Database Migration Service Release Notes

Features and enhancements

The following table shows the new features and enhancements introduced in AWS DMS version 3.4.4.

• AWS DMS now supports TLS encryption and TLS or SASL authentication using Amazon MSK and on-premises Kafka cluster as a target. For more information on using encryption and authentication for Kafka endpoints, see Connecting to Kafka using Transport Layer Security (TLS).

Issues resolved in AWS DMS 3.4.4

The issues resolved in AWS DMS 3.4.4 include the following:

• Improved AWS DMS logging on task failures when using Oracle endpoints.
• Improved AWS DMS task execution continues processing when Oracle source endpoints switch roles after Oracle Data Guard fail over.
• Improved error handling treats ORA—12561 as a recoverable error when using Oracle endpoints.
• Fixed an issue where EMPTY_BLOB() and EMPTY_CLOB() columns are migrated as null when using Oracle as a source.
• Fixed an issue where AWS DMS tasks fail to update records after add column DDL changes when using SQL Server as a source.
• Improved PostgreSQL as a source migration by supporting the TIMESTAMP WITH TIME ZONE data type.
• Fixed an issue where the afterConnectScript setting does not work during a full load when using PostgreSQL as a target.
• Introduced a new mapUnboundedNumericAsString setting to better handle the NUMERIC date type without precision and scale when using PostgreSQL endpoints.
• Fixed an issue where AWS DMS tasks fail with “0 rows affected” after stopping and resuming the task when using PostgreSQL as a source.
• Fixed an issue where AWS DMS fails to migrate the TIMESTAMP data type with the BC suffix when using PostgreSQL as a source.
• Fixed an issue where AWS DMS fails to migrate the TIMESTAMP value “±infinity” when using PostgreSQL as a source.
• Fixed an issue where empty strings are treated as NULL when using S3 as a source with the csvNullValue setting set to other values.
• Improved the timestampColumnName extra connection attribute in a full load with CDC to be sortable during CDC when using S3 as a target.
• Improved the handling of binary data types in hex format such as BYTE, BINARY, and BLOB when using S3 as a source.
• Fixed an issue where deleted records are migrated with special characters when using S3 as a target.
• Fixed an issue to handle empty key values when using Amazon DocumentDB (with MongoDB compatibility) as a target.
• Fixed an issue where AWS DMS fails to replicate NumberDecimal or Decimal128 columns when using MongoDB or Amazon DocumentDB (with MongoDB compatibility) as a source.
• Fixed an issue to allow CDC tasks to retry when there is a fail over on MongoDB or Amazon DocumentDB (with MongoDB compatibility) as a source.
• Added an option to remove the hexadecimal “0x” prefix to RAW data type values when using Kinesis, Kafka, or OpenSearch as a target.
• Fixed an issue where validation fails on fixed length character columns when using Db2 LUW as a source.
• Fixed an issue where validation fails when only the source data type or the target data type is FLOAT or DOUBLE.
• Fixed an issue where validation fails on NULL characters when using Oracle as a source.
• Fixed an issue where validation fails on XML columns when using Oracle as a source.
• Fixed an issue where AWS DMS tasks crash when there are nullable columns in composite keys using MySQL as a source.
• Fixed an issue where AWS DMS fails to validate both UNIQUEIDENTIFIER columns from SQL Server source endpoints and UUID columns from PostgreSQL target endpoints.
• Fixed an issue where a CDC task does not use an updated source table definition after it is modified.
• Improved AWS DMS fail over to treat task failures caused by an invalid user name or password as recoverable errors.
• Fixed an issue where AWS DMS tasks fail because of missing LSNs when using RDS for SQL Server as a source.

Change in Handling of Large Numeric Values for Streaming Targets

In AWS DMS version 3.5.1, there is a change in how large integer and high-precision numeric values are handled when streaming data to targets like Kafka and Kinesis. Specifically, AWS DMS changed its internal data type representation, handling these values as INT64 instead of INT8. This shift can result in different data formats on the streaming endpoints, particularly when the values exceed the limits of INT8. Consequently, the representation of these numeric types may differ from their previous formatting when streamed to destinations like Kafka and Kinesis, potentially impacting downstream systems and processes that consume the data from these targets.

Summary of Change

• In previous versions (e.g., 3.4.7/3.4.6), large integer values were represented as integers.
• Starting with version 3.5.1, these values may appear in scientific notation (e.g., 7.88129934789981E15), potentially leading to precision and formatting differences.

Affected Data Types

The recent change affects the representation of several numeric types when streamed to endpoints like Kafka and Kinesis. The impacted types are:

• Large integer types (e.g., bigint)
• Floating-point types (FLOAT, DOUBLE)
• High-precision decimal types (DECIMAL, NUMERIC)

Affected Scenarios

• Full load migrations to streaming targets
• Change Data Capture (CDC) to streaming targets

This change specifically impacts streaming endpoints such as Kafka and Kinesis, while non-streaming targets remain unaffected.

To mitigate this change, you can implement a data type transformation that reverts to the previous formatting, representing large numbers as integers. However, it's important to note that this workaround may not be suitable for all scenarios, as it could potentially introduce limitations or compatibility issues.

Recommendations

• Test your specific use case in a non-production environment before deploying AWS DMS version 3.5.1 or later to identify and address any impacts of this change.
• Affected customers can implement the change-data-type transformation workaround, if applicable, to revert to the previous formatting for large numbers as integers. However, this approach may not suit all scenarios.

We are reviewing this behavior to ensure consistent data type handling across endpoints in future releases.

The following table shows the new features and enhancements introduced in AWS Database Migration Service (AWS DMS) version 3.5.1.

New feature or enhancement | Description

• Support for PostgreSQL 15.x | AWS DMS version 3.5.1 supports PostgreSQL version 15.x. For more information, see Using PostgreSQL as a source and Using PostgreSQL as a target.
• Support for Amazon DocumentDB Elastic Clusters with sharded collections | AWS DMS version 3.5.1 supports Amazon DocumentDB Elastic Clusters with sharded collections. For more information, see Using Amazon DocumentDB as a target for AWS Database Migration Service.
• Support for using Amazon Amazon Redshift Serverless as a target endpoint. For more information, see Using an Amazon Redshift database as a target for AWS Database Migration Service.
• Enhanced PostgreSQL target endpoint settings for providing Babelfish support. For more information, see Using a PostgreSQL database as a target for AWS Database Migration Service.
• AWS DMS 3.5.1 improves the methodology of handling open transactions when starting a CDC-Only task from the Start Position for an Oracle source. For more information, see OpenTransactionWindow in the Endpoint settings when using Oracle as a source for AWS DMS section.
• Support for using Amazon Timestream as a target endpoint. For more information, see Using Amazon Timestream as a target for AWS Database Migration Service.

