optimizer

Due to the interplay of these diverse independent factors, in rare instances, the optimizer might select less than optimal execution plans.

Ideally, the optimizer would identify the most efficient access plan possible, but this goal is often impractical.

Note, however, that the optimizer is often unable to precisely estimate a query's cost, so expect there to be differences.

The output of the optimizer is a query execution plan, sometimes referred to as a query plan or just a plan.

You can use the REWRITE function without the ANSI argument to help understand how the optimizer generated the access plan for a given query.

No degree of query optimizer intelligence can eliminate the resource cost of these constructs when compared to a less complex query.

The optimizer's estimate could be very close to the actual value as long as the data distribution has not changed since the last RUNSTATS.

Further, the solidDB query optimizer recognizes the different nature of the in-memory tables by estimating execution costs in a new way.

It is possible that the query optimizer might have generated a remote statement that hits the remote server's limitation on performance.

Predicates with concrete values are important for the optimizer to be able to use available distribution statistics.

If the execution plan problems cannot be avoided, guidelines can be used to affect the optimizer plan to mitigate the performance problems.

The optimizer uses MAGIC values as a last resort when it is unable to accurately predict the selectivity of a predicate .

Many customers new to Informix do not realize they must update statistics, and see poor performance due to the lack of optimizer statistics.

An entry is added to the system catalog to indicate to the query optimizer that such a remote index exists.

Up-to-date statistics allow the optimizer to accurately assess the cost of different query plans, and choose a high-quality plan.

If the best indexes do not exist, the query optimizer generates a suboptimal query plan, but still stores information about these indexes.

The optimizer depends on accurate cardinality estimates to properly compute the cost of each query access plan considered.

db2exfmt is typically used for comprehensive performance analysis, as it provides the most optimizer information.

Information returned by sys. Dm_db_missing_index_details is updated when a query is optimized by the query optimizer, and is not persisted.

Specifies the name or ID of the indexes to be used by the query optimizer when it processes the statement.

Instead, the optimizer gathers up-to-date statistics on every database table referenced in a query.

Think of the optimizer as an explorer waiting to navigate a mountain represented by the data in the system.

Unlike other heart-failure devices , the Optimizer works by strengthening the pumping force of the heart.

You can access the results and reports of various statistics within Google Website Optimizer to see which variant is the most effective.

WideVine was a video optimizer and one of the last major independent digital rights managers.

An estimate source of Guess indicates that the optimizer has no selectivity information to use .

Almost every major database today uses some method of updating catalog statistics to provide the best information possible to its optimizer.

The view does not have to be referenced in the query for the optimizer to consider that view for a substitution.

WebSphere Federation Server uses its cost-based optimizer to determine the most efficient means of resolving the multi-source query.

The query optimizer, in consultation with the wrapper, determines how the data is to be accessed.

This set is input to the cost-based optimizer that decides whether to use any of the eligible indexes or not.

If such a plan is not possible , the query optimizer returns an error instead of deferring error detection to query execution .

The optimizer will compare the cost of each method to determine the best one to use.

Updating index statistics to make sure the query optimizer has up-to-date information about the distribution of data values in the tables.

Application developers have to be careful, as in certain situations distribution statistics are important for the DB2 optimizer.

If you do not implement UPDATE STATISTICS, the optimizer has inaccurate data to determine access paths.

This command updates the statistics in the system catalogs that the optimizer uses to determine the lowest-cost query plan.

Tuning such an environment by only analyzing the optimizer plans is not feasible due to the quantity of produced plans.

When cursors generate a keyset for a result set, the query optimizer estimates the number of rows that will be returned for that result set.

The total cost to the query optimizer for executing this operation and all operations preceding it in the same subtree.

Generate the access plan and see what the optimizer estimates the total cardinality (Number of rows) returned by the query will be.

if set to ANY, the optimizer is sensitive to the actual number of cpus that are online.

Consequently, the query optimizer had to make a guess, which may have resulted in the selection of an inefficient query plan.

The Missing Column Statistics event class indicates that column statistics that could have been useful for the optimizer are not available.

