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Two behaviors can cause huge batches to get progressively slower during execution. The maintenance of indexes, which adds work to the task and also tends to distort the geometry of the index trees. The accumulation of back versions, the deltas of the old rows that are marked for obsolescence once the update or delete operations are committed. Back versions do not become available for garbage collection until the transaction completes. In transactions involving back versions numbering in the tens to hundreds of thousands (or more), garbage collection may never win its battle to clean out obsolete versions. Under these conditions, only a backup and restore will sanitize the database completely.

A strategy for mitigating batch slowdown when other users are not accessing the input table is to deactivate the secondary indexes. This is simple to do:

ALTER INDEX index-name INACTIVE;

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DistId that represents a distributed transaction is actually a Guid. For many of these tests, only the last node of the TranId number changes. For instance, if we compare this test to the prior one, we see that the two local TranId numbers are as follows: TranId: c3cfb690-7094-4fed-a3ac-ada8d1fec8e9:1 TranId: c3cfb690-7094-4fed-a3ac-ada8d1fec8e9:2

When the large batch operation is finished, reactivate and rebuild the indexes:

ALTER INDEX index-name ACTIVE;

Unfortunately, you cannot disable the indexes that enforce primary and foreign keys without dropping the actual constraints. Often, it will be impracticable to drop constraints because of chained dependencies. For this reason and others, judicious partitioning of the task is recommended to help offset both the deterioration of index balance and the generation of an unnecessarily large number of new database pages. When posting large numbers of inserts in batch, it is preferable to partition them into groups and commit work about every 5,000 10,000 rows. The actual size of an optimal partition will vary according to the size of the row and the database page size. Optimal groups may be smaller or larger than this range.

Summary

When you need to partition huge batches, a stored procedure is often the way to go. You can make use of local counter variables, event messages, and return values to keep completing procedure calls in sync with the calling client.

The log for the DniScTransactionRequiresNewObj class (not shown) looks almost exactly like the last log. The only difference is that different transaction IDs are used. This class applies a Transaction attribute with TransactionOption.RequiresNew. This will force the creation of a new transaction even when one already exists. We can consider the following classes together since the results we see for them are exactly the same: DniScTransactionDisabledObj DniScTransactionNotSupportedObj DniScTransactionSupportedObj They all show that a transaction is not active at the time of the method call: ------Starting method call-----*Transaction at start of method: ***No current transaction*** This is as we expected, since none of the TransactionOption values used for these classes result in the creation of a new transaction. The DniScTransactionSupportedObj class uses the TransactionOption.Supported value. This supports an active transaction, but doesn t force the creation of one if it doesn t already exist. The DniScTransactionUsesSupportsObj class is interesting because it shows us what happens when one component calls another one with a different TransactionOption value. The class itself specifies TransactionOption.Required so we know that a transaction will be created. But during the method call, an instance of the DniScTransactionSupportedObj class is created and used. The previous call that we made to DniScTransactionSupportedObj did not have an active transaction. This time, we are calling it from a method that already has an active transaction, so the results should look different. What will the transaction state be when there is an active transaction Here are the merged logs for both of these classes (the caller and the called):

Firebird incorporates some features that can be implemented in database design to respond to DML operations that change the state of data, namely the posting of INSERT, UPDATE, and DELETE statements.

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