The assignee for this patent, patent number 8620954, is
Reporters obtained the following quote from the background information supplied by the inventors: "The present invention relates generally to database systems and more particularly to query optimization systems and methods for use in multi-tenant database systems, wherein a centralized computer or set of computing devices serve and store applications and data for use by multiple tenants.
"Multi-tenant database systems allow for users to access applications and/or data from a network source that, to the user, appears to be centralized (but might be distributed for backup, redundancy and/or performance reasons). An example of a multi-tenant system is a computing system that is accessible to multiple independent parties to provide those parties with application execution and/or data storage. Where there is an appearance of centralization, and network access, each subscribing party (e.g., a 'tenant') can access the system to perform application functions, including manipulating that tenant's data.
"With a multi-tenant system, the tenants have the advantage that they need not install software, maintain backups, move data to laptops to provide portability, etc. Rather, each tenant user need only be able to access the multi-tenant system to operate the applications and access that tenant's data. One such system usable for customer relationship management is the multi-tenant system accessible to
"In database systems, to access, retrieve and process stored data, a query is generated, automatically or manually, in accordance with the application program interface protocol for the database. In the case of a relational database, the standard protocol is the structured query language (SQL). SQL statements are used both for interactive queries for data from the database and for gathering data and statistics. The efficiency of the query method underlying the actual query is dependent in part on the size and complexity of the data structure scheme of the database and in part on the query logic used.
"Previous database query methods have been inefficient for multi-tenant databases because such methods do not understand, and fail to account for, the unique characteristics of each tenant's data. For example, while one tenant's data may include numerous short records having only one or two indexable fields, another tenant's data may include fewer, longer records having numerous indexable fields.
"In addition to these structural (schema) differences, the distribution of data among different tenants may be quite different, even when their schemas are similar. Modern relational databases rely on statistics-based query optimizers that make decisions about the best manner to answer a query given accurate table-level and column-level statistics that are gathered periodically. Importantly, however, because existing relational databases are not multi-tenant aware, these statistics cut across all tenants in the database. That is, the statistics that are gathered are not specific to any one tenant, but are in fact an aggregate or average of all tenants. This approach can lead to incorrect assumptions and query plans about any one tenant.
"As a specific example,
"In general, one goal of a query optimizer is to minimize the amount of data that must be read from disk (e.g., because disk access may be a slow operation). The optimizer therefore typically chooses tables or columns that are most 'selective'--that is, will yield the fewest rows when the query condition is evaluated. For instance, if a single query filters on two columns of a single table, and both columns are indexed, then the optimizer will use the index that has the highest number of distinct values because statistically for any given filter value a smaller number of rows are expected to be returned. If the optimizer knows that a certain column has a very high cardinality (number of distinct values) then the optimizer will choose to use an index on that column versus a similar index on a lower cardinality column. The optimizer assumes relatively even distribution of data and therefore reaches the conclusion that the high-cardinality column is likely to yield a smaller number of satisfying-rows for a given equality filter.
"Now consider in a multi-tenant system a physical column (shared by many tenants) that has a large number of distinct values for most tenants, but a small number of distinct values for a specific tenant. For this latter tenant the query optimizer will use this overall-high-cardinality column in error--because the optimizer is unaware that for this specific tenant the column is not selective.
"In the case of table joins, the optimizer's decisions may be even more important--deciding which table to retrieve first can have a profound impact on overall query performance. Here again, by using system-wide aggregate statistics the optimizer might choose a query plan that is incorrect or inefficient for a single tenant that does not conform to the 'normal' average of the entire database as determined from the gathered statistics.
"Accordingly, it is desirable to provide systems and methods for optimizing database queries, and for dynamically tuning a query optimizer, in a multi-tenant database system which overcome the above and other problems."
In addition to obtaining background information on this patent, VerticalNews editors also obtained the inventors' summary information for this patent: "The present invention provides methods and systems for optimizing database queries in a multi-tenant database system. In certain aspects, for example, the present invention provides methods for dynamically tuning a query optimizer based on particular data characteristics of the tenant whose data is being searched and the particular query being executed. The systems and methods of the present invention advantageously harness greater semantic knowledge about the use of data tables by the underlying relational database. By tracking tenant-level statistics on top of the typical system-gathered statistics (e.g.,
For more information, see this patent: Weissman, Craig; Moellenhoff, Dave; Wong, Simon;
Keywords for this news article include: inc.,
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