Read Uncommitted - Isolation Levels | OneNoughtOne

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Database Management SystemsIsolation Levels

Isolation Levels

LevelIntermediate

Duration90 mins

TopicIsolation Levels

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Read Uncommitted

The Wild West of Isolation Levels

In the spectrum of database isolation levels, Read Uncommitted occupies the extreme end—a territory where the database says, "I'll give you maximum speed, but you're on your own regarding data consistency." It's the isolation level that barely isolates anything, allowing transactions to read data that other transactions haven't yet committed.

This might sound dangerous, and in many contexts, it is. Yet Read Uncommitted exists for a reason, and understanding when and why to use it separates database novices from engineers who truly understand the consistency-performance continuum.

What You Will Learn

By the end of this page, you will understand the precise definition of Read Uncommitted, its behavior regarding all concurrency anomalies (dirty reads, non-repeatable reads, phantom reads), when it's actually appropriate to use, and why most production systems explicitly avoid it. You'll also learn how different database engines implement this level.

Formal Definition of Read Uncommitted

Read Uncommitted is the lowest isolation level defined by the SQL standard (ANSI SQL-92). At this level, a transaction may read data written by other concurrent transactions before those transactions commit. This means you can observe changes that might later be rolled back, leading to decisions based on data that never officially existed in the database.

SQL Standard Definition:

The SQL standard specifies isolation levels in terms of which phenomena they permit or prevent. Read Uncommitted is defined as the level that:

Permits Dirty Reads: A transaction can read uncommitted modifications made by other transactions
Permits Non-Repeatable Reads: A row read twice within a transaction may return different values
Permits Phantom Reads: New rows satisfying a query's predicate may appear between reads

set_isolation_read_uncommitted.sql
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-- Setting Read Uncommitted isolation level in various databases
 
-- SQL Server
SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
BEGIN TRANSACTION;
    SELECT * FROM Accounts WHERE balance > 1000;
    -- This read can see uncommitted data from other transactions
COMMIT;
 
-- Alternative: Use NOLOCK hint (SQL Server specific)
SELECT * FROM Accounts WITH (NOLOCK) WHERE balance > 1000;
 
-- MySQL/MariaDB
SET SESSION TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
START TRANSACTION;
    SELECT * FROM accounts WHERE balance > 1000;
COMMIT;
 
-- PostgreSQL Note: PostgreSQL treats Read Uncommitted as Read Committed
-- This is by design - PostgreSQL's MVCC architecture doesn't support true dirty reads
SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
-- Actually behaves as Read Committed in PostgreSQL
 
-- Oracle Note: Oracle does not support Read Uncommitted
-- The minimum isolation level in Oracle is Read Committed

Implementation Variance

Not all database systems honor Read Uncommitted as defined by the SQL standard. PostgreSQL, for example, silently upgrades Read Uncommitted to Read Committed. Oracle doesn't even offer Read Uncommitted. This means your application's behavior may differ significantly across database platforms if you rely on this isolation level.

The Dirty Read Phenomenon in Depth

A dirty read occurs when one transaction reads data that has been modified by another transaction that has not yet committed. The term "dirty" refers to the fact that the data is in an intermediate, potentially invalid state—it's "dirty" because it hasn't been cleaned up by a commit or rollback.

Why Dirty Reads Are Dangerous:

The fundamental problem with dirty reads is that they violate the consistency guarantee. If Transaction A reads data modified by Transaction B, and Transaction B subsequently rolls back, Transaction A has made decisions based on data that never actually existed in any committed state of the database.

