was one of the first requests from Facebook engineers. A separate blog post looked at 100 recent data race fixes in Infer's deployment in various bug categories, and for data races observed that 53 of them were inter-file (and thus involving multiple classes). See above for an example of RacerD's interprocedural capabilities.
One reaction to the challenge of developing effective static race detectors has been to ask the programmer to do more work to help the analyzer. Examples of this approach include the Clang Thread Safety Analyzer, the typing of locks in Rust, and the use/checking of @GuardedBy annotations in Java including in Google's Error Prone analyzer. When lock annotations are present they make the analyzer's life easier. It is possible to have a very effective race analysis without decreeing that such annotations must be present. This was essential for our deployment, since requiring lock annotations would have been a show stopper for converting many thousands of lines of code to a concurrent context. We believe that this finding should be transportable to new type systems and language designs, as well as to other analyses for existing languages.
Another reaction to difficulties in static race detection has been to instead develop dynamic analyses, automatic testing tools which work by running a program to attempt to find flaws. Google's Thread Sanitizer is a widely used and mature tool in this area, which has been used in production to find many bugs in C-family languages. The Thread Sanitizer authors explicitly call out limitations with static race analyzers as part of their motivation: “It seems unlikely that static detectors will work effectively in our environment: Google’s code is large and complex enough that it would be expensive to add the annotations required by a typical static detector”.
We have worked to limit the annotations that RacerD needs, for reasons similar those expressed by the Thread Sanitizer authors. And we have sought to bring the complementary benefits of static analysis — possibility of cheaper analysis and fast reporting, and ability to analyze code before it is placed in a context to run — to race detection. But we are interested as well in the future in leveraging ideas in the dynamic techniques to improve or add to our analysis for race detection.
There are a number of known limitations to the design of the race detector.
- It looks for races involving syntactically identical access paths, and misses races due to aliasing
- It misses races that arise from a locally declared object escaping its scope
- It uses a boolean locks abstraction, and so misses races where two accesses are mistakenly protected by different locks
- It assumes a deep ownership model, which misses races where local objects refer to or contain non-owned objects.
- It avoids reasoning about weak memory and Java's volatile keyword
Most of these limitations are consistent with the design goal of reducing false positives, even if they lead to false negatives. They also allow technical tradeoffs which are different than if we were to favour reduction of false negatives over false positives.
A different kind of limitation concerns the bugs searched for: Data races are the most basic form of concurrency error, but there are many types of concurrency issues out there that RacerD does not check for (but might in the future). Examples include deadlock, atomicity, and check-then-act bugs (shown below). You must look for these bugs yourself!
synchronized blindly as a means to fix every unprotected write
or read is not always safe. Even with RacerD, finding, understanding, and fixing
concurrency issues is difficult. If you would like to learn more about best
practices, Java Concurrency in Practice is an excellent
List of Issue Types
The following issue types are reported by this checker: