Implicit Conversions — Common SQL Performance… | OneNoughtOne

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Database Management SystemsCommon Performance Issues

Common SQL Performance Issues

LevelAdvanced

Duration90 mins

TopicCommon Performance Issues

2 / 5

Implicit Conversions

The Silent Index Killer

Imagine you've carefully indexed your database. Your queries should be blazingly fast. Yet some persist in scanning entire tables, ignoring perfectly good indexes. You've checked everything—the columns are indexed, the statistics are fresh, the query looks correct. What could possibly be wrong? Often, the culprit is implicit type conversion —also known as implicit casting, type coercion, or silent conversion . This occurs when the database engine automatically converts data from one type to another to satisfy a comparison, and in doing so, renders your index completely useless. The insidious nature of implicit conversions: They generate no errors or warnings — The query executes successfully They produce correct results — The data returned is accurate They're invisible in the query text — Nothing looks wrong They destroy performance — Full table scans occur silently This combination makes implicit conversions one of the most dangerous performance anti-patterns. Queries that 'work' in development become production nightmares, and the root cause is nearly invisible without careful analysis.

What You Will Learn

By the end of this page, you will understand how type systems work in major databases, why implicit conversions prevent index usage, how to detect conversion-related performance issues, and how to write conversion-safe SQL that maintains index eligibility.

How Implicit Conversions Work

When you compare values of different types in SQL, the database must convert one type to another before comparison can occur. This conversion follows a type precedence hierarchy defined by the SQL standard and extended by each database vendor. The Type Precedence Principle: When two different types must be compared, the database converts the type with lower precedence to the type with higher precedence . This ensures no data loss during conversion. General Type Precedence (highest to lowest): datetime types (highest) float / real decimal / numeric money (database-specific) bigint int smallint tinyint bit nvarchar / nchar (Unicode strings) varchar / char (non-Unicode strings) binary / varbinary (lowest) Why This Matters for Indexes: Indexes store values in a specific type. When you search with a different type, the database must decide: convert the search value, or convert every indexed value? Following type precedence, it converts the lower precedence side—which is often the column, not the literal value you're searching for.

implicit_conversion_example.sql
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-- Table definition
CREATE TABLE accounts (
    account_id INT PRIMARY KEY,
    account_number VARCHAR(20) NOT NULL,  -- Note: VARCHAR type
    balance DECIMAL(15,2)
);
 
-- Index on account_number
CREATE INDEX idx_account_number ON accounts(account_number);
 
-- Query with implicit conversion problem
SELECT * FROM accounts WHERE account_number = 12345;
--                                            ^^^^^
-- 12345 is an INTEGER literal, but account_number is VARCHAR
 
-- What the database must do:
-- Option A: Convert 12345 to '12345' (string) - preserves index
-- Option B: Convert account_number to integer for each row - breaks index
 
-- Per type precedence, INT > VARCHAR, so:
-- The database converts EACH VARCHAR account_number to INT
-- This applies a function to every row, preventing index use
 
-- Result: FULL TABLE SCAN despite index on account_number!
 
-- Correct approach: Use string literal
SELECT * FROM accounts WHERE account_number = '12345';
--                                            ^^^^^^^
-- Types match. Index used. Query runs 1000x faster.

The Devastating Irony

Implicit conversions are especially cruel because the query returns correct results. You don't get an error. You get the right data. You just get it 1000x slower than you should. This is why implicit conversions evade detection until production scale exposes them.

Common Implicit Conversion Scenarios

Understanding the most frequent scenarios where implicit conversions occur helps you recognize and avoid them in your own code. Scenario 1: Numeric Literals Against String Columns This is the most common case. Columns storing codes, IDs, or identifiers as strings are compared against numeric literals:

numeric_vs_string.sql
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-- Phone numbers stored as strings (to preserve leading zeros)
CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    phone_number VARCHAR(15)  -- Includes country codes like +1, leading zeros
);
 
-- BAD: Numeric literal against VARCHAR column
SELECT * FROM customers WHERE phone_number = 5551234567;
-- Database converts VARCHAR column to numeric → full scan
 
-- GOOD: String literal matches column type
SELECT * FROM customers WHERE phone_number = '5551234567';
 
-- ZIP codes (US ZIP codes can start with 0, e.g., "01234")
CREATE TABLE addresses (
    address_id INT PRIMARY KEY,
    zip_code VARCHAR(10)
);
 
-- BAD: Loses leading zeros AND breaks index
SELECT * FROM addresses WHERE zip_code = 01234;
-- This becomes integer 1234, missing "01234" entries entirely!
 
-- GOOD: Preserves value AND uses index
SELECT * FROM addresses WHERE zip_code = '01234';

Scenario 2: Unicode vs. Non-Unicode Strings Comparing NVARCHAR (Unicode) columns with regular string literals, or vice versa, triggers conversion:

unicode_conversion.sql
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-- Unicode column for international data
CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name NVARCHAR(100)  -- Unicode column
);
 
CREATE INDEX idx_product_name ON products(product_name);
 
-- BAD: Non-Unicode literal against NVARCHAR column (SQL Server)
SELECT * FROM products WHERE product_name = 'Widget';
-- 'Widget' is VARCHAR, column is NVARCHAR
-- Per precedence, NVARCHAR > VARCHAR, so column wins
-- But since column has higher precedence, no conversion on column
-- However, collation differences may still cause issues
 
-- BEST PRACTICE: Use N prefix for Unicode literals
SELECT * FROM products WHERE product_name = N'Widget';
-- N prefix makes literal NVARCHAR, matching column exactly
 
-- The opposite problem with VARCHAR column and NVARCHAR literal:
CREATE TABLE legacy_data (
    code VARCHAR(50)
);
CREATE INDEX idx_code ON legacy_data(code);
 
-- BAD: Unicode literal against VARCHAR column
SELECT * FROM legacy_data WHERE code = N'ABC123';
-- NVARCHAR literal has higher precedence than VARCHAR column
-- Column is converted to NVARCHAR → full table scan
 
-- GOOD: Match the column type
SELECT * FROM legacy_data WHERE code = 'ABC123';

Scenario 3: Date/Time Comparisons Date and time types have complex conversion rules, especially when mixing precision levels:

datetime_conversion.sql
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-- Datetime column
CREATE TABLE events (
    event_id INT PRIMARY KEY,
    event_time DATETIME  -- Millisecond precision
);
CREATE INDEX idx_event_time ON events(event_time);
 
