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Atomicity: A transaction is atomic if it s regarded as a single action rather than a collection of separate operations So, only when all the separate operations succeed does a transaction succeed and is committed to the database On the other hand, if a single operation fails during the transaction, everything is considered to have failed and must be undone (rolled back) if it has already taken place In the case of the order-entry system of the Northwind database, when you enter an order into the Orders and Order Details tables, data will be saved together in both tables, or it won t be saved at all Consistency: The transaction should leave the database in a consistent state whether or not it completed successfully The data modified by the transaction must comply with all the constraints placed on the columns in order to maintain data integrity.

You can also optimize all the indexes by calling the searchFactory.optimize() method, as shown in listing 9.11.

public void optimize() { FullTextSession session = SessionHolder.getFullTextSession(); session.getSearchFactory().optimize(); Optimize all indexes }

So, if a dataset doesn t have to be connected to a database, how do you populate it with data and save its data back to the database This is where data adapters come in. Think of data adapters as bridges between datasets and data sources. Without a data adapter, a dataset can t access any kind of data source. The data adapter takes care of all connection details for the dataset, populates it with data, and updates the data source. In this chapter, we ll cover the following: Datasets and data adapters How data is stored in a dataset with data tables, data rows, and data columns How to get different views of the data in a dataset

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When should you call optimize Optimizing is useful when many insertions or deletions have been made, particularly after initial data indexing or when you have reindexed a significant part of your data set.

In the case of Northwind, you can t have rows in the Order Details table without a corresponding row in the Orders table, as this would leave the data in an inconsistent state Isolation: Every transaction has a well-defined boundary that is, it is isolated from another transaction One transaction shouldn t affect other transactions running at the same time Data modifications made by one transaction must be isolated from the data modifications made by all other transactions A transaction sees data in the state it was in before another concurrent transaction modified it, or it sees the data after the second transaction has completed, but it doesn t see an intermediate state Durability: Data modifications that occur within a successful transaction are kept permanently within the system regardless of what else occurs.

Manipulating data in a dataset Persisting changes in the dataset back to the original data source Working with datasets and XML Using data tables without datasets Typed and untyped datasets

There s no need to optimize when you re about to index a large section of data unless you need to reclaim disk space. Optimization doesn t make indexing faster. On the other hand, when you ve finished a massive indexing, it s a good time to optimize. Subsequent queries will execute faster.

We ll begin this chapter with a quick presentation of all the new objects you ll need to understand in order to work with datasets and data adapters. You ll start by looking at the difference between datasets and data readers and then move on to look in more detail at how data is structured within a dataset and how a dataset works in collaboration with a data adapter.

A nice tool to see if an optimization is necessary is Luke. One of the panels shows how many files the index contains and the size of each (see section 2.6). Listing 9.12 shows how to use optimize after a complete reindexing.

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