AWS DMS version 3.5.1 includes the following resolved issues:

• The representation of large numeric values on streaming targets has been updated. Review the 'Handling of Large Numeric Values in Streaming Targets' documentation for details on potential impacts.
• Fixed an issue for Oracle source where CDC-only tasks had continuously growing inactive sessions, resulting in the following exception: ORA-00020: maximum number of processes exceeded on the source database.
• Fixed an issue for DocumentDB as a target where UPDATE statements were not properly replicated in some scenarios.
• Improved error handling for the data validation feature to properly fail the task when data validation is disabled for validation-only tasks.
• Fixed an issue for Amazon Redshift target where the DMS task would not retry applying changes on the target when the target has ParallelApplyThreads set greater than zero after connection termination, which would result in data loss.
• Fixed an issue for MySQL to MySQL replication of mediumtext data types with full-LOB mode.
• Fixed an issue for DMS tasks with BatchApplyEnabled set to true where DMS would stop replicating data after Secrets Manager rotated the password.
• Fixed an issue for MongoDB / DocDB source where range segmentation would not work properly when the primary key column contained a large value.
• Fixed an issue for PostgreSQL target where segmented (parallel) full load was failing due to "connection down" error.
• Fixed an issue for S3 target where a DMS task move operation was taking a very long time or never completing.
• Fixed an issue for PostgreSQL source where a DMS task would throw errors related to duplicates on the target after a task stop and resume.
• Fixed an issue for Oracle target where data validation would report false positive errors due to incorrectly replicated timezone for timestamp fields.
• Fixed an issue for SQL Server source where the DMS task could fail with an error indicating inability to read transaction log backups when Endpoint Setting "SafeguardPolicy": "EXCLUSIVE_AUTOMATIC_TRUNCATION" is specified.
• Fixed an issue for Oracle source where DMS task could fail on data validation due to incorrectly identified Primary Key values.
• Fixed an issue for streaming targets (Kinesis, Kafka) where the "EnableBeforeImage" task setting was working only for character data types.
• Fixed an issue for the Time Travel feature where DMS was creating zero byte time travel log files when the source is idle.
• Fixed an issue of excessive logging when BatchApplyEnabled is set to True.
• MongoDB endpoint setting FullLoadNoCursorTimeout specifies NoCursorTimeout for the full load cursor. NoCursorTimeout is a MongoDB connection setting that prevents the server from closing the cursor if idle.
• New filter function improves performance for migrating MongoDB databases using a single column for segmentation.
• Fixed an issue where DMS fails to create the target table on Amazon Redshift if the MongoDB collection has binary data type.
• New MongoDB SocketTimeoutMS extra connection attribute configures the connection timeout for MongoDB clients in units of milliseconds. If the value is less than or equal to zero, then the MongoDB client default is used.
• Fixed an issue handling null values if a primary key wasn't present in the table when migrating to Amazon Kinesis Data Streams as a target.
• Removed the limitation that data validation of NULL PK/UK values aren't supported.
• Fixed an issue where a few records were incorrectly migrated as NULL when migrating from Oracle to Amazon S3.
• Added the ability for DMS to handle open transactions when using Oracle Standby as a source.
• Fixed an issue where the task failed if the table had an SDO_GEOMETRY column present in the DDL when migrating from Oracle to Oracle.
• Fixed an issue where DMS occasionally skips an Oracle redo log sequence number when using Oracle as a source.
• Fixed an issue so that the DMS task fails when archive logs are missing when using Oracle as a source.
• Fixed an issue where spatial datatypes were not replicating during CDC when replicating from Oracle to Oracle.
• Fixed an issue where the target apply was failing with an ORA-01747 error when using Oracle as a target.
• Fixed an issue where a table reload operation wasn't generating CDC files when using Amazon S3 as a target.
• Fixed an issue to not initialize Availability Groups (AG) if the source is primary and AlwaysOnSharedSyncedBackupIsEnabled is set to true when using SQL Server Always On as a source.
• Fixed an issue where the replication task fails if AlwaysOnSharedSynchedBackupsIsEnabled is set to True when a source endpoint is SQL Server Always On Availability Group and is a secondary replica.
• Fixed an issue where CDC fails to migrate delete/update operations on the PostgreSQL source, which was introduced in 3.4.7 in supporting mapBooleanAsBoolean.

New features in AWS DMS 3.6.1

AWS DMS automatically fixes data inconsistencies identified through data validation between your source and target databases. The data resync feature is supported for Oracle and SQL Server as a source and Postgre SQL as a target databases.

Introduced support connecting to Amazon RDS and Aurora MariaDB, MySQL, and PostgreSQL endpoints through AWS Identity and Access Management (IAM) database authentication. With this enhancement you can use IAM to centrally manage access to your database resources, instead of managing access individually on each database endpoint.

Introduced support for PostgreSQL version 17. For more information, see: Using a PostgreSQL database as an AWS DMS source Using a PostgreSQL database as a target for AWS Database Migration Service

AWS DMS supports using PostgreSQL read replicas as source endpoints for Change Data Capture (CDC) replication, available with PostgreSQL version 16.x and later, starting with AWS DMS version 3.6.1. This feature allows you to leverage read replicas for CDC tasks. For more information, see Using a PostgreSQL database as an AWS DMS source.

AWS DMS version 3.6.1 includes the following resolved issues:

• Table selection rule filters now work correctly with data masking transformation rules. The fix ensures proper filtering when data masking transformations are applied.
• PostgreSQL target endpoints can handle unbound numeric data types through optimized memory allocation. This prevents task failures when migrating tables with large numeric values.
• Redshift target endpoints now maintain LOB data integrity during high-volume parallel migrations. The improved LOB handling prevents data corruption when using parallel apply threads.
• Fixed an issue with expressions in transformation rules that were incorrectly applied.
• Replication tasks now resume successfully when source tables are dropped and recreated during task stoppage. Improved metadata handling and status synchronization for recreated source tables.
• Improved primary replica detection for SQL Server sources in AlwaysOn configurations by handling case sensitivity variations.
• Resolved migration issues with TIME values containing fractions and hours exceeding 24 when loadUsingCSV is disabled.
• Data validation now handles NUL (0x00) characters correctly without failing. Improved validation processing for data containing NUL characters.
• Resolved validation comparison issues with UNIQUEIDENTIFIER columns for SQL Server sources.
• AWS DMS tasks now complete validation successfully when tables are in pre-load phase during CDC replication.
• Oracle source error logs now display the correct SQL statement during timezone query execution. Improved logging accuracy for better troubleshooting capabilities.
• SQL Server driver support is updated from ODBC 17 to ODBC 18.

New features in AWS DMS 3.6.0

Introduced two new metadata variables for transformation rules: $AR_M_MODIFIED_SCHEMA and $AR_M_MODIFIED_TABLE_NAME. These variables allow you to create more dynamic and flexible transformations that can adapt to schema or table name changes during your migration process.

Introduced support for replication of Large Object (LOB) columns when using the "No record found for applying an UPDATE: Insert the missing target record" error-handling option. With this enhancement, you can now accurately replicate LOB columns, ensuring complete and accurate data replication.

AWS DMS version 3.6.0 includes the following resolved issues:

• Resolved excessive memory consumption when using PostgreSQL as a source. Fixed an issue where the Write-Ahead Log (WAL) slot would continuously grow, leading to degraded performance. This update enhances the stability and efficiency of PostgreSQL migrations, especially for large databases or long-running tasks.

• Addressed the issue where wide Character Large Object (CLOB) and Character (CHAR) values containing non-ASCII characters (e.g., special symbols or international characters) were not replicated correctly. This fix ensures accurate replication of large text data, reducing errors, and maintaining data consistency.

• Resolved an issue where the Microsoft SQL Server source endpoint would incorrectly report very high latency. This fix provides more accurate performance metrics, allowing you to better monitor and optimize your SQL Server migration tasks.

• Fixed an issue with redundant calls to retrieve other tables' metadata, which resulted in degraded performance and latency after a table's metadata had been altered. This optimization improves overall task performance, especially for databases with frequent schema changes.

• Fixed an issue where non-standard UPDATE operations caused tasks to terminate unexpectedly without generating error messages. This fix ensures that such operations are processed correctly, avoiding task failures during complex update scenarios.

