How to Replicate Data Across All Shards in Python

Mocks a global table that replicates a key-value pair to every shard, ensuring reads return the same value from any shard.

Easy Python 3.9+ Aug 9, 2026 Database scaling & optimization 14 views 0 copies

sharding replication distributed systems dataclasses

Python code

35 lines

Python 3.9+

from dataclasses import dataclass
from typing import Dict, List


@dataclass
class Shard:
    id: str
    data: Dict[str, int]


class GlobalTable:
    def __init__(self, shards: List[Shard]):
        self._shards = {s.id: s for s in shards}

    def set_value(self, key: str, value: int) -> None:
        """Replicate a key-value pair to all shards."""
        for shard in self._shards.values():
            shard.data[key] = value

    def get_value(self, key: str) -> int:
        """Return the value from the first shard (same on all)."""
        first = next(iter(self._shards.values()))
        return first.data[key]


if __name__ == "__main__":
    shard_a = Shard(id="shard-a", data={"counter": 0})
    shard_b = Shard(id="shard-b", data={"counter": 10})
    table = GlobalTable([shard_a, shard_b])

    table.set_value("counter", 42)

    print(shard_a.data["counter"])
    print(shard_b.data["counter"])
    print(table.get_value("counter"))

Output

stdout

42
42
42

How it works

The GlobalTable class stores shards in a dictionary keyed by shard ID, and set_value iterates over all shards to write the same key-value pair to each one. The get_value method returns from the first shard, which is safe because replication guarantees consistency. The @dataclass decorator auto-generates __init__ methods for the Shard class, keeping the code concise. This pattern mirrors real-world data replication where writes fan out to all replicas for read consistency.

Common mistakes

Assuming shards are always in the same order when using `next(iter(...))` — use a consistent ordering strategy if needed
Forgetting that mutations via `set_value` are synchronous and block until all shards are updated
Ignoring failure handling when a shard write fails or is temporarily unavailable

Variations

Use a `for shard in self._shards.values()` loop with error logging for partial failures instead of silently succeeding
Store shards in a list and maintain a separate index for faster first-shard access

Real-world use cases

Testing distributed database logic where every read replica must return the same value after a write.
Simulating leaderless replication in a classroom or interview environment before implementing with actual databases.
Building a lightweight in-memory mock for integration tests that need multi-node consistency without external dependencies.

How to Replicate Data Across All Shards in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from Database scaling & optimization

Tutorials

Quizzes

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from Database scaling & optimization

Keep learning

Tutorials

Quizzes