serialize-data-formatsSerialize+deserialize across JSON|XML|YAML|Protobuf|MessagePack|Arrow/Parquet. Format selection, encode/decode patterns, perf tradeoffs, interop. Use → choose wire format for API, persist structured data to disk, exchange between langs, optimize size|speed, migrate formats.
Independent GitHub snapshot · Automated classification · Original documentation governs
Select+impl right serialization format → correct encode/decode + perf awareness.
| Format | Human Readable | Schema | Size | Speed | Best For |
|---|---|---|---|---|---|
| JSON | Yes | Optional (JSON Schema) | Medium | Medium | REST APIs, config, broad interop |
| XML | Yes | XSD, DTD | Large | Slow | Enterprise/legacy, SOAP, documents |
| YAML | Yes | Optional | Medium | Slow | Config files, CI/CD, Kubernetes |
| Protocol Buffers | No | Required (.proto) | Small | Fast | gRPC, microservices, mobile |
| MessagePack | No | None | Small | Fast | Real-time, embedded, Redis |
| Arrow/Parquet | No | Built-in | Very Small | Very Fast | Analytics, columnar queries, data lakes |
Decision tree:
→ Format selected w/ documented rationale.
If err: reqs conflict (human + fast) → prioritize primary use case + note tradeoff.
import json
from datetime import datetime, date
from dataclasses import dataclass, asdict
@dataclass
class Measurement:
sensor_id: str
value: float
unit: str
timestamp: datetime
# Custom encoder for non-standard types
class CustomEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, datetime):
return obj.isoformat()
if isinstance(obj, date):
return obj.isoformat()
if isinstance(obj, bytes):
import base64
return base64.b64encode(obj).decode('ascii')
return super().default(obj)
# Serialize
measurement = Measurement("sensor-01", 23.5, "celsius", datetime.now())
json_str = json.dumps(asdict(measurement), cls=CustomEncoder, indent=2)
# Deserialize
data = json.loads(json_str)
# R: JSON with jsonlite
library(jsonlite)
# Serialize
df <- data.frame(sensor_id = "sensor-01", value = 23.5, unit = "celsius")
json_str <- jsonlite::toJSON(df, auto_unbox = TRUE, pretty = TRUE)
# Deserialize
df_back <- jsonlite::fromJSON(json_str)
→ Round-trip preserves all types accurately.
If err: type lost (dates → strings) → add explicit conversion in deserialize.
.proto:
syntax = "proto3";
package sensors;
message Measurement {
string sensor_id = 1;
double value = 2;
string unit = 3;
int64 timestamp_ms = 4; // Unix milliseconds
}
message MeasurementBatch {
repeated Measurement measurements = 1;
}
Gen+use:
# Generate Python code
protoc --python_out=. sensors.proto
# Generate Go code
protoc --go_out=. sensors.proto
from sensors_pb2 import Measurement, MeasurementBatch
import time
# Serialize
m = Measurement(
sensor_id="sensor-01",
value=23.5,
unit="celsius",
timestamp_ms=int(time.time() * 1000)
)
binary = m.SerializeToString() # Compact binary
# Deserialize
m2 = Measurement()
m2.ParseFromString(binary)
→ Binary 3-10x smaller than JSON.
If err: protoc unavail → lang-native lib (betterproto Py).
import msgpack
from datetime import datetime
# Custom packing for datetime
def encode_datetime(obj):
if isinstance(obj, datetime):
return {"__datetime__": True, "s": obj.isoformat()}
return obj
def decode_datetime(obj):
if "__datetime__" in obj:
return datetime.fromisoformat(obj["s"])
return obj
data = {"sensor_id": "sensor-01", "value": 23.5, "ts": datetime.now()}
# Serialize (smaller than JSON, faster than JSON)
packed = msgpack.packb(data, default=encode_datetime)
# Deserialize
unpacked = msgpack.unpackb(packed, object_hook=decode_datetime, raw=False)
→ Output 15-30% smaller than JSON for typical payloads.
If err: lang lacks MessagePack → fallback JSON+gzip.
import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
# Create data
df = pd.DataFrame({
"sensor_id": ["s-01", "s-02", "s-01", "s-03"] * 1000,
"value": [23.5, 18.2, 24.1, 19.8] * 1000,
"unit": ["celsius"] * 4000,
"timestamp": pd.date_range("2025-01-01", periods=4000, freq="min")
})
# Write Parquet (columnar, compressed)
table = pa.Table.from_pandas(df)
pq.write_table(table, "measurements.parquet", compression="snappy")
# Read Parquet (can read specific columns without loading all data)
table_back = pq.read_table("measurements.parquet", columns=["sensor_id", "value"])
df_subset = table_back.to_pandas()
# R: Parquet with arrow
library(arrow)
# Write
df <- data.frame(sensor_id = rep("s-01", 1000), value = rnorm(1000))
arrow::write_parquet(df, "measurements.parquet")
# Read (with column selection — only reads selected columns from disk)
df_back <- arrow::read_parquet("measurements.parquet", col_select = c("value"))
→ Parquet 5-20x smaller than CSV for tabular.
If err: Arrow unavail → fastparquet (Py)|CSV+gzip fallback.
import json, msgpack, time
import pyarrow as pa, pyarrow.parquet as pq
data = [{"id": i, "value": i * 0.1, "label": f"item-{i}"} for i in range(10000)]
# JSON
start = time.perf_counter()
json_bytes = json.dumps(data).encode()
json_time = time.perf_counter() - start
# MessagePack
start = time.perf_counter()
msgpack_bytes = msgpack.packb(data)
msgpack_time = time.perf_counter() - start
print(f"JSON: {len(json_bytes):>8} bytes, {json_time*1000:.1f} ms")
print(f"MsgPack: {len(msgpack_bytes):>8} bytes, {msgpack_time*1000:.1f} ms")
→ Benchmarks guide format for prod.
If err: insufficient perf any format → consider compression (zstd, snappy) as orthogonal optimization.
!!python/object tags. Always safe loaders.design-serialization-schema — schema design, versioning, evolutionimplement-pharma-serialisation — pharma serialisation (diff domain, same naming)create-quarto-report — data output for reports