• Fixed an issue in Batch Optimized Apply mode where Large Object (LOB) lookup would fail to find a record when a DELETE operation and an INSERT operation were combined into a single UPDATE operation. This fix improves data consistency and integrity during migrations involving LOB data and complex Data Manipulation Language (DML) operations.

• Addressed an issue where tasks involving numerous transformations would crash during startup. This fix ensures stability and reliability for tasks with complex transformation logic, enabling you to confidently execute data processing tasks.

• Fixed a failure to capture special format Data Definition Language (DDL) changes during the Change Data Capture (CDC) phase. This fix ensures that all schema changes are properly replicated, maintaining schema consistency between source and target databases throughout the migration process.

New features in AWS DMS 3.5.4

Introduced data masking, allowing transforming sensitive data with options for digit randomization, masking, or hashing at the column level.

Introduced enhanced data validation performance, enabling faster processing for large datasets during full load and CDC migration tasks across select migration paths.

Fixed an issues for PostgreSQL source where degraded migration performance was observed while using filtering. Introduced disableUnicodeSourceFilter ECA to control this behavior.

Introduce support for all transformation rules for the enhanced throughput feature.

AWS DMS version 3.5.4 includes the following resolved issues:

• Fixed an issue for PostgreSQL source where certain events would not be replicated while using the test_decoding plugin.
• Fixed an issue for MySQL to Redshift migrations where a timestamp column would not be defined correctly on the target.
• Fixed an issue for Oracle source with binary reader where DMS task would crash after applying Oracle July 2024 PSU.
• Fixed an issue for MySQL endpoint where credentials would become corrupted when using secrets manager.
• Fixed an issue for Amazon DocumentDB/MongoDB endpoints where some records would be sent to the target twice causing duplicate key exception and AWS DMS task failure.
• Fixed an issue for RDS for SQL Server to noSQL migrations where the document structure was incorrect due to incorrect PK handling.
• Fixed an issue for Oracle source where data validation would report false positives for null or empty LOBs.
• Fixed an issue for PG - Babelfish target where AWS DMS task would fail while replicating tables with PK defined as uniqueidentifier.
• Fixed an issue for PostgreSQL source where AWS DMS MAZ failover would cause a fatal AWS DMS task failure.
• Fixed an issue where LOB data was not replicated correctly when the column order differed between the source and target.
• Fixed an issue for internal DMS database where the AWS DMS task would fail due to internal AWS DMS database concurency issues.
• Fixed an issues for internal AWS DMS database where the AWS DMS task would fail due to the lack of presence of certain internal database objects.
• Fixed an issue for Oracle source where data validation would return false positives for certain rare types of replicated events.
• Fixed an issue for the data validation feature where ceratin unicode data types were not compared properly which resulted in false positives.
• Fixed an issue for parquet target where zero-timestamp was replicated as null.
• Fixed an issue for Babelfish target where the GeoSpatial data type was not supported.
• Fixed an issue for Amazon S3 target where new column additions were not handeled properly when the before image setting was enabled.
• Fixed an issue for SQL Server source where AWS DMS could not automatically implement MS Replication prerequisites on sources using SQL Server 2022 with CU12 or above.
• Fixed an issue for PostgreSQL source where boolean data type would not be migrated correctly while MapBooleanAsBoolean was set to true and pglogical plugin was used.
• Fixed an issue for the TaskrecoveryTableEnabled setting, where the AWS DMS task would fail upon task stop when set to true.
• Fixed an issue where some transactions would be replicated twice when the TaskrecoveryTableEnabled setting was enabled.
• Fixed an issue for MySQL source where AWS DMS task would fail due to inability to read the BINLOG from MySQL v5.5.
• Data validation now prevents memory corruption during data type processing, eliminating fallback to row-by-row validation for affected partitions.
• S3 source to target replication now properly handles external table objects during full load and ongoing replication.
• S3 target CDC replication processes CSV format data correctly during ongoing replication phase.
• Enhanced S3 directory cleanup process prevents task interruptions during migrations.
• Data validation operation now optimizes transitions between validation phases, reducing unnecessary delays.
• Data validation now accurately processes unbound characters and TEXT data types, ensuring correct validation results.
• PostgreSQL source replication maintains connectivity during Multi-AZ failover events, preventing task failures.
• Data validation now correctly compares datetime values when using Babelfish as a target.
• MySQL source replication now correctly handles mid-table duplicate column additions, preventing task interruptions.
• MySQL source replication maintains column sequence integrity when multiple columns are added during CDC operations.
• DynamoDB target replication now correctly processes LOB data during CDC, ensuring complete data transfer.
• PostgreSQL source data validation now correctly interprets boolean data type mappings, producing accurate comparison results.
• Oracle source replication preserves trailing spaces in VARCHAR2(4000) columns when extended data type support is enabled.
• SQL Server source replication maintains connectivity during DDL operations on secondary replicas, preventing task interruptions.
• AWS Secrets Manager connection strings now support special characters while preserving security protocols.
• MongoDB and Amazon DocumentDB replication prevents record duplication that previously triggered key constraint errors.
• Oracle source replication accurately processes timestamp values across various session time zone configurations.
• Oracle source replication now handles data type conversions more robustly, preventing ORA-01460 errors.
• AWS DMS ignores DDL statements for tables not configured in table mappings, preventing unnecessary processing of source database schema changes for unmapped tables.
• When migrating data from source SQL Server view to target endpoints, the column length is correctly preserved and no columns are truncated.
• AWS DMS now correctly identifies the primary replica in Microsoft SQL Server Always On Availability Groups, resolving previous case sensitivity detection errors.
• Fixed an issue where AWS DMS tasks failed when migrating unbound numeric data types to PostgreSQL target endpoints.
• AWS DMS now correctly validates CHAR and VARCHAR data during migrations, eliminating false positive reports in validation tasks.
• AWS DMS prevents data corruption in Large Object (LOB) data during parellel batch operations when using Amazon Redshift as a target endpoint.
• Validation failures are prevented and queries are successfully executed on Amazon S3 target when source column filters are applied to VARCHAR, CHAR, DATE, and DATETIME data types.
• When using Amazon S3 targets, consistent data validation states are maintained throughout Change Data Capture (CDC) operations.
• Fixed an issue where data validation failed due to string formatting errors during record comparison operations.

New features in AWS DMS 3.5.4

Introduced data masking, allowing transforming sensitive data with options for digit randomization, masking, or hashing at the column level.

Introduced enhanced data validation performance, enabling faster processing for large datasets during full load and CDC migration tasks across select migration paths.

Fixed an issues for PostgreSQL source where degraded migration performance was observed while using filtering. Introduced disableUnicodeSourceFilter ECA to control this behavior.

Introduce support for all transformation rules for the enhanced throughput feature.