Timeline: Classic Dirty Read Scenario
Time	Transaction A (Reader)	Transaction B (Writer)	Account Balance
T1		BEGIN	$1,000 (committed)
T2		UPDATE: balance = $500	$500 (uncommitted)
T3	BEGIN		$500 (uncommitted)
T4	READ balance → $500 ⚠️		$500 (uncommitted)
T5	Process decision based on $500		$500 (uncommitted)
T6		ROLLBACK	$1,000 (committed)
T7	COMMIT (with incorrect data)		$1,000 (committed)

In this scenario, Transaction A read $500 and made a business decision (perhaps denying a loan application), but the real balance was always $1,000. Transaction B's modification was ephemeral—it never became part of the committed database state.

dirty_read_demonstration.sql
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-- Demonstration of Dirty Read vulnerability
-- Run these in two separate sessions concurrently
 
-- Session 1: Writer Transaction
BEGIN TRANSACTION;
    -- Initial balance: $1,000
    UPDATE BankAccounts 
    SET balance = balance - 500 
    WHERE account_id = 'ACC001';
    
    -- Simulate some processing time
    WAITFOR DELAY '00:00:10';
    
    -- Something goes wrong - rollback!
    ROLLBACK;
-- Balance is back to $1,000
 
-- Session 2: Reader Transaction (at Read Uncommitted)
SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
BEGIN TRANSACTION;
    -- This executes while Session 1 is in progress
    SELECT balance 
    FROM BankAccounts 
    WHERE account_id = 'ACC001';
    -- Returns $500 (dirty data!)
    
    -- Business logic based on dirty data
    IF (SELECT balance FROM BankAccounts WHERE account_id = 'ACC001') < 600
    BEGIN
        -- Deny loan application because "insufficient balance"
        INSERT INTO LoanDecisions (account_id, decision, reason)
        VALUES ('ACC001', 'DENIED', 'Insufficient balance');
    END
COMMIT;
-- Decision was made on data that never really existed!

Critical Implication

Dirty reads can cause cascading failures. If Transaction A makes decisions based on dirty data and those decisions affect other transactions, the corruption spreads. A single rollback in one transaction can invalidate logic in dozens of others that read its uncommitted changes.

Non-Repeatable Reads and Phantom Reads

While dirty reads are the signature problem of Read Uncommitted, this isolation level also permits all other concurrency anomalies. Understanding the full scope of what's allowed helps appreciate just how permissive this level truly is.

Non-Repeatable Reads at Read Uncommitted:

At Read Uncommitted, if you read the same row twice within a transaction, you might see different values—not because of uncommitted changes, but because another transaction committed between your reads. Since Read Uncommitted provides no read locks whatsoever, other transactions can modify and commit data freely.

non_repeatable_read_uncommitted.sql
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-- Non-Repeatable Read at Read Uncommitted level
 
-- Transaction A
SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
BEGIN TRANSACTION;
    -- First read
    SELECT price FROM Products WHERE product_id = 'P100';
    -- Returns: $99.99
    
    -- Some business logic processing...
    -- Meanwhile, Transaction B commits a price change
    
    -- Second read (same query)
    SELECT price FROM Products WHERE product_id = 'P100';
    -- Returns: $129.99 (different value!)
    
    -- Transaction A's calculations may now be inconsistent
    -- It used $99.99 for some logic and $129.99 for other logic
COMMIT;
 
-- Transaction B (executes and commits between A's two reads)
BEGIN TRANSACTION;
    UPDATE Products SET price = 129.99 WHERE product_id = 'P100';
COMMIT;

Phantom Reads at Read Uncommitted:

Similarly, phantom reads occur freely at Read Uncommitted. When you execute a range query twice, new rows may appear (or disappear) because other transactions are inserting or deleting rows that match your query's predicate.

phantom_read_uncommitted.sql
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-- Phantom Read at Read Uncommitted level
 
-- Transaction A
SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
BEGIN TRANSACTION;
    -- First query: count high-value orders
    SELECT COUNT(*) FROM Orders WHERE total_amount > 10000;
    -- Returns: 45 orders
    
    -- Business decision: "We have 45 premium customers this month"
    
    -- Meanwhile, Transaction B inserts new premium orders
    
    -- Second query (same predicate)
    SELECT COUNT(*) FROM Orders WHERE total_amount > 10000;
    -- Returns: 52 orders (phantom rows appeared!)
    
