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  • How To - Guide to Importing Data from a MySQL Database to Excel using MySQL for Excel

    - by Javier Treviño
    Fetching data from a database to then get it into an Excel spreadsheet to do analysis, reporting, transforming, sharing, etc. is a very common task among users. There are several ways to extract data from a MySQL database to then import it to Excel; for example you can use the MySQL Connector/ODBC to configure an ODBC connection to a MySQL database, then in Excel use the Data Connection Wizard to select the database and table from which you want to extract data from, then specify what worksheet you want to put the data into.  Another way is to somehow dump a comma delimited text file with the data from a MySQL table (using the MySQL Command Line Client, MySQL Workbench, etc.) to then in Excel open the file using the Text Import Wizard to attempt to correctly split the data in columns. These methods are fine, but involve some degree of technical knowledge to make the magic happen and involve repeating several steps each time data needs to be imported from a MySQL table to an Excel spreadsheet. So, can this be done in an easier and faster way? With MySQL for Excel you can. MySQL for Excel features an Import MySQL Data action where you can import data from a MySQL Table, View or Stored Procedure literally with a few clicks within Excel.  Following is a quick guide describing how to import data using MySQL for Excel. This guide assumes you already have a working MySQL Server instance, Microsoft Office Excel 2007 or 2010 and MySQL for Excel installed. 1. Opening MySQL for Excel Being an Excel Add-In, MySQL for Excel is opened from within Excel, so to use it open Excel, go to the Data tab located in the Ribbon and click MySQL for Excel at the far right of the Ribbon. 2. Creating a MySQL Connection (may be optional) If you have MySQL Workbench installed you will automatically see the same connections that you can see in MySQL Workbench, so you can use any of those and there may be no need to create a new connection. If you want to create a new connection (which normally you will do only once), in the Welcome Panel click New Connection, which opens the Setup New Connection dialog. Here you only need to give your new connection a distinctive Connection Name, specify the Hostname (or IP address) where the MySQL Server instance is running on (if different than localhost), the Port to connect to and the Username for the login. If you wish to test if your setup is good to go, click Test Connection and an information dialog will pop-up stating if the connection is successful or errors were found. 3.Opening a connection to a MySQL Server To open a pre-configured connection to a MySQL Server you just need to double-click it, so the Connection Password dialog is displayed where you enter the password for the login. 4. Selecting a MySQL Schema After opening a connection to a MySQL Server, the Schema Selection Panel is shown, where you can select the Schema that contains the Tables, Views and Stored Procedures you want to work with. To do so, you just need to either double-click the desired Schema or select it and click Next >. 5. Importing data… All previous steps were really the basic minimum needed to drill-down to the DB Object Selection Panel  where you can see the Database Objects (grouped by type: Tables, Views and Procedures in that order) that you want to perform actions against; in the case of this guide, the action of importing data from them. a. From a MySQL Table To import from a Table you just need to select it from the list of Database Objects’ Tables group, after selecting it you will note actions below the list become available; then click Import MySQL Data. The Import Data dialog is displayed; you can see some basic information here like the name of the Excel worksheet the data will be imported to (in the window title), the Table Name, the total Row Count and a 10 row preview of the data meant for the user to see the columns that the table contains and to provide a way to select which columns to import. The Import Data dialog is designed with defaults in place so all data is imported (all rows and all columns) by just clicking Import; this is important to minimize the number of clicks needed to get the job done. After the import is performed you will have the data in the Excel worksheet formatted automatically. If you need to override the defaults in the Import Data dialog to change the columns selected for import or to change the number of imported rows you can easily do so before clicking Import. In the screenshot below the defaults are overridden to import only the first 3 columns and rows 10 – 60 (Limit to 50 Rows and Start with Row 10). If the number of rows to be imported exceeds the maximum number of rows Excel can hold in its worksheet, a warning will be displayed in the dialog, meaning the imported number of rows will be limited by that maximum number (65,535 rows if the worksheet is in Compatibility Mode).  In the screenshot below you can see the Table contains 80,559 rows, but only 65,534 rows will be imported since the first row is used for the column names if the Include Column Names as Headers checkbox is checked. b. From a MySQL View Similar to the way of importing from a Table, to import from a View you just need to select it from the list of Database Objects’ Views group, then click Import MySQL Data. The Import Data dialog is displayed; identically to the way everything looks when importing from a table, the dialog displays the View Name, the total Row Count and the data preview grid. Since Views are really a filtered way to display data from Tables, it is actually as if we are extracting data from a Table; so the Import Data dialog is actually identical for those 2 Database Objects. After the import is performed, the data in the Excel spreadsheet looks like the following screenshot. Note that you can override the defaults in the Import Data dialog in the same way described above for importing data from Tables. Also the Compatibility Mode warning will be displayed if data exceeds the maximum number of rows explained before. c. From a MySQL Procedure Too import from a Procedure you just need to select it from the list of Database Objects’ Procedures group (note you can see Procedures here but not Functions since these return a single value, so by design they are filtered out). After the selection is made, click Import MySQL Data. The Import Data dialog is displayed, but this time you can see it looks different to the one used for Tables and Views.  Given the nature of Store Procedures, they require first that values are supplied for its Parameters and also Procedures can return multiple Result Sets; so the Import Data dialog shows the Procedure Name and the Procedure Parameters in a grid where their values are input. After you supply the Parameter Values click Call. After calling the Procedure, the Result Sets returned by it are displayed at the bottom of the dialog; output parameters and the return value of the Procedure are appended as the last Result Set of the group. You can see each Result Set is displayed as a tab so you can see a preview of the returned data.  You can specify if you want to import the Selected Result Set (default), All Result Sets – Arranged Horizontally or All Result Sets – Arranged Vertically using the Import drop-down list; then click Import. After the import is performed, the data in the Excel spreadsheet looks like the following screenshot.  Note in this example all Result Sets were imported and arranged vertically. As you can see using MySQL for Excel importing data from a MySQL database becomes an easy task that requires very little technical knowledge, so it can be done by any type of user. Hope you enjoyed this guide! Remember that your feedback is very important for us, so drop us a message: MySQL on Windows (this) Blog - https://blogs.oracle.com/MySqlOnWindows/ Forum - http://forums.mysql.com/list.php?172 Facebook - http://www.facebook.com/mysql Cheers!

