Here we present a few tips and tricks for squeezing maximum performance out of msgspec. They're presented in order from "sane, definitely a good idea" to "fast, but you may not want to do this".

Every call to a top-level encode function like msgspec.json.encode allocates some temporary internal state used for encoding. While fine for normal use, for maximum performance you'll want to create an Encoder (e.g. msgspec.json.Encoder) once and reuse it for all encoding calls, avoiding paying that setup cost for every call.

>>> import msgspec

>>> encoder = msgspec.json.Encoder()  # Create once

>>> for msg in msgs:
...     data = encoder.encode(msg)  # reuse multiple times

The same goes for decoding. If you're making multiple decode calls in a performance-sensitive code path, you'll want to create a Decoder (e.g. msgspec.json.Decoder) once and reuse it for each call. Since decoders are typed, you may need to create multiple decoders, one for each type.

>>> import msgspec

>>> decoder = msgspec.json.Decoder(list[int])  # Create once

>>> for data in input_buffers:
...     msg = decoder.decode(data)  # reuse multiple times

:doc:`structs` are msgspec's native way of expressing user-defined types. They're :ref:`fast to encode/decode <json-benchmark>` and :ref:`fast to use <struct-benchmark>`. If you have data with a known schema, we recommend defining a msgspec.Struct type (or types) for your schema and preferring that over other types like dict / dataclasses / etc.

By default, msgspec encodes all fields in a Struct type, including optional fields (those configured with a default value). If the default values are known on the decoding end (making serializing them redundant), it may be beneficial to omit default values from the encoded message. This can be done by configuring omit_defaults=True as part of the Struct definition. Omitting defaults reduces the size of the encoded message, and often also improves encoding and decoding performance (since there's less work to do).

For more information, see :ref:`omit_defaults`.

When decoding large inputs, sometimes you're only interested in a few specific fields. Since decoding large objects is inherently allocation heavy, it may be beneficial to define a smaller msgspec.Struct type that only has the fields you require.

For example, say you're interested in decoding some JSON from the Twitter API. A Tweet object has many nested fields on it - perhaps you only care about the tweet text, the user name, and the number of favorites. By defining struct types with only those fields, msgspec can avoid doing unnecessary work decoding fields that are never used.

>>> import msgspec

>>> class User(msgspec.Struct):
...     name: str

>>> class Tweet(msgspec.Struct):
...     user: User
...     full_text: str
...     favorite_count: int

We can then use these types to decode the example tweet json:

>>> tweet = msgspec.json.decode(example_json, type=Tweet)

>>> tweet.user.name
'Twitter Dev'

>>> tweet.user.favorite_count
70

Of course there are downsides to defining smaller "view" types, but if decoding performance is a bottleneck in your workflow, you may benefit from this technique.

For a more in-depth example of this technique, see the :doc:`examples/conda-repodata` example.

Every call to encode / Encoder.encode allocates a new bytes object for the output. msgspec exposes an alternative Encoder.encode_into (e.g. msgspec.json.Encoder.encode_into) that writes into a pre-allocated bytearray instead (possibly reallocating to increase capacity).

This has the following uses.

If you're encoding and writing messages to a socket/file in a hot loop, you may benefit from allocating a single bytearray buffer once and reusing it for every message.

For example:

encoder = msgspec.msgpack.Encoder()

# Allocate a single shared buffer
buffer = bytearray()

for msg in msgs:
    # Encode a message into the buffer at the start of the buffer.
    # Note that this overwrites any previous contents.
    encoder.encode_into(msg, buffer)

    # Write the buffer to the socket
    socket.sendall(buffer)

A few caveats:

• Encoder.encode_into will expand the capacity of buffer as needed to fit the message size. This means that if a large message is encountered the buffer will be expanded to be equally large, but won't be reduced back to normal afterwards (possibly bloating memory usage). You can use sys.getsizeof (or call bytearray.__sizeof__) directly to determine the actual capacity of the buffer, since len(buffer) will only reflect the part of the buffer that is written to.
• Small messages (for some definition of "small") likely won't see a performance improvement from using this method, and may instead see a slowdown. We recommend using a realistic benchmark to determine if this method can benefit your workload.

Some protocols require appending a suffix to an encoded message. One place where this comes up is when encoding line-delimited JSON, where every payload contains a JSON message followed by b"\n".

