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PATCHED Table Optimizer: A Solution for Fragmented Database Tables



Of the three MyISAM storage formats, static format is the simplest and most secure (least subject to corruption). It is also the fastest of the on-disk formats due to the ease with which rows in the data file can be found on disk: To look up a row based on a row number in the index, multiply the row number by the row length to calculate the row position. Also, when scanning a table, it is very easy to read a constant number of rows with each disk read operation.


The security is evidenced if your computer crashes while the MySQL server is writing to a fixed-format MyISAM file. In this case, myisamchk can easily determine where each row starts and ends, so it can usually reclaim all rows except the partially written one. MyISAM table indexes can always be reconstructed based on the data rows.




PATCHED Table Optimizer



Fixed-length row format is available only for tables having no BLOB or TEXT columns. Creating a table having such columns with an explicit ROW_FORMAT clause does not raise an error or warning; the format specification is ignored.


delete_flag is 1 for tables with static row format. Static tables use a bit in the row record for a flag that indicates whether the row has been deleted. delete_flag is 0 for dynamic tables because the flag is stored in the dynamic row header.


In two previous articles (part1 andpart 2), I showed some practical ways to use aSQL Servercalendar table to solve business day and date range problems. Next, I'lldemonstrate how you can use this table to solve scheduling problems, like environment-wideserver patch management.


This data allows us to determine what is the absolute last calendar day we canpatch a given server or pool (based on the last time it was patched), and stillbe in compliance with our rules. While we don't necessarily want to put offpatching, we do want to optimize for patching a server as late into its scheduleas possible. If we patch a server too soon, that just makes its next patchan earlier priority.


While you could use a separate table for this, I'm going to store informationabout deployment freezes in our OutageDates table (describedin an earlier tip). The next deployment freeze conveniently falls on the sameday all our servers are due for patching (and it also happens to be a Friday):


Next, I'll create a view called ValidPatchingDays which restricts our originalCalendar table to the days where we can perform patching (not on Fridays, weekends,holidays, or other outages like deployment freezes):


When we combine the ServerPatchDetails view with the ValidPatchingDays view,we can determine the last possible day a server can be patched to stay within itsrules, and be patched on a day when we can perform patches:


You can see that the last possible day to patch any server is June 22nd.The problem is, we can't patch all of those servers on June 22nd,both because we're limited to patching 5 servers a day, and we also can'tpatch all four servers in pool 2 on a single day. We need to introduce some rankingto bump servers back to a previous (also valid) day when necessary. Again, the calendartable can help us here, because we can get all valid patching days, as far backas we need. But how can we apply multiple business rules simultaneously?


Any method of scheduling many events can get complex rather quickly, and I wasn'ttrying to solve every aspect of patch scheduling in this post. I hope I'vedemonstrated how the calendar table can take a whole chunk of complexity out ofthe equation from the start.


Use the Resources table to view the hardware configuration of systems in the infrastructure that hosts the selected business service. In case of recommended or manual resizing, the table displays a summary of all the resized resources for which the simulation is generated.


Create custom optimizer rules based on the Data vs. Baseline condition type along with other condition types, such as the Data vs. Threshold or Formula condition types. Additionally, the Monitor Violations condition type is renamed to Good/Warn/Poor Samples Count. For more information, see Adding a custom Optimizer rule.


This article describes how to check dictionary statistics including statistics on fixed objects. Since version 10g, statistics on the data dictionary are mandatory for the cost-based optimizer to work properly. Dictionary statistics include the statistics on the tables and indexes owned by SYS (and other internal RDBMS schemas like SYSTEM) and the statistics on the fixed objects. Fixed objects are the internal X$ tables and the so called dynamic performance views or V$ views which are based upon them. These are not real tables and indexes, but rather memory structures. The statistics for the fixed objects need to be gathered manually; they are not updated by the automatic statistics gathering.


