flatbuffers/docs/source/FlexBuffers.md

FlexBuffers

FlatBuffers was designed around schemas, because when you want maximum performance and data consistency, strong typing is helpful.

There are however times when you want to store data that doesn't fit a schema, because you can't know ahead of time what all needs to be stored.

For this, FlatBuffers has a dedicated format, called FlexBuffers. This is a binary format that can be used in conjunction with FlatBuffers (by storing a part of a buffer in FlexBuffers format), or also as its own independent serialization format.

While it loses the strong typing, you retain the most unique advantage FlatBuffers has over other serialization formats (schema-based or not): FlexBuffers can also be accessed without parsing / copying / object allocation. This is a huge win in efficiency / memory friendly-ness, and allows unique use cases such as mmap-ing large amounts of free-form data.

FlexBuffers' design and implementation allows for a very compact encoding, combining automatic pooling of strings with automatic sizing of containers to their smallest possible representation (8/16/32/64 bits). Many values and offsets can be encoded in just 8 bits. While a schema-less representation is usually more bulky because of the need to be self-descriptive, FlexBuffers generates smaller binaries for many cases than regular FlatBuffers.

FlexBuffers is still slower than regular FlatBuffers though, so we recommend to only use it if you need it.

Usage

This is for C++, other languages may follow.

Include the header flexbuffers.h, which in turn depends on flatbuffers.h and util.h.

To create a buffer:

flexbuffers::Builder fbb;
fbb.Int(13);
fbb.Finish();

You create any value, followed by Finish. Unlike FlatBuffers which requires the root value to be a table, here any value can be the root, including a lonely int value.

You can now access the std::vector<uint8_t> that contains the encoded value as fbb.GetBuffer(). Write it, send it, or store it in a parent FlatBuffer. In this case, the buffer is just 3 bytes in size.

To read this value back, you could just say:

auto root = flexbuffers::GetRoot(my_buffer);
int64_t i = root.AsInt64();

FlexBuffers stores ints only as big as needed, so it doesn't differentiate between different sizes of ints. You can ask for the 64 bit version, regardless of what you put in. In fact, since you demand to read the root as an int, if you supply a buffer that actually contains a float, or a string with numbers in it, it will convert it for you on the fly as well, or return 0 if it can't. If instead you actually want to know what is inside the buffer before you access it, you can call root.GetType() or root.IsInt() etc.

Here's a slightly more complex value you could write instead of fbb.Int above:

fbb.Map([&]() {
  fbb.Vector("vec", [&]() {
    fbb.Int(-100);
    fbb.String("Fred");
    fbb.IndirectFloat(4.0f);
  });
  fbb.UInt("foo", 100);
});

This stores the equivalent of the JSON value { vec: [ -100, "Fred", 4.0 ], foo: 100 }. The root is a dictionary that has just two key-value pairs, with keys vec and foo. Unlike FlatBuffers, it actually has to store these keys in the buffer (which it does only once if you store multiple such objects, by pooling key values), but also unlike FlatBuffers it has no restriction on the keys (fields) that you use.

The map constructor uses a C++11 Lambda to group its children, but you can also use more conventional start/end calls if you prefer.

The first value in the map is a vector. You'll notice that unlike FlatBuffers, you can use mixed types. There is also a TypedVector variant that only allows a single type, and uses a bit less memory.

IndirectFloat is an interesting feature that allows you to store values by offset rather than inline. Though that doesn't make any visible change to the user, the consequence is that large values (especially doubles or 64 bit ints) that occur more than once can be shared. Another use case is inside of vectors, where the largest element makes up the size of all elements (e.g. a single double forces all elements to 64bit), so storing a lot of small integers together with a double is more efficient if the double is indirect.

Accessing it:

auto map = flexbuffers::GetRoot(my_buffer).AsMap();
map.size();  // 2
auto vec = map["vec"].AsVector();
vec.size();  // 3
vec[0].AsInt64();  // -100;
vec[1].AsString().c_str();  // "Fred";
vec[1].AsInt64();  // 0 (Number parsing failed).
vec[2].AsDouble();  // 4.0
vec[2].AsString().IsTheEmptyString();  // true (Wrong Type).
vec[2].AsString().c_str();  // "" (This still works though).
vec[2].ToString().c_str();  // "4" (Or have it converted).
map["foo"].AsUInt8();  // 100
map["unknown"].IsNull();  // true

Binary encoding

A description of how FlexBuffers are encoded is in the internals document.

Nesting inside a FlatBuffer

You can mark a field as containing a FlexBuffer, e.g.

a:[ubyte] (flexbuffer);

A special accessor will be generated that allows you to access the root value directly, e.g. a_flexbuffer_root().AsInt64().

Efficiency tips

• Vectors generally are a lot more efficient than maps, so prefer them over maps when possible for small objects. Instead of a map with keys x, y and z, use a vector. Better yet, use a typed vector. Or even better, use a fixed size typed vector.
• Maps are backwards compatible with vectors, and can be iterated as such. You can iterate either just the values (map.Values()), or in parallel with the keys vector (map.Keys()). If you intend to access most or all elements, this is faster than looking up each element by key, since that involves a binary search of the key vector.
• When possible, don't mix values that require a big bit width (such as double) in a large vector of smaller values, since all elements will take on this width. Use IndirectDouble when this is a possibility. Note that integers automatically use the smallest width possible, i.e. if you ask to serialize an int64_t whose value is actually small, you will use less bits. Doubles are represented as floats whenever possible losslessly, but this is only possible for few values. Since nested vectors/maps are stored over offsets, they typically don't affect the vector width.
• To store large arrays of byte data, use a blob. If you'd use a typed vector, the bit width of the size field may make it use more space than expected, and may not be compatible with memcpy. Similarly, large arrays of (u)int16_t may be better off stored as a binary blob if their size could exceed 64k elements. Construction and use are otherwise similar to strings.