Migration Risk Reference

Table

Data Types: MYSQL_GEOMETRY, MYSQL_POINT, MYSQL_LINESTRING, MYSQL_POLYGON, MYSQL_MULTIPOINT, MYSQL_MULTILINESTRING, MYSQL_MULTIPOLYGON, MYSQL_GEOMETRYCOLLECTION

High

The table contains columns which require special handling: <columns>.

You are storing geospatial data in your database. These will be converted to objects with a srid (int) coordinate reference and wkb (binary) content in WKB (well-known-binary) format

Table

Data Types: MYSQL_MEDIUMBLOB, MYSQL_LONGBLOB, MYSQL_MEDIUMTEXT, MYSQL_LONGTEXT

Medium

The table contains columns which could exceed the 16MB limit.

If you are storing >16MB in the record, the migration will fail as MongoDB documents cannot exceed 16MB. We strongly advise against storing large blobs in MongoDB, but if multi-document transactions aren't required you can use GridFS.

Table

Data Type

Medium

The following columns are at risk of data loss due to decimal precision: <columns>.

The specified columns have been configured to support more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Data Type

Medium

The following columns may be at risk of data loss due to decimal precision: <columns>.

The specified columns are using variable decimal precision and may contain values with more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Schema

High

Table <name> has an auto incrementing column.

MongoDB encourages the use of ObjectID for ID fields because incrementing IDs have difficulty sharding. MongoDB Relational Migrator can migrate your keys as-is, but you will need to write code to maintain this behavior. If you're using MongoDB Atlas, you can use Atlas Triggers to auto-increment your IDs.

Database

Schema

Medium

The <name> database has no foreign keys.

This will make the schema mapping more complicated as we cannot infer the relationship between your tables without them. You can use the synthetic foreign keys feature of Migrator to define logical relationships between your tables.

Database

Schema

Medium

There were views detected in <database>.

Views are supported in MongoDB but they must be converted into MQL. Consider using Query Converter to migrate your views.

Database

Unsupported Feature

High

The <name> database has triggers.

MongoDB has no native way to implement triggers. If you're using Atlas, consider using Query Converter to convert your existing triggers to Atlas Triggers to replicate the existing behavior.

Database

Unsupported Feature

Medium

There were routines detected in <database>.

MongoDB has no native way to represent routines. Consider using Query Converter to migrate your routines to application code.

Database

Performance

Medium

The total data size of the selected tables is greater than <limit> GB, at 100 GB.

Larger data migration jobs can require careful planning to maximize performance and reliability. The deployment considerations topic in the documentation provides advice which can help. For jobs that may run over multiple days, consider using the Kafka deployment model.

Table

Data Types: ORACLE_SDO_GEOMETRY, ORACLE_SDO_GTYPE, ORACLE_SDO_SRID, ORACLE_SDO_POINT, ORACLE_SDO_ELEM_INFO, ORACLE_SDO_ORDINATES

High

The table contains columns which require special handling: <columns>.

You are storing geospatial data in your database. These will be converted to objects with a srid (int) coordinate reference and wkb (binary) content in WKB (well-known-binary) format

Table

Data Types: ORACLE_BLOB, ORACLE_CLOB, ORACLE_NCLOB

Medium

The table contains columns which could exceed the 16MB limit.

If you are storing >16MB in the record, the migration will fail as MongoDB documents cannot exceed 16MB. We strongly advise against storing large blobs in MongoDB, but if multi-document transactions aren't required you can use GridFS.

Table

Data Type

Medium

The following columns are at risk of data loss due to decimal precision: <columns>.

The specified columns have been configured to support more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Data Type

Medium

The following columns may be at risk of data loss due to decimal precision: <columns>.

The specified columns are using variable decimal precision and may contain values with more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Data Types: ORACLE_TIMESTAMP_WITH_TIME_ZONE, ORACLE_TIMESTAMP_WITH_LOCAL_TIME_ZONE

Medium

Detected columns with <type> type. MongoDB stores times in UTC by default, and Relational Migrator may convert any local time representations into this form or to a direct string. Affected columns: <columns>.

MongoDB does not support storing timezones with time data. Consider converting to a desired timezone in your application or storing it as a string.

Table

Data Type: ORACLE_BFILE

Medium

Detected columns with type <type>. Migration to MongoDB is not currently supported for this type. Affected columns: <columns>.

ORACLE_BFILE is not supported by MongoDB. If the file must be stored in your database and multi-document transactions aren't required, you can use GridFS. Otherwise, we encourage storing your files on cloud storage & recording the URI instead.

Table

Data Types: ORACLE_LONG, ORACLE_LONG_RAW, ORACLE_UROWID

Medium

Detected columns with type <type>. Migration to MongoDB is not currently supported for this type. Affected columns: <columns>.

