ZebraPack format is like gobs version 2: serialization in Go, *but* extremely fast and friendly to other languages. Use Go as your schema. Strong typing. Well documented (and msgpack2 compatible) format so other languages can be readily supported.
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README.md

ZebraPack: a data description language and serialization format. Like Gobs version 2.0.

ZebraPack is a data definition language and serialization format. It removes gray areas from msgpack2 serialized data, and provides for declared schemas, sane data evolution, and more compact encoding.

It does all this while maintaining the possibility of easy compatibility with all the dynamic languages that already have msgpack2 support. If your favorite language (after Go, of course) has a library that reads msgpack2, then it would be only a day's work to adapt the library to read zebrapack: the schema are in msgpack2, and then one simply keeps a hashmap to translate between small integer <-> field names/type.

Why start with msgpack2? Quite simple: msgpack2 is simple, fast, and extremely portable. It has an implementation in every language you've heard of, and some you haven't (some 50 libraries are available). It has a well defined and short spec. The mspack1 vs msgpack2 terminology is a distinction we make here: msgpack1 spec poorly distringuished between strings and raw binary bytes, but that was remedied in msgpack2. Importantly, msgpack2 is dynamic-language friendly because it is largely self-describing.

We find only two problems with msgpack2: weak support for data evolution, and insufficiently strong typing.

The ZebraPack format addresses these problems. Moreover, ZebraPack is actually still binary compatible with msgpack2 spec. It just adopts a new convention about how to encode the field names of structs. Structs are encoded in msgpack2 using maps, as usual. Hence all data is still encoded precisely in the msgpack2 format. The only difference in ZebraPack is this convention: maps that represent structs are now keyed by integers. Rather than have string keys -- the convention for most msgpack2 language bindings -- in ZebraPack we use integers as keys for those maps that are representing structs. These integers are associated with a field name and type in a (separable) schema. The schema is also defined and encoded in msgpack2.

The resulting binary encoding is very similar in style to protobufs/Thrift/Capn'Proto. However it is much more friendly to other (dynamic) languages. Also it is screaming fast (see benchmarks below).

Once we have a schema, we can be very strongly typed, and be very efficient. We borrow the idea of field deprecation from FlatBuffers. For conflicting update detection, we use CapnProto's field numbering discipline. We add support for the omitempty tag. In fact, in ZebraPack, all fields are omitempty. If they are empty they won't be serialized on the wire. Like FlatBuffers and Protobufs, this enables one to define a very large schema of possibilities, and then only transmit a very small (efficient) portion that is currently relevant over the wire.

Full credit: the code here descends from the fantastic msgpack2 code generator https://github.com/tinylib/msgp by Philip Hofer.

By default we generate ZebraPack format encoders and decoders when the zebrapack tool is run. Note that we continue to offer straight msgpack2 serialization and deserialization with the -msgp flag to zebrapack.

At this time it is feature complete and production ready.

background and motivation

ZebraPack serialization. This one is all black and white. No gray areas.

ZebraPack is a data definition language and serialization format. It removes gray areas from msgpack2 serialized data, and provides for declared schemas, sane data evolution, and more compact encoding. It does all this while maintaining the possibility of easy compatibility with all the dynamic languages that already have msgpack2 support.

the main idea

//given this definition, defined in Go:
type A struct {
  Name     string      `zid:"0"`
  Bday     time.Time   `zid:"1"`
  Phone    string      `zid:"2"`
  Sibs     int         `zid:"3"`
  GPA      float64     `zid:"4" deprecated:"true"` // a deprecated field.
  Friend   bool        `zid:"5"`
}

then to serialize the following instance `a`, we would
print the schema information at the front of the file --
or detached completely and kept in a separate file --
in the form of a zebra.Schema (see https://github.com/glycerine/zebrapack/blob/master/zebra/zebra.go for the spec) structure. Then
the data follows as a map whose keys are now integers
instead of strings. A simple example:

original(msgpack2) ->        schema(msgpack2)      +    each instance(msgpack2)
--------                     --------------             -------------
a := A{                      zebra.StructT{             map{
  "Name":  "Atlanta",          0: {"Name", String},       0: "Atlanta",
  "Bday":  tm("1990-12-20"),   1: {"Bday", Timestamp},    1: "1990-12-20",
  "Phone": "650-555-1212",     2: {"Phone", String},      2: "650-555-1212",
  "Sibs":  3,                  3: {"Sibs", Int64},        3: 3,
  "GPA" :  3.95,               4: {"GPA", Float64},       4: 3.95,
  "Friend":true,               5: {"Friend", Bool},       5: true,
}                            }                          }

The central idea of ZebraPack: start with msgpack2, but when encoding a struct (in msgpack2 a struct is represented as a map), replace the key strings with small integers.

