Key Considerations to avoid Implicit Conversion issues in Oracle to Azure SQL Modernization

 

Overview 

This blog dives into the mechanics of implicit data type conversions and their impact during the post-migration performance optimization phase of heterogeneous database migrations. Drawing from our observed Engineering field patterns across diverse application architectures, this blog explores why certain platforms like ORMs, JDBC drivers, and cross-platform data models are more prone to implicit conversions than others and how they result in performance issues, break indexes, or cause query regressions. You'll gain actionable strategies to detect, mitigate, and design around these pitfalls to ensure a successful and performant data migration to platforms like Azure SQL. 

 

Understanding Implicit Conversions in Database Migrations 

When an application communicates with a database whether through JDBC, ODBC, ADO.NET, or any other data access API it sends parameters and query values in a specific data type format. However, if the type of the value provided by the application does not exactly match the data type of the target column in the database, the database engine attempts to reconcile the mismatch automatically. This automatic adjustment is known as implicit conversion. For instance, a value passed as a string from the application may be compared against a numeric or date column in the database. This occurs because many front-end systems and APIs transmit values as strings by default, even if the underlying business logic expects numbers or dates. Unless the application explicitly parses or casts these values to match the expected types, the database engine must decide how to handle the type mismatch during query execution. In such cases, the engine applies type conversion internally, either to the parameter or the column, based on its own rules. While this feature can simplify application development by allowing flexible data handling, it often introduces engine-specific behavior that becomes more visible during cross engine database migrations, where assumptions built into one system may not hold true in another.  

 

Impact of Implicit Conversions 

Implicit conversions can adversely affect database performance and functionality in several ways, some of which are discussed below: 

• Performance Degradation: When a database performs an implicit conversion, it may bypass indexes, resulting in slower query execution. For example, comparing a VARCHAR column to an INT value in SQL Server can trigger a table scan instead of an index seek, significantly increasing query time. 

• Migration-Specific Issues and Data Integrity Risks: Implicit conversions can cause data loss or incorrect results during a few instances and one such example is, when a column defined as VARCHAR2 in Oracle, which can store Unicode characters by default is mapped to a VARCHAR column in SQL Server, non-ASCII characters such as Chinese, Russian, or Korean may be silently replaced with incorrect characters/symbols. One example of scenario when this can happen: 

        Oracle VARCHAR2 stores Unicode if the database character set is UTF-8 (AL32UTF8), which is common in modern Oracle installations. 

        SQL Server VARCHAR is ANSI/code-page based, so non-ASCII characters are stored differently, unless the column is explicitly declared as                    NVARCHAR. 

-- In Real World this can happen on any other data types

• Maintenance Challenges: Queries relying on implicit conversions are harder to debug and optimize, as these conversions are not explicitly visible in the code and may only surface during performance regressions.  These queries forces the optimizer to compile an execution plan containing scans of large clustered indexes, or tables, instead of a seek resulting in degraded performance

• Execution Overhead and Resource Consumption: Implicit conversions increase execution times for both queries and API calls, as the engine must perform runtime casting operations. This can lead to higher CPU usage, increased logical reads, and memory pressure. 

 

Detection Methods 

Detecting implicit conversions is crucial for optimizing database performance post-migration. The following methods can be employed to detect: 

Query Store (QDS): Use QDS post-migration during load testing to track expensive queries based on cost and surface performance regressions caused by type mismatches. Review execution plans captured in QDS for conversion-related patterns. You can also use custom script like below to query the QDS:

USE <[Replace_with_actual_DB_name]> -- Replace with the actual database name 
GO 

SELECT TOP (100) DB_NAME() AS [Database], qt.query_sql_text AS [Consulta], 
rs.last_execution_time AS [Last Execution Time], 
rs.avg_cpu_time AS [Avg Worker Time], rs.max_cpu_time AS [Max Worker Time], rs.avg_duration AS [Avg Elapsed Time], 
rs.max_duration AS [Max Elapsed Time], rs.avg_logical_io_reads AS [Avg Logical Reads], 
rs.max_logical_io_reads AS [Max Logical Reads], rs.count_executions AS [Execution Count], 
q.last_compile_start_time AS [Creation Time], CAST(p.query_plan AS XML) AS [Query Plan] 
FROM sys.query_store_query_text AS qt JOIN sys.query_store_query AS q 
ON qt.query_text_id = q.query_text_id JOIN sys.query_store_plan AS p 
ON q.query_id = p.query_id JOIN sys.query_store_runtime_stats AS rs 
ON p.plan_id = rs.plan_id 
WHERE CAST(p.query_plan AS NVARCHAR(MAX)) LIKE '%CONVERT_IMPLICIT%' 
AND qt.query_sql_text NOT LIKE '%sys.query_store%' 
ORDER BY rs.avg_cpu_time DESC;

Execution Plans: For the expensive queries, in SSMS, hover over operators like Index Scan to inspect the Predicate. If implicit conversion exists, the plan includes something like “CONVERT_IMPLICIT(<data_type>, ..” 

XML Plan: For a confirmation of above, reviewing the underlying XML execution plan confirms whether implicit conversion is occurring and on which side of the comparison. This technique is particularly valuable when working with parameterized queries or when graphical plan warnings are insufficient. Look for elements like below in the XML plan:

<Warnings> <PlanAffectingConvert ConvertIssue="Seek Plan" Expression="CONVERT_IMPLICIT(.. </Warnings>

Plan Cache Inspection: Custom scripts can be written to scan the Azure SQL plan cache for any instances of CONVERT_IMPLICIT operations. Below is one such script that can be used to find.

SELECT TOP (100) DB_NAME(B.[dbid]) AS [Database], B.[text] AS [SQL_text], 
A.total_worker_time AS [Total Worker Time], A.total_worker_time / A.execution_count AS [Avg Worker Time], 
A.max_worker_time AS [Max Worker Time], A.total_elapsed_time / A.execution_count AS [Avg Elapsed Time], 
A.max_elapsed_time AS [Max Elapsed Time], A.total_logical_reads / A.execution_count AS [Avg Logical Reads],
 A.max_logical_reads AS [Max Logical Reads], A.execution_count AS [Execution Count], 
A.creation_time AS [Creation Time], C.query_plan AS [Query Plan] 
FROM sys.dm_exec_query_stats AS A WITH (NOLOCK) 
CROSS APPLY sys.dm_exec_sql_text(A.plan_handle) AS B CROSS APPLY sys.dm_exec_query_plan(A.plan_handle) AS C 
WHERE CAST(C.query_plan AS NVARCHAR(MAX)) LIKE '%CONVERT_IMPLICIT%' 
AND B.[dbid] = DB_ID() AND B.[text] NOT LIKE '%sys.dm_exec_sql_text%' 
ORDER BY A.total_worker_time DESC

XE event: Extended Events (XE) is valuable in support scenarios when Query Store or telemetry data alone can't pinpoint issues like implicit conversions, especially if plans aren't cached or historical data lacks detail. XE provides real-time capture of plan-affecting convert events, offering granular insights into query behavior that QS might miss during short-lived or dynamic workloads. However, use it sparingly due to overhead, as a targeted diagnostic tool rather than a broad solution. You can use below script to turn it off. Remember to stop and drop the event immediately when you are done collecting.

IF EXISTS (SELECT * FROM sys.server_event_sessions WHERE name = 'Detect_Conversion_Performance_Issues')
    DROP EVENT SESSION [Detect_Conversion_Performance_Issues] ON SERVER;
GO

CREATE EVENT SESSION [Detect_Conversion_Performance_Issues] ON SERVER 
ADD EVENT sqlserver.plan_affecting_convert(
    ACTION(sqlserver.database_name, sqlserver.sql_text)
    WHERE ([sqlserver].[database_name] = N'<Replace_with_your_DB_name>')  -- Replace your DB name
)
ADD TARGET package0.ring_buffer 
WITH (
    MAX_MEMORY = 4096 KB,
    EVENT_RETENTION_MODE = ALLOW_SINGLE_EVENT_LOSS,
    MAX_DISPATCH_LATENCY = 30 SECONDS,
    MEMORY_PARTITION_MODE = NONE,
    TRACK_CAUSALITY = OFF,
    STARTUP_STATE = OFF
);
GO