-- BAD: String comparison against DATETIME
SELECT * FROM events WHERE event_time = '2024-01-15';
-- '2024-01-15' is a string, must be converted
-- Usually works, but string format ambiguity can cause issues
 
-- BETTER: Explicit date literal or cast
SELECT * FROM events WHERE event_time = CAST('2024-01-15' AS DATE);
 
-- CAUTION: Different datetime types
CREATE TABLE high_precision_events (
    event_id INT PRIMARY KEY,
    event_time DATETIME2(7)  -- Nanosecond precision
);
 
-- Comparing DATETIME2 to DATETIME literal
DECLARE @dt DATETIME = '2024-01-15 10:30:00';
SELECT * FROM high_precision_events WHERE event_time = @dt;
-- DATETIME2 has higher precedence than DATETIME
-- @dt is converted to DATETIME2, but this works since variable is converted
-- No column conversion = index can be used
 
-- Problematic: Function returning older type
SELECT * FROM high_precision_events 
WHERE event_time = GETDATE();  -- GETDATE() returns DATETIME, not DATETIME2
-- If optimizer converts column rather than function result, index broken

Scenario 4: Decimal Precision Mismatches Decimal types with different precision/scale can trigger conversions:

decimal_conversion.sql
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-- High-precision decimal column
CREATE TABLE financial_transactions (
    transaction_id INT PRIMARY KEY,
    amount DECIMAL(19, 4)  -- 19 digits total, 4 after decimal
);
CREATE INDEX idx_amount ON financial_transactions(amount);
 
-- Potential issue: Parameter with different precision
DECLARE @search_amount DECIMAL(10, 2) = 1500.00;
SELECT * FROM financial_transactions WHERE amount = @search_amount;
-- DECIMAL(19,4) vs DECIMAL(10,2)
-- Usually fine, but precision differences can affect optimizer decisions
 
-- BEST PRACTICE: Match parameter precision to column precisely
DECLARE @search_amount DECIMAL(19, 4) = 1500.0000;
SELECT * FROM financial_transactions WHERE amount = @search_amount;
 
-- Float vs Decimal
CREATE TABLE measurements (
    measurement_id INT PRIMARY KEY,
    value DECIMAL(15, 6)
);
CREATE INDEX idx_value ON measurements(value);
 
-- BAD: Float literal
SELECT * FROM measurements WHERE value = 3.14159265359;
-- Unquoted decimal literals are sometimes interpreted as FLOAT
-- FLOAT has higher precedence → DECIMAL column converted → full scan
 
-- GOOD: Explicit DECIMAL
SELECT * FROM measurements WHERE value = CAST(3.14159265359 AS DECIMAL(15,6));

The Parameterization Trap

Application frameworks often parameterize queries for security (preventing SQL injection). However, they may use default types for parameters that don't match your column types. A Java String becomes NVARCHAR even against VARCHAR columns. A .NET integer becomes INT even against BIGINT columns. Always verify parameter types match column types.

Detecting Implicit Conversions

Because implicit conversions are invisible in query text, you need specific techniques to detect them. The execution plan is your primary tool. Detection Methods:

sqlserver_detect_conversion.sql
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-- Method 1: Check XML Execution Plan for CONVERT_IMPLICIT
SET SHOWPLAN_XML ON;
GO
SELECT * FROM accounts WHERE account_number = 12345;
GO
SET SHOWPLAN_XML OFF;
 
-- In the XML output, look for:
-- <ScalarOperator ScalarString="CONVERT_IMPLICIT(int,[account_number],0)">
-- This indicates implicit conversion on the column!
 
-- Method 2: Query the plan cache for implicit conversions
SELECT 
    qs.execution_count,
    qs.total_worker_time / 1000 AS total_cpu_ms,
    qs.total_elapsed_time / 1000 AS total_duration_ms,
    SUBSTRING(qt.text, (qs.statement_start_offset/2)+1,
        ((CASE qs.statement_end_offset
            WHEN -1 THEN DATALENGTH(qt.text)
            ELSE qs.statement_end_offset
        END - qs.statement_start_offset)/2) + 1) AS query_text,
    qp.query_plan
FROM sys.dm_exec_query_stats qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) qt
CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) qp
WHERE CAST(qp.query_plan AS NVARCHAR(MAX)) LIKE '%CONVERT_IMPLICIT%'
ORDER BY qs.total_elapsed_time DESC;
 
-- Method 3: Extended Events for real-time monitoring
CREATE EVENT SESSION [ImplicitConversions] ON SERVER
ADD EVENT sqlserver.plan_affecting_convert
ADD TARGET package0.ring_buffer
WITH (STARTUP_STATE = ON);
 
-- Check for warnings in graphical execution plan
-- Look for yellow warning triangles on operators
-- Hover to see "Type conversion in expression may affect cardinality estimate"

The 'ref' Column in MySQL EXPLAIN

In MySQL's EXPLAIN output, the 'ref' column shows what is compared to the index. 'const' means a constant value (good). 'func' means a function is applied (often indicates conversion). If you see 'func' where you expected 'const', investigate for implicit conversion.

Why Conversions Break Indexes

To understand why implicit conversions destroy index performance, we need to examine how indexes work at a mechanical level. B-Tree Index Structure: Most indexes are B-tree structures. They work by maintaining values in sorted order, enabling binary search (O(log n) lookups). The key insight: values must be compared in their indexed type . When you search for account_number = '12345' against a VARCHAR index: The database navigates the B-tree, comparing '12345' against index entries String comparison rules apply ('12345' < '2' because '1' < '2' alphabetically) The tree traversal finds '12345' efficiently When you search for account_number = 12345 (integer) against the same VARCHAR index: The integer 12345 cannot be directly compared to VARCHAR values To compare, one side must be converted Per type precedence, VARCHAR converts to INT (INT has higher precedence) Every index entry must be converted to INT before comparison This is equivalent to applying a function to every indexed value A function on the indexed column prevents index navigation Result: Full index/table scan

Converting Mermaid diagram...