    -- Report now shows inconsistent data within same transaction
COMMIT;
 
-- Transaction B (executes between A's queries)
BEGIN TRANSACTION;
    INSERT INTO Orders (customer_id, total_amount, order_date)
    VALUES 
        ('C201', 15000, GETDATE()),
        ('C202', 12500, GETDATE()),
        ('C203', 18000, GETDATE()),
        ('C204', 11000, GETDATE()),
        ('C205', 25000, GETDATE()),
        ('C206', 13500, GETDATE()),
        ('C207', 16000, GETDATE());
COMMIT;

All Anomalies Permitted

•Dirty Reads: Reading uncommitted changes that may be rolled back
•Non-Repeatable Reads: Same row returning different values on repeated reads
•Phantom Reads: New rows appearing/disappearing in range queries
•Lost Updates: Concurrent writes may overwrite each other (still possible)
•Write Skew: Complex anomalies where constraints are violated across transactions

Implementation Mechanics

Understanding how database engines implement Read Uncommitted reveals why it offers such high performance—and such low guarantees.

Lock-Based Implementation:

In traditional lock-based concurrency control, Read Uncommitted is implemented by simply not acquiring read locks. Write operations still acquire exclusive locks (to prevent concurrent writes to the same data), but read operations proceed without any locking whatsoever.

Lock Behavior at Read Uncommitted vs Other Levels
Operation	Read Uncommitted	Read Committed	Repeatable Read
SELECT (read)	No locks acquired	Short-term S lock	S lock held until commit
UPDATE (write)	X lock until commit	X lock until commit	X lock until commit
Block on uncommitted data?	No - reads dirty data	Yes - waits for commit	Yes - waits for commit
Can cause deadlocks?	Only with writes	Yes	Yes

MVCC Implementation:

Databases using Multi-Version Concurrency Control (MVCC), like PostgreSQL, maintain multiple versions of each row. At Read Uncommitted in MVCC systems, transactions could theoretically read the latest uncommitted version. However, most MVCC databases (PostgreSQL, for example) choose not to implement true dirty reads because:

The version chain already provides excellent read performance without blocking
The complexity of exposing uncommitted versions outweighs any benefit
Dirty reads are rarely needed in practice

This is why PostgreSQL treats Read Uncommitted as Read Committed—there's no performance benefit to true dirty reads in MVCC, only liability.

mvcc_versioning_concept.sql
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-- Conceptual MVCC version chain
-- PostgreSQL uses xmin (transaction ID that created row) 
-- and xmax (transaction ID that deleted/updated row)
 
-- Physical storage might look like:
-- ┌─────────────────────────────────────────────────────────┐
-- │ ROW VERSIONS for account_id = 'ACC001'                  │
-- ├─────────────────────────────────────────────────────────┤
-- │ Version 1: balance=$1000, xmin=100, xmax=150 (old)      │
-- │ Version 2: balance=$500,  xmin=150, xmax=200 (old)      │
-- │ Version 3: balance=$1500, xmin=200, xmax=∞ (current)    │
-- │ Version 4: balance=$1200, xmin=250, xmax=∞ (uncommitted)│
-- └─────────────────────────────────────────────────────────┘
 
-- At Read Committed: Sees Version 3 (latest committed)
-- At Read Uncommitted (true): Would see Version 4 (uncommitted)
-- PostgreSQL Read Uncommitted: Still sees Version 3 (acts as Read Committed)
 
-- SQL Server's implementation differs - it uses row versioning in tempdb
-- when READ_COMMITTED_SNAPSHOT is enabled, but true dirty reads 
-- bypass versioning entirely and read from buffer pool directly

Performance Source

The performance advantage of Read Uncommitted comes from avoiding lock waits entirely. In high-contention scenarios where many transactions read the same rows being updated, Read Uncommitted readers never block. They barrel through, reading whatever they find, without waiting for writers to commit.

SQL Server's NOLOCK Hint: A Deep Dive

In SQL Server environments, you'll frequently encounter the WITH (NOLOCK) table hint. This is functionally equivalent to running a query at Read Uncommitted isolation level, but applied at the statement level rather than the transaction level.

