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rca rp3013 cd player manualSearch support or find a product: Search Our apologies No results were found for your search query. Tips To return expected results, you can: Reduce the number of search terms. Each term you use focuses the search further. Check your spelling. A single misspelled or incorrectly typed term can change your result. If so, follow the appropriate link below to find the content you need. Our apologies Search results are not available at this time. Please try again later or use one of the other support options on this page. No results were found for your search query. If so, follow the appropriate link below to find the content you need. The most current documentation can be found at IBM Knowledge Center. You must aid business integration by linking changed-data events in DB2 databases on Linux, UNIX, and Windows with EAI solutions, message brokers, data transformation tools, and more. Investing in this book will save you many hours of work (and heartache) as it guides you around the many potential pitfalls to a successful conclusion. Compiled from many of author's successful projects, the book will bring you some of the best practices to implement your project smoothly and within time scales. The book has in-depth coverage of Event Publisher, which publishes changed-data events that can run updated data into crucial applications, assisting your business integration processes. Event Publisher also eliminates the hand coding typically required to detect DB2 data changes that are made by operational applications. We then go on to explore the world of Q replication in more depth. The latter chapters cover all the Q replication components and then talk about the different layers that need to be implemented—the DB2 database layer, the WebSphere MQ layer, and the Q replication layer. We conclude with a chapter on how to troubleshoot a problem. The Appendix (available online) demonstrates the implementation of 13 Q replication scenarios with step-by-step instructions.http://tsukanov-art-collection.ru/pict/da-88-manual.xml
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Any journey can be a bumpy ride, but after reading this book and going through the numerous examples, your journey will be a smoother one. In this first chapter, we will take you through the following discussion points: Why we want to replicate data. What is available today in the IBM world of data replication. The toolsets available to set up and administer a replication environment and look at the code that we need to install for a functioning Q replication solution. Introduce the architecture of Q replication. We look at the different types of replication available, namely the base replication methods of unidirectional, bidirectional, and peer-to-peer, and the replication architectures built on these base methods. Replicating XML data types and compressed tables. We look at some of the design points when considering replicating compressed table. Q replication conflict detection. Available transformation processing for both regular and XML data. What's wrong with just storing our data in one place. Well, in today's 24x7 world where being without data for even a short period of time could be catastrophic to our business, we need a method to be able to take a copy of our data and possibly more than one copy and store it securely in a different location. This copy should be complete and be stored as many miles away as possible. Also the amount of data that has to be stored is ever increasing and being generated at a fast rate, so our method needs to be able to handle large volumes of data very quickly. Overview of what is available today In the IBM software world today, there are a number of options available to replicate data: InfoSphere (formerly WebSphere) Replication Server InfoSphere CDC (formerly the Data Mirror suite of programs) The DB2 High Availability Disaster Recovery (HADR) functionality Traditional log shipping In this book, we will cover the first option InfoSphere Replication Server, which from now on, we will refer to as DB2 replication.http://www.energyair.co.uk/uploads/da-vinci-cribs-instruction-manuals.xml The other options are outside the scope of this book. The different replication options In the world of DB2 replication, we have two main options—SQL replication and Q replication, both of which involve replicating between source and target tables. Event publishing is a subset of Q replication, in that the target is not a table but a WebSphere MQ queue. The choice of replication solution depends on a number of factors, of which the fundamental ones are: Type of source Type of target Operating system support The DB2 Information Center contains a table, which compares the three types of replication. This table can be used as a quick checklist for determining the best solution to a given business requirement ( ). The SQL Apply program (Apply for short) reads from these change data tables and updates the target tables ( TAB3 and TAB4 ). The Q Apply program (Q Apply for short) then reads from these queues and updates the target tables. Replication toolset We have three ways of administering a replication environment. We can use: The Replication Center GUI The ASNCLP command interface Standard SQL We recommend that when you are new to replication, you should use the Replication Center and once you are confident with the process, you can then progress onto the ASNCLP interface. For defining production systems, we recommend using the ASNCLP interface, because the commands can be scripted. The ASNCLP interface generates SQL, which is run against the appropriate