By having a small schema description (essentially a lookup table with int->string mappings and a type identifier) either separate or serialized at the front of the serialization stream/file, we get known schema types up-front, plus compression and the ability to evolve our data without crashes. If you've ever had your msgpack crash your server because you tried to change the type of a field but keep the same name, then you know how fragile msgpack can be.

By default, today, we serialize the schema to a separate file so that the wire encoding is as fast as possible. However it is trivial to add/pre-pend the encoded schema to any file when you need to. The zebrapack generated Go code incorporates knowledge of the schema, so if you are only working in Go there is no need to zebrapack -write-schema to generate an external schema desription file. In summary, by default we behave like protobufs/thrift/capnproto, but dynamic languages and runtime type discovery can be supported in full fidelity.

The second easy idea: use the Go language struct definition syntax as our serialization schema. Why invent another format? Serialization for Go developers should be almost trivially easy. While we are focused on a serialization format for Go, because other language can read msgpack2, they can also readily parse the schema. The schema is stored in msgpack2 struct convention (and optionally json), rather than the ZebraPack struct convention, for bootstrapping.

background

Starting point: msgpack2 is great. It is has an easy to read spec, it defines a compact serialization format, and it has wide language support from both dynamic and compiled languages.

Nonetheless, data update conflicts still happen and can be hard to resolve. Encoders could use the guidance of a schema to avoid signed versus unsigned integer encodings.

For instance, sadly the widely emulated C-encoder for msgpack chooses to encode signed positive integers as unsigned integers. This causes crashes in readers who were expected a signed integer, which they may have originated themselves in the original struct.

Astonishing, but true: the existing practice for msgpack2 language bindings allows the data types to change as they are read and re-serialized. Simple copying of a serialized struct can change the types of data from signed to unsigned. This is horrible. Now we have to guess whether an unsigned integer was really intended because of the integer's range, or if data will be silently truncated or lost when coercing a 64-bit integer to a 63-bit signed integer--assuming such coercing ever makes logical sense, which it may not.

This kind of tragedy happens because of a lack of shared communication across time and space between readers and writers. It is easily addressed with a shared schema. ZebraPack, in its essense, is the agreement to follow that schema when binding msgpack2 to a new language.

While not always necessary, a schema provides many benefits, both for coordinating between people and for machine performance.

  • Stronger typing: readers know what is expected, in both type and size of the data delivered. Writers know what they should be writing.

  • Performance and compression: replacing struct/map field names with numbers provides immediate space savings and compression.

  • Conflict resolution: the Cap'nProto numbering and update conflict resolution method is used here. This method originated in the ProtocolBuffers scheme, and was enhanced after experience in Cap'nProto. How it works: Additions are always made by incrementing by one the largest number available prior to the addition. No gaps in numbering are allowed, and no numbers are ever deleted. To get the effect of deletion, use the deprecated tag (effectively a tombstone) instead. This allows the tools to help detect merge conflicts as soon as possible. If two people try to merge schemas where the same struct or field number is re-used, a schema compiler can automatically detect this update conflict, and flag the human to resolve the conflict before proceeding.

  • All fields optional. Just as in msgpack2, all fields are optional.

Design:

  • Schema language: the schema language for defining structs is identical to the Go language. Go is expressive and yet easily parsed by the standard library packages included with Go itself. There are already high-performance msgpack2 libraries available for go, https://github.com/tinylib/msgp and https://github.com/ugorji/go which make schema compilation easy.

  • Schema serialization: schemas are serialized using the msgpack2 encoding.

  • Requirement: zerbapack requires that the msgpack2 standard be adhered to. Strings and raw binary byte arrays are distinct, and must be marked distinctly; msgpack1 encoding is not allowed.

