In computing, just-in-time compilation (JIT), also known as dynamic translation, is a technique for improving the runtime performance of a computer program. JIT builds upon two earlier ideas in run-time environments: bytecode compilation and dynamic compilation. It converts code at runtime prior to executing it natively, for example bytecode into native machine code. The performance improvement over interpreters originates from caching the results of translating blocks of code, and not simply reevaluating each line or operand each time it is met (see Interpreted language). It also has advantages over statically compiling the code at development time, as it can recompile the code if this is found to be advantageous, and may be able to enforce security guarantees. Thus JIT can combine some of the advantages of interpretation and static compilation.
In a bytecode-compiled system, source code is translated to an intermediate representation known as bytecode. Bytecode is not the machine code for any particular computer, and may be portable among computer architectures. The bytecode may then be interpreted, or run, on a virtual machine. A just-in-time compiler can be used as a way to speed up execution of bytecode. At the time the bytecode is run, the just-in-time compiler will compile some or all of it to native machine code for better performance. This can be done per-file, per-function or even on any arbitrary code fragment; the code can be compiled when it is about to be executed (hence the name “just-in-time”).
In contrast, a traditional interpreted virtual machine will simply interpret the bytecode, generally with much lower performance. Some interpreters even interpret source code, without the step of first compiling to bytecode, with even worse performance. Statically compiled code or native code is compiled prior to deployment. A dynamic compilation environment is one in which the compiler can be used during execution.
A common goal of using JIT techniques is to reach or surpass the performance of static compilation, while maintaining the advantages of bytecode interpretation: Much of the “heavy lifting” of parsing the original source code and performing basic optimization is often handled at compile time, prior to deployment: compilation from bytecode to machine code is much faster than compiling from source. The deployed bytecode is portable, unlike native code. Since the runtime has control over the compilation, like interpreted bytecode, it can run in a secure sandbox. Compilers from bytecode to machine code are easier to write, because the portable bytecode compiler has already done much of the work.