simdjson strives to be at its fastest without tuning, and generally achieves this. However, there are still some scenarios where tuning can enhance performance.
- Reusing the parser for maximum efficiency
- Server Loops: Long-Running Processes and Memory Capacity
- Large files and huge page support
- Computed GOTOs
- Number parsing
- Visual Studio
- Downclocking
- Best Use of the DOM API
If you're using simdjson to parse multiple documents, or in a loop, you should make a parser once and reuse it. The simdjson library will allocate and retain internal buffers between parses, keeping buffers hot in cache and keeping memory allocation and initialization to a minimum. In this manner, you can parse terabytes of JSON data without doing any new allocation.
dom::parser parser;
// This initializes buffers and a document big enough to handle this JSON.
dom::element doc = parser.parse("[ true, false ]"_padded);
cout << doc << endl;
// This reuses the existing buffers, and reuses and *overwrites* the old document
doc = parser.parse("[1, 2, 3]"_padded);
cout << doc << endl;
// This also reuses the existing buffers, and reuses and *overwrites* the old document
dom::element doc2 = parser.parse("true"_padded);
// Even if you keep the old reference around, doc and doc2 refer to the same document.
cout << doc << endl;
cout << doc2 << endl;It's not just internal buffers though. The simdjson library reuses the document itself. The dom::element, dom::object and dom::array instances are references to the internal document. You are only borrowing the document from simdjson, which purposely reuses and overwrites it each time you call parse. This prevent wasteful and unnecessary memory allocation in 99% of cases where JSON is just read, used, and converted to native values or thrown away.
You are only borrowing the document from the simdjson parser. Don't keep it long term!
This is key: don't keep the document&, dom::element, dom::array, dom::object
or string_view objects you get back from the API. Convert them to C++ native values, structs and
arrays that you own.
The simdjson library automatically expands its memory capacity when larger documents are parsed, so that you don't unexpectedly fail. In a short process that reads a bunch of files and then exits, this works pretty flawlessly.
Server loops, though, are long-running processes that will keep the parser around forever. This means that if you encounter a really, really large document, simdjson will not resize back down. The simdjson library lets you adjust your allocation strategy to prevent your server from growing without bound:
-
You can set a max capacity when constructing a parser:
dom::parser parser(1000*1000); // Never grow past documents > 1MB for (web_request request : listen()) { dom::element doc; auto error = parser.parse(request.body).get(doc); // If the document was above our limit, emit 413 = payload too large if (error == CAPACITY) { request.respond(413); continue; } // ... }
This parser will grow normally as it encounters larger documents, but will never pass 1MB.
-
You can set a fixed capacity that never grows, as well, which can be excellent for predictability and reliability, since simdjson will never call malloc after startup!
dom::parser parser(0); // This parser will refuse to automatically grow capacity auto error = parser.allocate(1000*1000); // This allocates enough capacity to handle documents <= 1MB if (error) { cerr << error << endl; exit(1); } for (web_request request : listen()) { dom::element doc; error = parser.parse(request.body).get(doc); // If the document was above our limit, emit 413 = payload too large if (error == CAPACITY) { request.respond(413); continue; } // ... }
There is a memory allocation performance cost the first time you process a large file (e.g. 100MB). Between the cost of allocation, the fact that the memory is not in cache, and the initial zeroing of memory, on some systems, allocation runs far slower than parsing (e.g., 1.4GB/s). Reusing the parser mitigates this by paying the cost once, but does not eliminate it.
In large file use cases, enabling transparent huge page allocation on the OS can help a lot. We haven't found the right way to do this on Windows or OS/X, but on Linux, you can enable transparent huge page allocation with a command like:
echo always > /sys/kernel/mm/transparent_hugepage/enabledIn general, when running benchmarks over large files, we recommend that you report performance
numbers with and without huge pages if possible. Furthermore, you should amortize the parsing (e.g.,
by parsing several large files) to distinguish the time spent parsing from the time spent allocating
memory. If you are using the parse benchmarking tool provided with the simdjson library, you can
use the -H flag to omit the memory allocation cost from the benchmark results.
./parse largefile # includes memory allocation cost
./parse -H largefile # without memory allocation
For best performance, we use a technique called "computed goto" when the compiler supports it, it is also sometimes described as "Labels as Values". Though it is not part of the C++ standard, it is supported by many major compilers and it brings measurable performance benefits that are difficult to achieve otherwise. The computed gotos are automatically disabled under Visual Studio.