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  • SQL SERVER – SSMS: Disk Usage Report

    - by Pinal Dave
    Let us start with humor!  I think we the series on various reports, we come to a logical point. We covered all the reports at server level. This means the reports we saw were targeted towards activities that are related to instance level operations. These are mostly like how a doctor diagnoses a patient. At this point I am reminded of a dialog which I read somewhere: Patient: Doc, It hurts when I touch my head. Doc: Ok, go on. What else have you experienced? Patient: It hurts even when I touch my eye, it hurts when I touch my arms, it even hurts when I touch my feet, etc. Doc: Hmmm … Patient: I feel it hurts when I touch anywhere in my body. Doc: Ahh … now I get it. You need a plaster to your finger John. Sometimes the server level gives an indicator to what is happening in the system, but we need to get to the root cause for a specific database. So, this is the first blog in series where we would start discussing about database level reports. To launch database level reports, expand selected server in Object Explorer, expand the Databases folder, and then right-click any database for which we want to look at reports. From the menu, select Reports, then Standard Reports, and then any of database level reports. In this blog, we would talk about four “disk” reports because they are similar: Disk Usage Disk Usage by Top Tables Disk Usage by Table Disk Usage by Partition Disk Usage This report shows multiple information about the database. Let us discuss them one by one.  We have divided the output into 5 different sections. Section 1 shows the high level summary of the database. It shows the space used by database files (mdf and ldf). Under the hood, the report uses, various DMVs and DBCC Commands, it is using sys.data_spaces and DBCC SHOWFILESTATS. Section 2 and 3 are pie charts. One for data file allocation and another for the transaction log file. Pie chart for “Data Files Space Usage (%)” shows space consumed data, indexes, allocated to the SQL Server database, and unallocated space which is allocated to the SQL Server database but not yet filled with anything. “Transaction Log Space Usage (%)” used DBCC SQLPERF (LOGSPACE) and shows how much empty space we have in the physical transaction log file. Section 4 shows the data from Default Trace and looks at Event IDs 92, 93, 94, 95 which are for “Data File Auto Grow”, “Log File Auto Grow”, “Data File Auto Shrink” and “Log File Auto Shrink” respectively. Here is an expanded view for that section. If default trace is not enabled, then this section would be replaced by the message “Trace Log is disabled” as highlighted below. Section 5 of the report uses DBCC SHOWFILESTATS to get information. Here is the enhanced version of that section. This shows the physical layout of the file. In case you have In-Memory Objects in the database (from SQL Server 2014), then report would show information about those as well. Here is the screenshot taken for a different database, which has In-Memory table. I have highlighted new things which are only shown for in-memory database. The new sections which are highlighted above are using sys.dm_db_xtp_checkpoint_files, sys.database_files and sys.data_spaces. The new type for in-memory OLTP is ‘FX’ in sys.data_space. The next set of reports is targeted to get information about a table and its storage. These reports can answer questions like: Which is the biggest table in the database? How many rows we have in table? Is there any table which has a lot of reserved space but its unused? Which partition of the table is having more data? Disk Usage by Top Tables This report provides detailed data on the utilization of disk space by top 1000 tables within the Database. The report does not provide data for memory optimized tables. Disk Usage by Table This report is same as earlier report with few difference. First Report shows only 1000 rows First Report does order by values in DMV sys.dm_db_partition_stats whereas second one does it based on name of the table. Both of the reports have interactive sort facility. We can click on any column header and change the sorting order of data. Disk Usage by Partition This report shows the distribution of the data in table based on partition in the table. This is so similar to previous output with the partition details now. Here is the query taken from profiler. SELECT row_number() OVER (ORDER BY a1.used_page_count DESC, a1.index_id) AS row_number ,      (dense_rank() OVER (ORDER BY a5.name, a2.name))%2 AS l1 ,      a1.OBJECT_ID ,      a5.name AS [schema] ,       a2.name ,       a1.index_id ,       a3.name AS index_name ,       a3.type_desc ,       a1.partition_number ,       a1.used_page_count * 8 AS total_used_pages ,       a1.reserved_page_count * 8 AS total_reserved_pages ,       a1.row_count FROM sys.dm_db_partition_stats a1 INNER JOIN sys.all_objects a2  ON ( a1.OBJECT_ID = a2.OBJECT_ID) AND a1.OBJECT_ID NOT IN (SELECT OBJECT_ID FROM sys.tables WHERE is_memory_optimized = 1) INNER JOIN sys.schemas a5 ON (a5.schema_id = a2.schema_id) LEFT OUTER JOIN  sys.indexes a3  ON ( (a1.OBJECT_ID = a3.OBJECT_ID) AND (a1.index_id = a3.index_id) ) WHERE (SELECT MAX(DISTINCT partition_number) FROM sys.dm_db_partition_stats a4 WHERE (a4.OBJECT_ID = a1.OBJECT_ID)) >= 1 AND a2.TYPE <> N'S' AND  a2.TYPE <> N'IT' ORDER BY a5.name ASC, a2.name ASC, a1.index_id, a1.used_page_count DESC, a1.partition_number Using all of the above reports, you should be able to get the usage of database files and also space used by tables. I think this is too much disk information for a single blog and I hope you have used them in the past to get data. Do let me know if you found anything interesting using these reports in your environments. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

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  • SQL SERVER – Faster SQL Server Databases and Applications – Power and Control with SafePeak Caching Options