control tables to define and administer the replication environment. Therefore, in theory, it is possible for us to write our own SQL to do this. However, the SQL can be complicated and manual coding could result in errors, and therefore we recommend not using this method. The Replication Center GUI The Replication Center GUI can be used to set up and administer a Q replication scenario. See Chapter 6, Administration Tasks, for details on accessing and using the Replication Center. The launchpad screen is shown next.https://labroclub.ru/blog/hotpoint-dlb2650bdlwh-manual The Replication Center has a series of wizards, which are very useful if we are new to replication. The wizards take us through all the steps necessary to set up a replication environment. Using the Replication Center, it is possible to generate an SQL script for a particular function. The ability for the Replication Center to generate ASNCLP scripts in addition to SQL scripts is being planned for a future release. The ASNCLP command interface The ASNCLP interface (discussed in detail in Chapter 5, The ASNCLP Command Interface ) allows us to enter commands from the command line, and more importantly, allows us to combine various commands into a script file, which can then be run from the command line. In this book, we will focus on ASNCLP scripts. In the next section, we will look at the constituent components of Q replication. We will discuss the installation of both of these in some detail. With the current packaging, the Q replication code for homogeneous replication already comes bundled with the base code for DB2—all we have to install is a replication license key. The license for InfoSphere Replication Server is called isrs.lic and for InfoSphere Data Event Publisher the license is called isep.lic. Use the DB2 db2licm command to install the license key and to confirm that the license key has been successfully applied. Turning to the WebSphere MQ component, we can use either WebSphere MQ V6 or V7 with DB2 replication. The WebSphere MQ V6 Information Center can be found at. The WebSphere MQ V7 Information Center can be found at. For the procedure to install WebSphere MQ, consult the WebSphere MQ Information Center and search for install server. Version: 6.0.0.0. CMVC level: p000-L080610. Typically, these programs will be installed on different servers, in which case we have to pay attention to the machine clock time on the servers. Note The machine clock time on all servers involved in replication should be synchronized. The times on all servers need to be synchronized, because each captured record has a timestamp associated with it, and Q Apply will not apply in the future. Therefore, if the Q Capture server is ahead of the Q Apply server, then Q Apply will wait until it has reached the timestamp in the replicated record before applying it. In the next section, we will look at the different types of Q replication. Let's look at each of these in more detail. In the following sections, we talk about source and target tables. You may be wondering, what about views, triggers, and so on. You should check the Pre-setup evaluation section of Appendix A, for a list of objects to check for, before deciding on whether Q replication is the correct solution. Unidirectional replication In unidirectional replication, we can replicate all of the rows and columns of a source table or we can just replicate a subset of the rows and columns. We cannot really perform any transformation on this data. If we want to perform some sort of transformation, then we would need to replicate to a stored procedure, which we will discuss in detail in Appendix A. Replicating to a stored procedure Stored procedure replication is a subset of unidirectional replication in which the target is not a table as such, but a stored procedure, as shown in the following diagram: A stored procedure can transform the data and output the results to a target table. This target table is not known to Q Apply. These stored procedures can be written in SQL, C, or Java. An example of replicating to a stored procedure is shown in the Replication to a stored procedure section of Appendix A. Note Prior to DB2 9.7 the source table and the stored procedure must have the same name, and the target table name can be any name we like. Bidirectional replication In bidirectional replication, we replicate copies of tables between two servers, each of which has a copy of the table. Note that we can only set up bidirectional replication between two servers. Unlike unidirectional replication, where we can replicate a subset of rows and columns, this is not possible in bidirectional replication. The tables on both servers can have different names, but must have the same number of rows and columns. The columns must have identical column names of compatible data types. It is not possible to do any data transformation using this type of replication. Because we are updating records on both servers, it is possible that the same record will be updated at the same time on both servers. Note Although Q replication provides a conflict detection mechanism, we strongly advise that the driving application should be written or modified in such a way that such conflicts be avoided. The conflict detection provided by Q replication should be treated as a safety net and not the primary conflict resolution mechanism. This mechanism allows us to choose which data values are used to detect conflicts (key column values only, changed column values, or all column values) and which server should win if such a conflict is detected. Conflict detection is discussed in detail in the Q replication conflict detection section. One