The Function-on-Column Principle: The fundamental rule is: any operation applied to an indexed column prevents index seek operations . Implicit conversion is just a special case of this rule. These are equivalent from the optimizer's perspective: -- Explicit function on column (obviously breaks index) SELECT * FROM accounts WHERE CAST(account_number AS INT) = 12345; -- Implicit conversion (less obviously breaks index) SELECT * FROM accounts WHERE account_number = 12345; Both require evaluating a function (CAST/CONVERT) against every row's column value. The optimizer cannot 'seek' to a position in the index because the converted values aren't indexed—only the original values are. Why Not Convert the Literal Instead? You might ask: why doesn't the database convert 12345 to '12345' instead of converting every column value? The answer lies in type precedence rules and semantic correctness: Type precedence is absolute — The conversion direction is determined by type hierarchy, not by optimization considerations Semantic correctness — Converting string to integer might fail (e.g., 'ABC' can't become an integer). Converting integer to string always succeeds. Value space differences — '12345' and '012345' are different strings but the same integer. Converting the literal loses this distinction. Some databases (notably PostgreSQL in many cases) are smarter about this and will convert the literal when safe. But relying on this behavior is dangerous—always match types explicitly.

The Optimizer Isn't Stupid—It's Rule-Bound

Database optimizers follow deterministic rules to ensure consistent, correct behavior. Type conversion rules exist to prevent data loss and ensure semantic correctness. The optimizer sacrifices performance to guarantee correctness—it won't 'guess' that your conversion is safe.

Prevention Strategies

Preventing implicit conversions requires discipline at multiple levels: schema design, application code, and query writing. Strategy 1: Schema Design Discipline Choose column types that match how data will be queried:

schema_design.sql
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-- GOOD: Numeric data as numeric types (when appropriate)
CREATE TABLE orders (
    order_id BIGINT PRIMARY KEY,              -- Numeric, no leading zeros
    customer_id BIGINT NOT NULL,              -- Foreign key, numeric
    order_total DECIMAL(19,4) NOT NULL        -- Financial, exact precision
);
 
-- GOOD: Identifier columns as strings when they have string semantics
CREATE TABLE products (
    product_id INT PRIMARY KEY,
    sku VARCHAR(50) NOT NULL,                 -- SKUs often have letters, prefixes
    gtin VARCHAR(14) NOT NULL                 -- Barcodes can have leading zeros
);
 
-- BAD: Inconsistent types across related tables
CREATE TABLE orders (
    customer_id INT NOT NULL                  -- INT here
);
CREATE TABLE customers (
    customer_id BIGINT PRIMARY KEY            -- BIGINT here - MISMATCH!
);
-- Joins between these tables will have implicit conversions
 
-- GOOD: Consistent types for related columns
CREATE TABLE orders (
    customer_id BIGINT NOT NULL
);
CREATE TABLE customers (
    customer_id BIGINT PRIMARY KEY            -- Same type as foreign key
);
 
-- Consider: Document column types in a schema dictionary
-- Enforce type consistency through naming conventions:
--   _id suffix: always BIGINT
--   _code suffix: always VARCHAR
--   _amount suffix: always DECIMAL(19,4)

Strategy 2: Application-Layer Type Matching Ensure your application code uses the correct types when building queries:

application_type_matching.java
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// Java/JDBC Example
 
// BAD: Using wrong parameter type
String accountNumber = "12345";
PreparedStatement stmt = conn.prepareStatement(
    "SELECT * FROM accounts WHERE account_number = ?"
);
stmt.setLong(1, Long.parseLong(accountNumber));  // Sets as BIGINT!
// Database receives numeric value against VARCHAR column → conversion
 
// GOOD: Match parameter type to column type
stmt.setString(1, accountNumber);  // Sets as VARCHAR
// Types match → index used
 
// BAD: Java String for NVARCHAR column (SQL Server JDBC nuance)
stmt.setString(1, "value");  // May use VARCHAR
// NVARCHAR column requires setNString
 
// GOOD: Use setNString for NVARCHAR columns
stmt.setNString(1, "value");  // Ensures NVARCHAR parameter
 
// ORM Considerations (Hibernate/JPA)
// Ensure entity field types match column types precisely
 
@Entity
public class Account {
    @Column(name = "account_number", columnDefinition = "VARCHAR(20)")
    private String accountNumber;  // String field for VARCHAR column
    
    // NOT: private Long accountNumber; (would cause conversion)
}

Strategy 3: Explicit Casting to Correct Direction When conversions are unavoidable, cast the literal/parameter, not the column:

explicit_casting.sql
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-- Scenario: You receive a numeric input but column is VARCHAR
 
-- BAD: Let implicit conversion happen (converts column)
SELECT * FROM accounts WHERE account_number = @input_value;
-- If @input_value is INT and column is VARCHAR → column converted
 
-- GOOD: Explicitly cast the parameter/literal (preserves index)
SELECT * FROM accounts WHERE account_number = CAST(@input_value AS VARCHAR(20));
-- Or: = CONVERT(VARCHAR(20), @input_value)  -- SQL Server
-- Or: = @input_value::VARCHAR               -- PostgreSQL
 
-- Scenario: Date column vs string input
 
-- BAD: String literal against DATETIME column
SELECT * FROM events WHERE event_date = '2024-01-15';
-- String must be converted to datetime (usually safe, but risky)
 
-- BETTER: Explicit type ensures intended comparison
SELECT * FROM events WHERE event_date = CAST('2024-01-15' AS DATE);
 
-- Scenario: Decimal precision matching
 
-- BAD: Potential precision mismatch
SELECT * FROM transactions WHERE amount = 1500.00;
-- Literal may be interpreted as different precision/scale
 
-- GOOD: Explicit precision matching
SELECT * FROM transactions WHERE amount = CAST(1500.00 AS DECIMAL(19,4));

The Golden Rule

When in doubt, always cast the literal/parameter to match the column type, never the reverse. CAST(literal AS column_type) preserves index usage. Implicit conversion of the column always breaks it.