Syntax and Usage:

nolock_usage.sql
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-- NOLOCK hint usage patterns
 
-- Basic usage
SELECT * FROM Orders WITH (NOLOCK) WHERE order_date > '2024-01-01';
 
-- Multiple tables with NOLOCK
SELECT o.order_id, c.customer_name, p.product_name
FROM Orders o WITH (NOLOCK)
JOIN Customers c WITH (NOLOCK) ON o.customer_id = c.customer_id
JOIN Products p WITH (NOLOCK) ON o.product_id = p.product_id
WHERE o.status = 'PENDING';
 
-- READUNCOMMITTED is the full name (equivalent to NOLOCK)
SELECT * FROM Orders WITH (READUNCOMMITTED) WHERE status = 'SHIPPED';
 
-- Combining with other hints
SELECT * FROM LargeTable WITH (NOLOCK, INDEX(idx_date))
WHERE created_date > DATEADD(day, -7, GETDATE());
 
-- WARNING: These approaches are NOT equivalent for write operations
-- NOLOCK on UPDATE/DELETE still acquires write locks
UPDATE Orders WITH (NOLOCK) SET status = 'PROCESSED' 
WHERE order_id = 1001; -- NOLOCK ignored for writes!

Why NOLOCK Became Popular (And Why That's Concerning):

NOLOCK gained popularity in SQL Server environments as a "quick fix" for blocking issues. When queries started timing out due to lock contention, developers discovered that adding WITH (NOLOCK) made the blocking disappear. This led to its widespread—and often inappropriate—use.

The problem is that NOLOCK doesn't solve the underlying contention issue; it papers over it by accepting data inconsistency. Many teams sprinkle NOLOCK throughout their codebases without understanding the implications.

Hidden Dangers of NOLOCK

•Skipped Rows: During page splits or index reorganization, NOLOCK scans may skip rows entirely—not dirty reads, but missing rows
•Duplicate Rows: Similarly, rows may be read twice if they move during the scan
•Corrupted Reads: In extreme cases, NOLOCK can return rows with column values from different versions (torn reads)
•Half-Written LOBs: Large object (LOB) data may be read mid-update, returning partial or garbage data
•Index Corruption Visibility: NOLOCK may expose data from corrupted indexes that would normally be hidden

Beyond Dirty Reads

The most insidious NOLOCK problems aren't dirty reads—they're structural inconsistencies. A NOLOCK scan can skip rows or read them twice due to page movements, returning results that never existed in any database state, committed or uncommitted. This is worse than dirty reads because no rollback will fix it.

Legitimate Use Cases for Read Uncommitted

Despite its dangers, Read Uncommitted has legitimate applications. The key is understanding when data accuracy can be sacrificed for performance without causing business harm.

Criteria for Valid Read Uncommitted Usage:

Results are approximate by nature: If you're calculating rough estimates, minor inconsistencies don't matter
No business decisions depend on precision: Displaying informational metrics, not making transactional choices
The data is rarely or never updated: Historical/archived data, or append-only logs
Contention is extremely high: When blocking would cripple the system and approximate data is better than no data

Appropriate Use Cases

•Dashboard Metrics: Displaying approximate counts, totals, or averages where ±5% variance is acceptable
•Log Analysis Queries: Reading write-heavy log tables for pattern analysis where missing a few records is tolerable
•Reporting on Historical Data: Querying archive tables that are no longer being modified
•Cache Warming: Pre-loading data into application caches where the cache will be refreshed shortly anyway
•Database Health Monitoring: Querying system views to check table sizes, fragmentation levels, or blocked processes
•Full-Text Search Indexing: Building search indexes where minor temporary inconsistencies self-correct

legitimate_nolock_examples.sql
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-- Example 1: Dashboard showing approximate order counts
-- Acceptable: Small variance in displayed count won't affect decisions
SELECT COUNT(*) AS approximate_pending_orders
FROM Orders WITH (NOLOCK)
WHERE status = 'PENDING';
 
