Julia provides a variety of control flow constructs:
- Compound Expressions: begin and (;).
- Conditional Evaluation: if-elseif-else and ?: (ternary operator).
- Short-Circuit Evaluation: &&, || and chained comparisons.
- Repeated Evaluation: Loops: while and for.
- Exception Handling: try-catch, error and throw.
- Tasks (aka Coroutines): yieldto.
The first five control flow mechanisms are standard to high-level programming languages. Tasks are not so standard: they provide non-local control flow, making it possible to switch between temporarily-suspended computations. This is a powerful construct: both exception handling and cooperative multitasking are implemented in Julia using tasks. Everyday programming requires no direct usage of tasks, but certain problems can be solved much more easily by using tasks.
Sometimes it is convenient to have a single expression which evaluates several subexpressions in order, returning the value of the last subexpression as its value. There are two Julia constructs that accomplish this: begin blocks and (;) chains. The value of both compound expression constructs is that of the last subexpression. Here’s an example of a begin block:
julia> z = begin x = 1 y = 2 x + y end 3
Since these are fairly small, simple expressions, they could easily be placed onto a single line, which is where the (;) chain syntax comes in handy:
julia> z = (x = 1; y = 2; x + y) 3
This syntax is particularly useful with the terse single-line function definition form introduced in Functions. Although it is typical, there is no requirement that begin blocks be multiline or that (;) chains be single-line:
julia> begin x = 1; y = 2; x + y end 3 julia> (x = 1; y = 2; x + y) 3
Conditional evaluation allows portions of code to be evaluated or not evaluated depending on the value of a boolean expression. Here is the anatomy of the if-elseif-else conditional syntax:
if x < y println("x is less than y") elseif x > y println("x is greater than y") else println("x is equal to y") end
If the condition expression x < y is true, then the corresponding block is evaluated; otherwise the condition expression x > y is evaluated, and if it is true, the corresponding block is evaluated; if neither expression is true, the else block is evaluated. Here it is in action:
julia> function test(x, y) if x < y println("x is less than y") elseif x > y println("x is greater than y") else println("x is equal to y") end end test (generic function with 1 method) julia> test(1, 2) x is less than y julia> test(2, 1) x is greater than y julia> test(1, 1) x is equal to y
The elseif and else blocks are optional, and as many elseif blocks as desired can be used. The condition expressions in the if-elseif-else construct are evaluated until the first one evaluates to true, after which the associated block is evaluated, and no further condition expressions or blocks are evaluated.
Note that very short conditional statements (one-liners) are frequently expressed using Short-Circuit Evaluation in Julia, as outlined in the next section.
Unlike C, MATLAB, Perl, Python, and Ruby — but like Java, and a few other stricter, typed languages — it is an error if the value of a conditional expression is anything but true or false:
julia> if 1 println("true") end ERROR: type: non-boolean (Int64) used in boolean context
This error indicates that the conditional was of the wrong type: Int64 rather than the required Bool.
The so-called “ternary operator”, ?:, is closely related to the if-elseif-else syntax, but is used where a conditional choice between single expression values is required, as opposed to conditional execution of longer blocks of code. It gets its name from being the only operator in most languages taking three operands:
a ? b : c
The expression a, before the ?, is a condition expression, and the ternary operation evaluates the expression b, before the :, if the condition a is true or the expression c, after the :, if it is false.
The easiest way to understand this behavior is to see an example. In the previous example, the println call is shared by all three branches: the only real choice is which literal string to print. This could be written more concisely using the ternary operator. For the sake of clarity, let’s try a two-way version first:
julia> x = 1; y = 2; julia> println(x < y ? "less than" : "not less than") less than julia> x = 1; y = 0; julia> println(x < y ? "less than" : "not less than") not less than
If the expression x < y is true, the entire ternary operator expression evaluates to the string "less than" and otherwise it evaluates to the string "not less than". The original three-way example requires chaining multiple uses of the ternary operator together:
julia> test(x, y) = println(x < y ? "x is less than y" : x > y ? "x is greater than y" : "x is equal to y") test (generic function with 1 method) julia> test(1, 2) x is less than y julia> test(2, 1) x is greater than y julia> test(1, 1) x is equal to y
To facilitate chaining, the operator associates from right to left.