of the related subjects to conflict detection is the concept of which server takes precedence in a conflict, or to put it more bluntly, which server is the master and which is the slave. If there is a conflict, then whichever server takes precedence will not apply changes from the other server. This ensures that the servers remain in sync. There is a more egalitarian option, which is that no server takes precedence. In this situation, rows are applied irrespective of whether or not there is a conflict, which ultimately leads to a divergence of the contents of the databases, which is not good. The type of replication you choose will have implications on which server is defined as the master and which as the slave and what to do if a Q subscription is inadvertently inactivated. Peer-to-peer replication Peer-to-peer replication allows us to replicate data between two or more servers. This is different from bidirectional replication, which is only between two servers. Each server has a copy of the table (which can have a different schema and name), but must have the same number of rows and columns and these columns must have identical column names and compatible data types. In peer-to-peer replication, there is no such thing as a master or slave server—each server will have the most recent copy of the table—eventually. What this means is that there will be a slight delay between the first server having a copy of the table and the last server having that copy. This is an asynchronous process, so at any one time the tables might be different, but once applications stop updating them, then the tables will converge to the most recently updated value. If two applications update the same record at exactly the same time, then Q replication uses the server number allocated when the peer-to-peer environment was set up to determine the winner. This type of processing means that two columns are added to each of the tables in the Q replication environment, where the first column is a timestamp of when the row was last updated (GMT) and the second column is the machine number. These updates are performed through triggers on the tables. A variation on the bidirectional replication theme is that it would be nice to be able to replicate from one master to two slaves in a bidirectional manner. This requirement was addressed with B-Tree replication. A B-Tree replication structure is one, which looks like a tree (as shown in the preceding diagram). DB2A replicates with DB2B, DB2C, and DB2 in a bidirectional manner, but DB2B, DB2C, and DB2 do not replicate directly with each other—they have to replicate through DB2A, which is what differentiates B-Tree replication from peer-to-peer replication. For B-Tree replication, we can replicate between one master and many slaves in a bidirectional manner. In SQL replication terms, this was called Update Anywhere. Note that it is not possible to set up B-Tree replication using the Replication Center—we need to use ASNCLP commands, which is described in detail in the Bidirectional replication to two targets (B-Tree) of Appendix A. We can also replicate from one source to many targets in a unidirectional scenario, which we call a U-Tree scenario. In the preceding figure, DB2A replicates with DB2B, DB2C, and DB2 in a unidirectional manner (we can have more than three targets). Note, there is no radio button in the Replication Center to set up unidirectional U-Tree replication. What we have to do is set up unidirectional replication from DB2A to DB2B, and then for DB2A to DB2C, and so on. It is easier to use ASNCLP commands, which are described in detail in the Unidirectional replication to two targets (U-Tree) section of Appendix A. Replicating to a Consistent Change Data table Let's first look at the definition of Consistent Change Data ( CCD ) replication. There are three main uses of CCD table replication: To populate an operational data store To keep a history of changes made to the source table for audit purposes To enable multi-target update Consider the situation where we want to populate an Operational Data Store ( ODS ) with data from our live system. We want to replicate all operations apart from delete operations. Before the introduction of CCD tables, our only option was to use a stored procedure. One of the parameters that the Q Apply program passes to a stored procedure is the operation (insert, delete, and so on) that occurred on the source system. See the Replication to a stored procedure section of Appendix A. We can use CCD tables to keep a history of changes made to a table, or as a feed to InfoSphere DataStage. The multi-target update scenario uses Q replication to populate the CCD table and then uses SQL Replication to populate the multiple target tables as shown in the following diagram: In the Replicating to a CCD table section of Appendix A, we go through the steps needed to set up replication to a CCD table. We can only specify that a target table be a CCD table in a unidirectional setup. A CCD target table is made up of the following columns (only four of the metadata columns are compulsory, the other four are optional, and the order of the columns does not matter): The columns will be the before image columns and must be NULLABLE (because there is no before image when we insert a row, the before image is NULL ). Now let's look at what data is stored in a CCD table. With CCD tables we can specify that the target table is: COMPLETE or NONCOMPLETE and CONDENSED or NONCONSENSED. All target table loading options are valid for complete CCDs (automatic, manual, or no load). A noncomplete CCD table records all UPDATE operations at the source. The only valid load