-- Example 2: Finding large log tables for archival review
-- Acceptable: Missing a few rows in this analysis has no impact
SELECT 
    OBJECT_NAME(i.object_id) AS table_name,
    SUM(s.row_count) AS approximate_rows
FROM sys.dm_db_partition_stats s WITH (NOLOCK)
JOIN sys.indexes i ON s.object_id = i.object_id 
    AND s.index_id = i.index_id
WHERE i.type_desc = 'CLUSTERED'
GROUP BY i.object_id
HAVING SUM(s.row_count) > 1000000
ORDER BY approximate_rows DESC;
 
-- Example 3: Reading immutable audit log
-- Acceptable: Audit records are written once and never modified
SELECT event_type, event_timestamp, user_id, details
FROM AuditLog WITH (NOLOCK)
WHERE event_timestamp > DATEADD(hour, -24, GETDATE())
ORDER BY event_timestamp DESC;
 
-- Example 4: Checking for blocked processes (meta-query)
-- Acceptable: Monitoring shouldn't add to locking problems
SELECT blocking_session_id, wait_type, wait_time, session_id
FROM sys.dm_exec_requests WITH (NOLOCK)
WHERE blocking_session_id > 0;

The Newspaper Test

Before using Read Uncommitted, ask: "If this query returned completely wrong data, would it make the newspaper?" If the answer is yes—if incorrect data could cause financial loss, safety issues, regulatory violations, or reputational damage—do not use Read Uncommitted under any circumstances.

Anti-Patterns: When NOT to Use Read Uncommitted

Understanding where Read Uncommitted fails is as important as knowing where it succeeds. The following patterns represent dangerous misuses that have caused real-world data integrity disasters.

Never Use Read Uncommitted For

•Financial Calculations: Account balances, payment processing, invoice generation, tax calculations
•Inventory Management: Stock levels, availability checks, reservation systems
•User Authentication: Checking permissions, roles, session validity
•Data Export/Reporting for Compliance: Any report that may be audited or submitted to regulators
•Business Rule Validation: Checking constraints, quotas, limits before transactions
•ETL Processes: Extracting data for warehousing where consistency matters
•Backup/Replication: Creating consistent snapshots of data

antipattern_examples.sql
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-- ❌ ANTI-PATTERN 1: Financial Balance Check
-- NEVER do this - could cause overdrafts or double-spending
SELECT balance FROM Accounts WITH (NOLOCK) 
WHERE account_id = @AccountId;
 
IF @Balance >= @WithdrawalAmount
    UPDATE Accounts SET balance = balance - @WithdrawalAmount 
    WHERE account_id = @AccountId;
-- If balance was dirty and rolls back, you've created money from nothing
 
-- ❌ ANTI-PATTERN 2: Inventory Availability
-- NEVER do this - could result in overselling
DECLARE @Available INT;
SELECT @Available = quantity_on_hand 
FROM Inventory WITH (NOLOCK) 
WHERE product_id = @ProductId;
 
IF @Available >= @OrderQuantity
    INSERT INTO Orders (product_id, quantity) 
    VALUES (@ProductId, @OrderQuantity);
-- Dirty read of quantity could result in selling items that don't exist
 
-- ❌ ANTI-PATTERN 3: Permission Check
-- NEVER do this - could allow unauthorized access
IF EXISTS (
    SELECT 1 FROM UserRoles WITH (NOLOCK) 
    WHERE user_id = @UserId AND role = 'Admin'
)
BEGIN
    EXEC dbo.PerformAdminAction @Action;
END
-- User might be in middle of role revocation - dirty read shows admin access
 
-- ❌ ANTI-PATTERN 4: Aggregate Report for Regulatory Submission
-- NEVER do this - regulators require accurate data
SELECT 
    transaction_type,
    SUM(amount) as total_amount,
    COUNT(*) as transaction_count
FROM Transactions WITH (NOLOCK)
WHERE transaction_date BETWEEN @StartDate AND @EndDate
GROUP BY transaction_type;
-- Could include rolled-back transactions or miss in-flight committed ones

Real-World Consequences

In 2019, a major e-commerce platform experienced mass overselling during a flash sale because their inventory checks used NOLOCK. During the high-concurrency event, multiple transactions read the same dirty inventory count and each believed sufficient stock existed. The result: 340% overselling on popular items, costly refunds, and damaged customer trust.