This could be handled in python as:

import msgspec

json_msg = msgspec.json.encode(["my", "message"])

full_payload = json_msg + b'\n'

However, this results in an unnecessary copy of json_msg, which can be avoided by using msgspec.json.Encoder.encode_into.

import msgspec

encoder = msgspec.json.Encoder()

# Allocate a buffer. We recommend using a small non-empty buffer to
# avoid reallocating for small messages. Choose something larger than
# your common message size, but not excessively large.
buffer = bytearray(64)

# Encode into the existing buffer.
encoder.encode_into(["my", "message"], buffer)

# Append a newline character without copying
buffer.extend(b"\n")

# Write the full buffer to a socket/file/etc...
socket.sendall(buffer)

Some protocols require prepending a prefix to an encoded message. This comes up in Length-prefix framing , where every message is prefixed by its length stored as a fixed-width integer (e.g. a big-endian uint32). Like line-delimited JSON above, this is more efficient to do using Encoder.encode_into to avoid excessive copying.

import msgspec

encoder = msgspec.msgpack.Encoder()

# Allocate a buffer. We recommend using a small non-empty buffer to
# avoid reallocating for small messages. Choose something larger than
# your common message size, but not excessively large.
buffer = bytearray(64)

# Encode into the existing buffer, offset by 4 bytes at the front to
# store the length prefix.
encoder.encode_into(msg, buffer, 4)

# Encode the message length as a 4 byte big-endian integer, and
# prefix the message with it (without copying).
n = len(msg) - 4
buffer[:4] = n.to_bytes(4, "big")

# Write the buffer to a socket/file/etc...
socket.sendall(buffer)

msgspec supports both JSON and MessagePack protocols. The latter is less commonly used, but :ref:`can be more performant <msgpack-benchmark>`. If performance is an issue (and MessagePack is an acceptable solution), you may benefit from using it instead of JSON. And since msgspec supports both protocols with a consistent interface, switching from msgspec.json to msgspec.msgpack should be fairly painless.

Python processes with a large number of long-lived objects, or operations that allocate a large number of objects at once may suffer reduced performance due to Python's garbage collector (GC). By default, msgspec.Struct types implement a few optimizations to reduce the load on the GC (and thus reduce the frequency and duration of a GC pause). If you find that GC is still a problem, and are certain that your Struct types may never participate in a reference cycle, then you may benefit from setting gc=False on your Struct types. Depending on workload, this can result in a measurable decrease in pause time and frequency due to GC passes. See :ref:`struct-gc` for more details.

One touted benefit of JSON and MessagePack is that they're "self-describing" protocols. JSON objects serialize their field names along with their values. If both ends of a connection already know the field names though, serializing them may be an unnecessary cost. If you need higher performance (at the cost of more inscrutable message encoding), you can set array_like=True on a struct definition. Structs with this option enabled are encoded/decoded like array types, removing the field names from the encoded message. This can provide on average another ~2x speedup for decoding (and ~1.5x speedup for encoding).

>>> class Example(msgspec.Struct, array_like=True):
...     my_first_field: str
...     my_second_field: int

>>> x = Example("some string", 2)

>>> msg = msgspec.json.encode(x)

>>> msg
b'["some string",2]'

>>> msgspec.json.decode(msg, type=Example)
Example(my_first_field="some string", my_second_field=2)

When converting raw data into Python types, the internal machinery will treat different datastructures differently. Some type conversions are faster than others.

For example, this model:

class Example(msgspec.Struct):
    items: frozendict[str, int]  # requires Python3.15+ to be used

msg = b'{"items": {"pen": 1, "book": 2}}'
msgspec.json.decode(msg, type=Example)
# Example(items=frozendict({"pen": 1, "book": 2}))

As frozendict does not allow adding items one at a time, msgspec will first parse items as a regular :class:`dict`, and will then convert it to frozendict, which results in the total decoding operation having O(n*2) time complexity. Which might be slow on big dictionaries and consume more memory. Regular :class:`dict` would be more efficient to use.

The same can be said for :class:`tuple` vs :class:`list`:

>>> class Example(msgspec.Struct):
...     items: tuple[str, ...]

>>> msg = b'{"items": ["pen", "book"]}'

>>> msgspec.json.decode(msg, type=Example)
Example(items=("pen", "book"))

We would first create a list object and then convert it to variable-sized tuple, using double the memory and O(n*2) time to do that. This is true for all conversions, that require an intermediate representation.

To achieve the best performance, use collection types that can be constructed natively: list, set, frozenset, fixed-sized tuple, and dict.