    - by Pinal Dave
    Update: This blog post is written based on the SafePeak, which is available for free download. Today, I’d like to examine more closely one of my preferred technologies for accelerating SQL Server databases, SafePeak. Safepeak’s software provides a variety of advanced data caching options, techniques and tools to accelerate the performance and scalability of SQL Server databases and applications. I’d like to look more closely at some of these options, as some of these capabilities could help you address lagging database and performance on your systems. To better understand the available options, it is best to start by understanding the difference between the usual “Basic Caching” vs. SafePeak’s “Dynamic Caching”. Basic Caching Basic Caching (or the stale and static cache) is an ability to put the results from a query into cache for a certain period of time. It is based on TTL, or Time-to-live, and is designed to stay in cache no matter what happens to the data. For example, although the actual data can be modified due to DML commands (update/insert/delete), the cache will still hold the same obsolete query data. Meaning that with the Basic Caching is really static / stale cache.  As you can tell, this approach has its limitations. Dynamic Caching Dynamic Caching (or the non-stale cache) is an ability to put the results from a query into cache while maintaining the cache transaction awareness looking for possible data modifications. The modifications can come as a result of: DML commands (update/insert/delete), indirect modifications due to triggers on other tables, executions of stored procedures with internal DML commands complex cases of stored procedures with multiple levels of internal stored procedures logic. When data modification commands arrive, the caching system identifies the related cache items and evicts them from cache immediately. In the dynamic caching option the TTL setting still exists, although its importance is reduced, since the main factor for cache invalidation (or cache eviction) become the actual data updates commands. Now that we have a basic understanding of the differences between “basic” and “dynamic” caching, let’s dive in deeper. SafePeak: A comprehensive and versatile caching platform SafePeak comes with a wide range of caching options. Some of SafePeak’s caching options are automated, while others require manual configuration. Together they provide a complete solution for IT and Data managers to reach excellent performance acceleration and application scalability for  a wide range of business cases and applications. Automated caching of SQL Queries: Fully/semi-automated caching of all “read” SQL queries, containing any types of data, including Blobs, XMLs, Texts as well as all other standard data types. SafePeak automatically analyzes the incoming queries, categorizes them into SQL Patterns, identifying directly and indirectly accessed tables, views, functions and stored procedures; Automated caching of Stored Procedures: Fully or semi-automated caching of all read” stored procedures, including procedures with complex sub-procedure logic as well as procedures with complex dynamic SQL code. All procedures are analyzed in advance by SafePeak’s  Metadata-Learning process, their SQL schemas are parsed – resulting with a full understanding of the underlying code, objects dependencies (tables, views, functions, sub-procedures) enabling automated or semi-automated (manually review and activate by a mouse-click) cache activation, with full understanding of the transaction logic for cache real-time invalidation; Transaction aware cache: Automated cache awareness for SQL transactions (SQL and in-procs); Dynamic SQL Caching: Procedures with dynamic SQL are pre-parsed, enabling easy cache configuration, eliminating SQL Server load for parsing time and delivering high response time value even in most complicated use-cases; Fully Automated Caching: SQL Patterns (including SQL queries and stored procedures) that are categorized by SafePeak as “read and deterministic” are automatically activated for caching; Semi-Automated Caching: SQL Patterns categorized as “Read and Non deterministic” are patterns of SQL queries and stored procedures that contain reference to non-deterministic functions, like getdate(). Such SQL Patterns are reviewed by the SafePeak administrator and in usually most of them are activated manually for caching (point and click activation); Fully Dynamic Caching: Automated detection of all dependent tables in each SQL Pattern, with automated real-time eviction of the relevant cache items in the event of “write” commands (a DML or a stored procedure) to one of relevant tables. A default setting; Semi Dynamic Caching: A manual cache configuration option enabling reducing the sensitivity of specific SQL Patterns to “write” commands to certain tables/views. An optimization technique relevant for cases when the query data is either known to be static (like archive order details), or when the application sensitivity to fresh data is not critical and can be stale for short period of time (gaining better performance and reduced load); Scheduled Cache Eviction: A manual cache configuration option enabling scheduling SQL Pattern cache eviction based on certain time(s) during a day. A very useful optimization technique when (for example) certain SQL Patterns can be cached but are time sensitive. Example: “select customers that today is their birthday”, an SQL with getdate() function, which can and should be cached, but the data stays relevant only until 00:00 (midnight); Parsing Exceptions Management: Stored procedures that were not fully parsed by SafePeak (due to too complex dynamic SQL or unfamiliar syntax), are signed as “Dynamic Objects” with highest transaction safety settings (such as: Full global cache eviction, DDL Check = lock cache and check for schema changes, and more). The SafePeak solution points the user to the Dynamic Objects that are important for cache effectiveness, provides easy configuration interface, allowing you to improve cache hits and reduce cache global evictions. Usually this is the first configuration in a deployment; Overriding Settings of Stored Procedures: Override the settings of stored procedures (or other object types) for cache optimization. For example, in case a stored procedure SP1 has an “insert” into table T1, it will not be allowed to be cached. However, it is possible that T1 is just a “logging or instrumentation” table left by developers. By overriding the settings a user can allow caching of the problematic stored procedure; Advanced Cache Warm-Up: Creating an XML-based list of queries and stored procedure (with lists of parameters) for periodically automated pre-fetching and caching. An advanced tool allowing you to handle more rare but very performance sensitive queries pre-fetch them into cache allowing high performance for users’ data access; Configuration Driven by Deep SQL Analytics: All SQL queries are continuously logged and analyzed, providing users with deep SQL Analytics and Performance Monitoring. Reduce troubleshooting from days to minutes with database objects and SQL Patterns heat-map. The performance driven configuration helps you to focus on the most important settings that bring you the highest performance gains. Use of SafePeak SQL Analytics allows continuous performance monitoring and analysis, easy identification of bottlenecks of both real-time and historical data; Cloud Ready: Available for instant deployment on Amazon Web Services (AWS). As you can see, there are many options to configure SafePeak’s SQL Server database and application acceleration caching technology to best fit a lot of situations. If you’re not familiar with their technology, they offer free-trial software you can download that comes with a free “help session” to help get you started. You can access the free trial here. Also, SafePeak is available to use on Amazon Cloud. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Building dynamic OLAP data marts on-the-fly