AWS DMS version 3.5.4 includes the following resolved issues

• Fixed an issue for PostgreSQL source where certain events would not be replicated while using the test_decoding plugin.
• Fixed an issue for MySQL to Redshift migrations where a timestamp column would not be defined correctly on the target.
• Fixed an issue for Oracle source with binary reader where DMS task would crash after applying Oracle July 2024 PSU.
• Fixed an issue for MySQL endpoint where credentials would become corrupted when using secrets manager.
• Fixed an issue for Amazon DocumentDB/MongoDB endpoints where some records would be sent to the target twice causing duplicate key exception and AWS DMS task failure.
• Fixed an issue for RDS for SQL Server to noSQL migrations where the document structure was incorrect due to incorrect PK handling.
• Fixed an issue for Oracle source where data validation would report false positives for null or empty LOBs.
• Fixed an issue for PG - Babelfish target where AWS DMS task would fail while replicating tables with PK defined as uniqueidentifier.
• Fixed an issue for PostgreSQL source where AWS DMS MAZ failover would cause a fatal AWS DMS task failure.
• Fixed an issue where LOB data was not replicated correctly when the column order differed between the source and target.
• Fixed an issue for internal AWS DMS database where the AWS DMS task would fail due to internal AWS DMS database concurency issues.
• Fixed an issues for internal AWS DMS database where the AWS DMS task would fail due to the lack of presence of certain internal database objects.
• Fixed an issue for Oracle source where data validation would return false positives for certain rare types of replicated events.
• Fixed an issue for the data validation feature where ceratin unicode data types were not compared properly which resulted in false positives.
• Fixed an issue for parquet target where zero-timestamp was replicated as null.
• Fixed an issue for Babelfish target where the GeoSpatial data type was not supported.
• Fixed an issue for Amazon S3 target where new column additions were not handeled properly when the before image setting was enabled.
• Fixed an issue for Amazon S3 target where the AWS DMS task would crash after receiving alter table DDL when GlueCatalogGeneration is enabled.
• Fixed an issue for data validation feature where validation would fail on NUL (0x00) characters.
• Fixed an issue for babelfish endpoint where tables names with mixed case would be suspended.
• Fixed an issue for Amazon S3 source where files were not process due to a file name validation issue.
• Fixed an issue for Db2 LUW source where "table-type" option in selection rules was being ignored.
• Fixed an issue for Amazon Redshift target where data loss would occur when ParallelLoadThreads was >0 under certain conditions.
• Enhanced the data validation feature for Amazon Redshift target to support HandleCollationDiff setting.
• Fixed an issue for Amazon S3 target data validation where validation would fail when there were no other columns than the PK in the table.
• Fixed an issue for data validation feature where the CloudWatch metrics would be missing for validation which took a short amount of time to complete.
• Fixed an issue for data validation feature where the re-validation option was unavailable in certain situations.
• Fixed an issue where the maximum number of events per transaction was limited to 201,326,592 under certain conditions.
• Fixed an issue for MySQL to Amazon S3 migration where first DML executed after "add column" DDL would be missed resulting in data loss.
• Fixed a memory leak issue for batch apply feature which would occur under certain conditions.
• Fixed an issue where AWS DMS task start woudl take a very long time and never complete.
• Fixed an issue for MySQL source data validation where auto-incremented columns were not handled properly.
• Fixed an issue with Oracle endpoint where connectivity would not work with Kerberos authentication.
• Fixed an issue for Babelfish target where replication would fail for tables with primary keys defined as uniqueidentifier.
• Fixed an issue for PostgreSQL source where data loss would occur due to unknown events in the replication slot.
• Fixed an issue for the data validation feature where ceratin unicode data types were not compared properly which resulted in false positives.
• Fixed an issue with ColumnType definition where timestamp column types were not set correctly.
• Fixed an issue with S3 target parquet file format by handling zero timestamp values (these values should be converted to NULL on the target).
• Fixed an issue with MySQL source and target migrating Lob columns. Now DMS uses the column id from the target table instead of source table when deciding to which column we need to write the LOB Data.
• Fixed an issue with Babelfish target where we have restricted the fractional seconds precision for Datetime and Time types to solve rounding errors.
• Fixed an issue with MySQL 5.5 source, we have added retry mechanism to prevent task failure when DMS would fail to read binary log events during on going replication (CDC).
• Fixed an issue with PostgreSQL source where certain onging replication (CDC) events failed to be parsed correctly when using the test_decoding plugin for Postgres.
• Fixed an issue with DocumentDB target with parallel apply setting which was preventing the use of multiple threads while using this feature.
• Fixed an issue with Oracle HCC compression DIRECT INSERT with parallel DML hint causing missing and duplicate data.
• Fixed an issue with Oracle source, DMS task with binary reader were failing due to Oracle July 2024 CPU.
• Fixed an issue with TaskRecoveryTableEnabled enabled, where DMS attempts to update the target system table awsdms_txn_state after the target connection is terminated.
• Fixed an issue with PostgreSQL source where some transactions would be replicated twice when the TaskrecoveryTableEnabled setting was enabled.
• Fixed an issue with S3 source to S3 target where DMS task was not replicating data during full load and on going replication.
• Fixed an issue for S3 source where DMS task was seg faulting during on going replication for DMS vesion 3.5.3.
• Fixed an issue with DB2 soruce with CcsidMapping, CCSID mappin ECA is now properly applied to task when code page is 0 and data is migrated properly.
• Fixed an issue where DMS migration from Aurora PostgreSQL to Redshift Serverless was seeing issue with Boolean value.
• Data validation operation can now accurately processes unbound characters and TEXT data types, ensuring correct validation results.
• PostgreSQL source replication maintains connectivity during Multi-AZ failover events, preventing task failures.
• Data validation now correctly compares datetime values when using Babelfish as a target, improving cross-platform compatibility.
• MySQL source replication now correctly handles mid-table duplicate column additions, preventing task interruptions.
• MySQL source replication maintains column sequence integrity when multiple columns are added during CDC operations.
• DynamoDB target replication now correctly processes LOB data during CDC, ensuring complete data transfer.
• PostgreSQL source data validation now correctly interprets boolean data type mappings, producing accurate comparison results.
• Oracle source replication preserves trailing spaces in VARCHAR2(4000) columns when extended data type support is enabled.
• SQL Server source replication maintains connectivity during DDL operations on secondary replicas, preventing task interruptions.
• AWS Secrets Manager connection strings now support special characters while preserving security protocols.
• MongoDB and Amazon DocumentDB replication prevents record duplication that previously triggered key constraint errors.
• Oracle source replication accurately processes timestamp values across various session time zone configurations.
• Oracle source replication now handles data type conversions more robustly, preventing ORA-01460 errors and associated task failures.
• AWS DMS ignores DDL statements for tables not configured in table mappings, preventing unnecessary processing of source database schema changes for unmapped tables.
• When migrating data from source SQL Server view to target endpoints, the column length is correctly preserved and no columns are truncated.
• Fixed an issue where AWS DMS now correctly identifies the primary replica in Microsoft SQL Server Always On Availability Groups, resolving previous case sensitivity detection errors.
• Fixed an issue where AWS DMS tasks failed when migrating unbound numeric data types to PostgreSQL target endpoints.
• AWS DMS now correctly validates CHAR and VARCHAR data during migrations, eliminating false positive reports in validation tasks.
• AWS DMS prevents data corruption in Large Object (LOB) data during parellel batch operations when using Amazon Redshift as a target endpoint.
• Validation failures are prevented and queries are successfully executed on Amazon S3 target when source column filters are applied to VARCHAR, CHAR, DATE, and DATETIME data types.
• When using Amazon S3 targets, consistent data validation states are maintained throughout Change Data Capture (CDC) operations.
• Fixed an issue where data validation failed due to string formatting errors during record comparison operations.

