You can determine the table names with a Select on the system table sysupdstatwanted. Detected bottlenecks are output in text form to rapidly provide database administrators with an overview of the possible causes of performance problems. The analysis can be performed just once or at regular intervals. You can configure how long logs are kept using transaction DB59 in the integration data for the respective system. Data can be prepared furthermore by the list viewer building sums, min, max and average values, can be loaded to the local desktop or graphically displayed.
If you start the Database Analyzer on the DB server, you can omit the "-o" entry. The data is grouped by contents and stored in different files. By default, the logs are stored for 93 days. For more information, see note For a short time, lower hit rates may occur; e. User response In addition to enlarging the data cache note the paging risk in the operating system , search for the cause of the high read activity. Frequently, individual SQL statements cause a high percentage of the total logical and physical read activities.
Enlarging the cache only transfers the load from the disk to the CPU although an additional index, for example, could transform a read-intensive table scan into a cheap direct access. User response Enlargement of the data cache. This indicates a poor search strategy, caused either by the application missing or insufficient indexes or by poor formulation of SQL statements.
User response First of all, see if MaxDB Optimizer is able to find a more suitable strategy after updating the internal database statistics. If this does not produce the desired result, search for the statement that triggers the unfavorable search strategy. The runtime data collection has a small impact on the performance of the system. The runtime data includes information about number of executions, overall — minimum — maximum and average execution time, number of page accesses in memory and on disk, number of read and qualified rows, wait situations and more.
Shared SQL collects the execution times in microseconds. The Resource Monitor shows the execution times as milliseconds which is incorrect in the current version of the DBACockpit. The Shared SQL data helps analyzing the over all load in the systems. The Resource Monitor filters the commands to be displayed by the output criteria. This example shows a select reading data from table EK. It runs quite often with a high execution time. An optimization of the application or an index on table EK for the field FLAG could reduce the load in the system significantly.
A double-click on the command guides you to the complete statement. MaxDB needs the values of the input parameters for the explain. The following SQL statement shows all command executions after a reset: select c. Different input parameter values can lead to very different execution times. MaxDB can use different search strategies according to the input parameter values.
The Command Monitor stores the command ID, the monitoring values and the input parameter values when a command execution exceeds page accesses and execution time or falls below selectivity the given filter value.
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Explain shows the search strategy of the statement. Administrators can use those SQL statements manually, as well. This reduces the memory footprint of the Command Monitor significantly. Furthermore the new implementation has a very small impact on the performance of the system as long as users define reasonable filter values. A double click on the line of a command shows more detail information about the command.
The layout definition allows the selection of user defined output columns. Fetch Orders Number of fetch order for record transportation Result is Copied. YES: an internal result set was created Date execution date Time execution time 43 The Command Monitor collects the input parameter values belonging to the command executions. Explain show the search strategy found by the optimizer at the time of the explain execution.
It can also jump into the table definition view with the tables referenced by the SQL command. The table definition view shows the definition in the database, not the data dictionary definition of the WebAS. The search strategy can change with modified table definitions or updated statistics. The search strategy at the Explain time can be different to the search strategy at the execution time of the statement. The chapter Query Optimization provides more detailed information about the Explain command. Shared SQL stores this information with the command. Both, the Command and the Resource Monitor support this functionality.
The DBACockpit here shows the definition and more detailed information about the chosen table. The table size, optimizer statistics and the table data can be displayed in this view. Optimizer statistics can be updated as well. This is very helpful if no DBACockpit is available. The commands are sorted by the execution time by default.
Different variants show groups of runtime figures. This insures a proper overview.
A double click on a statement line guides to the detailed statement view. It shows the input parameter values and the Explain output according to the chosen command execution. A right click on the statement opens a new SQL editor with this statement. It can open the table definition editor for marked tables as well. With this approach the Command Monitor as of version 7.
The performance impact of the Command Monitor depends on the number of command executions to be logged. A high number of logged commands can lead to high memory consumptions. The Command Monitor settings remain after a database restart as of version 7. This table can become big with many logged command executions specially if the commands have many input parameters.
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This select joins the data from the relevant system tables and shows the command executions with their parameter values sorted by the execution runtime: select m. The Update Statistics collects the table size and number of rows only if they are not available in the File Directory. This can happen with tables created by older database version than 7. Or do you find that, in some cases, selecting large amounts of data is not practical. Large data sets can be costly to extract from a large database. When you think of these types of needs, you start to think of data warehousing.
From there you start thinking about big machines with large and costly-to-license commercial databases. Using UDFs, you can build functions that do complex data analysis inside of the database. The functionality of UDFs is not limited to traditional character and number data; exotic binary data like images and music can be analyzed as well. Additional examples will be shown using myPerl. With myPerl, you can even bypass the need to learn the internal structure of MySQL UDF structure and instead use a popular and easy to use scripting language like Perl. An overview of the major features of MySQL 5.
Multi-terabyte database deployments come with their own interesting set of challenges. This session will discuss case studies of large MySQL databases and will provide recommendations for:. MaxDB is a heavy-duty, SAP-certified open-source database that offers high availability, scalability, and a comprehensive feature set.
This session provides an overview of the major features of MaxDB, along with a discussion of the current state and future plans for the product.
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This session will provide an overview of the major features of MySQL Cluster, along with a discussion of the current state and future plans for the product. This session will take you on a detailed walk through the API, providing the attendee with the detailed knowledge required to get the most out of the C API and other APIs that are based on it. Larry will discuss the important business events in and around MySQL over the last year. He will also provide his analysis of how the hybrid MySQL business model has shaped and been shaped by events in the industry and in the community.
After a long day of dreadfully nice weather and stimulating Turkish culture, return to a more normal geekly existence by learning how the MySQL development model works. Our current development model will be discussed in detail -- with coverage on how it was designed, what works and what has failed. Using a combination of case studies, solid industry knowledge and glib wit, Larry Stefonic will outline the case for MySQL's use in the enterprise world. Have you ever wanted to extend MySQL in strange and wonderful ways?
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