If you wish to forcefully disable computed gotos, you can do so by compiling the code with
-DSIMDJSON_NO_COMPUTED_GOTO=1. It is not recommended to disable computed gotos if your compiler
supports it. In fact, you should almost never need to be concerned with computed gotos.
Some JSON files contain many floating-point values. It is the case with many GeoJSON files. Accurately parsing decimal strings into binary floating-point values with proper rounding is challenging. To our knowledge, it is not possible, in general, to parse streams of numbers at gigabytes per second using a single core. While using the simdjson library, it is possible that you might be limited to a few hundred megabytes per second if your JSON documents are densely packed with floating-point values.
- When possible, you should favor integer values written without a decimal point, as it simpler and faster to parse decimal integer values.
- When serializing numbers, you should not use more digits than necessary: 17 digits is all that is needed to exactly represent double-precision floating-point numbers. Using many more digits than necessary will make your files larger and slower to parse.
- When benchmarking parsing speeds, always report whether your JSON documents are made mostly of floating-point numbers when it is the case, since number parsing can then dominate the parsing time.
On Intel and AMD Windows platforms, Microsoft Visual Studio enables programmers to build either 32-bit (x86) or 64-bit (x64) binaries. We urge you to always use 64-bit mode. Visual Studio 2019 should default on 64-bit builds when you have a 64-bit version of Windows, which we recommend.
When compiling with Visual Studio, we recommend the flags /Ob2 /O2 or better. We do not recommend that you compile simdjson with architecture-specific flags such as arch:AVX2. The simdjson library automatically selects the best execution kernel at runtime.
Recent versions of Microsoft Visual Studio on Windows provides support for the LLVM Clang compiler. You only need to install the "Clang compiler" optional component (ClangCL). You may also get a copy of the 64-bit LLVM CLang compiler for Windows directly from LLVM. The simdjson library fully supports the LLVM Clang compiler under Windows. In fact, you may get better performance out of simdjson with the LLVM Clang compiler than with the regular Visual Studio compiler. Meanwhile the LLVM CLang compiler is binary compatible with Visual Studio which means that you can combine their binaries (executables and libraries).
Under Windows, we also support the GNU GCC compiler via MSYS2. The performance of 64-bit MSYS2 under Windows excellent (on par with Linux).
SIMD instructions are the public transportation of computing. Instead of using 4 distinct instructions to add numbers, you can replace them with a single instruction that does the same work. Though the one instruction is slightly more expensive, the energy used per unit of work is much less with SIMD. If you can increase your speed using SIMD instructions (NEON, SSE, AVX), you should expect to reduce your power usage.
The SIMD instructions that simdjson relies upon (SSE and AVX under x64, NEON under ARM) are routinely part of runtime libraries (e.g., Go, Glibc, LLVM, Rust, Java, PHP). What distinguishes the simdjson library is that it is built from the ground up to benefit from these instructions.
You should not expect the simdjson library to cause downclocking of your recent Intel CPU cores.
On some Intel processors, using SIMD instructions in a sustained manner on the same CPU core may result in a phenomenon called downclocking whereas the processor initially runs these instructions at a slow speed before reducing the frequency of the core for a short time (milliseconds). Intel refers to these states as licenses. On some current Intel processors, it occurs under two scenarios:
- Whenever 512-bit AVX-512 instructions are used.
- Whenever heavy 256-bit or wider instructions are used. Heavy instructions are those involving floating point operations or integer multiplications (since these execute on the floating point unit).
The simdjson library does not currently support AVX-512 instructions and it does not make use of heavy 256-bit instructions. We do use vectorized multiplications, but only using 128-bit registers. Thus there should be no downclocking due to simdjson on recent processors.
You may still be worried about which SIMD instruction set is used by simdjson. Thankfully, you can always determine and change which architecture-specific implementation is used by simdjson. Thus even if your CPU supports AVX2, you do not need to use AVX2. You are in control.
The simdjson API provides access to the JSON DOM (document-object-model) content as a tree of dom::element instances, each representing an object, an array or an atomic type (null, true, false, number). These dom::element instances are lightweight objects (e.g., spanning 16 bytes) and it might be advantageous to pass them by value, as opposed to passing them by reference or by pointer.