It is significant that like if-elseif-else, the expressions before and after the : are only evaluated if the condition expression evaluates to true or false, respectively:
julia> v(x) = (println(x); x) v (generic function with 1 method) julia> 1 < 2 ? v("yes") : v("no") yes "yes" julia> 1 > 2 ? v("yes") : v("no") no "no"
Short-circuit evaluation is quite similar to conditional evaluation. The behavior is found in most imperative programming languages having the && and || boolean operators: in a series of boolean expressions connected by these operators, only the minimum number of expressions are evaluated as are necessary to determine the final boolean value of the entire chain. Explicitly, this means that:
- In the expression a && b, the subexpression b is only evaluated if a evaluates to true.
- In the expression a || b, the subexpression b is only evaluated if a evaluates to false.
The reasoning is that a && b must be false if a is false, regardless of the value of b, and likewise, the value of a || b must be true if a is true, regardless of the value of b. Both && and || associate to the right, but && has higher precedence than || does. It’s easy to experiment with this behavior:
julia> t(x) = (println(x); true) t (generic function with 1 method) julia> f(x) = (println(x); false) f (generic function with 1 method) julia> t(1) && t(2) 1 2 true julia> t(1) && f(2) 1 2 false julia> f(1) && t(2) 1 false julia> f(1) && f(2) 1 false julia> t(1) || t(2) 1 true julia> t(1) || f(2) 1 true julia> f(1) || t(2) 1 2 true julia> f(1) || f(2) 1 2 false
You can easily experiment in the same way with the associativity and precedence of various combinations of && and || operators.
This behavior is frequently used in Julia to form an alternative to very short if statements. Instead of if <cond> <statement> end, one can write <cond> && <statement> (which could be read as: <cond> and then <statement>). Similarly, instead of if ! <cond> <statement> end, one can write <cond> || <statement> (which could be read as: <cond> or else <statement>).
For example, a recursive factorial routine could be defined like this:
julia> function factorial(n::Int) n >= 0 || error("n must be non-negative") n == 0 && return 1 n * factorial(n-1) end factorial (generic function with 1 method) julia> factorial(5) 120 julia> factorial(0) 1 julia> factorial(-1) ERROR: n must be non-negative in factorial at none:2
Boolean operations without short-circuit evaluation can be done with the bitwise boolean operators introduced in Mathematical Operations and Elementary Functions: & and |. These are normal functions, which happen to support infix operator syntax, but always evaluate their arguments:
julia> f(1) & t(2) 1 2 false julia> t(1) | t(2) 1 2 true
Just like condition expressions used in if, elseif or the ternary operator, the operands of && or || must be boolean values (true or false). Using a non-boolean value is an error:
julia> 1 && 2 ERROR: type: non-boolean (Int64) used in boolean context
Repeated Evaluation: Loops¶
There are two constructs for repeated evaluation of expressions: the while loop and the for loop. Here is an example of a while loop:
julia> i = 1; julia> while i <= 5 println(i) i += 1 end 1 2 3 4 5
The while loop evaluates the condition expression (i <= 5 in this case), and as long it remains true, keeps also evaluating the body of the while loop. If the condition expression is false when the while loop is first reached, the body is never evaluated.
The for loop makes common repeated evaluation idioms easier to write. Since counting up and down like the above while loop does is so common, it can be expressed more concisely with a for loop:
julia> for i = 1:5 println(i) end 1 2 3 4 5
Here the 1:5 is a Range object, representing the sequence of numbers 1, 2, 3, 4, 5. The for loop iterates through these values, assigning each one in turn to the variable i. One rather important distinction between the previous while loop form and the for loop form is the scope during which the variable is visible. If the variable i has not been introduced in an other scope, in the for loop form, it is visible only inside of the for loop, and not afterwards. You’ll either need a new interactive session instance or a different variable name to test this:
julia> for j = 1:5 println(j) end 1 2 3 4 5 julia> j ERROR: j not defined
See Scope of Variables for a detailed explanation of variable scope and how it works in Julia.