The report groups all columns of a given unsupported type into a single item.

• ORACLE_LONG data type is deprecated by Oracle and equivalent LOB data types are not supported in MongoDB. If the file must be stored in your database and multi-document transactions aren't required, you can use GridFS. Otherwise, we encourage storing your files on cloud storage & recording the URI instead.

• ORACLE_UROWID data type is essentially a FK to another row, stored as binary. We encourage migrating to using explicit IDs where possible, with FK enforcement, to assist data migration.

Table

Schema

High

Table <name> has an auto incrementing column.

MongoDB encourages the use of ObjectID for ID fields because incrementing IDs have difficulty sharding. MongoDB Relational Migrator can migrate your keys as-is, but you will need to write code to maintain this behavior. If you're using MongoDB Atlas, you can use Atlas Triggers to auto-increment your IDs.

Database

Schema

Medium

The <name> database has no foreign keys.

This will make the schema mapping more complicated as we cannot infer the relationship between your tables without them. You can use the synthetic foreign keys feature of Migrator to define logical relationships between your tables.

Database

Schema

Medium

There were views detected in <database>.

Views are supported in MongoDB but they must be converted into MQL. Consider using Query Converter to migrate your views.

Database

Unsupported Feature

High

The <name> database has triggers.

MongoDB has no native way to implement triggers. If you're using Atlas, consider using Query Converter to convert your existing triggers to Atlas Triggers to replicate the existing behavior.

Database

Unsupported Feature

Medium

There were routines detected in <database>.

MongoDB has no native way to represent routines. Consider using Query Converter to migrate your routines to application code.

Database

Unsupported Feature

Medium

A package is a schema object that groups logically related PL/SQL types, variables, constants, subprograms, cursors, and exceptions. A package is compiled and stored in the database, where many applications can share its contents.

MongoDB does not have any features that are equivalent to Oracle Packages. You can use the Query Converter to define logical relationships between your tables.

Database

Performance

Medium

The total data size of the selected tables is greater than <limit> GB, at 100 GB.

Larger data migration jobs can require careful planning to maximize performance and reliability. The deployment considerations topic in the documentation provides advice which can help. For jobs that may run over multiple days, consider using the Kafka deployment model.

Table

Data Types: POSTGRES_POINT, POSTGRES_LINE, POSTGRES_LSEG, POSTGRES_BOX, POSTGRES_PATH, POSTGRES_POLYGON, POSTGRES_CIRCLE

High

The table contains columns which require special handling: <columns>.

You are storing geospatial data in your database. These will be converted to objects with a srid (int) coordinate reference and wkb (binary) content in WKB (well-known-binary) format

Table

Data Types: POSTGRES_BYTEA, POSTGRES_TEXT, POSTGRES_BPCHAR

Medium

The table contains columns which could exceed the 16MB limit.

If you are storing >16MB in the record, the migration will fail as MongoDB documents cannot exceed 16MB. We strongly advise against storing large blobs in MongoDB, but if multi-document transactions aren't required you can use GridFS.

Table

Data Type

Medium

The following columns are at risk of data loss due to decimal precision: <columns>.

The specified columns have been configured to support more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Data Type

Medium

The following columns may be at risk of data loss due to decimal precision: <columns>.

The specified columns are using variable decimal precision and may contain values with more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Data Type: POSTGRES_TIMESTAMPTZ

Medium

Detected columns with <type> type. MongoDB stores times in UTC by default, and Relational Migrator may convert any local time representations into this form or to a direct string. Affected columns: <columns>.

MongoDB does not support storing timezones with time data. Consider converting to a desired timezone in your application or storing it as a string.

Table

Schema

High

Table <name> has an auto incrementing column.

MongoDB encourages the use of ObjectID for ID fields because incrementing IDs have difficulty sharding. MongoDB Relational Migrator can migrate your keys as-is, but you will need to write code to maintain this behavior. If you're using MongoDB Atlas, you can use Atlas Triggers to auto-increment your IDs.

Database

Schema

Medium

The <name> database has no foreign keys.

This will make the schema mapping more complicated as we cannot infer the relationship between your tables without them. You can use the synthetic foreign keys feature of Migrator to define logical relationships between your tables.

Database

Schema

Medium

There were views detected in <database>.

Views are supported in MongoDB but they must be converted into MQL. Consider using Query Converter to migrate your views.

Database

Unsupported Feature

Medium

There were materialized views detected in <database>

MongoDB supports On-Demand Materialized Views. You can either schedule their generation or use Atlas Triggers and $merge to maintain them.

Database

Unsupported Feature

High

The <name> database has triggers.

MongoDB has no native way to implement triggers. If you're using Atlas, consider using Query Converter to convert your existing triggers to Atlas Triggers to replicate the existing behavior.

Database

Unsupported Feature

Medium

There were routines detected in <database>.