    - by DrJohn
    At the forthcoming SQLBits conference, I will be presenting a session on how to dynamically build an OLAP data mart on-the-fly. This blog entry is intended to clarify exactly what I mean by an OLAP data mart, why you may need to build them on-the-fly and finally outline the steps needed to build them dynamically. In subsequent blog entries, I will present exactly how to implement some of the techniques involved. What is an OLAP data mart? In data warehousing parlance, a data mart is a subset of the overall corporate data provided to business users to meet specific business needs. Of course, the term does not specify the technology involved, so I coined the term "OLAP data mart" to identify a subset of data which is delivered in the form of an OLAP cube which may be accompanied by the relational database upon which it was built. To clarify, the relational database is specifically create and loaded with the subset of data and then the OLAP cube is built and processed to make the data available to the end-users via standard OLAP client tools. Why build OLAP data marts? Market research companies sell data to their clients to make money. To gain competitive advantage, market research providers like to "add value" to their data by providing systems that enhance analytics, thereby allowing clients to make best use of the data. As such, OLAP cubes have become a standard way of delivering added value to clients. They can be built on-the-fly to hold specific data sets and meet particular needs and then hosted on a secure intranet site for remote access, or shipped to clients' own infrastructure for hosting. Even better, they support a wide range of different tools for analytical purposes, including the ever popular Microsoft Excel. Extension Attributes: The Challenge One of the key challenges in building multiple OLAP data marts based on the same 'template' is handling extension attributes. These are attributes that meet the client's specific reporting needs, but do not form part of the standard template. Now clearly, these extension attributes have to come into the system via additional files and ultimately be added to relational tables so they can end up in the OLAP cube. However, processing these files and filling dynamically altered tables with SSIS is a challenge as SSIS packages tend to break as soon as the database schema changes. There are two approaches to this: (1) dynamically build an SSIS package in memory to match the new database schema using C#, or (2) have the extension attributes provided as name/value pairs so the file's schema does not change and can easily be loaded using SSIS. The problem with the first approach is the complexity of writing an awful lot of complex C# code. The problem of the second approach is that name/value pairs are useless to an OLAP cube; so they have to be pivoted back into a proper relational table somewhere in the data load process WITHOUT breaking SSIS. How this can be done will be part of future blog entry. What is involved in building an OLAP data mart? There are a great many steps involved in building OLAP data marts on-the-fly. The key point is that all the steps must be automated to allow for the production of multiple OLAP data marts per day (i.e. many thousands, each with its own specific data set and attributes). Now most of these steps have a great deal in common with standard data warehouse practices. The key difference is that the databases are all built to order. The only permanent database is the metadata database (shown in orange) which holds all the metadata needed to build everything else (i.e. client orders, configuration information, connection strings, client specific requirements and attributes etc.). The staging database (shown in red) has a short life: it is built, populated and then ripped down as soon as the OLAP Data Mart has been populated. In the diagram below, the OLAP data mart comprises the two blue components: the Data Mart which is a relational database and the OLAP Cube which is an OLAP database implemented using Microsoft Analysis Services (SSAS). The client may receive just the OLAP cube or both components together depending on their reporting requirements.  So, in broad terms the steps required to fulfil a client order are as follows: Step 1: Prepare metadata Create a set of database names unique to the client's order Modify all package connection strings to be used by SSIS to point to new databases and file locations. Step 2: Create relational databases Create the staging and data mart relational databases using dynamic SQL and set the database recovery mode to SIMPLE as we do not need the overhead of logging anything Execute SQL scripts to build all database objects (tables, views, functions and stored procedures) in the two databases Step 3: Load staging database Use SSIS to load all data files into the staging database in a parallel operation Load extension files containing name/value pairs. These will provide client-specific attributes in the OLAP cube. Step 4: Load data mart relational database Load the data from staging into the data mart relational database, again in parallel where possible Allocate surrogate keys and use SSIS to perform surrogate key lookup during the load of fact tables Step 5: Load extension tables & attributes Pivot the extension attributes from their native name/value pairs into proper relational tables Add the extension attributes to the views used by OLAP cube Step 6: Deploy & Process OLAP cube Deploy the OLAP database directly to the server using a C# script task in SSIS Modify the connection string used by the OLAP cube to point to the data mart relational database Modify the cube structure to add the extension attributes to both the data source view and the relevant dimensions Remove any standard attributes that not required Process the OLAP cube Step 7: Backup and drop databases Drop staging database as it is no longer required Backup data mart relational and OLAP database and ship these to the client's infrastructure Drop data mart relational and OLAP database from the build server Mark order complete Start processing the next order, ad infinitum. So my future blog posts and my forthcoming session at the SQLBits conference will all focus on some of the more interesting aspects of building OLAP data marts on-the-fly such as handling the load of extension attributes and how to dynamically alter the structure of an OLAP cube using C#.

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  • Ensure your view and function meta data is upto date.

    - by simonsabin
    You will see if you use views and functions that SQL Server holds the rowset metadata for this in system tables. This means that if you change the underlying tables, columns and data types your views and functions can be out of sync. This is especially the case with views and functions that use select * To get the metadata to be updated you need to use sp_refreshsqlmodule. This forces the object to be “re run” into the database and the meta data updated. Thomas mentioned sp_refreshview which is a...(read more)

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  • Oracle Database 12 c New Partition Maintenance Features by Gwen Lazenby

