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testland/eventual-consistency-tests

Build eventual-consistency tests for distributed infrastructure: multi-region replication convergence windows ("within 5s"), monotonic-read guarantees, anti-entropy self-healing, and CRDT merge semantics (OR-Set, G-Counter, LWW, vector clocks). Distinguishes "eventually" from "never" by asserting bounded convergence. Use when the consistency boundary is a cache cluster, replication topology, or CRDT store, not a CQRS command/query split (use cqrs-projection-tests for read-model lag after a command).

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name:
eventual-consistency-tests
description:
Build eventual-consistency tests for distributed infrastructure: multi-region replication convergence windows ("within 5s"), monotonic-read guarantees, anti-entropy self-healing, and CRDT merge semantics (OR-Set, G-Counter, LWW, vector clocks). Distinguishes "eventually" from "never" by asserting bounded convergence. Use when the consistency boundary is a cache cluster, replication topology, or CRDT store, not a CQRS command/query split (use cqrs-projection-tests for read-model lag after a command).
metadata:
{"keywords":"eventual-consistency, convergence, monotonic-reads, crdt, vector-clocks"}

eventual-consistency-tests

"Eventually consistent" is a system property; "eventually" without a bound is not testable. Tests bind the window, assert convergence, and verify conflict-resolution rules.

When to use

  • Distributed cache (Redis cluster), multi-region database, CRDT store, async-projection CQRS read model.
  • SLA promises "data converges within X seconds" - test the bound.
  • Replicated counters, sets, registers (CRDTs) - verify merge semantics.

Step 1 - Define the convergence window per workflow

Document target windows:

WorkflowTarget windowSource
Cart update visibility across regions≤ 2s P95SLA
Search index update after product change≤ 30sProduct spec
Audit log replication to backup region≤ 60sCompliance
User profile update across mobile + web≤ 5sUX requirement

Tests assert each.

Step 2 - Convergence-window assertion test

def test_cart_update_converges_within_2s_across_regions():
    cart_service_us.add_item(user_id="u1", sku="sku1")

    deadline = time.time() + 2.0
    while time.time() < deadline:
        eu_cart = cart_service_eu.get(user_id="u1")
        if any(item.sku == "sku1" for item in eu_cart.items):
            return
        time.sleep(0.05)

    pytest.fail("Cart did not converge across regions within 2s")

The exact window is per-system; the test pattern is deadline + poll + assert.

Step 3 - Monotonic-read test

Assert a session never sees a value older than one it already read:

def test_monotonic_reads_per_session():
    session = client.connect(read_preference="monotonic")
    initial = session.get("counter")  # = 5

    # Even if the write-leader replicates lazily, this session
    # never sees a value < initial
    for _ in range(100):
        v = session.get("counter")
        assert v >= initial, f"Read regressed: {initial} → {v}"

Step 4 - Anti-entropy / repair test

Test that replica divergence eventually self-heals via the store's background repair:

def test_anti_entropy_repairs_drift():
    # Simulate write to leader; suppress replication to follower
    leader.write("k1", "v1")
    pause_replication(leader, follower)
    leader.write("k1", "v2")
    resume_replication(leader, follower)

    # Manual replication path failed; rely on anti-entropy
    deadline = time.time() + 60
    while time.time() < deadline:
        if follower.read("k1") == "v2":
            return
        time.sleep(2.0)

    pytest.fail("Anti-entropy did not repair within 60s")

Step 5 - CRDT merge tests

For CRDT-based stores (Riak, Redis-CRDT, AntidoteDB, Yjs, Automerge), test the merge semantics directly. G-Counter (grow-only counter) merges to the max per actor:

def test_g_counter_merges_to_max_per_actor():
    """G-Counter (grow-only counter) merge = max per actor."""
    counter_a = GCounter(actor="a")
    counter_b = GCounter(actor="b")

    counter_a.increment(3)  # {a: 3}
    counter_b.increment(5)  # {b: 5}

    merged = counter_a.merge(counter_b)
    assert merged.value() == 8  # 3 + 5

Per CRDT theory: merge must be commutative, associative, idempotent (CmRDT) or use a join-semilattice (CvRDT).

LWW-register and OR-Set merge tests are in references/crdt-and-vector-clock-tests.md.

Step 6 - Vector-clock causality test

Assert vector clocks order causal events and flag concurrent ones: dominates(a, b) is true when a causally follows b, and false both ways when they are concurrent. Conflict-resolution rules use causality: dominates → prefer the descendant; concurrent → tiebreak per business rule (LWW, merge).

Full worked test is in references/crdt-and-vector-clock-tests.md.

Step 7 - Read-repair on inconsistent quorum

def test_read_repair_propagates_freshest_value():
    """Quorum read sees mismatched values; system writes back the freshest."""
    cluster.write("k1", "v1", consistency="quorum")
    pause_replication_to(node_3)
    cluster.write("k1", "v2", consistency="quorum")

    # node_3 still has v1; node_1 + node_2 have v2
    assert node_1.local_read("k1") == "v2"
    assert node_2.local_read("k1") == "v2"
    assert node_3.local_read("k1") == "v1"

    # Quorum read sees mismatch → triggers read-repair
    cluster.read("k1", consistency="quorum")
    time.sleep(2.0)

    # node_3 should now have v2
    assert node_3.local_read("k1") == "v2"

Step 8 - Bounded staleness assertion

Distinct from window: "all reads no more than X seconds stale":

def test_bounded_staleness_under_2_seconds():
    leader.write("counter", time.time())
    time.sleep(2.5)  # exceed bound

    for replica in replicas:
        ts = float(replica.read("counter"))
        staleness = time.time() - ts
        assert staleness <= 2.0, f"Replica {replica} stale by {staleness:.2f}s"

Anti-patterns

Anti-patternWhy it failsFix
Test "eventually consistent" with no time boundUntestable; can hangDefine + assert window (Step 1, Step 2)
Skip CRDT merge property testsSubtle merge bugs shipStep 5
Read after write, expect immediate freshnessDefeats async replicationTest the contracted window
Use single-region cluster for testsDoesn't surface cross-region driftMulti-region setup or simulation
No anti-entropy testDrift accumulates; never detectedStep 4

Limitations

  • Real-world convergence depends on load + network + clock drift; quiet-test-bench results don't predict prod.
  • Some "eventually consistent" stores (DynamoDB strong reads) have modes that bypass eventual semantics - verify which mode tests exercise.
  • CRDT property tests benefit from property-based testing (qa-property-based) - combine.

References

  • Fowler - Event Sourcing - replay determinism foundation
  • Fowler - CQRS - read-model eventual-consistency framing
  • saga-transaction-tests, event-sourcing-tests, cqrs-projection-tests - sister skills
  • CRDT theory - Shapiro et al., "A comprehensive study of Convergent and Commutative Replicated Data Types" (INRIA report)
  • mvcc-isolation-tests (in the qa-concurrency plugin) - per-DB transaction isolation (different consistency dimension)
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testland
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https://github.com/testland/qa
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