option for noncomplete CCD tables is no load. For condensed CCD tables, a primary key is required, which is used in case of an update conflict. When added to the CCD table, all of the rows become INSERT operations. No primary key is required. Before we move on, let's quickly look at CCD tables in an SQL Replication environment. In SQL Replication, there is the concept of internal and external CCD tables, which does not exist in Q replication. In SQL Replication terminology, all Q replication CCD tables are external CDD tables. Event Publishing The Event Publishing functionality captures changes to source tables and converts committed transactional data to messages in an XML format. Each message can contain an entire transaction or only a row-level change. These messages are put on WebSphere MQ message queues and read by a message broker or other applications. We can publish subsets of columns and rows from source tables so that we publish only the data that we need. Replicating XML data types From DB2 9.5 onwards, we can replicate tables, which contain columns of data type XML, and an example is shown in the Unidirectional replication for an XML data type section of Appendix A. We can set up unidirectional, bidirectional, and peer-to-peer replication. From DB2 9.7 onwards, in unidirectional replication, we can use XML expressions to transform XML data between the source and target tables. Examples of supported and unsupported XML expressions are shown next. Supported XML expressions include XMLATTRIBUTES, XMLCOMMENT, XMLCAST, XMLCONCAT, XMLDOCUMENT, XMLELEMENT, XMLFOREST, XMLNAMESPACES, XMLPARSE, XMLPI, XMLQUERY, XMLROW, XMLSERIALIZE, XMLTEXT, and XMLVALIDATE. Unsupported XML expressions include XMLAGG, XMLGROUP, XMLTABLE, XMLXSROBJECTID, and XMLTRANSFORM. The issue with replicating a compressed table, is what happens if the compression dictionary is changed while Q Capture is down. Once Q Capture is started again, then it will try and read logs and records that were compressed with the previous compression dictionary, and not succeed. To address this, when a table has both the COMPRESS YES and DATA CAPTURE CHANGES options set, then the table can have two dictionaries: an active data compression dictionary and a historical compression dictionary. Note We should not create more than one data compression dictionary while Q Capture is down. If a table is set to DATA CAPTURE NONE, then if a second dictionary exists, it will be removed during the next REORG TABLE operation or during table truncate operations ( LOAD REPLACE, IMPORT REPLACE, or TRUNCATE TABLE ). Replicating large objects If a row change involves columns with large object ( LOB ) data, Q Capture copies the LOB data directly from the source table to the send queue. If we are replicating or publishing data from LOB columns in a source table, then Q Capture will automatically divide the LOB data into multiple messages to ensure that the messages do not exceed the MAX MESSAGE SIZE value of the Replication Queue Map used to transport the data. If we are going to replicate LOB data, then we need to ensure that the MAXDEPTH value for the Transmission Queue and Administration Queue on the source system, and the Receive Queue on the target system, is large enough to account for divided LOB messages. If we select columns that contain LOB data types for a Q subscription, we need to make sure that the source table enforces at least one unique database constraint (a unique index, primary key, and so on). Note that we do not need to select the columns that make up this uniqueness property for the Q subscription. Not at the present time. Yes, see the Q replication in a DPF environment section. So now let's move on to looking at Q replication filtering and transformations. It is only possible to filter rows for replication in a unidirectional scenario, and this is done in the Q subscription. For an example, see the Creating a Q subscription section of Chapter 6. What about the number of columns we want to replicate—can we replicate just a subset of the source table columns. For the latest release of code, we can subset the columns to be replicated. Any filtering of rows or columns in unidirectional replication is specified at Q subscription definition time. At this time, we can specify: Which columns to replicate and how they map to columns at the target table (or to parameters in a stored procedure) A search condition to determine which rows from the source table are replicated As stated at the beginning of this chapter, Q replication is built for speed with transformations not being a major factor. However, although Q replication does not have the transformation capabilities of SQL Replication, it does have some transformation capabilities, which are described in the following sections. Before and After SQL—alternatives In Q replication, there is no concept of before and after SQL, as there is in SQL Replication. In a unidirectional setup, we can use SQL expressions to transform data between the source and target tables. We can map multiple source columns to a single target column, or to create other types of computed columns at the target. An example is shown in the Q subscription for unidirectional replication section of Chapter 5. Stored procedure processing If we want to perform transformations with Q replication, then we need to use stored procedure processing. This allows us to call external routines to perform all the transformations we want. The Replication to a stored procedure section of Appendix A shows an example of how to set up Q replication to a stored procedure.What is conflict detection. Let's start by defining what we mean by a conflict. A