In general, the for loop construct can iterate over any container. In these cases, the alternative (but fully equivalent) keyword in is typically used instead of =, since it makes the code read more clearly:
julia> for i in [1,4,0] println(i) end 1 4 0 julia> for s in ["foo","bar","baz"] println(s) end foo bar baz
Various types of iterable containers will be introduced and discussed in later sections of the manual (see, e.g., Multi-dimensional Arrays).
It is sometimes convenient to terminate the repetition of a while before the test condition is falsified or stop iterating in a for loop before the end of the iterable object is reached. This can be accomplished with the break keyword:
julia> i = 1; julia> while true println(i) if i >= 5 break end i += 1 end 1 2 3 4 5 julia> for i = 1:1000 println(i) if i >= 5 break end end 1 2 3 4 5
The above while loop would never terminate on its own, and the for loop would iterate up to 1000. These loops are both exited early by using the break keyword.
In other circumstances, it is handy to be able to stop an iteration and move on to the next one immediately. The continue keyword accomplishes this:
julia> for i = 1:10 if i % 3 != 0 continue end println(i) end 3 6 9
This is a somewhat contrived example since we could produce the same behavior more clearly by negating the condition and placing the println call inside the if block. In realistic usage there is more code to be evaluated after the continue, and often there are multiple points from which one calls continue.
Multiple nested for loops can be combined into a single outer loop, forming the cartesian product of its iterables:
julia> for i = 1:2, j = 3:4 println((i, j)) end (1,3) (1,4) (2,3) (2,4)
When an unexpected condition occurs, a function may be unable to return a reasonable value to its caller. In such cases, it may be best for the exceptional condition to either terminate the program, printing a diagnostic error message, or if the programmer has provided code to handle such exceptional circumstances, allow that code to take the appropriate action.
Exceptions are thrown when an unexpected condition has occurred. The built-in Exceptions listed below all interrupt the normal flow of control.
For example, the sqrt function throws a DomainError() if applied to a negative real value:
julia> sqrt(-1) ERROR: DomainError sqrt will only return a complex result if called with a complex argument. try sqrt(complex(x)) in sqrt at math.jl:284
You may define your own exceptions in the following way:
julia> type MyCustomException <: Exception end
The throw function¶
Exceptions can be created explicitly with throw. For example, a function defined only for nonnegative numbers could be written to throw a DomainError if the argument is negative:
julia> f(x) = x>=0 ? exp(-x) : throw(DomainError()) f (generic function with 1 method) julia> f(1) 0.36787944117144233 julia> f(-1) ERROR: DomainError in f at none:1
Note that DomainError without parentheses is not an exception, but a type of exception. It needs to be called to obtain an Exception object:
julia> typeof(DomainError()) <: Exception true julia> typeof(DomainError) <: Exception false
Additionally, some exception types take one or more arguments that are used for error reporting:
julia> throw(UndefVarError(:x)) ERROR: x not defined
This mechanism can be implemented easily by custom exception types following the way UndefVarError is written:
julia> type UndefVarError <: Exception var::Symbol end julia> showerror(io::IO, e::UndefVarError) = print(io, e.var, " not defined")
The error function is used to produce an ErrorException that interrupts the normal flow of control.
