Root Cause and Mitigation Patterns for Phantom Reads Under PostgreSQL Transaction Isolation Levels

1. Introduction & Overview

Concurrency anomalies in relational databases are often misunderstood, especially when translating ANSI SQL isolation level definitions to specific database engine implementations like PostgreSQL. A textbook example is the Phantom Read anomaly.

In this article, we dissect the mechanics of Phantom Reads using Python and SQLAlchemy against a PostgreSQL database. We also analyze an unexpected runtime failure encountered during testing—a missing optional C-extension dependency (orjson) in an automated diagnostic CLI—and extract architectural lessons on building resilient database validation tooling.

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2. Architecture and Verification Scenario

To demonstrate how transaction isolation levels behave under concurrent writes, we developed an automated reproduction test using Python's SQLAlchemy targeting PostgreSQL. The goal was to programmatically trigger and observe a Phantom Read by executing range queries concurrently with range-overlapping insertions.

sequenceDiagram
    participant Conn1 as "Transaction 1 (Reader)"
    participant DB as "PostgreSQL Engine"
    participant Conn2 as "Transaction 2 (Writer)"

    Conn1->>DB: "BEGIN (READ COMMITTED)"
    Conn1->>DB: "First Range Query (COUNT)"
    DB-->>Conn1: "Result: 2 rows"
    Note over Conn1,Conn2: "Transaction 1 remains open"
    Conn2->>DB: "BEGIN & INSERT (New matching row) & COMMIT"
    DB-->>Conn2: "Commit successful"
    Conn1->>DB: "Second Range Query (COUNT)"
    DB-->>Conn1: "Result: 3 rows (Phantom Read detected)"
    Conn1->>DB: "ROLLBACK"

💡 For immediate deployment: The complete source code suite (ZIP) for this architecture is available on Gumroad for $0+ (Pay What You Want).

Core Design Logic

• Dynamic Table Lifecycle Management: In each test iteration, the target schema/table is cleanly torn down, rebuilt, and seeded with deterministic fixture rows.
• Concurrent Session Control: Using separate connection handles from sqlalchemy.create_engine, the script orchestrates distinct database sessions (Conn1 and Conn2) with explicit transaction boundaries to simulate race conditions.
• Structured Result Serialization: Verification metrics and raw execution traces are collected as structured payloads and dumped via standard output as formatted JSON.

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3. Dissecting the Runtime Failure and Root Cause

During execution in the target staging environment, the test runner crashed before running any database assertions:

Traceback (most recent call last):
  File "/home/phenox/gemini-sandbox/TOAI_Workspace/V2_SandBox/V2_PROD_20261021_175521_test.py", line 4, in <module>
    import orjson
ModuleNotFoundError: No module named 'orjson'

Post-Mortem Root Cause Analysis

1. Hard External Dependency vs. Portability: The script relied on orjson—a high-performance JSON library implemented in Rust/C—for serialized output and imported it globally at module load time (import orjson). However, the runtime execution container was a minimal environment without pre-installed wheels, and the execution runner lacked a dynamic fallback mechanism.
2. Disconnect Between Logic Correctness and Environment Realities: No matter how sound the concurrency and transaction management code was, the diagnostic runner failed during bootstrap import resolution. When developing infrastructure assessment and database diagnostic CLIs, hard-requiring compiled native dependencies without a standard-library fallback creates brittle operational workflows.

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4. Remediation Strategies and Implementation

To resolve this failure mode, two approaches can be considered:

Approach A: Strict Pre-requisite Packaging

Enforce hard requirements in your dependency manifests (pyproject.toml or requirements.txt) and guarantee container builds install them before test execution:

pip install orjson pydantic sqlalchemy psycopg2-binary

Approach B: Graceful Standard Library Fallback (Recommended)

Make high-throughput native libraries optional enhancements, falling back to Python’s built-in json module when running in ad-hoc, untrusted, or minimal scratch environments.

import sys

try:
    import orjson
    HAS_ORJSON = True
except ImportError:
    import json
    HAS_ORJSON = False

def write_output(data: dict) -> None:
    """
    Safely serialize and write payload to standard output,
    leveraging orjson for speed if available, or standard json as fallback.
    """
    if HAS_ORJSON:
        sys.stdout.buffer.write(orjson.dumps(data, option=orjson.OPT_INDENT_2))
    else:
        sys.stdout.write(json.dumps(data, indent=2, ensure_ascii=False))
    sys.stdout.write("\n")

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5. Key Engineering Takeaways

1. Designing Portable Diagnostic Utilities

While C-extension packages like orjson offer substantial throughput improvements for high-volume data processing, diagnostic and verification CLI tools must prioritize zero-setup portability. Treating performance libraries as optional accelerations with standard-library defaults guarantees diagnostic tools execute reliably across heterogeneous CI/CD nodes and production jump hosts.

2. PostgreSQL Isolation Nuances

In PostgreSQL, the default READ COMMITTED mode reads a fresh snapshot on every query statement, naturally allowing concurrent commits to change row counts (as observed in the Phantom Read diagram).

Switching to REPEATABLE READ in PostgreSQL uses Snapshot Isolation, which freezes the snapshot to the start of the transaction and prevents classic Phantom Reads for plain SELECT statements. However, when concurrent transactions attempt mutations on the same range or update conflicting rows, serialization anomalies can still occur unless explicit pessimistic row locks (SELECT ... FOR UPDATE) or true serializability (SERIALIZABLE isolation with SSI - Serializable Snapshot Isolation) are applied.

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