New features in AWS DMS 3.5.3

• AWS DMS has enhanced its PostgreSQL source endpoint to support Babelfish datatypes. For more information, see Using a PostgreSQL database as an AWS DMS source.
• AWS DMS supports S3 Parquet as a source. For more information, see Using Amazon S3 as a source for AWS DMS.
• AWS DMS supports PostgreSQL version 16.x. For more information, see Using a PostgreSQL database as an AWS DMS source and Using a PostgreSQL database as a target for AWS Database Migration Service.
• AWS DMS Serverless provides significantly improved throughput performance for full-load migrations from Oracle to Amazon Redshift. For more information, see Enhanced Throughput for Full-Load Oracle to Amazon Redshift and Amazon S3 Migrations.
• AWS DMS supports ongoing replication from transaction log backups for RDS for SQL Server sources. This feature is available only to new and modified AWS DMS source endpoints reading from RDS for SQL Server. For more information, see Setting up ongoing replication on a cloud SQL Server DB instance.
• AWS DMS version 3.5.3 includes the following resolved issues:

AWS DMS version 3.5.3 includes the following resolved issues:

• Fixed an issue for the data validation feature where AWS DMS would not honor source filtering when a rule action was set to override-validation-function in table mappings.
• Fixed an issue for MySQL as a source where CDC migration would fail with UTF16 encoding.
• Fixed an issue for the data validation feature where AWS DMS would not properly apply the HandleCollationDiff task setting when column filtering was used.
• Fixed an issue for the data validation feature where the AWS DMS task would hang with a target is null" error.
• Fixed an issue for PostgreSQL to PostgreSQL migrations where a AWS DMS task would fail while inserting LOB data into the target during CDC replication.
• Fixed an issue for PostgreSQL as a source where data loss occurring in certain edge-case scenarios.
• Fixed an issue for MySQL as a source where CDC replication would fail with MySQL verion 5.5.
• Fixed an issue for Oracle as a source where AWS DMS wouldn't replicate UPDATE statements correctly for IOT tables with supplemental logging enabled on all columns.
• Fixed an issue for MySQL to Amazon Redshift migrations where the AWS DMS task would fail due to LOBs exceeding the maximum size allowed by Amazon Redshift.
• Fixed an issue for the data validation feature where the AWS DMS task would fail with SkipLobColumns = true when a primary key was on the last column in the source table.
• Fixed an issue for the data validation feature where AWS DMS doesn't skip rows with null unique keys properly.
• Fixed an issue for the data validation feature where the validation would fail with a syntax error on Oracle versions prior to 12.2.
• Fixed an issue for PostgreSQL as a target where the task would hang during the full-load phase after a table error caused by invalid data.
• Enhanced the data validation feature to allow revalidation on a CDC validation-only task.
• Fixed an issue for S3 as a target where the AWS DMS task would fail with an Out of Memory condition with CdcMaxBatchInterval set.
• Upgraded the AWS DMS Oracle source driver from v12.2 to v19.18.
• Enhanced logging for SQL Server as a source to show warnings on LOB truncation during CDC.
• Enhanced the Oracle source binary reader to support the following: Big Endian platform Parallel DML hints with HCC compression Advanced Oracle Compressions with Golden Gate enabled
• Fixed an issue for MongoDB source where AWS DMS task would fail to resume after AWS DMS upgrade when AWS DMS swap file was present.
• Fixed an issue for MySQL source where the JSON data type was not being hadled propely with Batch Apply enabled.
• Fixed an issue for SQL Server to PostgreSQL migration where data validaiton would report false positives in certain situations.
• Fixed an issue for Amazon Redshift target where the EmptyAsNull ECA would not work correctly.
• Enhanced logging for SQL Server source to unclude the storage unit value.
• Fixed an issue where for targets using CSV files to load data AWS DMS task was showing a memory leak.
• Fixed an issue for Amazon S3 target where CdcMaxBatchInterval and CdcMinFileSize were not respected when cdcInsertAndUpdateOnly setting was enabled.
• Enhanced logging for Kafka target
• Enhanced logging for Oracle source with binary reader to properly indicate tables being skipped due to missing primary keys.
• Enhanced logging for SQL Server source in AlwaysOn configuration to properly indicate missing permissions.
• Enhanced logging for migrations with disabled DDL replication to indicate unexpected target table structure after its modified outside of AWS DMS.
• Fixed an issue for Db2 target where the task would fail when the AWS DMS status table is enabled.
• Fixed an issue for MongoDB / Amazon DocumentDB endpoints where the credentials could not be retrieved from Secret Manager which resulted in an error.
• Fixed an issue for MongoDB / Amazon DocumentDB where the task would fail with ParallelApply enabled while replicating certain sequence of events.
• Enhanced logging for Amazon Redshift target to include more detailed information in default logging level.
• Fixed an issue for Amazon S3 target where DMS task would crash after receiving alter table DDL when GlueCatalogGeneration is enabled.
• Fixed an issue for data validation feature where validation would fail on NUL (0x00) characters.
• Fixed an issue for babelfish endpoint where tables names with mixed case would be suspended.
• Fixed an issue for Amazon S3 source where files were not process due to a file name validation issue.
• Fixed an issue for Db2 LUW source where "table-type" option in selection rules was being ignored.
• Fixed an issue for Amazon Redshift target where data loss would occur when ParallelLoadThreads was >0 under certain conditions.
• Enhanced the data validation feature for Amazon Redshift target to support HandleCollationDiff setting.
• Fixed an issue for Amazon S3 target data validation where validation would fail when there were no other columns than the PK in the table.
• Fixed an issue for data validation feature where the CloudWatch metrics would be missing for validation which took a short amount of time to complete.
• Fixed an issue for data validation feature where the re-validation option was unavailable in certain situations.
• Fixed an issue where the maximum number of events per transaction was limited to 201,326,592 under certain conditions.
• Fixed an issue for MySQL to Amazon S3 migration where first DML executed after "add column" DDL would be missed resulting in data loss.
• Fixed a memory leak issue for batch apply feature which would occur under certain conditions.
• Fixed an issue where AWS DMS task start woudl take a very long time and never complete.
• Fixed an issue for MySQL source data validation where auto-incremented columns were not handled properly.
• Fixed an issue with Oracle endpoint where connectivity would not work with Kerberos authentication.
• Fixed an issue for Babelfish target where replication would fail for tables with primary keys defined as uniqueidentifier.
• Fixed an issue for PostgreSQL source where data loss would occur due to unknown events in the replication slot.
• Fixed an issue for the data validation feature where ceratin unicode data types were not compared properly which resulted in false positives.
• Fixed an issue with ColumnType definition where timestamp column types were not set correctly.
• Fixed an issue with S3 target parquet file format by handling zero timestamp values (these values should be converted to NULL on the target).
• Fixed an issue with MySQL source and target migrating Lob columns. Now DMS uses the column id from the target table instead of source table when deciding to which column we need to write the LOB Data.
• Fixed an issue with Babelfish target where we have restricted the fractional seconds precision for Datetime and Time types to solve rounding errors.
• Fixed an issue with MySQL 5.5 source, we have added retry mechanism to prevent task failure when DMS would fail to read binary log events during on going replication (CDC).
• Fixed an issue with PostgreSQL source where certain onging replication (CDC) events failed to be parsed correctly when using the test_decoding plugin for Postgres.
• Fixed an issue with DocumentDB target with parallel apply setting which was preventing the use of multiple threads while using this feature.
• Fixed an issue with Oracle HCC compression DIRECT INSERT with parallel DML hint causing missing and duplicate data.
• Fixed an issue with Oracle source, DMS task with binary reader were failing due to Oracle July 2024 CPU.
• Fixed an issue with TaskRecoveryTableEnabled enabled, where DMS attempts to update the target system table awsdms_txn_state after the target connection is terminated.
• Fixed an issue with PostgreSQL source where some transactions would be replicated twice when the TaskrecoveryTableEnabled setting was enabled.
• Fixed an issue with S3 source to S3 target where DMS task was not replicating data during full load and on going replication.
• Fixed an issue for S3 source where DMS task was seg faulting during on going replication for DMS vesion 3.5.3.
• Fixed an issue with DB2 soruce with CcsidMapping, CCSID mappin ECA is now properly applied to task when code page is 0 and data is migrated properly.
• Fixed an issue where DMS migration from Aurora PostgreSQL to Redshift Serverless was seeing issue with Boolean value.
• Data validation operation can now accurately processes unbound characters and TEXT data types, ensuring correct validation results.
• PostgreSQL source replication maintains connectivity during Multi-AZ failover events, preventing task failures.
• Data validation now correctly compares datetime values when using Babelfish as a target, improving cross-platform compatibility.
• MySQL source replication now correctly handles mid-table duplicate column additions, preventing task interruptions.
• MySQL source replication maintains column sequence integrity when multiple columns are added during CDC operations.
• DynamoDB target replication now correctly processes LOB data during CDC, ensuring complete data transfer.
• PostgreSQL source data validation now correctly interprets boolean data type mappings, producing accurate comparison results.
• Oracle source replication preserves trailing spaces in VARCHAR2(4000) columns when extended data type support is enabled.
• SQL Server source replication maintains connectivity during DDL operations on secondary replicas, preventing task interruptions.
• AWS Secrets Manager connection strings now support special characters while preserving security protocols.
• MongoDB and Amazon DocumentDB replication prevents record duplication that previously triggered key constraint errors.
• Oracle source replication accurately processes timestamp values across various session time zone configurations.
• Oracle source replication now handles data type conversions more robustly, preventing ORA-01460 errors and associated task failures.
• AWS DMS ignores DDL statements for tables not configured in table mappings, preventing unnecessary processing of source database schema changes for unmapped tables.
• When migrating data from source SQL Server view to target endpoints, the column length is correctly preserved and no columns are truncated.
• Fixed an issue where AWS DMS now correctly identifies the primary replica in Microsoft SQL Server Always On Availability Groups, resolving previous case sensitivity detection errors.
• Fixed an issue where AWS DMS tasks failed when migrating unbound numeric data types to PostgreSQL target endpoints.
• AWS DMS now correctly validates CHAR and VARCHAR data during migrations, eliminating false positive reports in validation tasks.
• AWS DMS prevents data corruption in Large Object (LOB) data during parellel batch operations when using Amazon Redshift as a target endpoint.
• Validation failures are prevented and queries are successfully executed on Amazon S3 target when source column filters are applied to VARCHAR, CHAR, DATE, and DATETIME data types.
• When using Amazon S3 targets, consistent data validation states are maintained throughout Change Data Capture (CDC) operations.
• Fixed an issue where data validation failed due to string formatting errors during record comparison operations.