Suppose we want to stop execution immediately if the square root of a negative number is taken. To do this, we can define a fussy version of the sqrt function that raises an error if its argument is negative:
julia> fussy_sqrt(x) = x >= 0 ? sqrt(x) : error("negative x not allowed") fussy_sqrt (generic function with 1 method) julia> fussy_sqrt(2) 1.4142135623730951 julia> fussy_sqrt(-1) ERROR: negative x not allowed in fussy_sqrt at none:1
If fussy_sqrt is called with a negative value from another function, instead of trying to continue execution of the calling function, it returns immediately, displaying the error message in the interactive session:
julia> function verbose_fussy_sqrt(x) println("before fussy_sqrt") r = fussy_sqrt(x) println("after fussy_sqrt") return r end verbose_fussy_sqrt (generic function with 1 method) julia> verbose_fussy_sqrt(2) before fussy_sqrt after fussy_sqrt 1.4142135623730951 julia> verbose_fussy_sqrt(-1) before fussy_sqrt ERROR: negative x not allowed in fussy_sqrt at none:1
Warnings and informational messages¶
Julia also provides other functions that write messages to the standard error I/O, but do not throw any Exceptions and hence do not interrupt execution.:
julia> info("Hi"); 1+1 INFO: Hi 2 julia> warn("Hi"); 1+1 WARNING: Hi 2 julia> error("Hi"); 1+1 ERROR: Hi in error at error.jl:21
The try/catch statement¶
The try/catch statement allows for Exceptions to be tested for. For example, a customized square root function can be written to automatically call either the real or complex square root method on demand using Exceptions :
julia> f(x) = try sqrt(x) catch sqrt(complex(x, 0)) end f (generic function with 1 method) julia> f(1) 1.0 julia> f(-1) 0.0 + 1.0im
It is important to note that in real code computing this function, one would compare x to zero instead of catching an exception. The exception is much slower than simply comparing and branching.
try/catch statements also allow the Exception to be saved in a variable. In this contrived example, the following example calculates the square root of the second element of x if x is indexable, otherwise assumes x is a real number and returns its square root:
julia> sqrt_second(x) = try sqrt(x) catch y if isa(y, DomainError) sqrt(complex(x, 0)) elseif isa(y, BoundsError) sqrt(x) end end sqrt_second (generic function with 1 method) julia> sqrt_second([1 4]) 2.0 julia> sqrt_second([1 -4]) 0.0 + 2.0im julia> sqrt_second(9) 3.0 julia> sqrt_second(-9) ERROR: DomainError sqrt will only return a complex result if called with a complex argument. try sqrt(complex(x)) in sqrt at math.jl:284 in sqrt_second at none:7
The power of the try/catch construct lies in the ability to unwind a deeply nested computation immediately to a much higher level in the stack of calling functions. There are situations where no error has occurred, but the ability to unwind the stack and pass a value to a higher level is desirable. Julia provides the rethrow, backtrace and catch_backtrace functions for more advanced error handling.
In code that performs state changes or uses resources like files, there is typically clean-up work (such as closing files) that needs to be done when the code is finished. Exceptions potentially complicate this task, since they can cause a block of code to exit before reaching its normal end. The finally keyword provides a way to run some code when a given block of code exits, regardless of how it exits.
For example, here is how we can guarantee that an opened file is closed:
f = open("file") try # operate on file f finally close(f) end
When control leaves the try block (for example due to a return, or just finishing normally), close(f) will be executed. If the try block exits due to an exception, the exception will continue propagating. A catch block may be combined with try and finally as well. In this case the finally block will run after catch has handled the error.
Tasks (aka Coroutines)¶
Tasks are a control flow feature that allows computations to be suspended and resumed in a flexible manner. This feature is sometimes called by other names, such as symmetric coroutines, lightweight threads, cooperative multitasking, or one-shot continuations.
When a piece of computing work (in practice, executing a particular function) is designated as a Task, it becomes possible to interrupt it by switching to another Task. The original Task can later be resumed, at which point it will pick up right where it left off. At first, this may seem similar to a function call. However there are two key differences. First, switching tasks does not use any space, so any number of task switches can occur without consuming the call stack. Second, switching among tasks can occur in any order, unlike function calls, where the called function must finish executing before control returns to the calling function.
This kind of control flow can make it much easier to solve certain problems. In some problems, the various pieces of required work are not naturally related by function calls; there is no obvious “caller” or “callee” among the jobs that need to be done. An example is the producer-consumer problem, where one complex procedure is generating values and another complex procedure is consuming them. The consumer cannot simply call a producer function to get a value, because the producer may have more values to generate and so might not yet be ready to return. With tasks, the producer and consumer can both run as long as they need to, passing values back and forth as necessary.
