YourSQL: A High-Performance Database System Leveraging In
... The early filtering approach is usually considered one of the most effective ways to achieve the acceleration of dataintensive queries. The level of potential improvement varies depending on selectivity, which represents a fraction of relevant rows from a row set (e.g., a table, a view or the result ...
... The early filtering approach is usually considered one of the most effective ways to achieve the acceleration of dataintensive queries. The level of potential improvement varies depending on selectivity, which represents a fraction of relevant rows from a row set (e.g., a table, a view or the result ...
Joins for Hybrid Warehouses: Exploiting Massive Parallelism in Hadoop and Enterprise Data Warehouses.
... parallel database. It has an optimizer, indexing support and sophisticated SQL engine. On the HDFS side, we assume a scan-based processing engine without any indexing support. This is true for all the existing SQL-on-Hadoop systems, such as MapReduce-based Hive [39], Spark-based Shark [42], and Impa ...
... parallel database. It has an optimizer, indexing support and sophisticated SQL engine. On the HDFS side, we assume a scan-based processing engine without any indexing support. This is true for all the existing SQL-on-Hadoop systems, such as MapReduce-based Hive [39], Spark-based Shark [42], and Impa ...
chapter 7_ database administration
... data to enter in the WAREHOUSE column. The only possibility would be NULL. Therefore, if every column not included in a view can accept nulls, you can add new rows using the INSERT command. There is another problem, however. Suppose the user attempts to add a row to the HOUSEWARES view containing th ...
... data to enter in the WAREHOUSE column. The only possibility would be NULL. Therefore, if every column not included in a view can accept nulls, you can add new rows using the INSERT command. There is another problem, however. Suppose the user attempts to add a row to the HOUSEWARES view containing th ...
How Achaeans Would Construct Columns in Troy
... from the TPC-H benchmark [13] as a running example below. Figure 1(a) shows the logical query plan for query 6. Below, let’s see how we can push down one or more operators in query 6 to a UDF. Scan Pushdown. First of all, we need to push down the scan operator to the UDF. This is because we need to ...
... from the TPC-H benchmark [13] as a running example below. Figure 1(a) shows the logical query plan for query 6. Below, let’s see how we can push down one or more operators in query 6 to a UDF. Scan Pushdown. First of all, we need to push down the scan operator to the UDF. This is because we need to ...
Chapter 14: Query Optimization
... By: for each relation r in S let S1 = S – r . If only left-deep trees are considered, time complexity of finding best join order is O(n 2n) ...
... By: for each relation r in S let S1 = S – r . If only left-deep trees are considered, time complexity of finding best join order is O(n 2n) ...
Pizza Parlor Point-Of-Sales System CMPS 342 Database Systems
... that have to do with inventory, ordering, accounts payable, and labor, among others. Based on their answers we planned accordingly. From a business perspective we will be able to query historical data and produce total sales per day/week/month; sales based on pizza's sold; types of pizzas; salad and ...
... that have to do with inventory, ordering, accounts payable, and labor, among others. Based on their answers we planned accordingly. From a business perspective we will be able to query historical data and produce total sales per day/week/month; sales based on pizza's sold; types of pizzas; salad and ...
The BUCKY Object-Relational Benchmark
... also contribute to an extensible type system. The last item in the BUCKY list of tested features, ADT support, maps to the third item in Stonebraker's list. Finally, we dier on the last item in his list|the BUCKY benchmark includes no trigger tests. While we agree that advanced trigger support is a ...
... also contribute to an extensible type system. The last item in the BUCKY list of tested features, ADT support, maps to the third item in Stonebraker's list. Finally, we dier on the last item in his list|the BUCKY benchmark includes no trigger tests. While we agree that advanced trigger support is a ...
ppt
... By: for each relation r in S let S1 = S – r . If only left-deep trees are considered, time complexity of finding best join order is O(n 2n) ...
... By: for each relation r in S let S1 = S – r . If only left-deep trees are considered, time complexity of finding best join order is O(n 2n) ...
Oracle/SQL Tutorial - Department of Math/CS
... values(313, ’DBS’, 7411, null, 150000.42, ’10-OCT-94’, null); If there are already some data in other ...
... values(313, ’DBS’, 7411, null, 150000.42, ’10-OCT-94’, null); If there are already some data in other ...
Data security best practices ® A practical guide to implementing
... to address the shortcomings of traditional row and column access control methods. They represent a second layer of security that complements the current table-privileges security model. More specifically, the table-privileges security model is applied first to determine whether a user is allowed to ...
... to address the shortcomings of traditional row and column access control methods. They represent a second layer of security that complements the current table-privileges security model. More specifically, the table-privileges security model is applied first to determine whether a user is allowed to ...
A Self-managing Data Cache for Edge-Of
... XML) and the database server (base tables) which complicates the task of consistency maintenance. Finally, space is not managed efficiently due to the redundant storage of multiple copies of the same data as part of different query results. The limitations of dynamic content caching solutions and the i ...
... XML) and the database server (base tables) which complicates the task of consistency maintenance. Finally, space is not managed efficiently due to the redundant storage of multiple copies of the same data as part of different query results. The limitations of dynamic content caching solutions and the i ...