ALTER EVENT SESSION [Detect_Conversion_Performance_Issues] ON SERVER STATE = START;

-- View the raw Extended Events buffer
SELECT 
    s.name AS session_name,
    t.target_name,
    CAST(t.target_data AS XML) AS raw_buffer_xml
FROM sys.dm_xe_sessions s
JOIN sys.dm_xe_session_targets t ON s.address = t.event_session_address
WHERE s.name = 'Detect_Conversion_Performance_Issues';

Documentation Reference: Microsoft docs on conversion and precedence help explain engine behavior and mappings around implicit conversion triggers. This close look at them along with app developers during the schema/code conversion phase can help better understanding and mitigation.

 

Implicit Conversion: Real-World Example 

To evaluate the impact of implicit conversions in Azure SQL during post-migration scenarios, we created a synthetic workload example using a table named dbo.Customers. It contains one million rows and includes columns such as AccountNumber, CustomerName, PhoneNumber, and JoinDate. The AccountNumber, CustomerName, and PhoneNumber columns were initially defined as VARCHAR, and Nonclustered indexes were created on these fields to enable efficient lookups. From the application layer, parameters were passed using NVARCHAR, which mirrors typical real-world ORM behavior particularly in Java-based applications or when migrating from Oracle, where VARCHAR2 frequently stores Unicode characters. This deliberate mismatch allows us to study the real performance consequences of implicit conversions in Azure SQL’s query execution engine. Although enabling SET STATISTICS XML ON can expose implicit conversions during query execution, our approach tries to reflect how these issues are usually uncovered in real-world scenarios where customers are less aware of this issue. In this case, we used Query Store and execution plan XML inspection 

Problem: Implicit Conversion Due to Type Mismatch:  

A NVARCHAR parameter from the application is compared against a VARCHAR column in the database. This scenario highlights a silent performance regression that can go unnoticed post-migration without detailed plan inspection.   

Query Used: DECLARE ACC NVARCHAR(20) = N’ACC000500’; 

SELECT CustomerID, AccountNumber, CustomerName, PhoneNumber 
FROM dbo.Customers 
WHERE AccountNumber = ACC;

Execution Plan Behavior: 

 

                                                                                                                       Fig: 1            

• SQL Server applies an implicit CONVERT_IMPLICIT(nvarchar, AccountNumber) on the column side as we can see from Fig 1 when you hover to Index Scan and see the Predicate 

• This disables the use of the nonclustered index on AccountNumber, leading to an Index Scan  

• The XML plan includes a <Warning> tag under <PlanAffectingConvert> 

• Extended Events monitoring consistently shows "plan_affecting_convert" warnings indicating suboptimal query plans caused by these conversions 

What's Missing: 

• Type alignment between the query parameter and the column. 

• Awareness that even matching string lengths won’t help if encoding mismatches exist. 

Impact: 

• Index Seek is lost, and full scans are triggered. 

• Higher Exec times and Overall costs observed. 

 

Mitigation via Explicit CAST – Matching the Column’s Type: 

In some cases, especially during post-migration tuning, application teams may not be able to change the database schema, but developers can update the query to explicitly align data types. This scenario simulates such a mitigation where an NVARCHAR parameter is explicitly cast to VARCHAR to match the column’s data type and avoid implicit conversions. 

Query Used: DECLARE ACC NVARCHAR(20) = N'ACC000500'; SELECT CustomerID, AccountNumber, CustomerName, PhoneNumber FROM dbo.Customers WHERE AccountNumber = CAST(@acc AS VARCHAR(20)); -- Explicit use of CAST

 

Execution Plan Behavior: 

 

                                                                                                               Fig: 2 

• The CAST operation ensures that the parameter side matches the VARCHAR column type. 

• SQL performs an Index Seek on the IX_AccountNumber index. The Seek Predicates as seen in Fig 2 confirms this showing Scalar “Operator(CONVERT..” 

• No <Warning> tag appears in the XML execution plan indicating the absence of implicit conversions. 

 

What's Fixed: 

• Type mismatch is resolved on the query side without altering the database schema. 

• The query is now SARGable, enabling index usage. 

Impact: 

• Index Seek is lost, and full scans are triggered. 