Strategy 4: Code Review and Static Analysis Implement processes to catch conversions before they reach production: Schema-Aware Linters — Tools that know your schema can flag type mismatches in queries Mandatory EXPLAIN Reviews — Require execution plan analysis for new/modified queries Type Documentation — Maintain a column-type reference and enforce consistency Prepared Statement Analysis — Review parameter types in application code against schema

Real-World Case Studies

Let's examine real-world scenarios where implicit conversions caused production issues and how they were resolved. Case Study 1: The Mysterious Slow Lookups Situation: An e-commerce platform experiences slow product lookups. Products are retrieved by SKU, and there's an index on the SKU column. Yet queries take several seconds. Investigation:

case_study_1.sql
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-- Schema (legacy design)
CREATE TABLE products (
    product_id INT PRIMARY KEY,
    sku VARCHAR(20) NOT NULL,
    name NVARCHAR(100),
    price DECIMAL(10,2)
);
CREATE INDEX idx_products_sku ON products(sku);
 
-- Application query (from ORM)
-- Entity class had field: private Long sku; (WRONG TYPE!)
-- Generated query:
SELECT * FROM products WHERE sku = 1234567890;
--                               ^^^^^^^^^^^
-- Numeric literal against VARCHAR column
 
-- Execution plan showed:
-- |--Table Scan (despite index on sku)
-- Predicate: CONVERT_IMPLICIT(bigint, [sku], 0) = 1234567890
 
-- Root cause: ORM entity mapped sku as Long, not String
-- Fix: Changed entity field to String, regenerated mappings
 
-- Result:
-- Before: 4.2 seconds (full scan of 5 million products)
-- After: 2 milliseconds (index seek)
-- Improvement: 2,100x faster

Case Study 2: The Join Performance Cliff Situation: A reporting query joining orders and customers runs fine in development but times out in production. Investigation:

case_study_2.sql
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-- Table schemas (created by different teams at different times)
CREATE TABLE customers (
    customer_id BIGINT PRIMARY KEY,  -- Team A chose BIGINT
    name VARCHAR(100)
);
 
CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT NOT NULL,         -- Team B chose INT
    order_total DECIMAL(10,2)
);
 
CREATE INDEX idx_orders_customer ON orders(customer_id);
 
-- The problematic join
SELECT c.name, SUM(o.order_total)
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.name;
 
-- Execution plan revealed:
-- Hash Join (expected Nested Loop with index)
-- Predicates: CONVERT_IMPLICIT(bigint, [o].[customer_id]) = [c].[customer_id]
 
-- For each order row, customer_id was converted from INT to BIGINT
-- This prevented index use on the orders table
-- 10 million orders × full scan = timeout
 
-- Fix: Altered orders table to match type
ALTER TABLE orders ALTER COLUMN customer_id BIGINT NOT NULL;
-- (Required careful migration, foreign key handling)
 
-- Result:
-- Before: Timeout after 5 minutes
-- After: 3.4 seconds
-- Improvement: Query actually completes!

Case Study 3: The Unicode Trap Situation: A .NET application's string searches are slow despite proper indexing. Investigation:

case_study_3.sql
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-- Schema (ASCII-only column for legacy compatibility)
CREATE TABLE documents (
    doc_id INT PRIMARY KEY,
    doc_code VARCHAR(50) NOT NULL,  -- VARCHAR, not NVARCHAR
    content NVARCHAR(MAX)
);
CREATE INDEX idx_doc_code ON documents(doc_code);
 
-- .NET SqlCommand (using SqlDbType.NVarChar by default for strings)
using (var cmd = new SqlCommand("SELECT * FROM documents WHERE doc_code = @code", conn))
{
    cmd.Parameters.Add("@code", SqlDbType.NVarChar).Value = "DOC123";
    // NVarChar parameter vs VARCHAR column!
}
 
-- Effectively executing:
SELECT * FROM documents WHERE doc_code = N'DOC123';
--                                       ^^^^^^^^
-- Unicode literal against non-Unicode column
 
-- NVARCHAR has higher precedence → VARCHAR column converted
-- Full table scan despite index
 
-- Fix: Explicitly specify VARCHAR parameter type
cmd.Parameters.Add("@code", SqlDbType.VarChar, 50).Value = "DOC123";
 
-- Or use database-level workaround (not recommended):
-- ALTER DATABASE [MyDB] SET PARAMETERIZATION FORCED;
-- (Forces all literals to parameters, may not help)
 
-- Result:
-- Before: 2.8 seconds
-- After: 1 millisecond
-- Improvement: 2,800x faster

Common Theme in Case Studies

In all cases, the queries returned correct data. There were no errors. The only symptom was poor performance. This is why implicit conversions are so dangerous—they fail silently, often not manifesting until production scale.

Database-Specific Behaviors

Different database systems handle implicit conversions with varying degrees of strictness and optimization. Understanding these differences is crucial for multi-database environments. SQL Server: Follows strict type precedence Most aggressive about implicit conversions CONVERT_IMPLICIT visible in execution plans Extended Event plan_affecting_convert for monitoring Unicode (NVARCHAR) vs. non-Unicode (VARCHAR) particularly problematic PostgreSQL: Generally more intelligent about conversion direction Often converts literals rather than columns when safe Still affected by type mismatches in some cases Explicit type system makes conversions visible in EXPLAIN Less prone to accidental conversion issues MySQL: More lenient with implicit conversions May convert in either direction depending on context Silent failures common; EXPLAIN doesn't always show conversion String/number comparisons particularly unpredictable log_queries_not_using_indexes helps detect symptoms Oracle: Strict type precedence similar to SQL Server TO_NUMBER, TO_CHAR, TO_DATE visible in predicate information Explicit casting often required for optimal plans SQL Tuning Advisor can identify conversion issues

Conversion Behavior Comparison
Database	Strictness	Plan Visibility	Common Trap
SQL Server	High	CONVERT_IMPLICIT in XML plan	NVARCHAR vs VARCHAR parameters
PostgreSQL	Medium	Cast notation (::type) in Filter	Integer literals against text columns
MySQL	Low	Often invisible; ref='func' hint	String/number comparison ambiguity
Oracle	High	TO_* functions in predicates	DATE format and NLS settings

Don't Rely on Database 'Smarts'

Even databases that sometimes optimize conversions correctly can fail in edge cases. Explicit type matching is always safer than hoping the optimizer will make the right choice. Write defensive SQL that doesn't depend on optimizer intelligence.