    - by hamsun
    One of my favourite new features in Oracle Database 12c is the ability to perform partition maintenance operations on multiple partitions. This means we can now add, drop, truncate and merge multiple partitions in one operation, and can split a single partition into more than two partitions also in just one command. This would certainly have made my life slightly easier had it been available when I administered a data warehouse at Oracle 9i. To demonstrate this new functionality and syntax, I am going to create two tables, ORDERS and ORDERS_ITEMS which have a parent-child relationship. ORDERS is to be partitioned using range partitioning on the ORDER_DATE column, and ORDER_ITEMS is going to partitioned using reference partitioning and its foreign key relationship with the ORDERS table. This form of partitioning was a new feature in 11g and means that any partition maintenance operations performed on the ORDERS table will also take place on the ORDER_ITEMS table as well. First create the ORDERS table - SQL CREATE TABLE orders ( order_id NUMBER(12), order_date TIMESTAMP, order_mode VARCHAR2(8), customer_id NUMBER(6), order_status NUMBER(2), order_total NUMBER(8,2), sales_rep_id NUMBER(6), promotion_id NUMBER(6), CONSTRAINT orders_pk PRIMARY KEY(order_id) ) PARTITION BY RANGE(order_date) (PARTITION Q1_2007 VALUES LESS THAN (TO_DATE('01-APR-2007','DD-MON-YYYY')), PARTITION Q2_2007 VALUES LESS THAN (TO_DATE('01-JUL-2007','DD-MON-YYYY')), PARTITION Q3_2007 VALUES LESS THAN (TO_DATE('01-OCT-2007','DD-MON-YYYY')), PARTITION Q4_2007 VALUES LESS THAN (TO_DATE('01-JAN-2008','DD-MON-YYYY')) ); Table created. Now the ORDER_ITEMS table SQL CREATE TABLE order_items ( order_id NUMBER(12) NOT NULL, line_item_id NUMBER(3) NOT NULL, product_id NUMBER(6) NOT NULL, unit_price NUMBER(8,2), quantity NUMBER(8), CONSTRAINT order_items_fk FOREIGN KEY(order_id) REFERENCES orders(order_id) on delete cascade) PARTITION BY REFERENCE(order_items_fk) tablespace example; Table created. Now look at DBA_TAB_PARTITIONS to get details of what partitions we have in the two tables – SQL select table_name,partition_name, partition_position position, high_value from dba_tab_partitions where table_owner='SH' and table_name like 'ORDER_%' order by partition_position, table_name; TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 Just as an aside it is also now possible in 12c to use interval partitioning on reference partitioned tables. In 11g it was not possible to combine these two new partitioning features. For our first example of the new 12cfunctionality, let us add all the partitions necessary for 2008 to the tables using one command. Notice that the partition specification part of the add command is identical in format to the partition specification part of the create command as shown above - SQL alter table orders add PARTITION Q1_2008 VALUES LESS THAN (TO_DATE('01-APR-2008','DD-MON-YYYY')), PARTITION Q2_2008 VALUES LESS THAN (TO_DATE('01-JUL-2008','DD-MON-YYYY')), PARTITION Q3_2008 VALUES LESS THAN (TO_DATE('01-OCT-2008','DD-MON-YYYY')), PARTITION Q4_2008 VALUES LESS THAN (TO_DATE('01-JAN-2009','DD-MON-YYYY')); Table altered. Now look at DBA_TAB_PARTITIONS and we can see that the 4 new partitions have been added to both tables – SQL select table_name,partition_name, partition_position position, high_value from dba_tab_partitions where table_owner='SH' and table_name like 'ORDER_%' order by partition_position, table_name; TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q1_2008 5 TIMESTAMP' 2008-04-01 00:00:00' ORDER_ITEMS Q1_2008 5 ORDERS Q2_2008 6 TIMESTAMP' 2008-07-01 00:00:00' ORDER_ITEM Q2_2008 6 ORDERS Q3_2008 7 TIMESTAMP' 2008-10-01 00:00:00' ORDER_ITEMS Q3_2008 7 ORDERS Q4_2008 8 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 8 Next, we can drop or truncate multiple partitions by giving a comma separated list in the alter table command. Note the use of the plural ‘partitions’ in the command as opposed to the singular ‘partition’ prior to 12c– SQL alter table orders drop partitions Q3_2008,Q2_2008,Q1_2008; Table altered. Now look at DBA_TAB_PARTITIONS and we can see that the 3 partitions have been dropped in both the two tables – TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q4_2008 5 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 5 Now let us merge all the 2007 partitions together to form one single partition – SQL alter table orders merge partitions Q1_2005, Q2_2005, Q3_2005, Q4_2005 into partition Y_2007; Table altered. TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Y_2007 1 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Y_2007 1 ORDERS Q4_2008 2 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 2 Splitting partitions is a slightly more involved. In the case of range partitioning one of the new partitions must have no high value defined, and in list partitioning one of the new partitions must have no list of values defined. I call these partitions the ‘everything else’ partitions, and will contain any rows contained in the original partition that are not contained in the any of the other new partitions. For example, let us split the Y_2007 partition back into 4 quarterly partitions – SQL alter table orders split partition Y_2007 into (PARTITION Q1_2007 VALUES LESS THAN (TO_DATE('01-APR-2007','DD-MON-YYYY')), PARTITION Q2_2007 VALUES LESS THAN (TO_DATE('01-JUL-2007','DD-MON-YYYY')), PARTITION Q3_2007 VALUES LESS THAN (TO_DATE('01-OCT-2007','DD-MON-YYYY')), PARTITION Q4_2007); Now look at DBA_TAB_PARTITIONS to get details of the new partitions – TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q4_2008 5 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 5 Partition Q4_2007 has a high value equal to the high value of the original Y_2007 partition, and so has inherited its upper boundary from the partition that was split. As for a list partitioning example let look at the following another table, SALES_PAR_LIST, which has 2 partitions, Americas and Europe and a partitioning key of country_name. SQL select table_name,partition_name, high_value from dba_tab_partitions where table_owner='SH' and table_name = 'SALES_PAR_LIST'; TABLE_NAME PARTITION_NAME HIGH_VALUE -------------- --------------- ----------------------------- SALES_PAR_LIST AMERICAS 'Argentina', 'Canada', 'Peru', 'USA', 'Honduras', 'Brazil', 'Nicaragua' SALES_PAR_LIST EUROPE 'France', 'Spain', 'Ireland', 'Germany', 'Belgium', 'Portugal', 'Denmark' Now split the Americas partition into 3 partitions – SQL alter table sales_par_list split partition americas into (partition south_america values ('Argentina','Peru','Brazil'), partition north_america values('Canada','USA'), partition central_america); Table altered. Note that no list of values was given for the ‘Central America’ partition. However it should have inherited any values in the original ‘Americas’ partition that were not assigned to either the ‘North America’ or ‘South America’ partitions. We can confirm this by looking at the DBA_TAB_PARTITIONS view. SQL select table_name,partition_name, high_value from dba_tab_partitions where table_owner='SH' and table_name = 'SALES_PAR_LIST'; TABLE_NAME PARTITION_NAME HIGH_VALUE --------------- --------------- -------------------------------- SALES_PAR_LIST SOUTH_AMERICA 'Argentina', 'Peru', 'Brazil' SALES_PAR_LIST NORTH_AMERICA 'Canada', 'USA' SALES_PAR_LIST CENTRAL_AMERICA 'Honduras', 'Nicaragua' SALES_PAR_LIST EUROPE 'France', 'Spain', 'Ireland', 'Germany', 'Belgium', 'Portugal', 'Denmark' In conclusion, I hope that DBA’s whose work involves maintaining partitions will find the operations a bit more straight forward to carry out once they have upgraded to Oracle Database 12c. Gwen Lazenby is a Principal Training Consultant at Oracle. She is part of Oracle University's Core Technology delivery team based in the UK, teaching Database Administration and Linux courses. Her specialist topics include using Oracle Partitioning and Parallelism in Data Warehouse environments, as well as Oracle Spatial and RMAN.

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  • SQL SERVER – Script to Update a Specific Column in Entire Database

    - by Pinal Dave
    Last week, I have received a very interesting question and I find in email and I really liked the question as I had to play around with SQL Script for a while to come up with the answer he was looking for. Please read the question and I believe that all of us face this kind of situation. “Pinal, In our database we have recently introduced ModifiedDate column in all of the tables. Now onwards any update happens in the row, we are updating current date and time to that field. Now here is the issue, when we added that field we did not update it with a default value because we were not sure when we will go live with the system so we let it be NULL. Now modification to the application went live yesterday and we are now updating this field. Here is where I need your help. We need to update all the tables in our database where we have column created ModifiedDate and now want to update with current datetime. As our system is already live since yesterday there are several thousands of the rows which are already updated with real world value so we do not want to update those values. Essentially, in our entire database where ever there is a ModifiedDate column and if it is NULL we want to update that with current date time?  Do you have a script for it?” Honestly I did not have such a script. This is very specific required but I was able to come up with two different methods how he can use this method. Method 1 : Using INFORMATION_SCHEMA SELECT 'UPDATE ' + T.TABLE_SCHEMA + '.' + T.TABLE_NAME + ' SET ModifiedDate = GETDATE() WHERE ModifiedDate IS NULL;' FROM INFORMATION_SCHEMA.TABLES T INNER JOIN INFORMATION_SCHEMA.COLUMNS C ON T.TABLE_NAME = C.TABLE_NAME AND c.COLUMN_NAME ='ModifiedDate' WHERE T.TABLE_TYPE = 'BASE TABLE' ORDER BY T.TABLE_SCHEMA, T.TABLE_NAME; Method 2: Using DMV SELECT 'UPDATE ' + SCHEMA_NAME(t.schema_id) + '.' + t.name + ' SET ModifiedDate = GETDATE() WHERE ModifiedDate IS NULL;' FROM sys.tables AS t INNER JOIN sys.columns c ON t.OBJECT_ID = c.OBJECT_ID WHERE c.name ='ModifiedDate' ORDER BY SCHEMA_NAME(t.schema_id), t.name; Above scripts will create an UPDATE script which will do the task which is asked. We can pretty much the update script to any other SELECT statement and retrieve any other data as well. Click to Download Scripts Reference: Pinal Dave (http://blog.sqlauthority.com)  Filed under: PostADay, SQL, SQL Authority, SQL Joins, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • More on PHP and Oracle 11gR2 Improvements to Client Result Caching