Better Alternatives to Read Uncommitted

If you're considering Read Uncommitted for performance reasons, there are usually better solutions that provide similar performance without sacrificing consistency.

Alternative Approaches:

Read Uncommitted Alternatives
Problem	Instead of Read Uncommitted	Why It's Better
Long-running reports blocking writers	Database snapshots or read replicas	Get consistent point-in-time data without blocking
High lock contention	Enable Read Committed Snapshot Isolation (RCSI)	Readers don't block writers, but still see committed data
Need for fast approximate counts	Use pre-aggregated summary tables	Periodic refresh provides good-enough data without dirty reads
Dashboard needs real-time-ish metrics	Change Data Capture (CDC) to streaming system	Near-real-time with guaranteed consistency
Hot table with frequent reads/writes	Partition the table to reduce contention	Operations on different partitions don't interfere

rcsi_alternative.sql
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-- Alternative: Enable Read Committed Snapshot Isolation (SQL Server)
-- This gives you MVCC-like behavior at Read Committed level
 
-- Enable at database level (requires exclusive access)
ALTER DATABASE YourDatabase SET READ_COMMITTED_SNAPSHOT ON;
 
-- Now reads at Read Committed level will:
-- - Not block on writers
-- - See the last COMMITTED version of rows
-- - Never return dirty data
-- - Never cause deadlocks with writers
 
-- This is almost always preferable to READ UNCOMMITTED
-- You get the concurrency benefits without dirty read risks
 
-- PostgreSQL uses this by default (MVCC)
-- Oracle uses this by default (consistent reads)
 
-- For approximate counts, consider computed columns or indexed views:
CREATE VIEW dbo.OrderSummary
WITH SCHEMABINDING
AS
SELECT 
    status,
    COUNT_BIG(*) as order_count,
    SUM(ISNULL(total_amount, 0)) as total_amount
FROM dbo.Orders
GROUP BY status;
 
-- Create a unique clustered index to materialize the view
CREATE UNIQUE CLUSTERED INDEX IX_OrderSummary 
ON dbo.OrderSummary (status);
 
-- Now queries against OrderSummary are instant and consistent
SELECT status, order_count FROM dbo.OrderSummary;

RCSI is Usually the Answer

If you're using SQL Server and considering NOLOCK for performance, enable Read Committed Snapshot Isolation instead. It provides the concurrency benefits (readers don't block writers) while maintaining data consistency. The overhead is minimal for most workloads, and it eliminates an entire class of bugs.

Summary: Read Uncommitted

We've thoroughly examined Read Uncommitted—the lowest rung on the isolation ladder. Let's consolidate the key insights:

Key Takeaways

•Definition: Read Uncommitted allows transactions to read uncommitted modifications from other transactions (dirty reads)
•All Anomalies Permitted: Dirty reads, non-repeatable reads, and phantom reads are all possible at this level
•Implementation: Achieved by not acquiring read locks; writes still use exclusive locks
•Platform Variance: PostgreSQL and Oracle don't truly implement dirty reads—PostgreSQL treats it as Read Committed
•NOLOCK Dangers: Beyond dirty reads, NOLOCK can cause skipped rows, duplicate reads, and torn pages during concurrent modifications
•Legitimate Uses: Approximate metrics, log analysis, historical data queries, system monitoring—where accuracy isn't critical
•Never Use For: Financial calculations, inventory, security, compliance reporting, or any data affecting business decisions
•Better Alternatives: RCSI, read replicas, materialized views, and proper indexing usually solve the same performance problems more safely

What's Next:

We've seen the chaos that Read Uncommitted permits. In the next page, we examine Read Committed—the default isolation level in most database systems. You'll learn how adding a single guarantee (preventing dirty reads) dramatically improves data consistency while still providing reasonable concurrency.

Page Complete

You now understand Read Uncommitted isolation in comprehensive detail—its definition, behavior, implementation, appropriate uses, and dangerous anti-patterns. This knowledge helps you recognize when dirty reads might be acceptable and, more importantly, when they absolutely are not.