Julia provides the functions produce and consume for solving this problem. A producer is a function that calls produce on each value it needs to produce:
julia> function producer() produce("start") for n=1:4 produce(2n) end produce("stop") end;
To consume values, first the producer is wrapped in a Task, then consume is called repeatedly on that object:
julia> p = Task(producer) Task julia> consume(p) "start" julia> consume(p) 2 julia> consume(p) 4 julia> consume(p) 6 julia> consume(p) 8 julia> consume(p) "stop"
One way to think of this behavior is that producer was able to return multiple times. Between calls to produce, the producer’s execution is suspended and the consumer has control.
A Task can be used as an iterable object in a for loop, in which case the loop variable takes on all the produced values:
julia> for x in Task(producer) println(x) end start 2 4 6 8 stop
Note that the Task() constructor expects a 0-argument function. A common pattern is for the producer to be parameterized, in which case a partial function application is needed to create a 0-argument anonymous function. This can be done either directly or by use of a convenience macro:
function mytask(myarg) ... end taskHdl = Task(() -> mytask(7)) # or, equivalently taskHdl = @task mytask(7)
produce and consume do not launch threads that can run on separate CPUs. True kernel threads are discussed under the topic of Parallel Computing.
Core task operations¶
While produce and consume illustrate the essential nature of tasks, they are actually implemented as library functions using a more primitive function, yieldto. yieldto(task,value) suspends the current task, switches to the specified task, and causes that task’s last yieldto call to return the specified value. Notice that yieldto is the only operation required to use task-style control flow; instead of calling and returning we are always just switching to a different task. This is why this feature is also called “symmetric coroutines”; each task is switched to and from using the same mechanism.
yieldto is powerful, but most uses of tasks do not invoke it directly. Consider why this might be. If you switch away from the current task, you will probably want to switch back to it at some point, but knowing when to switch back, and knowing which task has the responsibility of switching back, can require considerable coordination. For example, produce needs to maintain some state to remember who the consumer is. Not needing to manually keep track of the consuming task is what makes produce easier to use than yieldto.
In addition to yieldto, a few other basic functions are needed to use tasks effectively. current_task() gets a reference to the currently-running task. istaskdone(t) queries whether a task has exited. istaskstarted(t) queries whether a task has run yet. task_local_storage manipulates a key-value store specific to the current task.
Tasks and events¶
Most task switches occur as a result of waiting for events such as I/O requests, and are performed by a scheduler included in the standard library. The scheduler maintains a queue of runnable tasks, and executes an event loop that restarts tasks based on external events such as message arrival.
The basic function for waiting for an event is wait. Several objects implement wait; for example, given a Process object, wait will wait for it to exit. wait is often implicit; for example, a wait can happen inside a call to read to wait for data to be available.
In all of these cases, wait ultimately operates on a Condition object, which is in charge of queueing and restarting tasks. When a task calls wait on a Condition, the task is marked as non-runnable, added to the condition’s queue, and switches to the scheduler. The scheduler will then pick another task to run, or block waiting for external events. If all goes well, eventually an event handler will call notify on the condition, which causes tasks waiting for that condition to become runnable again.
A task created explicitly by calling Task is initially not known to the scheduler. This allows you to manage tasks manually using yieldto if you wish. However, when such a task waits for an event, it still gets restarted automatically when the event happens, as you would expect. It is also possible to make the scheduler run a task whenever it can, without necessarily waiting for any events. This is done by calling schedule(task), or using the @schedule or @async macros (see Parallel Computing for more details).
Tasks have a state field that describes their execution status. A task state is one of the following symbols:
|:runnable||Currently running, or available to be switched to|
|:waiting||Blocked waiting for a specific event|
|:queued||In the scheduler’s run queue about to be restarted|
|:done||Successfully finished executing|
|:failed||Finished with an uncaught exception|