    - by christopher.jones
    Oracle 11.2 brought several improvements to Client Result Caching. CRC is way for the results of queries to be cached in the database client process for reuse.  In an Oracle OpenWorld presentation "Best Practices for Developing Performant Application" my colleague Luxi Chidambaran had a (non-PHP generated) graph for the Niles benchmark that shows a DB CPU reduction up to 600% and response times up to 22% faster when using CRC. Sometimes CRC is called the "Consistent Client Cache" because Oracle automatically invalidates the cache if table data is changed.  This makes it easy to use without needing application logic rewrites. There are a few simple database settings to turn on and tune CRC, so management is also easy. PHP OCI8 as a "client" of the database can use CRC.  The cache is per-process, so plan carefully before caching large data sets.  Tables that are candidates for caching are look-up tables where the network transfer cost dominates. CRC is really easy in 11.2 - I'll get to that in a moment.  It was also pretty easy in Oracle 11.1 but it needed some tiny application changes.  In PHP it was used like: $s = oci_parse($c, "select /*+ result_cache */ * from employees"); oci_execute($s, OCI_NO_AUTO_COMMIT); // Use OCI_DEFAULT in OCI8 <= 1.3 oci_fetch_all($s, $res); I blogged about this in the past.  The query had to include a specific hint that you wanted the results cached, and you needed to turn off auto committing during execution either with the OCI_DEFAULT flag or its new, better-named alias OCI_NO_AUTO_COMMIT.  The no-commit flag rule didn't seem reasonable to me because most people wouldn't be specific about the commit state for a query. Now in Oracle 11.2, DBAs can now nominate tables for caching, either with CREATE TABLE or ALTER TABLE.  That means you don't need the query hint anymore.  As well, the no-commit flag requirement has been lifted.  Your code can now look like: $s = oci_parse($c, "select * from employees"); oci_execute($s); oci_fetch_all($s, $res); Since your code probably already looks like this, your DBA can find the top queries in the database and simply tune the system by turning on CRC in the database and issuing an ALTER TABLE statement for candidate tables.  Voila. Another CRC improvement in Oracle 11.2 is that it works with DRCP connection pooling. There is some fine print about what is and isn't cached, check the Oracle manuals for details.  If you're using 11.1 or non-DRCP "dedicated servers" then make sure you use oci_pconnect() persistent connections.  Also in PHP don't bind strings in the query, although binding as SQLT_INT is OK.

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  • Difference between DISTINCT and VALUES in DAX

    - by Marco Russo (SQLBI)
    I recently got a question about differences between DISTINCT and VALUES in DAX and thanks to Jeffrey Wang I created a simple example to describe the difference. Consider the two tables below: Fact and Dim tables, having a single column with the same name of the table. A relationship exists between Fact[Fact] and Dim[Dim]. This relationship generates a referential integrity violations in table Fact for rows containing C, which doesn’t exist in table Dim. In this case, an empty row is virtually inserted...(read more)

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  • Implicit Permissions Due to Ownership Chaining or Scopes in SQL Server

    I have audited for permissions on my databases because users seem to be accessing the tables, but I don't see permissions which give them such rights. I've gone through every Windows group that has access to my SQL Server and into the database, but with no success. How are the users accessing these tables? The Future of SQL Server Monitoring "Being web-based, SQL Monitor 2.0 enables you to check on your servers from almost any location" Jonathan Allen.Try SQL Monitor now.

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  • How to suggest using an ORM instead of stored procedures?

    - by Wayne M
    I work at a company that only uses stored procedures for all data access, which makes it very annoying to keep our local databases in sync as every commit we have to run new procs. I have used some basic ORMs in the past and I find the experience much better and cleaner. I'd like to suggest to the development manager and rest of the team that we look into using an ORM Of some kind for future development (the rest of the team are only familiar with stored procedures and have never used anything else). The current architecture is .NET 3.5 written like .NET 1.1, with "god classes" that use a strange implementation of ActiveRecord and return untyped DataSets which are looped over in code-behind files - the classes work something like this: class Foo { public bool LoadFoo() { bool blnResult = false; if (this.FooID == 0) { throw new Exception("FooID must be set before calling this method."); } DataSet ds = // ... call to Sproc if (ds.Tables[0].Rows.Count > 0) { foo.FooName = ds.Tables[0].Rows[0]["FooName"].ToString(); // other properties set blnResult = true; } return blnResult; } } // Consumer Foo foo = new Foo(); foo.FooID = 1234; foo.LoadFoo(); // do stuff with foo... There is pretty much no application of any design patterns. There are no tests whatsoever (nobody else knows how to write unit tests, and testing is done through manually loading up the website and poking around). Looking through our database we have: 199 tables, 13 views, a whopping 926 stored procedures and 93 functions. About 30 or so tables are used for batch jobs or external things, the remainder are used in our core application. Is it even worth pursuing a different approach in this scenario? I'm talking about moving forward only since we aren't allowed to refactor the existing code since "it works" so we cannot change the existing classes to use an ORM, but I don't know how often we add brand new modules instead of adding to/fixing current modules so I'm not sure if an ORM is the right approach (too much invested in stored procedures and DataSets). If it is the right choice, how should I present the case for using one? Off the top of my head the only benefits I can think of is having cleaner code (although it might not be, since the current architecture isn't built with ORMs in mind so we would basically be jury-rigging ORMs on to future modules but the old ones would still be using the DataSets) and less hassle to have to remember what procedure scripts have been run and which need to be run, etc. but that's it, and I don't know how compelling an argument that would be. Maintainability is another concern but one that nobody except me seems to be concerned about.

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  • Re-generating SQL Server Logins

    SQL Server stores all login information on security catalog system tables. By querying the system tables, SQL statements can be re-generated to recover logins, including password, default schema/database, server/database role assignments, and object level permissions. A comprehensive permission report can also be produced by combining information from the system metadata. The Future of SQL Server Monitoring "Being web-based, SQL Monitor 2.0 enables you to check on your servers from almost any location" Jonathan Allen.Try SQL Monitor now.

