molap-server

When analysts need to interactively explore data across multiple dimensions — slicing, dicing, rolling up, drilling down — dynamically generating SQL queries (as ROLAP does) introduces unacceptable latency. Every user action requires the database to compute aggregations on-the-fly, which can take seconds or minutes for large datasets.

MOLAP (Multidimensional Online Analytical Processing) stores data in proprietary multidimensional databases (MDDBs) as pre-calculated arrays of data cubes. Because the cubes are computed during the data load phase (not at query time), user requests are served with near-instant response — no SQL generation or computation is needed at query time.

1. Data loading phase (pre-computation): When data is loaded from the warehouse into the MDDB, proprietary software pre-calculates all possible aggregations and stores them in multidimensional arrays (data cubes).
2. Storage format: Data is stored in specialized multidimensional arrays, not relational tables. Each cell in the array corresponds to a specific combination of dimension values.
3. Sparse matrix technology: Since most cells in a high-dimensional cube are empty (no sales for a specific product in a specific city on a specific day), MOLAP uses sparse matrix techniques to efficiently store only non-empty cells.
4. Query phase: When a user requests data, the MOLAP engine directly retrieves pre-calculated values from the MDDB — no SQL, no joins, no aggregations.
5. Application layer: The MOLAP engine resides in the application layer, providing a simple interface for users of all skill levels.

Strengths:

• Lightning-fast response time (data already pre-calculated)
• Simple interface compatible with both experienced and inexperienced users
• Fast indexing to previously summarized data

Weaknesses:

• Limited data volumes — cannot store detailed transactional data
• Storage waste with sparse (scattered) datasets despite sparse matrix optimization
• Proprietary format — vendor lock-in risk
• Not suitable for users who need detailed, row-level data

• Pre-calculated cubes: All aggregations computed during load, not at query time
• MDDB storage: Proprietary multidimensional arrays, not relational tables
• Sparse matrix technology: Efficiently stores mostly-empty cubes
• Instant response: No SQL generation or computation at query time
• Limited detail: Stores summaries, not detailed transactional data

• Built from: OLAP Servers — MOLAP is one of four server types
• Built from: Multidimensional Data Model — MOLAP implements the model directly
• Contrasts with: ROLAP Server — MOLAP pre-computes; ROLAP computes dynamically
• Related: HOLAP Server — HOLAP combines MOLAP aggregations with ROLAP detail
• Builds into: OLAP Operations — MOLAP serves pre-computed results for all operations
• Related: Data Warehouse Scale — MOLAP is limited by storage costs for large datasets

• Storage explosion: Pre-computing all possible aggregations for many dimensions creates enormous storage requirements.
• Refresh latency: When warehouse data is refreshed, MOLAP cubes must be re-computed — this can take hours.
• Vendor lock-in: MDDB formats are proprietary — migrating data between MOLAP vendors requires re-extraction and re-computation.
• No detailed data: MOLAP cannot answer “show me all individual transactions” — it only stores aggregated data.