• Higher Exec times and Overall costs observed. 

What's Still Missing: 

• This still creates a long-term maintainability concern, especially when many queries or columns are affected. 

• Developers must remember to manually CAST in every affected query, increasing code complexity and the chance of inconsistency. 

• Missed CASTs in other queries can still cause implicit conversions, so the issue isn’t eliminated just patched locally. 

 

Fix at DB end – Parameter Usage aligned Schema Column type 

This fix involves altering the column type to NVARCHAR, aligning it with the NVARCHAR parameter passed from the application. It eliminates implicit conversions and enables index seeks, improving performance. However, it’s a database-side adjustment,  the ideal long-term fix lies in ensuring the application sends parameters matching the original column type. 

 

Query Used: DECLARE ACC NVARCHAR(20) = N'ACC000500'; 

SELECT CustomerID, AccountNumber, CustomerName, PhoneNumber 
FROM dbo.Customers 
WHERE AccountNumber = CAST(@acc AS VARCHAR(20));

Execution Plan Behavior: 

 

                                                                                                                 Fig: 3 

 

• The CAST operation ensures that the parameter side matches the VARCHAR column type. 

• As seen in Fig 3 an Index Seek is performed on the updated IX_AccountNumber index. The Seek Predicates confirm this showing “Scalar Operator(..” 

• No <Warning> tag appears in the XML execution plan indicating the absence of implicit conversions. 

 

What's Fixed: 

• The fix is schema-driven and works universally, ensuring consistent performance across tools and interfaces. 

• Encoding alignment between the parameter and column removes conversion logic entirely, making query plans stable and predictable. 

Impact: 

• Indexes remain fully usable without manual intervention in queries. 

• Application code stays clean, no casts or workarounds are needed. 

• This is the most sustainable fix but may require coordination with application and DB teams. 

What's Still Missing: 

• Some of implications around data type changes will be around data types consuming additional storage, for example NVARCHAR consumes 2 bytes/char, increasing storage when compared to VARCHAR. 

 

Implicit vs Explicit vs Aligned: Execution Plan Behavior Comparison  

 

 

Best Practices for Managing Implicit Conversions 

 

Refactoring Application: Legacy systems, especially those using dynamic SQL or lacking strict type enforcement, are prone to implicit conversion issues. Refactor your application code to leverage strongly typed variables and parameter declarations to ensure data type consistency at the source, minimizing implicit conversions during query execution. 

Explicit Data Type Casting: Use CAST or CONVERT functions to explicitly define conversions, reducing reliance on implicit behavior. In our example we have used CAST, but a CONVERT function would have worked equally well. Both approaches explicitly align the parameter type to the column and avoid implicit conversions, enabling index seek. 

Data Type Alignment: When you are performing heterogenous migrations which involve different database engines, ensure data types are consistent between source and target DB engines. Check official documents thoroughly to know and see the nuances around your data and application convertibility and know the implications like additional storage, collation changes etc. that can negatively affect your business. 

Indexing: Create indexes on columns frequently involved in WHERE filters and JOIN predicates with matching data types to avoid implicit conversions that would cause index seeks to degrade into scans and ensures optimal index utilization by the optimizer.

Early Testing: Conduct thorough post-migration testing using QDS to identify, then drill down on execution plans and performance metrics to identify and resolve conversion-related issues. Early collaboration between Developer and DBA teams will be crucial. 

Tools and Scripts: Utilize SQL Server Migration Assistant (SSMA) for Oracle to identify and change mappings early when you know your application needs. Additionally, use can use custom scripts or third-party tools if necessary to detect implicit conversions in the plan cache. 

 

References 

 

https://learn.microsoft.com/sql/t-sql/data-types/data-type-conversion-database-engine 

https://learn.microsoft.com/sql/t-sql/data-types/data-type-precedence-transact-sql 

 

Final Thoughts

We hope that this post has helped you gain actionable strategies to detect, mitigate, and design around implicit conversions in order to ensure a successful and performant data migration to platforms such as SQL Server or Azure SQL. If you have feedback or suggestions for improving this post, please contact the Azure Databases SQL Customer Success Engineering Team. Thanks for your support!