To calculate migration complexity, migVisor gathers information about the database features in use, types and quantities of schema objects, database-side code (such as stored procedures), and infrastructure setup. These data points are used to calculate the “migration complexity score”.
migVisor detects or computes features present at the source databases and evaluates them with respect to the target database engine. This calculation generates a migration complexity score of each detected feature. The score value factors both the feature value and the number of occurrences of the feature in the source. The individual scores are then combined to produce the overall score for the given source-to-target pair.
Migration Complexity Scoring Process
The scoring process follows these steps:
mMC runs queries or commands against the source system, detecting the type and quantities of detected features, host attributes, configuration, cluster topology, schema objects, schema object types, and other size and usage statistics or metadata.
mConsole assigns a score for each detected feature or schema object. The score is dependent on the feature’s compatibility with the specific target technology (PostgreSQL, MySQL, etc.).
migVisor factors in the number of occurrences of each feature detected.
Feature scores are processed by a proprietary normalization and weighting formula.
The overall score is computed by combining individual adjusted feature scores. This generates a single high/medium/low migration complexity score.
Migration Complexity Determinants
Migration complexity scores represent an overall level of effort that is required to migrate a source to the desired target platform. A higher score indicates a more complex migration effort. If migVisor detects a feature in the source that is not supported at all in the target technology, the score for that feature will be high. However, if migVisor detects a feature that is fully supported in the target, the score contribution for this feature will be very low.
migVisor does not require each feature to have an exact equivalent in the target database. Instead, it evaluates potential similar target features or capabilities which may perform the same function as the source feature. For each source feature migVisor checks for both exact and near-match equivalents on the target database. migVisor considers these factors:
Is the target feature fully compatible with the source feature?
Is the target feature partially-compatible with the source feature?
Can custom server-side code produce the equivalent functionality in the target database?
Objects that remain the same when moved to the target will require the least amount of effort to migrate.
Migration Complexity Evaluation
Numeric values that determine the migration complexity level fall within the following ranges after applying migVisor’s normalization and weighting algorithms on the score:
0 ≤ (score) ≤ 30
31 ≤ (score) ≤ 70
71 ≤ (score) ≤ 100
Complexity Impact Representation
Each detected feature has some contribution to the overall score.
The feature contribution is represented as a colored icon, with the following meaning:
Red: High Complexity
Orange: Medium Complexity
Green: Low Complexity