The manufacturing of composite structures is expensive due to high material cost and the amount of manual labor needed. Current manufacturing technologies, e.g. filament winding, braiding or fiber placement technologies are offering a possibility of automated manufacturing to lower these costs. Nevertheless, these technologies are limited when it comes to structures with complex fiber architecture and small geometries. A new approach to overcome these limitations is the fiber patch placement technology. The preform of a composite structure is built up with small and dry fiber patches in a sequential order. Fiber discontinuities are necessarily occurring between two patches and are influencing the mechanical properties. To optimize strength of the composite those discontinuities have to be distributed within the preform. With increased number of patches, the number of combinations of patch positions is increasing significantly. This paper presents a method based on an ant colony algorithm for an efficient calculation of an optimized distribution, which enables to use the potential of patched laminates.


Design variables: The input variables to the objective function. These variables are changed by the optimizer within a pre-set range of values called as the bounds of the variables. In this example, the dimensions of the H-Notch patch are the design variables.


To optimize the H-Notch patch, click on the Optimize button. To select an objective function, use the OBJECTIVE FUNCTION Gallery drop down. Since the goal is to maximize the gain of the antenna, click on Maximize Gain. To set up the design variables, click on the Design Variables tab. Click on the checkboxes present on the left-hand side of the properties to choose the required design variables. The optimizer would change these chosen properties to obtain a maximum gain for the antenna. To set up the constraints, click on the Constraints tab and select S11 (dB) from the Constraint Function. Select


In the model building stage, the optimizer makes a surrogate model from the design space, and the specified objective and the constraints function. It diversely goes through the design space and performs analysis on these sample points.


So, the X-axis shows the number of samples and the Y-axis shows the value of the analysis function value at that sample. The bottom left side show the current sample value and the bottom right side shows the design variables. The optimizer within decides and takes appropriate number of samples to build the model. After the model is built, the optimizer starts running iterations.


As explained in the CSS2.1 specification, table layout in general is usually a matter of taste and will vary depending on design choices. Browsers will, however, automatically apply certain constraints that will define how tables are laid out. This happens when the table-layout property is set to auto (the default). But these constraints can be lifted when table-layout is set to fixed.


Introduce new row in catalog property table SERVER_OPERATION_ENCRYPTION_LEVEL and default to PER_EXECUTION to keep backward compatibility, value can be changed to PER_PROJECT creating one key or certificate pair for each project. A full cleanup is required before changing from PER_EXECUTION to PER_PROJECT. Two new store procedures are introduced for a full cleanup.


If a table variable is joined with other tables in SQL Server, it may result in slow performance due to inefficient query plan selection because SQL Server does not support statistics or track number of rows in a table variable while compiling a query plan.


In SQL Server 2012 SP2, a new trace flag is introduced that allows the query optimizer to use information about the number of rows inserted into a table variable in order to select more efficient query plan. Enable trace flag 2453 to activate this behavior.Notes:


In some scenarios, enabling trace flag 2453 may result in some degradation of performance, due to additional compilation required to account for actual number of rows inserted into a table variable during execution time. Typically, you would benefit from this trace flag if a table variable has significant amount of rows joined with other tables, or has more than one row and used at the outer side of a nested loop join operator with a plan on the inner side that processes large amount of rows.


Similar behavior may be achieved on other versions of SQL Server through using OPTION (RECOMPILE) query hint. However, the query hint requires detecting and modifying all queries suffering from poor plan choice due to large amount of work driven by table variables, while enabling the trace flag 2453 can impact existing workloads.


In SQL Server 2012 SP2 a new Dynamic Management Function (DMF) was added to provide access to positioning information for keywords indexed in a document. The new DMF is similar to existing DMF sys.dm_fts_index_keywords_by_document, and has the following syntax:sys. dm_fts_index_keywords_position_by_document ( DB_ID('database_name'), OBJECT_ID('table_name') )


When a record is deleted in a table or index, such a delete operation never physically removes records from pages, it only marks them as having been deleted, or ghosted. This is a performance optimization that allows delete operations to complete more quickly. A background task called the ghost cleanup task then physically removes all the deleted records. Several extended events have been added in Service Pack 2 to provide insights into the various phases of this task: 2ff7e9595c


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