  • All language bindings must respect the declared type in the ZebraPack schema when writing data. For example, this means that signed and unsigned declarations must be respected.

benchmarking

Based on the implementation now available in https://github.com/glycerine/zebrapack, we measure read and write speed with the -fast-strings -no-rtti optimizations on. Benchmarks from https://github.com/glycerine/go_serialization_benchmarks of this struct:

type A struct {
    Name     string
    BirthDay time.Time
    Phone    string
    Siblings int
    GPA      float64
    Friend   bool
}

read performance

zebrapack -fast-strings -no-rtti jockeys for the top position with go-capnproto-version-1, Gencode, FlatBuffers, and gogoprotobuf. In the sampling below it comes out fastest, but this varies occassionally run-by-run. Nonetheless, we see a very strong showing amongst strong company. Moreover, our zero allocation profile and serialization directly to and from Go structs are advantages. As is typical for binary formats, ZebraPack is about 20x faster than Go's JSON handling.

benchmark                                       iter           time/iter         bytes alloc       allocs
---------                                       ----           ---------         -----------       ------
BenchmarkZebraPackUnmarshal-4               10000000           227 ns/op           0 B/op          0 allocs/op
BenchmarkGencodeUnmarshal-4                 10000000           229 ns/op         112 B/op          3 allocs/op
BenchmarkFlatBuffersUnmarshal-4             10000000           232 ns/op          32 B/op          2 allocs/op
BenchmarkGogoprotobufUnmarshal-4            10000000           232 ns/op          96 B/op          3 allocs/op
BenchmarkCapNProtoUnmarshal-4               10000000           258 ns/op           0 B/op          0 allocs/op
BenchmarkMsgpUnmarshal-4                     5000000           296 ns/op          32 B/op          2 allocs/op
BenchmarkGoprotobufUnmarshal-4               2000000           688 ns/op         432 B/op          9 allocs/op
BenchmarkProtobufUnmarshal-4                 2000000           707 ns/op         192 B/op         10 allocs/op
BenchmarkGobUnmarshal-4                      2000000           886 ns/op         112 B/op          3 allocs/op
BenchmarkHproseUnmarshal-4                   1000000          1045 ns/op         320 B/op         10 allocs/op
BenchmarkCapNProto2Unmarshal-4               1000000          1359 ns/op         608 B/op         12 allocs/op
BenchmarkXdrUnmarshal-4                      1000000          1659 ns/op         239 B/op         11 allocs/op
BenchmarkBinaryUnmarshal-4                   1000000          1907 ns/op         336 B/op         22 allocs/op
BenchmarkVmihailencoMsgpackUnmarshal-4       1000000          2085 ns/op         384 B/op         13 allocs/op
BenchmarkUgorjiCodecMsgpackUnmarshal-4        500000          2620 ns/op        3008 B/op          6 allocs/op
BenchmarkUgorjiCodecBincUnmarshal-4           500000          2795 ns/op        3168 B/op          9 allocs/op
BenchmarkSerealUnmarshal-4                    500000          3271 ns/op        1008 B/op         34 allocs/op
BenchmarkJsonUnmarshal-4                      200000          5576 ns/op         495 B/op          8 allocs/op

write performance

zebrapack -fast-strings -no-rtti consistently dominates the field. This is mostly due to the use of the highly tuned https://github.com/tinylib/msgp library (in 3rd place here), which is then sped up further by using integer keys instead of strings.