The two versions differ in internal details like the indexing system, optimizer, and storage engine.

When the query optimizer generates a query plan, it analyzes what are the best indexes for a particular filter condition.

The optimizer calculates and compares the costs of each join method and selects the best one to use when evaluating query execution plans.

To understand how the optimizer ends up with 0. 0014% as the filter factor, first look at the six elements in the IN list predicate.

The optimizer considers the indexes on the new view, even if it is not directly referenced in the FROM clause of queries.

The optimizer periodically re- optimizes queries to verify that the cached plan is still relatively efficient .

The number of times the subquery and its function is executed can vary with different access paths chosen by the optimizer.

Optimizer guidelines to help aid in plan compilation as stop-gap measure to eventual fixes that are provided in the product.

Bugfix in optimizer because in rare cases it did not optimize using the specified frequencies.

I'll show you how the DB2 optimizer works, and give you guidelines for writing SQL that will get the most out of the optimizer.

Keep in mind that this catalog data is what the optimizer uses to make access path decisions.

Specifies that the query optimizer use an optimization or locking strategy with this table and for this statement.

The optimizer takes account of these changes and selects the optimal access plan when the job is executed.

This means the optimizer will always attempt to formulate an access path for each query that reduces overall cost .

Besides the inputting and outputting data, the framework can be divided into four parts: interpolator, measurer, optimizer and transformer.

The optimizer takes into account both the cost of disk access operations and the estimated CPU cost of each operation.

The optimizer chooses Case 4 since it determined that it has the minimum number of row accesses and therefore should be efficient.

In particular, it is rather hard for the optimizer to determine the costs and the selectivity of such an index scan.

USE PLAN can be used to force the query optimizer to use a specified query plan for a query.

For each query, the query optimizer compares the execution cost of many alternative execution plans.

The optimizer would chalk out a query plan before performing the actual join operation itself by making use of the statistical data.

Therefore, if the optimizer's estimate is accurate, this access path should be a very efficient access path.

Higher values for this parameter can influence the optimizer to choose access plans that are more conservative in buffer pool usage.

If the query optimizer decides to use an indexed view in a query plan, the indexed view is treated the same way as a base table.

If more than one UNION hint is specified, the query optimizer selects the least expensive strategy from those hints specified.

Note: the query optimizer will not use the R-Tree index unless the statistics on the table are up to date.

With few exceptions, the Adaptive Server Anywhere optimizer devises a suitable access plan based only on the semantics of each statement.

Second bugfix in optimizer to let us again optimize for none integer frequencies.

The query optimizer bases query-cost estimates on the number of rows to be retrieved from each table.

The optimizer can use its knowledge of the appliance components to determine the best join order in a complex join.

For SQL statements with variables, the optimizer uses a default filter factor to determine the best access path at bind time.

For queries that match the active profile, the optimizer makes the best possible effort to generate the plan requested by the guideline.

Normally, the optimizer selects an access plan for a query every time the query is executed.

If more than one join hint is specified , the optimizer selects the least expensive join strategy from the allowed ones .

The optimizer bases query cost estimates on the number of rows to be retrieved from each table.

Therefore, DB2 as a proven relational engine with a cutting-edge optimizer is insufficiently exploited.

The SQL explain facility provides detailed information about the access plan that the query optimizer chooses for an SQL statement.

The optimizer chooses a table scan when most data of the table must be read anyway, or when table do not have indexes.

It is recommended that you do not override the choices the optimizer makes .

The database server query optimizer automatically uses an index when a suitable index exists and when using one will improve performance.

You want indexing of XML data for efficient query processing and good scalability, and the use of a first-rate query optimizer.

The estimated selectivity of predicates provides the information on which the optimizer bases its cost estimates.

The query optimizer will give priority to an execution plan involving an MQT over a plan that involves access to remote tables.

Unfortunately, LLVM in its current state is really designed as a static compiler optimizer and back end.

In a dynamic language, fewer such clues are available for the optimizer, making optimization choices harder.

