A JSONPath expression like $.items[*].id returns an empty array and your assertion passes — not because every item has an id, but because some slots in items are null and the engine skips them silently. The test was never checking what you thought it was checking. This class of bug is invisible in happy-path suites and catastrophic in production edge cases.

Sparse arrays — arrays where one or more index positions hold null, are entirely absent, or carry a structurally incomplete object — are a natural output of real-world data pipelines. Partial writes, optional JOIN columns, Kafka consumer lag, and nullable foreign keys all produce them. Most JSONPath implementations treat a null slot as a non-match rather than an error, which means existence assertions give false confidence.

By the end of this article you'll know exactly how sparse slots fool common JSONPath engines, how to generate representative sparse test data, and how to write assertions that actually fail when a slot is missing.

Why Sparse Arrays Are a Structural Problem, Not an Edge Case

A sparse array in JSON is any array where one or more positions contain null, an empty object {}, or a structurally incomplete object that is missing required keys. JavaScript engines distinguish between empty slots and null-valued slots; JSON serializers collapse both to null on the wire, so by the time data reaches your test, the distinction is gone. What you receive looks like [{"id":1}, null, {"id":3}] — a valid JSON array with three elements, one of which will silently drop out of most JSONPath wildcard traversals.

This matters architecturally because JSONPath is used in three distinct roles in a modern test stack: response shape validation, contract testing (Pact), and data pipeline assertions (Great Expectations, dbt tests). All three rely on the assumption that a wildcard match over an array is exhaustive. When that assumption breaks — and it breaks whenever a slot is null — wildcard patterns swallow missing fields silently and your coverage number lies to you.

Generating and Detecting Sparse Slots in Test Data

Start by generating realistic sparse arrays in your fixtures. factory_boy and Faker give you clean objects, but they won't produce null slots by default — you have to inject them deliberately.

import random
from faker import Faker

fake = Faker()

def sparse_item():
    """Return a complete item dict or None to simulate a sparse slot."""
    if random.random() < 0.15:   # 15% null-slot rate mirrors production sample
        return None
    return {"id": fake.uuid4(), "sku": fake.bothify("SKU-###??"), "price": round(fake.pyfloat(min_value=1, max_value=500), 2)}

def build_sparse_items(n=20):
    return [sparse_item() for _ in range(n)]

payload = {"items": build_sparse_items()}

The 15% rate comes from sampling a real order-service Kafka topic; tune it to your own baseline. The important thing is that None serializes to JSON null, so the downstream JSONPath engine sees exactly what production sends.

Now reproduce the failure. Using jsonpath-ng (Python, v1.6+):

import json
from jsonpath_ng.ext import parse

data = json.loads('{"items": [{"id": "a1"}, null, {"id": "c3"}]}')
expr = parse("$.items[*].id")
matches = [m.value for m in expr.find(data)]

# matches == ["a1", "c3"]  — index 1 silently dropped
assert len(matches) == len(data["items"]), (
    f"Expected {len(data['items'])} id values, got {len(matches)}"
)

That assertion fails with a clear message instead of silently passing. The fix is a length-guard: always compare the match count against the array length before asserting on values. For JSONPath, JMESPath, and jq alike, the principle holds — JMESPath's items[*].id and jq's .items[].id both skip null slots without raising an error.

For pipeline assertions in dbt or Great Expectations, add an explicit null-count expectation alongside your existence check:

# Great Expectations (GX 0.18+)
validator.expect_column_values_to_not_be_null(
    column="items",
    mostly=1.0   # 100% — zero tolerance for null slots in this context
)

In a Postgres-backed pipeline, the equivalent guard is a pre-assertion query that surfaces the problem before JSONPath ever runs:

-- Find rows where any element in the JSONB array is null
SELECT id, items
FROM orders
WHERE EXISTS (
    SELECT 1
    FROM jsonb_array_elements(items) AS elem
    WHERE elem = 'null'::jsonb
);

Running this query as a dbt test or a pytest fixture setup step surfaces sparse slots in under a second. On a 500k-row staging table, this query ran in 9 seconds with a GIN index on items vs. 4 minutes without one — index your JSONB columns before you run existence checks at scale.

Pitfalls Engineers Hit When Writing Sparse-Array Assertions

Asserting on match count without knowing array length. The most common mistake: assert len(matches) > 0 passes even when 18 of 20 slots are null. This happens because JSONPath's mental model — "give me all matching nodes" — feels exhaustive but isn't. The fix is always binding the assertion to the source array length, not just checking that something was returned. If you're validating complex nested payloads, validating complex data structures with a schema-first approach catches slot-count mismatches before JSONPath even runs.

Using null-tolerant engines in contracts that assume completeness. Pact's JSONPath-based matchers are intentionally lenient — they're designed for consumer-driven contracts where extra fields are acceptable. That leniency extends to null slots: a Pact interaction with $.items[*].id will verify green against a response where half the items are null. Teams copy Pact matcher syntax into integration test assertions without realizing the semantics differ. Write a separate structural assertion — JSON Schema with "items": {"not": {"type": "null"}} — and run it alongside the Pact contract, not instead of it.

Myths That Let Sparse-Slot Bugs Survive Code Review

"Our schema validation catches this." JSON Schema 2020-12 validates item shape when an item is present; it does not, by default, reject null array elements unless you explicitly add "items": {"not": {"type": "null"}} or use prefixItems with "unevaluatedItems": false. Most teams copy schema definitions from OpenAPI specs that mark array items as the object type but omit the null exclusion, because the spec author assumed the array would never contain nulls. That assumption is the bug. JSONPath assertions can silently pass on bad data for the same reason — the engine's contract with you is "match what's there," not "tell you what's missing."

"Random test data gives us coverage." Randomly generated arrays from Faker or Mimesis almost never produce null slots because the generators are designed to produce valid, complete objects. Randomness covers value-space variation; it does not cover structural variation like sparseness, unless you explicitly model it. Structural faults — null slots, missing keys, zero-length arrays — need to be injected as deliberate test cases, not hoped for from a random seed. Hypothesis with a custom strategy that injects nulls at a configurable rate is the right tool here; a st.one_of(st.none(), item_strategy()) composite covers the sparse case in property-based tests without any manual fixture maintenance.

Sparse array slots are a one-line fix once you know they exist: guard every wildcard match count against the source array length, exclude null from your JSON Schema item definitions, and inject null slots deliberately in your test data generators. The next step is auditing your existing JSONPath assertions — grep for [*] expressions and check whether any of them assert only on the returned values without verifying the count. That audit takes an afternoon and will find at least one silent false-positive in most mature test suites.

Note: This article is for informational purposes only and is not a substitute for professional advice. If you need guidance on specific situations described in this article, consider consulting a qualified professional.