Database Management SystemsCommon Performance Issues

Common SQL Performance Issues

LevelAdvanced

Duration90 mins

TopicCommon Performance Issues

2 / 5

Implicit Conversions

The Silent Index Killer

Imagine you've carefully indexed your database. Your queries should be blazingly fast. Yet some persist in scanning entire tables, ignoring perfectly good indexes. You've checked everything—the columns are indexed, the statistics are fresh, the query looks correct. What could possibly be wrong? Often, the culprit is implicit type conversion —also known as implicit casting, type coercion, or silent conversion . This occurs when the database engine automatically converts data from one type to another to satisfy a comparison, and in doing so, renders your index completely useless. The insidious nature of implicit conversions: They generate no errors or warnings — The query executes successfully They produce correct results — The data returned is accurate They're invisible in the query text — Nothing looks wrong They destroy performance — Full table scans occur silently This combination makes implicit conversions one of the most dangerous performance anti-patterns. Queries that 'work' in development become production nightmares, and the root cause is nearly invisible without careful analysis.

What You Will Learn

By the end of this page, you will understand how type systems work in major databases, why implicit conversions prevent index usage, how to detect conversion-related performance issues, and how to write conversion-safe SQL that maintains index eligibility.

How Implicit Conversions Work

When you compare values of different types in SQL, the database must convert one type to another before comparison can occur. This conversion follows a type precedence hierarchy defined by the SQL standard and extended by each database vendor. The Type Precedence Principle: When two different types must be compared, the database converts the type with lower precedence to the type with higher precedence . This ensures no data loss during conversion. General Type Precedence (highest to lowest): datetime types (highest) float / real decimal / numeric money (database-specific) bigint int smallint tinyint bit nvarchar / nchar (Unicode strings) varchar / char (non-Unicode strings) binary / varbinary (lowest) Why This Matters for Indexes: Indexes store values in a specific type. When you search with a different type, the database must decide: convert the search value, or convert every indexed value? Following type precedence, it converts the lower precedence side—which is often the column, not the literal value you're searching for.

implicit_conversion_example.sql
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-- Table definition
CREATE TABLE accounts (
    account_id INT PRIMARY KEY,
    account_number VARCHAR(20) NOT NULL,  -- Note: VARCHAR type
    balance DECIMAL(15,2)
);
 
-- Index on account_number
CREATE INDEX idx_account_number ON accounts(account_number);
 
-- Query with implicit conversion problem
SELECT * FROM accounts WHERE account_number = 12345;
--                                            ^^^^^
-- 12345 is an INTEGER literal, but account_number is VARCHAR
 
-- What the database must do:
-- Option A: Convert 12345 to '12345' (string) - preserves index
-- Option B: Convert account_number to integer for each row - breaks index
 
-- Per type precedence, INT > VARCHAR, so:
-- The database converts EACH VARCHAR account_number to INT
-- This applies a function to every row, preventing index use
 
-- Result: FULL TABLE SCAN despite index on account_number!
 
-- Correct approach: Use string literal
SELECT * FROM accounts WHERE account_number = '12345';
--                                            ^^^^^^^
-- Types match. Index used. Query runs 1000x faster.

The Devastating Irony

Implicit conversions are especially cruel because the query returns correct results. You don't get an error. You get the right data. You just get it 1000x slower than you should. This is why implicit conversions evade detection until production scale exposes them.

Common Implicit Conversion Scenarios

Understanding the most frequent scenarios where implicit conversions occur helps you recognize and avoid them in your own code. Scenario 1: Numeric Literals Against String Columns This is the most common case. Columns storing codes, IDs, or identifiers as strings are compared against numeric literals:

numeric_vs_string.sql
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-- Phone numbers stored as strings (to preserve leading zeros)
CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    phone_number VARCHAR(15)  -- Includes country codes like +1, leading zeros
);
 
-- BAD: Numeric literal against VARCHAR column
SELECT * FROM customers WHERE phone_number = 5551234567;
-- Database converts VARCHAR column to numeric → full scan
 
-- GOOD: String literal matches column type
SELECT * FROM customers WHERE phone_number = '5551234567';
 
-- ZIP codes (US ZIP codes can start with 0, e.g., "01234")
CREATE TABLE addresses (
    address_id INT PRIMARY KEY,
    zip_code VARCHAR(10)
);
 
-- BAD: Loses leading zeros AND breaks index
SELECT * FROM addresses WHERE zip_code = 01234;
-- This becomes integer 1234, missing "01234" entries entirely!
 
-- GOOD: Preserves value AND uses index
SELECT * FROM addresses WHERE zip_code = '01234';

Scenario 2: Unicode vs. Non-Unicode Strings Comparing NVARCHAR (Unicode) columns with regular string literals, or vice versa, triggers conversion:

unicode_conversion.sql
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-- Unicode column for international data
CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name NVARCHAR(100)  -- Unicode column
);
 
CREATE INDEX idx_product_name ON products(product_name);
 
-- BAD: Non-Unicode literal against NVARCHAR column (SQL Server)
SELECT * FROM products WHERE product_name = 'Widget';
-- 'Widget' is VARCHAR, column is NVARCHAR
-- Per precedence, NVARCHAR > VARCHAR, so column wins
-- But since column has higher precedence, no conversion on column
-- However, collation differences may still cause issues
 
-- BEST PRACTICE: Use N prefix for Unicode literals
SELECT * FROM products WHERE product_name = N'Widget';
-- N prefix makes literal NVARCHAR, matching column exactly
 
-- The opposite problem with VARCHAR column and NVARCHAR literal:
CREATE TABLE legacy_data (
    code VARCHAR(50)
);
CREATE INDEX idx_code ON legacy_data(code);
 
-- BAD: Unicode literal against VARCHAR column
SELECT * FROM legacy_data WHERE code = N'ABC123';
-- NVARCHAR literal has higher precedence than VARCHAR column
-- Column is converted to NVARCHAR → full table scan
 
-- GOOD: Match the column type
SELECT * FROM legacy_data WHERE code = 'ABC123';

Scenario 3: Date/Time Comparisons Date and time types have complex conversion rules, especially when mixing precision levels:

datetime_conversion.sql
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-- Datetime column
CREATE TABLE events (
    event_id INT PRIMARY KEY,
    event_time DATETIME  -- Millisecond precision
);
CREATE INDEX idx_event_time ON events(event_time);
 
-- BAD: String comparison against DATETIME
SELECT * FROM events WHERE event_time = '2024-01-15';
-- '2024-01-15' is a string, must be converted
-- Usually works, but string format ambiguity can cause issues
 