MongoDB has no native way to represent routines. Consider using Query Converter to migrate your routines to application code.

Database

Performance

Medium

The total data size of the selected tables is greater than <limit> GB, at 100 GB.

Larger data migration jobs can require careful planning to maximize performance and reliability. The deployment considerations topic in the documentation provides advice which can help. For jobs that may run over multiple days, consider using the Kafka deployment model.

Table

Data Types: SQL_SERVER_GEOGRAPHY, SQL_SERVER_GEOMETRY

High

The table contains columns which require special handling: <columns>.

You are storing geospatial data in your database. These will be converted to objects with a srid (int) coordinate reference and wkb (binary) content in WKB (well-known-binary) format

Table

Data Types: SQLSERVER_TEXT, SQLSERVER_NTEXT, SQLSERVER_IMAGE, SQLSERVER_VARCHAR, SQLSERVER_NVARCHAR, SQLSERVER_VARBINARY

Medium

The table contains columns which could exceed the 16MB limit.

If you are storing >16MB in the record, the migration will fail as MongoDB documents cannot exceed 16MB. We strongly advise against storing large blobs in MongoDB, but if multi-document transactions aren't required you can use GridFS.

Table

Data Type

Medium

The following columns are at risk of data loss due to decimal precision: <columns>.

The specified columns have been configured to support more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Data Type

Medium

The following columns may be at risk of data loss due to decimal precision: <columns>.

The specified columns are using variable decimal precision and may contain values with more decimal precision than is supported in a Decimal128 field. During migration, these values will be rounded to 34 significant figures.

Table

Data Type: SQL_SERVER_DATETIMEOFFSET

Medium

Detected columns with <type> type. MongoDB stores times in UTC by default, and Relational Migrator may convert any local time representations into this form or to a direct string. Affected columns: <columns>.

MongoDB does not support storing timezones with time data. Consider converting to a desired timezone in your application or storing it as a string.

Table

Data Type: FILESTREAM

Medium

Detected columns with type <type>. Migration to MongoDB is not currently supported for this type. Affected columns: <columns>.

FILESTREAM is not supported by MongoDB. FILESTREAM data type not supported by MongoDB. If the file must be stored in your database and multi-document transactions aren't required, you can use GridFS.

Table

Data Types: SQLSERVER_HIERARCHYID, SQLSERVER_ROWVERSION, SQLSERVER_SQL_VARIANT, SQLSERVER_UNIQUEIDENTIFIER

Medium

Detected columns with type <type>. Migration to MongoDB is not currently supported for this type. Affected columns: <columns>.

The report groups all columns of a given unsupported type into a single item.

• There is no direct equivalent for the SQLSERVER_HIERARCHYID within MongoDB. Instead, embedded documents should be used to model hierarchical data.

• SQLSERVER_ROWVERSION (ROWVERSION) data type is used for version-stamping table rows and there no direct equivalent within MongoDB. You can simulate this using an Integer or a String field to store and increment version information manually. Alternatively, change streams could also be used for version tracking.

• SQLSERVER_SQL_VARIANT data type is a flexible way to store data of various SQL Server supported data types. MongoDB does not have an exact equivalent, however the flexible schema model allows for similar storage by dynamically typing fields.

• MongoDB does not have a native GUID data type. SQLSERVER_UNIQUEIDENTIFIER types will be migrated to Strings. You can use ObjectIDs, or generate these UUIDs in your application code.

Table

Schema

High

Table <name> has an auto incrementing column.

MongoDB encourages the use of ObjectID for ID fields because incrementing IDs have difficulty sharding. MongoDB Relational Migrator can migrate your keys as-is, but you will need to write code to maintain this behavior. If you're using MongoDB Atlas, you can use Atlas Triggers to auto-increment your IDs.

Database

Schema

Medium

The <name> database has no foreign keys.

This will make the schema mapping more complicated as we cannot infer the relationship between your tables without them. You can use the synthetic foreign keys feature of Migrator to define logical relationships between your tables.

Database

Schema

Medium

There were views detected in <database>.

Views are supported in MongoDB but they must be converted into MQL. Consider using Query Converter to migrate your views.

Database

Unsupported Feature

High

The <name> database has triggers.

MongoDB has no native way to implement triggers. If you're using Atlas, consider using Query Converter to convert your existing triggers to Atlas Triggers to replicate the existing behavior.

Database

Unsupported Feature

Medium

There were routines detected in <database>.

MongoDB has no native way to represent routines. Consider using Query Converter to migrate your routines to application code.

Database

Performance

Medium

The total data size of the selected tables is greater than <limit> GB, at 100 GB.

Larger data migration jobs can require careful planning to maximize performance and reliability. The deployment considerations topic in the documentation provides advice which can help. For jobs that may run over multiple days, consider using the Kafka deployment model.