When serializing large databases, it is often better to write out many independent JSON documents, instead of one large monolithic document containing many records. The simdjson library provides high-speed access to files or streams containing multiple small JSON documents separated by ASCII white-space characters. Given an input such as
{"text":"a"}
{"text":"b"}
{"text":"c"}
...... you want to read the entries (individual JSON documents) as quickly and as conveniently as possible. Importantly, the input might span several gigabytes, but you want to use a small (fixed) amount of memory. Ideally, you'd also like the parallelize the processing (using more than one core) to speed up the process.
The main motivation for this piece of software is to achieve maximum speed and offer a better quality of life in parsing files containing multiple small JSON documents.
The JavaScript Object Notation (JSON) RFC7159 is a handy serialization format. However, when serializing a large sequence of values as an array, or a possibly indeterminate-length or never- ending sequence of values, JSON may be inconvenient.
Consider a sequence of one million values, each possibly one kilobyte when encoded -- roughly one gigabyte. It is often desirable to process such a dataset incrementally without having to first read all of it before beginning to produce results.
Before parsing anything, simdjson first preprocesses the JSON text by identifying all structural indexes
(i.e. the starting position of any JSON value, as well as any important operators like ,, :, ] or
}) and validating UTF8. This stage is referred to stage 1. However, during this process, simdjson has
no knowledge of whether parsed a valid document, multiple documents, or even if the document is complete.
Then, to iterate through the JSON text during parsing, we use what we call a JSON iterator that will navigate
through the text using these structural indexes. This JSON iterator is not visible though, but it is the
key component to make parsing work.
Prior to iterate_many, most people who had to parse a multiline JSON file would proceed by reading the
file line by line, using a utility function like std::getline or equivalent, and would then use
the parse on each of those lines. From a performance point of view, this process is highly
inefficient, in that it requires a lot of unnecessary memory allocation and makes use of the
getline function, which is fundamentally slow, slower than the act of parsing with simdjson
(more on this here).
Unlike the popular parser RapidJson, our DOM does not require the buffer once the parsing job is completed, the DOM and the buffer are completely independent. The drawback of this architecture is that we need to allocate some additional memory to store our ParsedJson data, for every document inside a given file. Memory allocation can be slow and become a bottleneck, therefore, we want to minimize it as much as possible.
To achieve a minimum amount of allocations, we opted for a design where we create only one parser object and therefore allocate its memory once, and then recycle it for every document in a given file. But, knowing that they often have largely varying size, we need to make sure that we allocate enough memory so that all the documents can fit. This value is what we call the batch size. As of right now, we need to manually specify a value for this batch size, it has to be at least as big as the biggest document in your file, but not too big so that it submerges the cached memory. The bigger the batch size, the fewer we need to make allocations. We found that 1MB is somewhat a sweet spot.
- When the user calls
iterate_many, we return adocument_streamwhich the user can iterate over to receive parsed documents. - We call stage 1 on the first batch_size bytes of JSON in the buffer, detecting structural indexes for all documents in that batch.
- The
document_streamowns adocumentinstance that keeps track of the current document position in the stream using a JSON iterator. To obtain a valid document, thedocument_streamreturns a reference to its document instance. - Each time the user calls
++to read the next document, the JSON iterator moves to the start the next document. - When we reach the end of the batch, we call stage 1 on the next batch, starting from the end of the last document, and go to step 3.
But how can we make use of threads if they are available? We found a pretty cool algorithm that allows us to quickly identify the position of the last JSON document in a given batch. Knowing exactly where the end of the last document in the batch is, we can safely parse through the last document without any worries that it might be incomplete. Therefore, we can run stage 1 on the next batch concurrently while parsing the documents in the current batch. Running stage 1 in a different thread can, in best cases, remove almost entirely its cost and replaces it by the overhead of a thread, which is orders of magnitude cheaper. Ain't that awesome!
Thread support is only active if thread supported is detected in which case the macro SIMDJSON_THREADS_ENABLED is set. Otherwise the library runs in single-thread mode.
A document_stream instance uses at most two threads: there is a main thread and a worker thread.
Since we want to offer flexibility and not restrict ourselves to a specific file format, we support any file that contains any amount of valid JSON document, separated by one or more character that is considered whitespace by the JSON spec. Anything that is not whitespace will be parsed as a JSON document and could lead to failure.
