A system and method for processing a database query is described. In response to detection that a database query involves a star or snowflake join operation, a join operator in a preliminary query plan can be split into a build operator and a probe operator. The probe operator can be placed in a final query plan in the same place as the join operator in the preliminary query plan, while the build operator can be placed beneath the probe operator in the final query plan, between an exchange operator and the exchange operator's child from the preliminary query plan.

Splitting of a join operation to allow parallelization

In the new age, digital data is the most important source of acquiring knowledge. For this purpose, collect data from various sources like websites, blogs, webpages, and most important databases. Database and relational databases both provide help to decision making in the future work. Nowadays these approaches become time and resource consuming there for new concept use name data warehouse. Which can analyze many databases at a time on a common plate from with very efficient way. In this paper, we will discuss the database and migration from the database to the data warehouse. Data Warehouse (DW) is the special type of a database that stores a large amount of data. DW schemas organize data in two ways in which star schema and snowflakes schema. Fact and dimension tables organize in them. Distinguished by normalization of tables. Nature of data leads the designer to follow the DW schemas on the base of data, time and resources factor. Both design-modeling techniques compare with the experiment on the same data and results of applying the same query on them. After the performance evaluation, using bitmap indexing to improve the schemas performance. We also present the design modeling techniques with respect to data mining and improve query optimization technique to save time and resource in the analysis of data.

A Review of Star Schema and Snowflakes Schema

A system for query planning and execution with source and/or sink operators is provided. In some implementations, the system performs operations comprising identifying a plurality of operators for executing a query, allocating space in memory for storing data when a second operator comprises a collection operator for collecting the data, generating at least a portion of the data based on executing a first operator, and providing the data (e.g., access to the data) to another operator which occurs after the first operator. In some implementations, a query plan for execution of the query can be generated to include the first operator and exclude the collection operator. Related systems, methods, and articles of manufacture are also described.

Query planning and execution with source and sink operators

A system for query execution and planning with pipelining and pump operators is provided. In some implementations, the system performs operations comprising identifying a plurality of operators for executing a query, the plurality of operators including a first operator and a second operator, generating a pump operator when the first operator comprises a table scan operator, and/or generating a query plan for execution of the query, the query plan comprising the pump operator and the second operator. The operations can further include executing the pump operator to at least retrieve a portion of a database table, generate a first data portion based on the portion of the database table, and provide the first data portion for the second operator. Related systems, methods, and articles of manufacture are also described.

Query execution pipelining with pump operators

Techniques are described herein to generate and to execute a query execution plan using static data buffering. After receiving a query with a clause that requires multiple iterations to execute, a database management system (DBMS) generates a plurality of plans that vary the order in which the database operations are executed. Within each plan, the DBMS identifies sets of rows within that plan that contain static data during execution of the query. Then, an additional step is added to each plan that includes loading the static set of rows in a database buffer cache. One or more database operations, from an iteration other than the first iteration, may be performed against the cached static set of rows. For each plan generated in this manner, a cost analysis model is applied, and the plan with the lowest estimated computational cost is selected for use as the query execution plan.

Static data caching for queries with a clause that requires multiple iterations to execute

A system and method for processing a database query is described. The method can, in response to detection that a database query involves a star or snowflake join operation, determine a selectivity ratio for each of a plurality of dimension tables. The selectivity ratio having a lower value can correspond to a more restrictive dimension table. Thereafter, a table ordering can be created beginning with a fact table and continuing with each of the dimension tables in ascending order of their corresponding selectivity ratios. Then a query plan involving join operations between successive tables in the table ordering can be created.

Star and snowflake join query performance

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Splitting of a join operation to allow parallelization

Star and snowflake join query performance