Unlike APC and Wincache, it is only an opcode cache and optimizer, so it does not contain the user cache components.

When an application sends a query to Informix, the Informix optimizer decides whether the query should be sent to IWA.

This may be caused by the optimizer choosing not to access the table at all, or because an indexed view is accessed instead .

As you've seen in the steps above, the optimizer selects the optimal access plan based on available statistics.

The OPTIMIZATION_GOAL option often has a major effect on the execution plan chosen by the optimizer, and is stored along with the query.

Forces the query optimizer to try a plan that works for the maximum potential row size, possibly at the expense of performance.

Alternatively, you can resort to very clumsy casting, and hope that the compiler's optimizer works out what you really want to do.

The optimizer expresses its chosen method in the form of an access plan.

The value ANY allows the optimizer to determine the number of ATQs for a given query plan.

In particular, the query optimizer is less likely to match the query to an indexed view or an index on a computed column.

As mentioned earlier, the DB2 optimizer is only as good as the catalog statistics are current.

The optimizer will choose dynamic hash join if none of the joined columns has index.

As well, the optimizer makes use of indexes and keys to improve its guess of the number of rows.

Finally, we discuss a few enhancements to the db2support utility for gathering optimizer-related information.

However, you would still like the optimizer to find the best access plan based on the values that users have passed into the query.

The optimizer uses data distribution information generated by UPDATE STATISTICS to calculate selectivity for the filters in the query.

Once the profile is enabled, the optimizer automatically matches optimization guidelines to the appropriate statement.

In many cases, this will provide the optimizer with nearly as accurate statistics as the first command, but will return results much faster.

When this attribute is not specified, the optimizer makes a cost-based decision to select an access plan for the given table.

This defines indexes and keys in server terms and enables the query optimizer to consider any indexes that may exist on this table.

The optimizer uses data-distribution statistics to determine whether a column contains unique values.

The input to the optimizer consists of the query , the database schema ( table and index definitions ), and the database statistics .

Using FORCE ORDER does not affect possible role reversal behavior of the query optimizer.

Contains information about using the USE PLAN query hint to force the query optimizer to use a specific query plan for a query.

When distribution statistics are generated for the column "TYPE, " the optimizer knows the real frequency of each model.

As the optimizer is not aware of the uneven distribution of the STATE values, it decides to use the index I_TYPE.

If value is set to 2, the optimizer would uses estimated costs to determine an execution plan regardless of the transaction isolation mode.

Expert Optimizer is installed in some 170 clinker kilns worldwide producing more than 100 million tons of cement a year.

When you provide more detailed search conditions by having more predicates in a query, the optimizer has a chance to make better choices.

Instructs the query optimizer to use a particular value for a local variable when the query is compiled and optimized.

Using guidelines, a workaround can be provided quickly while a permanent optimizer fix is developed.

The number of processors that are used by the query optimizer typically provides optimal performance.

Updating the catalog statistics would provide the optimizer with the most current map to navigate quickly through the terrain.

In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array .

Neither is the optimizer affected by whether or not a query contains a view.

The illustration shows a query optimizer plan executed with a degree of parallelism equal to 4 and involving a two-table join.

Without these statistics, the optimizer may generate very inefficient data access plans, causing poor database performance.

The Index Consultant retrieves the plan for each statement in the workload, as chosen by the Adaptive Server Anywhere query optimizer.

New SET OPTIMIZATION ENVIRONMENT options are also introduced for influencing optimizer consideration of star join plans.

Users are only required to compose the XML profile document, insert it into the database, and instruct the optimizer to use that profile.

Noting the partition numbers confirms that the query optimizer is performing partition elimination .

As a consequence, the optimizer usually makes sub-optimal decisions, leading to poor performance.

Regardless of schema (3NF or Star), the Teradata query optimizer relies on statistics to help it determine the best way to access data.

Since sampling is not supported on nicknames, the Design Advisor uses the optimizer's estimates for statistics.

This can be useful in the event of the generation of a non-optimzal plan by the DB2 optimizer.