benchmark                                       iter           time/iter          bytes alloc      allocs
---------                                       ----           ---------          -----------      ------
BenchmarkZebraPackMarshal-4                 10000000           115 ns/op           0 B/op          0 allocs/op
BenchmarkGogoprotobufMarshal-4              10000000           148 ns/op          64 B/op          1 allocs/op
BenchmarkMsgpMarshal-4                      10000000           161 ns/op         128 B/op          1 allocs/op
BenchmarkGencodeMarshal-4                   10000000           176 ns/op          80 B/op          2 allocs/op
BenchmarkFlatBufferMarshal-4                 5000000           347 ns/op           0 B/op          0 allocs/op
BenchmarkCapNProtoMarshal-4                  3000000           506 ns/op          56 B/op          2 allocs/op
BenchmarkGoprotobufMarshal-4                 3000000           617 ns/op         312 B/op          4 allocs/op
BenchmarkGobMarshal-4                        2000000           887 ns/op          48 B/op          2 allocs/op
BenchmarkProtobufMarshal-4                   2000000           912 ns/op         200 B/op          7 allocs/op
BenchmarkHproseMarshal-4                     1000000          1052 ns/op         473 B/op          8 allocs/op
BenchmarkCapNProto2Marshal-4                 1000000          1214 ns/op         436 B/op          7 allocs/op
BenchmarkBinaryMarshal-4                     1000000          1427 ns/op         256 B/op         16 allocs/op
BenchmarkVmihailencoMsgpackMarshal-4         1000000          1772 ns/op         368 B/op          6 allocs/op
BenchmarkXdrMarshal-4                        1000000          1802 ns/op         455 B/op         20 allocs/op
BenchmarkJsonMarshal-4                       1000000          2500 ns/op         536 B/op          6 allocs/op
BenchmarkUgorjiCodecBincMarshal-4             500000          2514 ns/op        2784 B/op          8 allocs/op
BenchmarkSerealMarshal-4                      500000          2729 ns/op         912 B/op         21 allocs/op
BenchmarkUgorjiCodecMsgpackMarshal-4          500000          3274 ns/op        2752 B/op          8 allocs/op

deprecating fields

to actually deprecate a field, you start by adding the deprecated:"true" tag:

type A struct {
  Name     string      `zid:"0"`
  Bday     time.Time   `zid:"1"`
  Phone    string      `zid:"2"`
  Sibs     int         `zid:"3"`
  GPA      float64     `zid:"4" deprecated:"true"` // a deprecated field.
  Friend   bool        `zid:"5"`
}

In addition, you'll want to change the type of the deprecated field, substituting struct{} for the old type. By converting the type of the deprecated field to struct{}, it will no longer takes up any space in the Go struct. This saves space. Even if a struct evolves heavily in time (rare), the changes will cause no extra overhead in terms of memory. It also allows the compiler to detect and reject any new writes to the field that are using the old type.

// best practice for deprecation of fields, to save space + get compiler support for deprecation
type A struct {
  Name     string      `zid:"0"`
  Bday     time.Time   `zid:"1"`
  Phone    string      `zid:"2"`
  Sibs     int         `zid:"3"`
  GPA      struct{}    `zid:"4" deprecated:"true"` // a deprecated field should have its type changed to struct{}, as well as being marked deprecated:"true".
  Friend   bool        `zid:"5"`
}

Rules for safe data changes: To preserve forwards/backwards compatible changes, you must never remove a field from a struct, once that field has been defined and used. In the example above, the zid:"4" tag must stay in place, to prevent someone else from ever using 4 again. This allows sane data forward evolution, without tears, fears, or crashing of servers. The fact that struct{} fields take up no space also means that there is no need to worry about loss of performance when deprecating. We retain all fields ever used for their zebra ids, and the compiled Go code wastes no extra space for the deprecated fields.

NB: There is one exception to this struct{} consumes no space rule: if the newly deprecated struct{} field happens to be the very last field in a struct, it will take up one pointer worth of space. If you want to deprecate the last field in a struct, if possible you should move it up in the field order (e.g. make it the first field in the Go struct), so it doesn't still consume space; reference https://github.com/golang/go/issues/17450.

schema

what does a schema look like? See https://github.com/glycerine/zebrapack/blob/master/testdata/my.go and https://github.com/glycerine/zebrapack/blob/master/testdata/my.z.json for example:

First here is (a shortened version of) the go file that we parsed. The zebraSchemaId64 is a random number generated with a quick command line call to zebrapack -genid. Assigning a zebraSchemaId64 in your Go source/schema can avoid format ambiguity.

package main

import (
    "time"
)

const zebraSchemaId64 = 0x6eb25cc0f9a3e

func main() {}

type A struct {
    Name   string    `zid:"0" msg:"name"` 
    Bday   time.Time `zid:"1"`
    Phone  string    `zid:"2" msg:"phone,omitempty"`
    Sibs   int       `zid:"3" msg:",omitempty"`
    GPA    float64   `zid:"4"`
    Friend bool      `zid:"5"`
}

Second, here is the (json version) of the zebrapack schema (stored canonically in msgpack2) that corresponds:

{
    "SourcePath": "testdata/my.go",
    "SourcePackage": "main",
    "ZebraSchemaId": 1947397430155838,
    "Structs": [
        {
            "StructName": "A",
            "Fields": [
                {
                    "Zid": 0,
                    "FieldGoName": "Name",
                    "FieldTagName": "name",
                    "FieldTypeStr": "string",
                    "FieldCategory": 23,
                    "FieldPrimitive": 2,
                    "FieldFullType": {
                        "Kind": 2,
                        "Str": "string"
                    }
                },
                {
                    "Zid": 1,
                    "FieldGoName": "Bday",
                    "FieldTagName": "Bday",
                    "FieldTypeStr": "time.Time",
                    "FieldCategory": 23,
                    "FieldPrimitive": 20,
                    "FieldFullType": {
                        "Kind": 20,
                        "Str": "Time"
                    }
                },
                {
                    "Zid": 2,
                    "FieldGoName": "Phone",
                    "FieldTagName": "phone",
                    "FieldTypeStr": "string",
                    "FieldCategory": 23,
                    "FieldPrimitive": 2,
                    "FieldFullType": {
                        "Kind": 2,
                        "Str": "string"
                    },
                    "OmitEmpty": true
                },
                {
                    "Zid": 3,
                    "FieldGoName": "Sibs",
                    "FieldTagName": "Sibs",
                    "FieldTypeStr": "int",
                    "FieldCategory": 23,
                    "FieldPrimitive": 13,
                    "FieldFullType": {
                        "Kind": 13,
                        "Str": "int"
                    },
                    "OmitEmpty": true
                },
                {
                    "Zid": 4,
                    "FieldGoName": "GPA",
                    "FieldTagName": "GPA",
                    "FieldTypeStr": "float64",
                    "FieldCategory": 23,
                    "FieldPrimitive": 4,
                    "FieldFullType": {
                        "Kind": 4,
                        "Str": "float64"
                    }
                },
                {
                    "Zid": 5,
                    "FieldGoName": "Friend",
                    "FieldTagName": "Friend",
                    "FieldTypeStr": "bool",
                    "FieldCategory": 23,
                    "FieldPrimitive": 18,
                    "FieldFullType": {
                        "Kind": 18,
                        "Str": "bool"
                    }
                }
            ]
        }
    ]
}

The official, machine-readable definition of the zebrapack format is given in this file: https://github.com/glycerine/zebrapack/blob/master/zebra/zebra.go

command line flags

  $ zebrapack -h

  Usage of zebrapack:

  -msgp
        generate msgpack2 serializers instead of ZebraPack;
        for backward compatiblity or serializing the zebra
        schema itself.

  -fast-strings
        for speed when reading a string in a message that won't be
     reused, this flag means we'll use unsafe to cast the string
     header and avoid allocation.

  -file go generate
        input file (or directory); default is $GOFILE, which
     is set by the go generate command.

  -genid
        generate a fresh random zebraSchemaId64 value to
     include in your Go source schema

  -io
        create Encode and Decode methods (default true)

  -marshal
        create Marshal and Unmarshal methods (default true)

  -no-embedded-schema
        don't embed the schema in the generated files

  -o string
        output file (default is {input_file}_gen.go

  -schema-to-go string
        (standalone functionality) path to schema in msgpack2
     format; we will convert it to Go, write the Go on stdout,
     and exit immediately

  -tests
        create tests and benchmarks (default true)

  -unexported
        also process unexported types

  -write-schema string
        write schema to this file; - for stdout

zebrapack -msgp as a msgpack2 code-generator

msg:",omitempty" tags on struct fields

If you're using zebrapack -msgp to generate msgpack2 serialization code, then you can use the omitempty tag on your struct fields.

In the following example,

type Hedgehog struct {
   Furriness string `msg:",omitempty"`
}

If Furriness is the empty string, the field will not be serialized, thus saving the space of the field name on the wire.

It is safe to re-use structs even with omitempty. For reference:

from https://github.com/tinylib/msgp/issues/154:

The only special feature of UnmarshalMsg and DecodeMsg (from a zero-alloc standpoint) is that they will use pre-existing fields in an object rather than allocating new ones. So, if you decode into the same object repeatedly, things like slices and maps won't be re-allocated on each decode; instead, they will be re-sized appropriately. In other words, mutable fields are simply mutated in-place.

