Three OLAP server architectures that implement the multidimensional data model differently — storing and processing data in relational tables (ROLAP), pre-computed multidimensional arrays (MOLAP), or a hybrid of both (HOLAP). The choice determines query speed, data volume capacity, storage cost, and vendor dependency.
The fundamental tradeoff is computation timing — when should aggregations be calculated? ROLAP computes at query time (slow but flexible), MOLAP computes at load time (fast but rigid), and HOLAP splits the difference (summary at load time, detail at query time).
| Dimension | ROLAP | MOLAP | HOLAP |
|---|---|---|---|
| Storage | Relational tables (rows/columns) | Proprietary MDDB (multidimensional arrays) | Both: ROLAP for detail, MOLAP for aggregations |
| Computation timing | At query time (dynamic SQL) | At load time (pre-computed) | Aggregations pre-computed; detail at query time |
| Query speed | Slow | Fastest | Fast for summaries, moderate for detail |
| Data volume | Unlimited (terabytes+) | Limited (summaries only) | Large (detail in ROLAP) |
| Storage efficiency | High (no pre-computation overhead) | Low (sparse matrix waste) | Medium (only aggregations pre-computed) |
| Vendor lock-in | None (standard RDBMS) | High (proprietary format) | Moderate (MOLAP component is proprietary) |
| Best for | Large data, infrequent complex queries | Interactive dashboards, small datasets | Enterprise with mixed query patterns |
- Data volumes are very large (terabytes to petabytes)
- Existing RDBMS investment should be leveraged
- Query latency is acceptable (scheduled reports, not interactive dashboards)
- Vendor independence is important
- Interactive, exploratory analysis is the primary use case
- Data volumes are manageable (gigabytes to low terabytes)
- Fast response time is critical
- Detailed transactional data is not needed in the OLAP layer
- Both summary speed and detail access are required
- Data volumes exceed MOLAP capacity
- Users need both dashboard-style analysis and drill-through to detail
- Organization can manage the added configuration complexity
The ROLAP/MOLAP/HOLAP spectrum reveals that there is no universal optimal OLAP architecture — the right choice depends on the query pattern distribution. If 80% of queries are summaries and 20% need detail, HOLAP’s split architecture is ideal. If 100% of queries are summaries on small data, MOLAP wins. If queries are unpredictable and data is massive, ROLAP is the only viable option. Modern cloud warehouses (BigQuery, Redshift, Snowflake) have essentially made ROLAP fast enough that MOLAP’s speed advantage is less critical than it once was.
- ROLAP Server — relational, dynamic, scalable
- MOLAP Server — pre-computed, fast, limited
- HOLAP Server — hybrid, best of both
- OLAP Servers — the overarching category
- Multidimensional Data Model — what all three implement
- OLAP Operations — operations each server type executes