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This instance relocation uses a new featured dubbed Oracle Omotion.

This instance relocation uses a new featured dubbed Oracle Omotion.

This is a similar approach to instance relocation that was first introduced by Savantis Systems with their DB-Switch invention, an offshoot of the Database Area Network (DAN) approach.

Cons of ASSM: §         Slow for full-table scans: Several studies have shown that large-table full-table scans (FTS) will run longer with ASSM than standard bitmaps. ASSM FTS tablespaces are consistently slower than freelist FTS operations. This implies that ASSM may not be appropriate for decision support systems and warehouse applications unless partitioning is used with Oracle Parallel Query. §        Slower for high-volume concurrent inserts: Numerous experts have conducted studies that show that tables with high volume bulk loads perform faster with traditional multiple freelists. §         ASSM will influence index clustering: For row ordered tables, ASSM can adversely affect the clustering_factor for indexes. Bitmap freelists are less likely to place adjacent tows on physically adjacent data blocks, and this can lower the clustering_factor and the cost-based optimizer's propensity to favor an index range scan.

Pros of ASSM: §          Varying row sizes: ASSM is better than a static pctused. The bitmaps make ASSM tablespaces better at handling rows with wide variations in row length. §         Reducing buffer busy waits: ASSM will remove buffer busy waits better than using multiple freelists. When a table has multiple freelists, all purges must be parallelized to reload the freelists evenly, and ASSM has no such limitation. §         Great for Real Application Clusters: The bitmap freelists remove the need to define multiple freelists groups for RAC and provide overall improved freelist management over traditional freelists.

Cons of ASSM: §         Slow for full-table scans: Several studies have shown that large-table full-table scans (FTS) will run longer with ASSM than standard bitmaps. ASSM FTS tablespaces are consistently slower than freelist FTS operations. This implies that ASSM may not be appropriate for decision support systems and warehouse applications unless partitioning is used with Oracle Parallel Query. §          Slower for high-volume concurrent inserts: Numerous experts have conducted studies that show that tables with high volume bulk loads perform faster with traditional multiple freelists. §          ASSM will influence index clustering: For row ordered tables, ASSM can adversely affect the clustering_factor for indexes. Bitmap freelists are less likely to place adjacent tows on physically adjacent data blocks, and this can lower the clustering_factor and the cost-based optimizer's propensity to favor an index range scan.

One obvious method of reducing pings between instances is to isolate the transactions that use a specific data set to a specific server in the RAC cluster.

Some of the good RAC practices, to put it quite frankly, waste disk and memory space to improve data sharing and dispersal characteristics.

An example of an efficient RAC object is one that is used by only a single instance at a time. To achieve this singularity of use, the rows-per-block (RPB) of the data object must be reduced.

Enterprise Manager A graphical all-purpose tool that can be used to identify when a spike occurred, drill down to the cause, and examine ADDM recommendations. The benefit of a graphical representation of performance data is visible (pun intended). Data visualizations display any skew directly.

Automatic Database Diagnostic Monitor (ADDM) An expert system that automatically identifies and recommends solutions for many instance-wide performance problems. Best used for longer-duration performance problems (that is, problems that are continuous or ongoing for a large proportion of the snapshot interval). The symptoms and problems are available by running the ADDM report, and through Enterprise Manager.

Active Session History (ASH) An all-purpose tool providing data that is useful when investigating system-wide problems, shorter-duration spikes, or smaller-scoped problems (for example, for a specific user, or SQL, or a module/action).The advantage of using ASH data when compared to other diagnostic information is that the data is of a finer granularity. This allows you to look at a problem to identify how the symptoms "build up," or allows you to determine exactly which resources are involved and who is using them. The ASH data can be queried directly or accessed via a targeted ASH report.

Unfortunately data modeling is not yet covered by the Unified Modeling Language (UML), even though persistence-related issues are clearly an important aspect of object-oriented software project.

The good news is that the Object Management Group (OMG) issued an RFP for an official UML Data Modeling Profile in December 2005.

This page summarizes the data modeling profile for UML Class Diagrams, that I apply in Agile Database Techniques, The Object Primer 3rd Edition, and Refactoring Databases.  First some important definitions:

Here’s a handy little query I made up the other day to quickly digest any of the segment statistics from the AWR and grab the top objects for the cluster, reporting on each instance.

Any time you see heavy concurrency problems during inserts on table data blocks there should always be one first place to look: space management. Since ancient versions of OPS it has been a well-known fact that freelists are the enemy of concurrency.

Statistics are somewhat fallible in that they are seldom 100 percent accurate, but in most cases they do sufficiently indicate what was intended. Be sure you understand what each statistic represents and the units used (there is a big difference between microseconds and centiseconds).

Time-breakdown statistics (Time Model) make it significantly easier to determine the type of operations that are consuming resources in the database.

DB time: Time spent by all user processes in the database (that is,. non-idle wait time + CPU time).

Each time you execute this statement; Oracle will convert it to an ASCII function and apply a hashing algorithm over it; than it will check if this SQL statement is already present in the SHARED POOL.

If the statement is in the SHARED POOL, Oracle will reuse (soft parse) it together with its execution plan. If the statement is not in the SHARED POOL, Oracle will have to do a hard parse.

Oracle’ CBO can generate more optimized execution plan if the he knows upfront the values of the filter predicated, meaning if the values are literals and not bind variables.