Installing and Implementing smart optimizer on the new CS-Cart site, as well as making any changes to improve site speed.

The optimizer can use data distributions to calculate selectivity for the filters in a query.

Tuning the Universal Server's query optimizer for particular types of complex data can vastly improve querying speed on those data types.

Describes how the query optimizer uses statistics to develop optimal query plans.

The optimizer chose this query plan because the estimated cost for its execution was the lowest among all the evaluated plans.

The RDBMS optimizer accomplishes this substitution in an automatic way.

During the search process of PSO, the chaotic local optimizer was introduced to raise its resulting precision and convergence rate.

It is not an optimizer, compressor, or token reducer, and it is not able to flush a browser's cache.

During plan generation for the query, the query optimizer communicates with the non-relational wrapper.

Also, if any of the listed options are incorrectly set, this will prevent the optimizer from using the indexes on the views.

The query optimizer does not recalculate the profile for tables automatically.

Table level statistics are decoded and loaded into the data distribution cache for the optimizer to use.

The inconsistent statistics may also mislead the optimizer into choosing a bad access path.

Forces the query optimizer to relax the estimated recompile threshold for a query.

Given accurate statistics, the query optimizer generally comes up with correct selectivity estimates.

WebSphere Federation Server uses its cost-based optimizer to determine the most efficient method of joining the data.

The optimizer chooses between the two access methods using a cost-based analysis.

The important information used by the optimizer for a tablespace is the following.

As you can see, the optimizer creates a virtual table to process the IN-list sub-query.

In general, the query optimizer selects a plan that will return the entire result set in the shortest amount of time.

OPTIMIZE FOR can counteract the parameter detection behavior of the optimizer or can be used when you create plan guides.

The query optimizer always generates a serial execution plan for a query that is part of a dynamic cursor.

Relationships between tables provide valuable input to the Teradata database optimizer.

Forces the query optimizer not to recompile a query due to changes in statistics.

If used improperly, the optimizer might perform an optimization that would break your application.

Google Website Optimizer serves different pages to different users, with hundreds of variations possible.

Google Website Optimizer serves different pages to different users, with hundreds of variations possible.

Both of these commands will create a directory called DB2CAEM under OPTIMIZER directory in the resulting db2support. zip file.

The query optimizer does not consider the index when creating query execution plans.

Plan forcing removes the ability of the query optimizer to adapt to changing data sizes and distributions, new indexes, and other variables.

This example illustrates the effect that partial statistics have on the ability of the optimizer to estimate the cardinality.

When the IN predicate is used in its join form, the optimizer still considers it for statistical correlation detection.

Access methods refer to the ways that the optimizer reads (or rather retrieves) data from database tables.

The DB2 optimizer uses these statistics to determine the most efficient access plan for any given query, which improves query performance.

The optimizer re-evaluates its choice of matching predicates when considering alternate join strategies.

The optimizer will decide whether an index can be used to evaluate a filter.

Access plan describes the access path that the DB2 optimizer has chosen for accessing data for an SQL statement.

The Filter factor for each predicate indicates the proportion of data the optimizer estimates will satisfy the predicate.

This table stores the column statistics that are stored as histograms and used by the optimizer.

Comparing real cardinalities to the optimizer's estimates can help you identify query planning problems.

Fast forward-only cursors vary in when they acquire their locks depending on the execution plan chosen by the query optimizer.

Although this is an option, the query optimizer can generally pick the best optimization method without hints being specified.

With three predicates applied in the WHERE clause, assuming they are independent results in the optimizer underestimating the cardinality.

The query optimizer may use indexed views to speed up the query execution.

If not, further make sure that all the optimizer-related parameters discussed in this article are identical.

The optimizer, in the absence of current statistics, is choosing to do a table scan on both tables involved in the join.

With this feature, the query optimizer can immediately consider the index when determining an access plan.

After the query optimizer creates the plan, the query optimizer chooses the most efficient physical operator for each logical operator.

The query optimization class is used to direct the optimizer to use different degrees of optimization when compiling SQL queries.

