Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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CHAPTER 7 ■ CONCURRENCY AND MULTI-VERSIONING 243 3. They then pull all of the rows from the transactional system—a full SELECT * FROM ➥ TABLE—to get the data warehouse initially populated. 4. To refresh the data warehouse, they remember what time it is right now again. For example, suppose an hour has gone by and it is now 10:00 am on the source system. They will remember that fact. They then pull all changed records since 9:00 am—the moment before they started the first pull—and merge them in. ■Note This technique may “pull” the same record twice in two consecutive refreshes. This is unavoidable due to the granularity of the clock. A MERGE operation will not be affected by this (i.e., update existing record in the data warehouse or insert a new record). They believe that they now have all of the records in the data warehouse that were modified since they did the initial pull. They may actually have all of the records, but just as likely they may not. This technique does work on some other databases—ones that employ a locking system whereby reads are blocked by writes and vice versa. But in a system where you have non-blocking reads, the logic is flawed. To see the flaw in this example, all we need to do is assume that at 9:00 am there was at least one open, uncommitted transaction. At 8:59:30 am, it had updated a row in the table we were to copy. At 9:00 am, when we started pulling the data, reading the data in this table, we would not see the modifications to that row; we would see the last committed version of it. If it was locked when we got to it in our query, we would read around the lock. If it was committed by the time we got to it, we would still read around it since read consistency permits us to read only data that was committed in the database when our statement began. We would not read that new version of the row during the 9:00 am initial pull, but nor would we read the modified row during the 10:00 am refresh. The reason? The 10:00 am refresh would only pull records modified since 9:00 am that morning—but this record was modified at 8:59:30 am. We would never pull this changed record. In many other databases where reads are blocked by writes and a committed but inconsistent read is implemented, this refresh process would work perfectly. If at 9:00 am—when we did the initial pull of data—we hit that row and it was locked, we would have blocked and waited for it, and read the committed version. If it were not locked, we would just read whatever was there, committed. So, does this mean the preceding logic just cannot be used? No, it means that we need to get the “right now” time a little differently. We need to query V$TRANSACTION and find out which is the earliest of the current time and the time recorded in the START_TIME column of this view. We will need to pull all records changed since the start time of the oldest transaction (or the current SYSDATE value if there are no active transactions): select nvl( min(to_date(start_time,'mm/dd/rr hh24:mi:ss')),sysdate) from v$transaction; In this example, that would be 8:59:30 am—when the transaction that modified the row started. When we go to refresh the data at 10:00 am, we pull all of the changes that had occurred since that time, and when we merge these into the data warehouse, we’ll have
244 CHAPTER 7 ■ CONCURRENCY AND MULTI-VERSIONING An Explanation for Higher Than Expected I/O on Hot Tables Another situation where it is vital that you understand read consistency and multi-versioning is when you are faced with a query that in production, under a heavy load, uses many more I/Os than you observe in your test or development systems, and you have no way to account for it. You review the I/O performed by the query and note that it is much higher than you have ever seen—much higher than seems possible. You restore the production instance on test and discover that the I/O is way down. But in production it is still very high (but seems to vary: sometimes it is high, sometimes it is low, and sometimes it is in the middle). The reason, as we’ll see, is that in your test system, in isolation, you do not have to undo other transactions’ changes. In production, however, when you read a given block, you might have to undo (roll back) the changes of many transactions, and each rollback could involve I/O to retrieve the undo and apply it. This is probably a query against a table that has many concurrent modifications taking place—you are seeing the reads to the undo segment taking place, the work that Oracle is performing to restore the block back the way it was when your query began. You can see the effects of this easily in a single session, just to understand what is happening. We’ll start with a very small table: ops$tkyte@ORA10GR1> create table t ( x int ); Table created. ops$tkyte@ORA10GR1> insert into t values ( 1 ); 1 row created. ops$tkyte@ORA10GR1> exec dbms_stats.gather_table_stats( user, 'T' ); PL/SQL procedure successfully completed. ops$tkyte@ORA10GR1> select * from t; X ---------- 1 Now we’ll set our session to use the SERIALIZABLE isolation level, so that no matter how many times we run a query in our session, the results will be “as of” that transaction’s start time: ops$tkyte@ORA10GR1> alter session set isolation_level=serializable; Session altered. Now, we’ll query that small table and observe the amount of I/O performed: ops$tkyte@ORA10GR1> set autotrace on statistics ops$tkyte@ORA10GR1> select * from t; X ---------- 1
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Expert Oracle Database Architecture
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Contents Foreword . . . . . . . . .
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■CONTENTS v Block Buffer Cache .
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