This continues to hold true, and missing fields on the wire will zero the field in any re-used struct.

NB: Under tuple encoding (https://github.com/tinylib/msgp/wiki/Preprocessor-Directives), for example //msgp:tuple Hedgehog, then all fields are always serialized and the omitempty tag is ignored.

notices

Copyright (c) 2016, Jason E. Aten, Ph.D.

LICENSE: MIT. See https://github.com/glycerine/zebrapack/blob/master/LICENSE

from the original https://github.com/tinylib/msgp README

MessagePack Code Generator Build Status

This is a code generation tool and serialization library for MessagePack. You can read more about MessagePack in the wiki, or at msgpack.org.

Why?

Quickstart

In a source file, include the following directive:

//go:generate zebrapack

The zebrapack command will generate serialization methods for all exported type declarations in the file. If you add the flag -msgp, it will generate msgpack2 rather than ZebraPack format.

For other language's use, schemas can can be written to a separate file using zebrapack -file my.go -write-schema at the shell. (By default schemas are not written to the wire, just as in protobufs/CapnProto/Thrift.)

You can read more about the code generation options here.

Use

Field names can be set in much the same way as the encoding/json package. For example:

type Person struct {
    Name       string `msg:"name"`
    Address    string `msg:"address"`
    Age        int    `msg:"age"`
    Hidden     string `msg:"-"` // this field is ignored
    unexported bool             // this field is also ignored
}

By default, the code generator will satisfy msgp.Sizer, msgp.Encodable, msgp.Decodable, msgp.Marshaler, and msgp.Unmarshaler. Carefully-designed applications can use these methods to do marshalling/unmarshalling with zero heap allocations.

While msgp.Marshaler and msgp.Unmarshaler are quite similar to the standard library's json.Marshaler and json.Unmarshaler, msgp.Encodable and msgp.Decodable are useful for stream serialization. (*msgp.Writer and *msgp.Reader are essentially protocol-aware versions of *bufio.Writer and *bufio.Reader, respectively.)

Features

  • Extremely fast generated code
  • Test and benchmark generation
  • JSON interoperability (see msgp.CopyToJSON() and msgp.UnmarshalAsJSON())
  • Support for complex type declarations
  • Native support for Go's time.Time, complex64, and complex128 types
  • Generation of both []byte-oriented and io.Reader/io.Writer-oriented methods
  • Support for arbitrary type system extensions
  • Preprocessor directives
  • File-based dependency model means fast codegen regardless of source tree size.

Consider the following:

const Eight = 8
type MyInt int
type Data []byte

type Struct struct {
    Which  map[string]*MyInt `msg:"which"`
    Other  Data              `msg:"other"`
    Nums   [Eight]float64    `msg:"nums"`
}

As long as the declarations of MyInt and Data are in the same file as Struct, the parser will determine that the type information for MyInt and Data can be passed into the definition of Struct before its methods are generated.

Extensions

MessagePack supports defining your own types through "extensions," which are just a tuple of the data "type" (int8) and the raw binary. You can see a worked example in the wiki.

Status

Mostly stable, in that no breaking changes have been made to the /msgp library in more than a year. Newer versions of the code may generate different code than older versions for performance reasons. I (@philhofer) am aware of a number of stability-critical commercial applications that use this code with good results. But, caveat emptor.

You can read more about how msgp maps MessagePack types onto Go types in the wiki.

Here some of the known limitations/restrictions:

  • Identifiers from outside the processed source file are assumed (optimistically) to satisfy the generator's interfaces. If this isn't the case, your code will fail to compile.
  • Like most serializers, chan and func fields are ignored, as well as non-exported fields.
  • Encoding of interface{} is limited to built-ins or types that have explicit encoding methods.

If the output compiles, then there's a pretty good chance things are fine. (Plus, we generate tests for you.) Please, please, please file an issue if you think the generator is writing broken code.

Performance

If you like benchmarks, see here and above in the ZebraPack benchmarks section; see here for the benchmark source code.

As one might expect, the generated methods that deal with []byte are faster for small objects, but the io.Reader/Writer methods are generally more memory-efficient (and, at some point, faster) for large (> 2KB) objects.