SQL Server 2014 In-Memory OLTP TDM White Paper
... Indexes on memory-optimized tables are not stored as traditional B-trees. Memory-optimized tables support nonclustered hash indexes, stored as hash tables with linked lists connecting all the rows that hash to the same value and memory-optimized nonclustered indexes, which are stored using special B ...
... Indexes on memory-optimized tables are not stored as traditional B-trees. Memory-optimized tables support nonclustered hash indexes, stored as hash tables with linked lists connecting all the rows that hash to the same value and memory-optimized nonclustered indexes, which are stored using special B ...
A Modular Query Optimizer Architecture for Big Data - CMU 15-721
... Listing 1 shows the representation of the previous query in DXL, where we give the required output columns, sorting columns, data distribution and logical query. Metadata (e.g., tables and operators definitions) are decorated with metadata ids (Mdid’s) to allow requesting further information during ...
... Listing 1 shows the representation of the previous query in DXL, where we give the required output columns, sorting columns, data distribution and logical query. Metadata (e.g., tables and operators definitions) are decorated with metadata ids (Mdid’s) to allow requesting further information during ...
CH10
... How to select data from multiple tables To return a result set that contains data from two tables, you join the tables. To do that, you can use a JOIN clause. Most of the time, you’ll want to code an inner join so that rows are only included when the key of a row in the first table matches the ...
... How to select data from multiple tables To return a result set that contains data from two tables, you join the tables. To do that, you can use a JOIN clause. Most of the time, you’ll want to code an inner join so that rows are only included when the key of a row in the first table matches the ...
A Survey on Query Processing and Optimization
... A select-project-join query, an optimizer has to make many choices, the most important being: access methods, join order, join algorithms, and pipeling. [2] Access Method: The optimizer needs to pick an access method for each table in the query. Typically there are many choices, including a direct t ...
... A select-project-join query, an optimizer has to make many choices, the most important being: access methods, join order, join algorithms, and pipeling. [2] Access Method: The optimizer needs to pick an access method for each table in the query. Typically there are many choices, including a direct t ...
Using DDL Statements Questions
... A. When the database is not being used by any user B. When the database is newly created C. It can be created any time, even when a user is using the database D. None of the above Answer: C. An index can be created to speed up the query process. DML operations are always slower when indexes exist. O ...
... A. When the database is not being used by any user B. When the database is newly created C. It can be created any time, even when a user is using the database D. None of the above Answer: C. An index can be created to speed up the query process. DML operations are always slower when indexes exist. O ...
Understanding and Creating Queries
... and Sort & Filter functions. For more complex tasks such as retrieving data records that meet particular criteria, you can use queries. Queries are questions that you ask of your database. By using queries, you can retrieve data from a single table or from multiple tables and display all data togeth ...
... and Sort & Filter functions. For more complex tasks such as retrieving data records that meet particular criteria, you can use queries. Queries are questions that you ask of your database. By using queries, you can retrieve data from a single table or from multiple tables and display all data togeth ...
Expressive Query Construction through Direct Manipulation of
... joins and aggregation, and allows the user to see, in context, intermediate tuples produced in any part of the query. Using our system, the user can express a relationally complete [17] set of query operators plus calculation, aggregation, outer joins, sorting, and nesting (see Appendix A for detail ...
... joins and aggregation, and allows the user to see, in context, intermediate tuples produced in any part of the query. Using our system, the user can express a relationally complete [17] set of query operators plus calculation, aggregation, outer joins, sorting, and nesting (see Appendix A for detail ...
Expressive Query Construction through Direct Manipulation of
... joins and aggregation, and allows the user to see, in context, intermediate tuples produced in any part of the query. Using our system, the user can express a relationally complete [17] set of query operators plus calculation, aggregation, outer joins, sorting, and nesting (see Appendix A for detail ...
... joins and aggregation, and allows the user to see, in context, intermediate tuples produced in any part of the query. Using our system, the user can express a relationally complete [17] set of query operators plus calculation, aggregation, outer joins, sorting, and nesting (see Appendix A for detail ...
Progressive Optimization in a Shared-Nothing
... When joining two tables in a parallel database environment, the table data must be physically located in the same database partition where the join operation takes place. If not, the data must be transferred from one partition to the designated joining partition. This movement of data is accomplishe ...
... When joining two tables in a parallel database environment, the table data must be physically located in the same database partition where the join operation takes place. If not, the data must be transferred from one partition to the designated joining partition. This movement of data is accomplishe ...
OLAP Query Evaluation in a Database Cluster: a Performance Study
... replicas of the dimension tables at all nodes. Fact tables are by far the largest tables, so accessing these tables in parallel should yield the most significant speedup. However, the difficult open question remains: How many nodes should be used to evaluate the query in parallel? To be more flexibl ...
... replicas of the dimension tables at all nodes. Fact tables are by far the largest tables, so accessing these tables in parallel should yield the most significant speedup. However, the difficult open question remains: How many nodes should be used to evaluate the query in parallel? To be more flexibl ...