The following table shows the new features and enhancements introduced in AWS Database Migration Service (AWS DMS) version 3.4.6.

• AWS DMS introduces Time Travel, a feature granting customers flexibility on their logging capabilities, and enhancing their troubleshooting experience. With Time Travel, you can store and encrypt AWS DMS logs using Amazon S3, and view, download and obfuscate the logs within a certain time frame.
• AWS DMS now supports Microsoft Azure SQL Managed Instance as a source. Using AWS DMS, you can now perform live migrations from Microsoft Azure SQL Managed Instance to any AWS DMS supported target.
• AWS DMS now supports Google Cloud SQL for MySQL as a source. Using AWS DMS, you can now perform live migrations from Google Cloud SQL for MySQL to any AWS DMS supported target.
• AWS DMS now supports parallel load for partitioned data to Amazon S3, improving the load times for migrating partitioned data from supported database engine source data to Amazon S3. This feature creates Amazon S3 sub-folders for each partition of the table in the database source, allowing AWS DMS to run parallel processes to populate each sub-folder.
• AWS DMS now supports Apache Kafka multi-topic targets with a single task. Using AWS DMS, you can now replicate multiple schemas from a single database to different Apache Kafka target topics using the same task. This eliminates the need to create multiple separate tasks in situations where many tables from the same source database need to be migrated to different Kafka target topics.

The issues resolved in AWS DMS 3.4.6 include the following:

• Fixed an issue where columns from UPDATE statements were populated to incorrect columns if the primary key column is not the first column when using Amazon S3 as a target with CSV format.
• Fixed an issue where AWS DMS tasks might crash when using the pglogical plugin with NULL values in BYTEA columns under limited LOB mode when using PostgreSQL as a source.
• Fixed an issue where AWS DMS tasks might crash when a large number of source tables are deleted when using PostgreSQL as a source.
• Improved Amazon S3 date-based folder partitioning by introducing a new Amazon S3 setting DatePartitionTimezone to allow partitioning on non-UTC dates.
• Supported the mapping between data type TIMESTAMP WITH TIME ZONE from sources to TIMESTAMPTZ when using Amazon Redshift as a target.
• Improved the performance of CDC for tasks without wildcard selection rules when using MongoDB or Amazon DocumentDB as a source.
• Fixed an issue where schema names with underscore wildcard and length less than 8 were not captured by AWS DMS tasks when using Db2 LUW as a source.
• Fixed an issue where AWS DMS instances ran out of memory under large data volume when using OpenSearch Service as a target.
• Improved the performance of data validation by supporting full load validation only tasks.
• Fixed an issue where AWS DMS tasks failed to resume after forced failover when using Sybase as a source.
• Fixed an issue where AWS DMS sent warning Invalid BC timestamp was encountered in column incorrectly.

Issues resolved in the DMS 3.4.6 maintenance release dated 13-December-2022:

• Fixed an issue for SAP ASE as a source so that the ODBC driver can support character sets.
• Fixed an issue for SQL Server as a source where LOB lookup was not working properly, when primary key has a datetime datatype, with precision in milliseconds.
• For migrations from SQL Server to Amazon Redshift, improved mapping so that the SQL Server 'datetimeoffset' format is mapped to the Amazon Redshift 'timestamptz' format.
• Fixed an issue where DMS task crashes when SkipLobColumns is True, there is a LOB on the source, the Primary Key is on the last column, and a data difference is detected by validation.
• Fixed an issue for MySQL as a source with data validation enabled, where a DMS task crash occurs while using a table that has a composite unique key that has null values.
• Fixed an issue for MySQL as a source, where a table is getting suspended with Overflow error when the columns are altered to add precision.
• Upgrade MySQL ODBC driver to 8.0.23
• Introduced a new MySQL endpoint setting skipTableSuspensionForPartitionDdl to allow the user to skip table suspension for partition DDL changes during CDC, so that DMS can now support DDL changes for partitioned MySQL tables.
• Fixed an issue for MongoDB to Amazon Redshift migrations, where DMS fails to create the target table on Amazon Redshift if the MongoDB collection has binary data type.
• Fixed an issue for Amazon Redshift as a target, where DMS task crashes with BatchApplyEnabled set to true.
• Fixed an issue for Amazon Redshift as a target, where with parallel-load set to type=partitions-auto, parallel segments were writing bulk CSV files to the same table directory and interfering with each other.
• Fixed an issue with Amazon Redshift as a target, where during CDC the target column is of type boolean while the source is of type character varying.
• Improved the task log to identify a DDL change that fails to replicate to Amazon Redshift as a target.
• Fixed an issue for validation with PostgreSQL, where the validation fails when boolean data types are present.
• Fixed an issue for PostgreSQL as a source, so that the full load uses the ExecuteTimeout field in Extra connection attributes.
• Fixed an issue for PostgreSQL as a source, so that a task will fail if it's reading LSNs which are greater than the requested task resume LSN for more than 60 min to indicate that the is a problem with the replication slot being used.
• Fixed an issue for PostgreSQL as a source, where timestamptz before 1970-01-01 were not migrated correctly during CDC.
• Fixed an issue for PostgreSQL as a source, where DMS was truncating character varying datatype values during CDC.
• Fixed an issue for PostgreSQL as a source where resuming a previously stopped task replay misses one or more transactions during CDC.
• Fixed an issue for S3 as a target, where the resultant CSV file header is off by one column when AddColumnName is true and TimestampColumnName is "".
• Fixed an issue for S3 as a source, where a DMS task in full load was only releasing the used memory after the entire table was loaded to the target database.
• Fixed an issue for S3 as a target, where a table reload operation missed generating CDC files.