-- BETTER: Explicit date literal or cast
SELECT * FROM events WHERE event_time = CAST('2024-01-15' AS DATE);
 
-- CAUTION: Different datetime types
CREATE TABLE high_precision_events (
    event_id INT PRIMARY KEY,
    event_time DATETIME2(7)  -- Nanosecond precision
);
 
-- Comparing DATETIME2 to DATETIME literal
DECLARE @dt DATETIME = '2024-01-15 10:30:00';
SELECT * FROM high_precision_events WHERE event_time = @dt;
-- DATETIME2 has higher precedence than DATETIME
-- @dt is converted to DATETIME2, but this works since variable is converted
-- No column conversion = index can be used
 
-- Problematic: Function returning older type
SELECT * FROM high_precision_events 
WHERE event_time = GETDATE();  -- GETDATE() returns DATETIME, not DATETIME2
-- If optimizer converts column rather than function result, index broken

Scenario 4: Decimal Precision Mismatches Decimal types with different precision/scale can trigger conversions:

decimal_conversion.sql
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-- High-precision decimal column
CREATE TABLE financial_transactions (
    transaction_id INT PRIMARY KEY,
    amount DECIMAL(19, 4)  -- 19 digits total, 4 after decimal
);
CREATE INDEX idx_amount ON financial_transactions(amount);
 
-- Potential issue: Parameter with different precision
DECLARE @search_amount DECIMAL(10, 2) = 1500.00;
SELECT * FROM financial_transactions WHERE amount = @search_amount;
-- DECIMAL(19,4) vs DECIMAL(10,2)
-- Usually fine, but precision differences can affect optimizer decisions
 
-- BEST PRACTICE: Match parameter precision to column precisely
DECLARE @search_amount DECIMAL(19, 4) = 1500.0000;
SELECT * FROM financial_transactions WHERE amount = @search_amount;
 
-- Float vs Decimal
CREATE TABLE measurements (
    measurement_id INT PRIMARY KEY,
    value DECIMAL(15, 6)
);
CREATE INDEX idx_value ON measurements(value);
 
-- BAD: Float literal
SELECT * FROM measurements WHERE value = 3.14159265359;
-- Unquoted decimal literals are sometimes interpreted as FLOAT
-- FLOAT has higher precedence → DECIMAL column converted → full scan
 
-- GOOD: Explicit DECIMAL
SELECT * FROM measurements WHERE value = CAST(3.14159265359 AS DECIMAL(15,6));

The Parameterization Trap

Application frameworks often parameterize queries for security (preventing SQL injection). However, they may use default types for parameters that don't match your column types. A Java String becomes NVARCHAR even against VARCHAR columns. A .NET integer becomes INT even against BIGINT columns. Always verify parameter types match column types.

Detecting Implicit Conversions

Because implicit conversions are invisible in query text, you need specific techniques to detect them. The execution plan is your primary tool. Detection Methods:

sqlserver_detect_conversion.sql
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-- Method 1: Check XML Execution Plan for CONVERT_IMPLICIT
SET SHOWPLAN_XML ON;
GO
SELECT * FROM accounts WHERE account_number = 12345;
GO
SET SHOWPLAN_XML OFF;
 
-- In the XML output, look for:
-- <ScalarOperator ScalarString="CONVERT_IMPLICIT(int,[account_number],0)">
-- This indicates implicit conversion on the column!
 
-- Method 2: Query the plan cache for implicit conversions
SELECT 
    qs.execution_count,
    qs.total_worker_time / 1000 AS total_cpu_ms,
    qs.total_elapsed_time / 1000 AS total_duration_ms,
    SUBSTRING(qt.text, (qs.statement_start_offset/2)+1,
        ((CASE qs.statement_end_offset
            WHEN -1 THEN DATALENGTH(qt.text)
            ELSE qs.statement_end_offset
        END - qs.statement_start_offset)/2) + 1) AS query_text,
    qp.query_plan
FROM sys.dm_exec_query_stats qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) qt
CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) qp
WHERE CAST(qp.query_plan AS NVARCHAR(MAX)) LIKE '%CONVERT_IMPLICIT%'
ORDER BY qs.total_elapsed_time DESC;
 
-- Method 3: Extended Events for real-time monitoring
CREATE EVENT SESSION [ImplicitConversions] ON SERVER
ADD EVENT sqlserver.plan_affecting_convert
ADD TARGET package0.ring_buffer
WITH (STARTUP_STATE = ON);
 
-- Check for warnings in graphical execution plan
-- Look for yellow warning triangles on operators
-- Hover to see "Type conversion in expression may affect cardinality estimate"

The 'ref' Column in MySQL EXPLAIN

In MySQL's EXPLAIN output, the 'ref' column shows what is compared to the index. 'const' means a constant value (good). 'func' means a function is applied (often indicates conversion). If you see 'func' where you expected 'const', investigate for implicit conversion.

Why Conversions Break Indexes

To understand why implicit conversions destroy index performance, we need to examine how indexes work at a mechanical level. B-Tree Index Structure: Most indexes are B-tree structures. They work by maintaining values in sorted order, enabling binary search (O(log n) lookups). The key insight: values must be compared in their indexed type . When you search for account_number = '12345' against a VARCHAR index: The database navigates the B-tree, comparing '12345' against index entries String comparison rules apply ('12345' < '2' because '1' < '2' alphabetically) The tree traversal finds '12345' efficiently When you search for account_number = 12345 (integer) against the same VARCHAR index: The integer 12345 cannot be directly compared to VARCHAR values To compare, one side must be converted Per type precedence, VARCHAR converts to INT (INT has higher precedence) Every index entry must be converted to INT before comparison This is equivalent to applying a function to every indexed value A function on the indexed column prevents index navigation Result: Full index/table scan

Converting Mermaid diagram...