conflict occurs in bidirectional replication when the same record is processed at the same time on the two servers. We then have to decide which server is the winner, which we do when we set up the Q subscription for the table. When do conflicts occur. In unidirectional replication, the only time we need conflict detection is if we are updating the target table outside of Q replication, which is not recommended. Q subscription for unidirectional replication section of Chapter 5, covers scenarios where the target table is updated outside of Q replication. There is no conflict detection with Event Publishing. We need conflict detection in multi-directional replication. Let's first look at bidirectional replication and then move on to peer-to-peer replication. Bidirectional replication uses data values (which we can choose) to detect and resolve conflicts. The choice of data values is determined by the CONFLICT RULE parameter we specify when we create a Q subscription. Q Apply detects the following conflicts: A row is not found in the target table A row is a duplicate of a row that already exists in the target table With this conflict rule, Q Capture sends the least amount of data to Q Apply for conflict checking. No before values are sent, only the after values for any changed columns are sent. Q Apply detects the following conflicts: A row is not found in the target table A row is a duplicate of a row that already exists in the target table A row is updated at both servers simultaneously and the same column values changed If a row is updated at both servers simultaneously and the different column values changed, then there is no conflict. With this conflict rule, Q Apply merges updates that affect different columns into the same row. Because Q Apply requires the before values for changed columns for this conflict action, Q Capture sends the before values of changed columns. All column values: Q Apply attempts to update or delete the target row by checking all columns that are in the target table. With this conflict rule, Q Capture sends the greatest amount of data to Q Apply for conflict checking. Note If we replicate the LOB columns, then conflicts are not detected. See the Replicating large objects section for more information. In a peer-to-peer configuration, conflict detection and resolution are managed automatically by Q Apply in a way that assures data convergence. We do not need to set anything up, as was discussed in the Peer-to-peer replication section. The Conflict detection examples section of Appendix A walks through a couple of conflict detection examples. In the second way, we can use HADR to provide local resilience and Q replication to provide remote resilience, as shown in the following diagram: Each side will have four data partitions and a catalog partition with one DAS instance per box. The instance name is db2i001 and the database name is TP1. The RED side is shown next. There are five database configuration files ( DB CFG ) and one database manager configuration file ( DBM CFG ). Note the following: MQ installed on RED01 Replication control tables created on partition 0 Q Capture and Q Apply run on RED01 EXPLAIN tables defined on RED01 Tables with referential integrity The first design point deals with tables, which have referential integrity. We need to ensure that all related parent and child tables are on the same partition. If we do not do this and start replication, then we will get ASN7628E errors. Table load and insert considerations If we want to load from the application, then the staging table should be partitioned similarly to the detailed table so that we can make use of collocation (therefore, we need the same partition group and same partition key). If we want to insert from the application, then the staging table should be defined on partition 1 ONLY. We also need to perform simple housekeeping tasks on the staging table, for example regular online reorganizations.We discussed various DB2 replication sources including XML data and compressed data and looked at filtering and transformations. Finally, we covered operating Q replication in HADR and DPF environments. Now that we have an overview of what Q replication is, we can look at the Q replication components in more detail, which is what we will do in the next chapter. Before joining IBM he worked as a database administrator in the airline industry as well as various financial institutions in the UK and Europe. He has held various positions during his time at IBM, including in the Software Business Services team and the global BetaWorks organization. His current position is a DB2 technical specialist in the Software Business. He was involved with Information Integrator (the forerunner of Replication Server) since its inception, and has helped numerous customers design and implement Q replication solutions, as well as speaking about Q replication at various conferences. Pav Kumar-Chatterjee has co-authored the “DB2 pureXML Cookbook” (978-0-13-815047-1) published in August 2009. Packt Publishing Limited. All rights reserved. Try searching our site: We look forward to supporting the Db2 community with technical content that enables our System Z colleagues to remain active and valued contributors to the use of technology within their organizations. We invite you to shape the future of IBM, including product roadmaps, by submitting ideas that matter to you the most. Here's how it works: Post your ideas If you have not registered on this portal please register at. To complete registration you will need to open the email you will receive from Aha to confirm your identity. 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