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  • Generate MERGE statements from a table

    - by Bill Graziano
    We have a requirement to build a test environment where certain tables get reset from production every night.  These are mainly lookup tables.  I played around with all kinds of fancy solutions and finally settled on a series of MERGE statements.  And being lazy I didn’t want to write them myself.  The stored procedure below will generate a MERGE statement for the table you pass it.  If you have identity values it populates those properly.  You need to have primary keys on the table for the joins to be generated properly.  The only thing hard coded is the source database.  You’ll need to update that for your environment.  We actually used a linked server in our situation. CREATE PROC dba_GenerateMergeStatement (@table NVARCHAR(128) )ASset nocount on; declare @return int;PRINT '-- ' + @table + ' -------------------------------------------------------------'--PRINT 'SET NOCOUNT ON;--'-- Set the identity insert on for tables with identitiesselect @return = objectproperty(object_id(@table), 'TableHasIdentity')if @return = 1 PRINT 'SET IDENTITY_INSERT [dbo].[' + @table + '] ON; 'declare @sql varchar(max) = ''declare @list varchar(max) = '';SELECT @list = @list + [name] +', 'from sys.columnswhere object_id = object_id(@table)SELECT @list = @list + [name] +', 'from sys.columnswhere object_id = object_id(@table)SELECT @list = @list + 's.' + [name] +', 'from sys.columnswhere object_id = object_id(@table)-- --------------------------------------------------------------------------------PRINT 'MERGE [dbo].[' + @table + '] AS t'PRINT 'USING (SELECT * FROM [source_database].[dbo].[' + @table + ']) as s'-- Get the join columns ----------------------------------------------------------SET @list = ''select @list = @list + 't.[' + c.COLUMN_NAME + '] = s.[' + c.COLUMN_NAME + '] AND 'from INFORMATION_SCHEMA.TABLE_CONSTRAINTS pk , INFORMATION_SCHEMA.KEY_COLUMN_USAGE cwhere pk.TABLE_NAME = @tableand CONSTRAINT_TYPE = 'PRIMARY KEY'and c.TABLE_NAME = pk.TABLE_NAMEand c.CONSTRAINT_NAME = pk.CONSTRAINT_NAMESELECT @list = LEFT(@list, LEN(@list) -3)PRINT 'ON ( ' + @list + ')'-- WHEN MATCHED ------------------------------------------------------------------PRINT 'WHEN MATCHED THEN UPDATE SET'SELECT @list = '';SELECT @list = @list + ' [' + [name] + '] = s.[' + [name] +'],'from sys.columnswhere object_id = object_id(@table)-- don't update primary keysand [name] NOT IN (SELECT [column_name] from INFORMATION_SCHEMA.TABLE_CONSTRAINTS pk , INFORMATION_SCHEMA.KEY_COLUMN_USAGE c where pk.TABLE_NAME = @table and CONSTRAINT_TYPE = 'PRIMARY KEY' and c.TABLE_NAME = pk.TABLE_NAME and c.CONSTRAINT_NAME = pk.CONSTRAINT_NAME)-- and don't update identity columnsand columnproperty(object_id(@table), [name], 'IsIdentity ') = 0 --print @list PRINT left(@list, len(@list) -3 )-- WHEN NOT MATCHED BY TARGET ------------------------------------------------PRINT ' WHEN NOT MATCHED BY TARGET THEN';-- Get the insert listSET @list = ''SELECT @list = @list + '[' + [name] +'], 'from sys.columnswhere object_id = object_id(@table)SELECT @list = LEFT(@list, LEN(@list) - 1)PRINT ' INSERT(' + @list + ')'-- get the values listSET @list = ''SELECT @list = @list + 's.[' +[name] +'], 'from sys.columnswhere object_id = object_id(@table)SELECT @list = LEFT(@list, LEN(@list) - 1)PRINT ' VALUES(' + @list + ')'-- WHEN NOT MATCHED BY SOURCEprint 'WHEN NOT MATCHED BY SOURCE THEN DELETE; 'PRINT ''PRINT 'PRINT ''' + @table + ': '' + CAST(@@ROWCOUNT AS VARCHAR(100));';PRINT ''-- Set the identity insert OFF for tables with identitiesselect @return = objectproperty(object_id(@table), 'TableHasIdentity')if @return = 1 PRINT 'SET IDENTITY_INSERT [dbo].[' + @table + '] OFF; 'PRINT ''PRINT 'GO'PRINT '';

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  • How to Plug a Small Hole in NetBeans JSF (Join Table) Code Generation

    - by MarkH
    I was asked recently to provide an assist with designing and building a small-but-vital application that had at its heart some basic CRUD (Create, Read, Update, & Delete) functionality, built upon an Oracle database, to be accessible from various locations. Working from the stated requirements, I fleshed out the basic application and database designs and, once validated, set out to complete the first iteration for review. Using SQL Developer, I created the requisite tables, indices, and sequences for our first run. One of the tables was a many-to-many join table with three fields: one a primary key for that table, the other two being primary keys for the other tables, represented as foreign keys in the join table. Here is a simplified example of the trio of tables: Once the database was in decent shape, I fired up NetBeans to let it have first shot at the code. NetBeans does a great job of generating a mountain of essential code, saving developers what must be millions of hours of effort each year by building a basic foundation with a few clicks and keystrokes. Lest you think it (or any tool) can do everything for you, however, occasionally something tosses a paper clip into the delicate machinery and makes you open things up to fix them. Join tables apparently qualify.  :-) In the case above, the entity class generated for the join table (New Entity Classes from Database) included an embedded object consisting solely of the two foreign key fields as attributes, in addition to an object referencing each one of the "component" tables. The Create page generated (New JSF Pages from Entity Classes) worked well to a point, but when trying to save, we were greeted with an error: Transaction aborted. Hmm. A quick debugger session later and I'd identified the issue: when trying to persist the new join-table object, the embedded "foreign-keys-only" object still had null values for its two (required value) attributes...even though the embedded table objects had populated key attributes. Here's the simple fix: In the join-table controller class, find the public String create() method. It will look something like this:     public String create() {        try {            getFacade().create(current);            JsfUtil.addSuccessMessage(ResourceBundle.getBundle("/Bundle").getString("JoinEntityCreated"));            return prepareCreate();        } catch (Exception e) {            JsfUtil.addErrorMessage(e, ResourceBundle.getBundle("/Bundle").getString("PersistenceErrorOccured"));            return null;        }    } To restore balance to the force, modify the create() method as follows (changes in red):     public String create() {         try {            // Add the next two lines to resolve:            current.getJoinEntityPK().setTbl1id(current.getTbl1().getId().toBigInteger());            current.getJoinEntityPK().setTbl2id(current.getTbl2().getId().toBigInteger());            getFacade().create(current);            JsfUtil.addSuccessMessage(ResourceBundle.getBundle("/Bundle").getString("JoinEntityCreated"));            return prepareCreate();        } catch (Exception e) {            JsfUtil.addErrorMessage(e, ResourceBundle.getBundle("/Bundle").getString("PersistenceErrorOccured"));            return null;        }    } I'll be refactoring this code shortly, but for now, it works. Iteration one is complete and being reviewed, and we've met the milestone. Here's to happy endings (and customers)! All the best,Mark

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  • Temporary Object Caching Explained

    - by Paul White
    SQL Server 2005 onward caches temporary tables and table variables referenced in stored procedures for reuse, reducing contention on tempdb allocation structures and catalogue tables.  A number of things can prevent this caching (none of which are allowed when working with table variables): Named constraints (bad idea anyway, since concurrent executions can cause a name collision) DDL after creation (though what is considered DDL is interesting) Creation using dynamic SQL Table created in a...(read more)

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  • Contiguous Time Periods

    It is always better, and more efficient, to maintain referential integrity by using constraints rather than triggers. Sometimes it is not at all obvious how to do this, and the history table, and other temporal data tables, presented problems for checking data that were difficult to solve with constraints. Suddenly, Alex Kuznetsov came up with a good solution, and so now history tables can benefit from more effective integrity checking. Joe explains...