Whitespace Characters:
- Space
- Linefeed
- Carriage return
- Horizontal tab
If your documents are all objects or arrays, then you may even have nothing between them.
E.g., [1,2]{"32":1} is recognized as two documents.
Some official formats (non-exhaustive list):
- Newline-Delimited JSON (NDJSON)
- JSON lines (JSONL)
- Record separator-delimited JSON (RFC 7464) <- Not supported by JsonStream!
- More on Wikipedia...
See basics.md for an overview of the API.
From jsonlines.org:
-
Better than CSV
["Name", "Session", "Score", "Completed"] ["Gilbert", "2013", 24, true] ["Alexa", "2013", 29, true] ["May", "2012B", 14, false] ["Deloise", "2012A", 19, true]
CSV seems so easy that many programmers have written code to generate it themselves, and almost every implementation is different. Handling broken CSV files is a common and frustrating task. CSV has no standard encoding, no standard column separator and multiple character escaping standards. String is the only type supported for cell values, so some programs attempt to guess the correct types.
JSON Lines handles tabular data cleanly and without ambiguity. Cells may use the standard JSON types.
The biggest missing piece is an import/export filter for popular spreadsheet programs so that non-programmers can use this format.
-
Easy Nested Data
{"name": "Gilbert", "wins": [["straight", "7♣"], ["one pair", "10♥"]]} {"name": "Alexa", "wins": [["two pair", "4♠"], ["two pair", "9♠"]]} {"name": "May", "wins": []} {"name": "Deloise", "wins": [["three of a kind", "5♣"]]}JSON Lines' biggest strength is in handling lots of similar nested data structures. One .jsonl file is easier to work with than a directory full of XML files.
Some users would like to know where the document they parsed is in the input array of bytes.
It is possible to do so by accessing directly the iterator and calling its current_index()
method which reports the location (in bytes) of the current document in the input stream.
You may also call the source() method to get a std::string_view instance on the document
and error() to check if there were any error.
Let us illustrate the idea with code:
auto json = R"([1,2,3] {"1":1,"2":3,"4":4} [1,2,3] )"_padded;
simdjson::ondemand::parser parser;
simdjson::ondemand::document_stream stream;
auto error = parser.iterate_many(json).get(stream);
if( error ) { /* do something */ }
auto i = stream.begin();
size_t count{0};
for(; i != stream.end(); ++i) {
auto doc = *i;
if(!i.error()) {
std::cout << "got full document at " << i.current_index() << std::endl;
std::cout << i.source() << std::endl;
count++;
} else {
std::cout << "got broken document at " << i.current_index() << std::endl;
return false;
}
}
This code will print:
got full document at 0
[1,2,3]
got full document at 9
{"1":1,"2":3,"4":4}
got full document at 29
[1,2,3]
Some users may need to work with truncated streams. The simdjson may truncate documents at the very end of the stream that cannot possibly be valid JSON (e.g., they contain unclosed strings, unmatched brackets, unmatched braces). After iterating through the stream, you may query the truncated_bytes() method which tells you how many bytes were truncated. If the stream is made of full (whole) documents, then you should expect truncated_bytes() to return zero.
Consider the following example where a truncated document ({"key":"intentionally unclosed string ) containing 39 bytes has been left within the stream. In such cases, the first two whole documents are parsed and returned, and the truncated_bytes() method returns 39.
auto json = R"([1,2,3] {"1":1,"2":3,"4":4} {"key":"intentionally unclosed string )"_padded;
simdjson::ondemand::parser parser;
simdjson::ondemand::document_stream stream;
auto error = parser.iterate_many(json,json.size()).get(stream);
if(error) { std::cerr << error << std::endl; return; }
for(auto i = stream.begin(); i != stream.end(); ++i) {
std::cout << i.source() << std::endl;
}
std::cout << stream.truncated_bytes() << " bytes "<< std::endl; // returns 39 bytesThis will print:
[1,2,3]
{"1":1,"2":3,"4":4}
39 bytes
Importantly, you should only call truncated_bytes() after iterating through all of the documents since the stream cannot tell whether there are truncated documents at the very end when it may not have accessed that part of the data yet.