The Function-on-Column Principle: The fundamental rule is: any operation applied to an indexed column prevents index seek operations . Implicit conversion is just a special case of this rule. These are equivalent from the optimizer's perspective: -- Explicit function on column (obviously breaks index) SELECT * FROM accounts WHERE CAST(account_number AS INT) = 12345; -- Implicit conversion (less obviously breaks index) SELECT * FROM accounts WHERE account_number = 12345; Both require evaluating a function (CAST/CONVERT) against every row's column value. The optimizer cannot 'seek' to a position in the index because the converted values aren't indexed—only the original values are. Why Not Convert the Literal Instead? You might ask: why doesn't the database convert 12345 to '12345' instead of converting every column value? The answer lies in type precedence rules and semantic correctness: Type precedence is absolute — The conversion direction is determined by type hierarchy, not by optimization considerations Semantic correctness — Converting string to integer might fail (e.g., 'ABC' can't become an integer). Converting integer to string always succeeds. Value space differences — '12345' and '012345' are different strings but the same integer. Converting the literal loses this distinction. Some databases (notably PostgreSQL in many cases) are smarter about this and will convert the literal when safe. But relying on this behavior is dangerous—always match types explicitly.

The Optimizer Isn't Stupid—It's Rule-Bound

Database optimizers follow deterministic rules to ensure consistent, correct behavior. Type conversion rules exist to prevent data loss and ensure semantic correctness. The optimizer sacrifices performance to guarantee correctness—it won't 'guess' that your conversion is safe.

Prevention Strategies

Preventing implicit conversions requires discipline at multiple levels: schema design, application code, and query writing. Strategy 1: Schema Design Discipline Choose column types that match how data will be queried:

schema_design.sql
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-- GOOD: Numeric data as numeric types (when appropriate)
CREATE TABLE orders (
    order_id BIGINT PRIMARY KEY,              -- Numeric, no leading zeros
    customer_id BIGINT NOT NULL,              -- Foreign key, numeric
    order_total DECIMAL(19,4) NOT NULL        -- Financial, exact precision
);
 
-- GOOD: Identifier columns as strings when they have string semantics
CREATE TABLE products (
    product_id INT PRIMARY KEY,
    sku VARCHAR(50) NOT NULL,                 -- SKUs often have letters, prefixes
    gtin VARCHAR(14) NOT NULL                 -- Barcodes can have leading zeros
);
 
-- BAD: Inconsistent types across related tables
CREATE TABLE orders (
    customer_id INT NOT NULL                  -- INT here
);
CREATE TABLE customers (
    customer_id BIGINT PRIMARY KEY            -- BIGINT here - MISMATCH!
);
-- Joins between these tables will have implicit conversions
 
-- GOOD: Consistent types for related columns
CREATE TABLE orders (
    customer_id BIGINT NOT NULL
);
CREATE TABLE customers (
    customer_id BIGINT PRIMARY KEY            -- Same type as foreign key
);
 
-- Consider: Document column types in a schema dictionary
-- Enforce type consistency through naming conventions:
--   _id suffix: always BIGINT
--   _code suffix: always VARCHAR
--   _amount suffix: always DECIMAL(19,4)

Strategy 2: Application-Layer Type Matching Ensure your application code uses the correct types when building queries:

application_type_matching.java
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// Java/JDBC Example
 
// BAD: Using wrong parameter type
String accountNumber = "12345";
PreparedStatement stmt = conn.prepareStatement(
    "SELECT * FROM accounts WHERE account_number = ?"
);
stmt.setLong(1, Long.parseLong(accountNumber));  // Sets as BIGINT!
// Database receives numeric value against VARCHAR column → conversion
 
// GOOD: Match parameter type to column type
stmt.setString(1, accountNumber);  // Sets as VARCHAR
// Types match → index used
 
// BAD: Java String for NVARCHAR column (SQL Server JDBC nuance)
stmt.setString(1, "value");  // May use VARCHAR
// NVARCHAR column requires setNString
 
// GOOD: Use setNString for NVARCHAR columns
stmt.setNString(1, "value");  // Ensures NVARCHAR parameter
 
// ORM Considerations (Hibernate/JPA)
// Ensure entity field types match column types precisely
 
@Entity
public class Account {
    @Column(name = "account_number", columnDefinition = "VARCHAR(20)")
    private String accountNumber;  // String field for VARCHAR column
    
    // NOT: private Long accountNumber; (would cause conversion)
}

Strategy 3: Explicit Casting to Correct Direction When conversions are unavoidable, cast the literal/parameter, not the column:

explicit_casting.sql
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-- Scenario: You receive a numeric input but column is VARCHAR
 
-- BAD: Let implicit conversion happen (converts column)
SELECT * FROM accounts WHERE account_number = @input_value;
-- If @input_value is INT and column is VARCHAR → column converted
 
-- GOOD: Explicitly cast the parameter/literal (preserves index)
SELECT * FROM accounts WHERE account_number = CAST(@input_value AS VARCHAR(20));
-- Or: = CONVERT(VARCHAR(20), @input_value)  -- SQL Server
-- Or: = @input_value::VARCHAR               -- PostgreSQL
 
-- Scenario: Date column vs string input
 
-- BAD: String literal against DATETIME column
SELECT * FROM events WHERE event_date = '2024-01-15';
-- String must be converted to datetime (usually safe, but risky)
 
-- BETTER: Explicit type ensures intended comparison
SELECT * FROM events WHERE event_date = CAST('2024-01-15' AS DATE);
 
-- Scenario: Decimal precision matching
 
-- BAD: Potential precision mismatch
SELECT * FROM transactions WHERE amount = 1500.00;
-- Literal may be interpreted as different precision/scale
 
-- GOOD: Explicit precision matching
SELECT * FROM transactions WHERE amount = CAST(1500.00 AS DECIMAL(19,4));

The Golden Rule

When in doubt, always cast the literal/parameter to match the column type, never the reverse. CAST(literal AS column_type) preserves index usage. Implicit conversion of the column always breaks it.