New features in AWS DMS 3.5.2

AWS DMS now supports validating data in Amazon Redshift targets.

AWS DMS now supports using Microsoft SQL Server version 2022 as a source and target.

AWS DMS now supports IBM Db2 LUW as a target. Using AWS DMS, you can now perform live migrations from IBM Db2 LUW to IBM Db2 LUW.

AWS DMS version 3.5.2 includes the following resolved issues:

• Added support for segmented full load with IBM Db2 as a target.
• Enhanced the handling of invalid timestamp settings and unsupported table operations for Timestream as a target.
• Fixed an issue where a task was crashing while using a filter on a column that DMS added dynamically using a transformation rule.
• Added logging to show when DMS is reading from transaction swap files.
• Fixed an issue for S3 as a target where a task would crash when CdcInsertsAndUpdates is true and PreserveTransactions is true.
• Fixed an issue where the source filter-operator when set to a negative operator had incorrect behavior if the same column had a transformation rule defined.
• Enhanced logging to show when DMS temporarily pauses reading from the source to improve performance.
• Fixed an issue for source filters where DMS applies escaped characters to newly created tables during CDC.
• Fixed an issue for PostgreSQL as a target where DMS replicates deletes as null values.
• Enhanced logging for Oracle as a source to remove extraneous error codes.
• Improved logging for Oracle as a source to show DMS' lack of support for full LOB mode for the XMLTYPE data type.
• Fixed an issue for MySQL as a target where corrupted column metadata could cause task crashes or data loss.
• Fixed an issue during full load where DMS ignores a filter that a transformation rule adds to a new column.
• Fixed an issue for S3 as a target where data validation would fail while migrating multiple tables with different validation partitioning definitions.
• Fixed an issue for CDC-only tasks where the task would crash when TaskRecoveryTableEnabled is true.
• Fixed an issue for MySQL to MariaDB migrations where DMS doesn't migrate MySQL v8 tables with tf8mb4_0900_ai_ci collation.
• Fixed an issue for the Batch Apply feature where the task would fail under certain conditions.
• Added support for non UTF-8 characters for Amazon DocumentDB endpoints.
• Fixed an issue for the Batch Apply feature where the DMS task crashes while replicating large transactions.
• Fixed an issue for Db2 as a source where DMS would replicate an INSERT to the target, despite being rolled back on the source.
• Fixed an issue where validation was not respecting source filters.
• Fixed an issue for MongoDB source where DMS task would fail to resume after AWS DMS upgrade when AWS DMS swap file was present.
• Fixed an issue for MySQL source where the JSON data type was not being hadled propely with Batch Apply enabled.
• Fixed an issue for MySQL source where CDC replicaiton would fail with MySQL verion 5.5.
• Fixed an issue for LOB migration where the AWS DMS task would crash while processing certain event types.
• Fixed an issue for Data Validation feature where the Validation-only task would hang on certain DDL events.
• Fixed an issue for data validation feature where the the HandleCollationDiff setting was not applied when filters were present.
• Fixed an issue for MySQL source where UTF-16 encoded enum values were not migrated correctly.
• Enhanced logging for SQL Server source to unclude the storage unit value.
• Fixed a memory leak issue for batch apply feature which would occur under certain conditions.
• Fixed an issue for Amazon S3 target where CdcMaxBatchInterval and CdcMinFileSize were not respected when cdcInsertAndUpdateOnly setting was enabled.
• Enhanced logging for Kafka target
• Enhanced logging for Oracle source with binary reader to properly indicate tables being skipped due to missing primary keys.
• Enhanced logging for SQL Server source in AlwaysOn configuration to properly indicate missing permissions.
• Enhanced logging for migrations with disabled DDL replication to indicate unexpected target table structure after its modified outside of AWS DMS.
• Fixed an issue for Db2 target where the task would fail when the AWS DMS status table is enabled.
• Fixed an issue for MongoDB / Amazon DocumentDB endpoints where the credentials could not be retrieved from Secret Manager which resulted in an error.
• Fixed an issue for MongoDB / Amazon DocumentDB where the task would fail with ParallelApply enabled while replicating certain sequence of events.
• Enhanced logging for Amazon Redshift target to include more detailed information in default logging level.
• Fixed an issue for Amazon S3 target where DMS task would crash after receiving alter table DDL when GlueCatalogGeneration is enabled.
• Fixed an issue for data validation feature where validation would fail on NUL (0x00) characters.
• Fixed an issue for babelfish endpoint where tables names with mixed case would be suspended.
• Fixed an issue for Amazon Redshift target where data loss would occur when ParallelLoadThreads was >0 under certain conditions.
• Fixed an issue for Amazon S3 target data validation where validation would fail when there were no other columns than the PK in the table.
• Fixed an issue for data validation feature where the CloudWatch metrics would be missing for validation which took a short amount of time to complete.
• Fixed an issue for data validation feature where the re-validation option was unavailable in certain situations.
• Fixed an issue where the maximum number of events per transaction was limited to 201,326,592 under certain conditions.
• Fixed an issue where a reload of multiple tables was canceled when at least one of the table was invalid.
• Fixed an issue for MySQL to S3 migration where first DML executed after "add column" DDL would be missed resulting in data loss.
• Fixed a memory leak issue for batch apply feature which would occur under certain conditions.
• Fixed an issue where AWS DMS task start woudl take a very long time and never complete.
• Fixed an issue for PostgreSQL source where data loss would occur due to unknown events in the replication slot.
• Fixed an issue with MySQL source and target migrating Lob columns. Now DMS uses the column id from the target table instead of source table when deciding to which column we need to write the LOB Data.
• Fixed an issue with MySQL 5.5 Source, added retry mechanism to prevent task failure when DMS would fail to read binary log events during on-going replication (CDC).
• Fixed an issue with PostgreSQL Source where certain onging replication (CDC) events failed to be parsed correctly when using the test_decoding plugin for Postgres.
• Fixed an issue with DocumentDB target with parallel apply setting which was preventing the use of multiple threads while using this feature.
• Fixed an issue with Oracle HCC compression DIRECT INSERT with parallel DML hint causing missing and duplicate data.
• Fixed an issue with Oracle Source, DMS task with binary reader were failing due to Oracle July 2024 CPU.
• Fixed an issue with TaskRecoveryTableEnabled enabled, where DMS attempts to update the target system table awsdms_txn_state after the target connection is terminated.
• Fixed an issue with PostgreSQL source where some transactions would be replicated twice when the TaskrecoveryTableEnabled setting was enabled.
• Fixed an issue with S3 source to S3 target where DMS task was not replicating data during full load and on going replication.
• Fixed an issue for S3 source where DMS task was seg faulting during on going replication for DMS vesion 3.5.3
• Fixed an issue with DB2 soruce with CcsidMapping, CCSID mappin ECA is now properly applied to task when code page is 0 and data is migrated properly.
• Fixed an issue where DMS migration from Aurora PostgreSQL to Redshift Serverless was seeing issue with Boolean value.
• Data validation operation can now accurately processes unbound characters and TEXT data types, ensuring correct validation results.
• PostgreSQL source replication maintains connectivity during Multi-AZ failover events, preventing task failures.
• Data validation now correctly compares datetime values when using Babelfish as a target, improving cross-platform compatibility.
• MySQL source replication now correctly handles mid-table duplicate column additions, preventing task interruptions.
• MySQL source replication maintains column sequence integrity when multiple columns are added during CDC operations.
• DynamoDB target replication now correctly processes LOB data during CDC, ensuring complete data transfer.
• PostgreSQL source data validation now correctly interprets boolean data type mappings, producing accurate comparison results.
• Oracle source replication preserves trailing spaces in VARCHAR2(4000) columns when extended data type support is enabled.
• SQL Server source replication maintains connectivity during DDL operations on secondary replicas, preventing task interruptions.
• AWS Secrets Manager connection strings now support special characters while preserving security protocols.
• MongoDB and Amazon DocumentDB replication prevents record duplication that previously triggered key constraint errors.
• Oracle source replication accurately processes timestamp values across various session time zone configurations.
• Oracle source replication now handles data type conversions more robustly, preventing ORA-01460 errors and associated task failures.
• AWS DMS ignores DDL statements for tables not configured in table mappings, preventing unnecessary processing of source database schema changes for unmapped tables.
• When migrating data from source SQL Server view to target endpoints, the column length is correctly preserved and no columns are truncated.
• Fixed an issue where AWS DMS now correctly identifies the primary replica in Microsoft SQL Server Always On Availability Groups, resolving previous case sensitivity detection errors.
• Fixed an issue where AWS DMS tasks failed when migrating unbound numeric data types to PostgreSQL target endpoints.
• AWS DMS now correctly validates CHAR and VARCHAR data during migrations, eliminating false positive reports in validation tasks.
• AWS DMS prevents data corruption in Large Object (LOB) data during parellel batch operations when using Amazon Redshift as a target endpoint.
• Validation failures are prevented and queries are successfully executed on Amazon S3 target when source column filters are applied to VARCHAR, CHAR, DATE, and DATETIME data types.
• When using Amazon S3 targets, consistent data validation states are maintained throughout Change Data Capture (CDC) operations.
• Fixed an issue where data validation failed due to string formatting errors during record comparison operations.