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  • Taking a Projects Development to the Next Level

    - by user1745022
    I have been looking for some advice for a while on how to handle a project I am working on, but to no avail. I am pretty much on my fourth iteration of improving an "application" I am working on; the first two times were in Excel, the third Time in Access, and now in Visual Studio. The field is manufacturing. The basic idea is I am taking read-only data from a massive Sybase server, filtering it and creating much smaller tables in Access daily (using delete and append Queries) and then doing a bunch of stuff. More specifically, I use a series of queries to either combine data from multiple tables or group data in specific ways (aggregate functions), and then I place this data into a table (so I can sort and manipulate data using DAO.recordset and run multiple custom algorithms). This process is then repeated multiple times throughout the database until a set of relevant tables are created. Many times I will create a field in a query with a value such as 1.1 so that when I append it to a table I can store information in the field from the algorithms. So as the process continues the number of fields for the tables change. The overall application consists of 4 "back-end" databases linked together on a shared drive, with various output (either front-end access applications or Excel). So my question is is this how many data driven applications that solve problems essentially work? Each backend database is updated with fresh data daily and updating each takes around 10 seconds (for three) and 2 minutes(for 1). Project Objectives. I want/am moving to SQL Server soon. Front End will be a Web Application (I know basic web-development and like the administration flexibility) and visual-studio will be IDE with c#/.NET. Should these algorithms be run "inside the database," or using a series of C# functions on each server request. I know you're not supposed to store data in a database unless it is an actual data point, and in Access I have many columns that just hold calculations from algorithms in vba. The truth is, I have seen multiple professional Access applications, and have never seen one that has the complexity or does even close to what mine does (for better or worse). But I know some professional software applications are 1000 times better then mine. So Please Please Please give me a suggestion of some sort. I have been completely on my own and need some guidance on how to approach this project the right way.

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  • cannot find table or object

    - by jeni
    hi all, Am running asp.net,c# application with sql server 2005. I got some problems with database tables.I got an inconsistency errors in some tables.I tried to run the below dbcc command to remove the inconsistent datas; DBCC CHECKTABLE ('Customer',repair_allow_data_loss) WITH ALL_ERRORMSG At first i run DBCC CHECKTABLE ('Customer') it is working.but now it is not working, i got an error as Cannot find a table or object with the name "Customer". Check the system catalog. Is my commands wrong.

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  • Designing Efficient SQL: A Visual Approach

    Sometimes, it is a great idea to push away the keyboard when tackling the problems of an ill-performing, complex, query, and take up pencil and paper instead. By drawing a diagram to show of all the tables involved, the joins, the volume of data involved, and the indexes, you'll see more easily the relative efficiency of the possible paths that your query could take through the tables.

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  • Ubuntu 12.04 - PPTP VPN is the only Internet Access

    - by user212553
    I know this has been covered. I've read dozens of posts but still have questions. I have a work server whose traffic should never leave my house without encryption. The VPN is PPTP. Currently I have a cron job that checks the status of the ppp0 adapter each minute. If the connection drops, which it does fairly often, it shuts key components down. It's fairly easy to restart PPTP with "nmcli con up id 'myVPNServer'" but there's no assurance it will reconnect and I need a better way to stop traffic (other than killing apps) when ppp0 is down. The two options I've seen discussed are the firewall (UFW, Firestarter, IPTables) or the route tables. I could be easily swayed to consider the firewall option but I focused on the route tables since no new function needs to be started. My questions involve the way the route tables change and then specifics on rules. When I start the PPTP VPN the route tables change. That suggests that if the VPN drops, the table will change back, defeating my stated intent of preventing external traffic. How can I make "sticky" changes to the route table that will persist even if the VPN connection drops? Perhaps the check boxes "Ignore automatically obtained routes" or "Use this connection only for resources on it's network" (which are part of the VPN configuration options)? It would seem that, if I can force the active VPN route table to stay in effect, even when the VPN drops, that this will effectively kill any external traffic should the VPN drop. This will give me the latitude to run a routine to restart the VPN from the command line (assuming the route table rules don't prevent me re-establishing the connection). My route table, with the VPN active is (ip route list): Any comments on what 10.10.1.1 is? $ ip route list default dev ppp0 proto static 10.10.1.1 dev ppp0 proto kernel scope link src 10.10.1.11 VPN_Server_IP_Address via 192.168.1.1 dev eth0 proto static VPN_Server_IP_Address via 192.168.1.1 dev eth0 src 192.168.1.60 169.254.0.0/16 dev eth0 scope link metric 1000 192.168.1.0/24 dev eth0 proto kernel scope link src 192.168.1.60 metric 1

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  • No Significant Fragmentation? Look Closer…

    If you are relying on using 'best-practice' percentage-based thresholds when you are creating an index maintenance plan for a SQL Server that checks the fragmentation in your pages, you may miss occasional 'edge' conditions on larger tables that will cause severe degradation in performance. It is worth being aware of patterns of data access in particular tables when judging the best threshold figure to use.

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  • No Significant Fragmentation? Look Closer…

    If you are relying on using 'best-practice' percentage-based thresholds when you are creating an index maintenance plan for a SQL Server that checks the fragmentation in your pages, you may miss occasional 'edge' conditions on larger tables that will cause severe degradation in performance. It is worth being aware of patterns of data access in particular tables when judging the best threshold figure to use.

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  • A Look At Style Sheet Languages

    Style sheet languages are computer languages which was introduced when the market demanded for a new way or method of designing a website other than the use of tables and spacers. In the past, tables... [Author: Margarette Mcbride - Web Design and Development - May 17, 2010]

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  • Stairway to T-SQL DML Level 11: How to Delete Rows from a Table

    You may have data in a database that was inserted into a table by mistake, or you may have data in your tables that is no longer of value. In either case, when you have unwanted data in a table you need a way to remove it. The DELETE statement can be used to eliminate data in a table that is no longer needed. In this article you will see the different ways to use the DELETE statement to identify and remove unwanted data from your SQL Server tables.

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