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Tuesday, March 11, 2014

Mondrian Architecture - Storage and aggregation strategies

Storage and aggregation strategies 

OLAP Servers are generally categorized according to how they store their data:
  • A MOLAP (multidimensional OLAP) server stores all of its data on disk in structures optimized for multidimensional access. Typically, data is stored in dense arrays, requiring only 4 or 8 bytes per cell value.
  • A ROLAP (relational OLAP) server stores its data in a relational database. Each row in a fact table has a column for each dimension and measure.
Three kinds of data need to be stored: fact table data (the transactional records), aggregates, and dimensions.

MOLAP databases store fact data in multidimensional format, but if there are more than a few dimensions, this data will be sparse, and the multidimensional format does not perform well. A HOLAP (hybrid OLAP) system solves this problem by leaving the most granular data in the relational database, but stores aggregates in multidimensional format.

Pre-computed aggregates are necessary for large data sets, otherwise certain queries could not be answered without reading the entire contents of the fact table. MOLAP aggregates are often an image of the in-memory data structure, broken up into pages and stored on disk. ROLAP aggregates are stored in tables. In some ROLAP systems these are explicitly managed by the OLAP server; in other systems, the tables are declared as materialized views, and they are implicitly used when the OLAP server issues a query with the right combination of columns in the group by clause.

The final component of the aggregation strategy is the cache. The cache holds pre-computed aggregations in memory so subsequent queries can access cell values without going to disk. If the cache holds the required data set at a lower level of aggregation, it can compute the required data set by rolling up.

The cache is arguably the most important part of the aggregation strategy because it is adaptive. It is difficult to choose a set of aggregations to pre-compute which speed up the system without using huge amounts of disk, particularly those with a high dimensionality or if the users are submitting unpredictable queries. And in a system where data is changing in real-time, it is impractical to maintain pre-computed aggregates. A reasonably sized cache can allow a system to perform adequately in the face of unpredictable queries, with few or no pre-computed aggregates.

Mondrian's aggregation strategy is as follows:
  • Fact data is stored in the RDBMS. Why develop a storage manager when the RDBMS already has one?
  • Read aggregate data into the cache by submitting group by queries. Again, why develop an aggregator when the RDBMS has one?
  • If the RDBMS supports materialized views, and the database administrator chooses to create materialized views for particular aggregations, then Mondrian will use them implicitly. Ideally, Mondrian's aggregation manager should be aware that these materialized views exist and that those particular aggregations are cheap to compute. It should even offer tuning suggestings to the database administrator.
The general idea is to delegate unto the database what is the database's. This places additional burden on the database, but once those features are added to the database, all clients of the database will benefit from them. Multidimensional storage would reduce I/O and result in faster operation in some circumstances, but I don't think it warrants the complexity at this stage.

A wonderful side-effect is that because Mondrian requires no storage of its own, it can be installed by adding a JAR file to the class path and be up and running immediately. Because there are no redundant data sets to manage, the data-loading process is easier, and Mondrian is ideally suited to do OLAP on data sets which change in real time


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