AWS DMS 3.5.0 Beta

AWS DMS 3.5.0 is a beta version of the replication instance engine. AWS DMS supports this version the same as all previous releases. But we recommend that you test AWS DMS 3.5.0 Beta before using it for production purposes.

The following table shows the new features and enhancements introduced in AWS Database Migration Service (AWS DMS) version 3.5.0 Beta.

• Time Travel for Oracle and Microsoft SQL Server: You can now use Time Travel in all AWS Regions with DMS-supported Oracle, Microsoft SQL Server, and PostgreSQL source endpoints, and DMS-supported PostgreSQL and MySQL target endpoints.
• S3 validation: AWS DMS now supports validating replicated data in Amazon S3 target endpoints.
• Glue Catalog Integration: You can now integrate an AWS Glue Data Catalog with your Amazon S3 target endpoint and query Amazon S3 data through other AWS services such as Amazon Athena.
• Parallel apply for DocumentDB as a target: Using DocumentDB as the target with new ParallelApply* task settings, AWS DMS now supports a maximum of 5000 records per second during CDC replication.
• Customer Centric Logging: You can now examine and manage task logs more effectively with AWS DMS version 3.5.0.
• SASL_PLAIN mechanism for Kafka target endpoints: You can now use SASL_PLAIN authentication to support Kafka MSK target endpoints.
• Replication of XA transactions in MySQL: You can now use XA transactions on your MySQL DMS source. Prior to DMS 3.5.0, DML changes applied as part of XA transactions weren’t replicated correctly.
• Oracle Extended Data Types: AWS DMS now supports replication of extended data types in Oracle version 12.2 and higher.
• Db2 LUW PureScale Environment: AWS DMS now supports replication from a Db2 LUW PureScale environment. This functionality is only supported using the Start processing changes from source change position option.
• SQL Server source with READ_COMMITTED_SNAPSHOT option: When using a Microsoft SQL Server source database with the READ_COMMITTED_SNAPSHOT option set to TRUE, you can replicate DML changes correctly by setting the forceDataRowLookup connection attribute.

AWS DMS 3.5.0 includes the following resolved issues:

• Fixed an issue for Oracle source where filtering rules weren't working as expected for a numeric column when data type transformation to string existed for the same column.
• Improved efficiency of connection handling with SQL Server source in AlwaysOn configuration by eliminating unnecessary connections to replicas that aren't used by DMS.
• Fixed an issues with SQL Server Source where HIERARCHYID data type was replicated as VARCHAR(250) instead of HIERARCHYID to SQL Server target.
• Fixed an issue when moving a task with an S3 target would take a very long time, appear frozen or never complete.
• Introduced support for SASL Plain authentication method for Kafka MSK target endpoint.
• Fixed an issue for Opensearch 2.x target where parallel load or parallel apply would fail due to lack of support for _type parameter.
• Removed a limitation where only one filter could be applied on a column.
• Fixed an issue for Kinesis, Kafka and S3 targets where data in LOB columns added during CDC wasn't replicated.
• Upgraded the MongoDB driver to v1.23.2.
• Upgraded the Kafka driver from 1.5.3 to 1.9.2.
• Fixed an issue for S3 target where the AddTrailingPaddingCharacter endpoint setting was not working when data contained the character specified as the delimiter for the S3 target.
• Fixed an issue for Kinesis target where a task would crash when PK value was empty and detailed debug was enabled.
• Fixed an issue for an S3 target where column names where shifted by one position when AddColumnName was set to true and TimestampColumnName was set to "".
• Improved warning logging for LOB truncation for SQL Server source to include the select statement used to retrieve the LOB.
• Introduced meaningful error message and eliminated task crash issue in situations where DMS task was failing with no error message due incorrect TDE password for Oracle as a source.
• Removed limitations of DMS not being able to replicate PostgreSQL CTAS (create table as selected) DDLs during CDC.
• Fixed an issue for PostgreSQL source with S3 target where columns were misaligned on the target when support for LOBs was disabled and LOBs were present.
• Fixed an issue for MySQL source where task memory consumption was increasing continuously.
• Set this attribute to true to convert the timestamp value of 'TIMESTAMP WITH TIME ZONE' and 'TIMESTAMP WITH LOCAL TIME ZONE' columns to UTC. By default the value of this attribute is 'false' and data is replicated using the source database timezone.
• Fixed an issue for Oracle source with S3 target where tables were not suspended while the DataTruncationErrorPolicy task setting was set to SUSPEND_TABLE.
• Fixed an issues for SQL Server source where task would fail or become unresponsive when selection rule contained comma separated list of tables.
• Fixed an issue for MongoDB and DocumentDB endpoints where secret manager based authentication wasn't working.
• Fixed an issues for Oracle source with Oracle target where data was being truncated for multi-byte VARCHAR columns with NLS_NCHAR_CHARACTERSET set to UTF8.
• Added an Extra Connection Attribute (ECA) filterTransactionsOfUser to allow DMS to ignore transactions from a specified user when replicating from Oracle using LogMiner.
• Fixed an issue for SQL Server where a task would not fail on missing LSN.