Strategy 4: Code Review and Static Analysis Implement processes to catch conversions before they reach production: Schema-Aware Linters — Tools that know your schema can flag type mismatches in queries Mandatory EXPLAIN Reviews — Require execution plan analysis for new/modified queries Type Documentation — Maintain a column-type reference and enforce consistency Prepared Statement Analysis — Review parameter types in application code against schema

Real-World Case Studies

Let's examine real-world scenarios where implicit conversions caused production issues and how they were resolved. Case Study 1: The Mysterious Slow Lookups Situation: An e-commerce platform experiences slow product lookups. Products are retrieved by SKU, and there's an index on the SKU column. Yet queries take several seconds. Investigation:

case_study_1.sql
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-- Schema (legacy design)
CREATE TABLE products (
    product_id INT PRIMARY KEY,
    sku VARCHAR(20) NOT NULL,
    name NVARCHAR(100),
    price DECIMAL(10,2)
);
CREATE INDEX idx_products_sku ON products(sku);
 
-- Application query (from ORM)
-- Entity class had field: private Long sku; (WRONG TYPE!)
-- Generated query:
SELECT * FROM products WHERE sku = 1234567890;
--                               ^^^^^^^^^^^
-- Numeric literal against VARCHAR column
 
-- Execution plan showed:
-- |--Table Scan (despite index on sku)
-- Predicate: CONVERT_IMPLICIT(bigint, [sku], 0) = 1234567890
 
-- Root cause: ORM entity mapped sku as Long, not String
-- Fix: Changed entity field to String, regenerated mappings
 
-- Result:
-- Before: 4.2 seconds (full scan of 5 million products)
-- After: 2 milliseconds (index seek)
-- Improvement: 2,100x faster

Case Study 2: The Join Performance Cliff Situation: A reporting query joining orders and customers runs fine in development but times out in production. Investigation:

case_study_2.sql
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-- Table schemas (created by different teams at different times)
CREATE TABLE customers (
    customer_id BIGINT PRIMARY KEY,  -- Team A chose BIGINT
    name VARCHAR(100)
);
 
CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT NOT NULL,         -- Team B chose INT
    order_total DECIMAL(10,2)
);
 
CREATE INDEX idx_orders_customer ON orders(customer_id);
 
-- The problematic join
SELECT c.name, SUM(o.order_total)
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.name;
 
-- Execution plan revealed:
-- Hash Join (expected Nested Loop with index)
-- Predicates: CONVERT_IMPLICIT(bigint, [o].[customer_id]) = [c].[customer_id]
 
-- For each order row, customer_id was converted from INT to BIGINT
-- This prevented index use on the orders table
-- 10 million orders × full scan = timeout
 
-- Fix: Altered orders table to match type
ALTER TABLE orders ALTER COLUMN customer_id BIGINT NOT NULL;
-- (Required careful migration, foreign key handling)
 
-- Result:
-- Before: Timeout after 5 minutes
-- After: 3.4 seconds
-- Improvement: Query actually completes!

Case Study 3: The Unicode Trap Situation: A .NET application's string searches are slow despite proper indexing. Investigation:

case_study_3.sql
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-- Schema (ASCII-only column for legacy compatibility)
CREATE TABLE documents (
    doc_id INT PRIMARY KEY,
    doc_code VARCHAR(50) NOT NULL,  -- VARCHAR, not NVARCHAR
    content NVARCHAR(MAX)
);
CREATE INDEX idx_doc_code ON documents(doc_code);
 
-- .NET SqlCommand (using SqlDbType.NVarChar by default for strings)
using (var cmd = new SqlCommand("SELECT * FROM documents WHERE doc_code = @code", conn))
{
    cmd.Parameters.Add("@code", SqlDbType.NVarChar).Value = "DOC123";
    // NVarChar parameter vs VARCHAR column!
}
 
-- Effectively executing:
SELECT * FROM documents WHERE doc_code = N'DOC123';
--                                       ^^^^^^^^
-- Unicode literal against non-Unicode column
 
-- NVARCHAR has higher precedence → VARCHAR column converted
-- Full table scan despite index
 
-- Fix: Explicitly specify VARCHAR parameter type
cmd.Parameters.Add("@code", SqlDbType.VarChar, 50).Value = "DOC123";
 
-- Or use database-level workaround (not recommended):
-- ALTER DATABASE [MyDB] SET PARAMETERIZATION FORCED;
-- (Forces all literals to parameters, may not help)
 
-- Result:
-- Before: 2.8 seconds
-- After: 1 millisecond
-- Improvement: 2,800x faster

Common Theme in Case Studies

In all cases, the queries returned correct data. There were no errors. The only symptom was poor performance. This is why implicit conversions are so dangerous—they fail silently, often not manifesting until production scale.

Database-Specific Behaviors

Different database systems handle implicit conversions with varying degrees of strictness and optimization. Understanding these differences is crucial for multi-database environments. SQL Server: Follows strict type precedence Most aggressive about implicit conversions CONVERT_IMPLICIT visible in execution plans Extended Event plan_affecting_convert for monitoring Unicode (NVARCHAR) vs. non-Unicode (VARCHAR) particularly problematic PostgreSQL: Generally more intelligent about conversion direction Often converts literals rather than columns when safe Still affected by type mismatches in some cases Explicit type system makes conversions visible in EXPLAIN Less prone to accidental conversion issues MySQL: More lenient with implicit conversions May convert in either direction depending on context Silent failures common; EXPLAIN doesn't always show conversion String/number comparisons particularly unpredictable log_queries_not_using_indexes helps detect symptoms Oracle: Strict type precedence similar to SQL Server TO_NUMBER, TO_CHAR, TO_DATE visible in predicate information Explicit casting often required for optimal plans SQL Tuning Advisor can identify conversion issues

Conversion Behavior Comparison
Database	Strictness	Plan Visibility	Common Trap
SQL Server	High	CONVERT_IMPLICIT in XML plan	NVARCHAR vs VARCHAR parameters
PostgreSQL	Medium	Cast notation (::type) in Filter	Integer literals against text columns
MySQL	Low	Often invisible; ref='func' hint	String/number comparison ambiguity
Oracle	High	TO_* functions in predicates	DATE format and NLS settings

Don't Rely on Database 'Smarts'

Even databases that sometimes optimize conversions correctly can fail in edge cases. Explicit type matching is always safer than hoping the optimizer will make the right choice. Write defensive SQL that doesn't depend on optimizer intelligence.

Implicit Conversions

The Silent Index Killer

How Implicit Conversions Work

Common Implicit Conversion Scenarios

Detecting Implicit Conversions

Why Conversions Break Indexes

Prevention Strategies

Real-World Case Studies

Database-Specific Behaviors

There’s more to understand.

Implicit Conversions

The Silent Index Killer

How Implicit Conversions Work

Common Implicit Conversion Scenarios

Detecting Implicit Conversions

Why Conversions Break Indexes

Prevention Strategies

Real-World Case Studies

Database-Specific Behaviors

There’s more to understand.