The optimizer can decide to not use an index even if it could be used.

This drastic step leaves the optimizer with no access to essential statistical information.

For very expensive SQL statements, the optimizer may benefit from seeing actual variable values (literals) instead of parameter markers.

Sometimes we hope to influence the optimizer more directly, with minimum application changes if possible.

The CPU speed (in milliseconds per instruction) is used by the SQL optimizer to estimate the cost of performing certain operations.

The tuning process relies on the query optimizer, and the recommendations it generates are based in part on the underlying hardware.

When the DB2 optimizer forms a query access plan for a query, it divides the work among the database partitions which work in parallel.

Accurate index information and statistics are fundamental to cost-based decisions in the query optimizer.

The query optimizer uses this statistical information to choose the most efficient plan for retrieving or updating data.

In certain cases, as determined by the optimizer, the intermediate temp table is not required, which is reflected in the plan.

The push-down decision is made by the wrapper during the planning phase, while working with the query optimizer.

as data changes or indexes are added or dropped , the query optimizer automatically adapts its behavior by using alternative plans.

Dynamic SQL needs to be prepared execution by the UDB optimizer in order to create an access plan for the statement.

The following statement returns the value Index, which means that the query optimizer probed an index to estimate the selectivity.

However, we want to spend a bit more time on star schema optimizer support, which is an offshoot of XPS.

To do this, the optimizer uses the RTL to create fast or more compact code (or both, when possible).

If the query selects fewer columns than the corresponding matching MQT, the optimizer can eliminate unneeded columns.

In general, the basic statistics available to the DB2 optimizer does not detect data correlation.

Should the data change, the explicit estimate may become inaccurate and may force the optimizer to select poor plans.

After the optimizer has done its work, the compiler converts the query plan into executable code segments called snippets.

The number of requests optimized by the optimizer bypass.

Added information about when the query optimizer does not use a parallel execution plan.

Approximate query execution costs are estimated by the query optimizer.

Recommend the best mix of indexes for databases by using the query optimizer to analyze queries in a workload.

Optimal database performance requires the database optimizer to do its job well.

The query optimizer does not consider constraints that are defined WITH NOCHECK.

The optimizer will deduce key value selection based on the functional dependency of the column within the row.

Awareness of all applicable remote indexes can help the federated query optimizer generate better execution plans.

Gets a value that indicates whether the runtime optimizer is disabled.

Even if we do update statistics for the table, the optimizer will have no choice but to sequentially scan it.

The total number of rows, estimated by the optimizer, is 2. 176.

With the new stats available to DB2's optimizer, access to the data can be dramatically improved.

Zero disables the optimizer, and higher values use more aggressive optimizations.

Specifies that any indexed views are not expanded to access underlying tables when the query optimizer processes the query.

Provides the source for selectivity estimates used by the query optimizer.

Helps the query optimizer estimate the selectivity of query predicates on string patterns.

In particular, the optimizer may choose to use the Clustered Hash Group By query execution algorithm.

SET FORCEPLAN essentially overrides the logic used by the query optimizer to process a Transact-SQL SELECT statement.

To get an idea of how the optimizer calculates QUALIFIED_ROWS and how accurate it is, refer again to Figure 9.

In the figure below, asynchrony has been enabled, and the optimizer has chosen to place an ATQ operator above the rightmost (pink) SHIP.

To ensure that the optimizer selects the best query plan, it is important to keep internal statistics current and accurate.

When the index name is not specified, optimizer picks the cheapest single XML index access plan.

The query optimizer typically selects the most efficient method when executing queries.

The optimizer transparently rewrites the request to use the materialized view.

A new feature of the query optimizer supports a new type of index scan, called an index self-join path.

Additional information helpful to the query optimizer is provided.

At each operator in the access plan, the optimizer estimates the cardinality output from the operator.

Spatial predicates that tell the DB2 optimizer under which conditions a spatial index could be exploited.

If the transaction isolation mode is Repeatable Read, then the optimizer would base its choice purely on the estimated costs.

The optimizer can then compare the input variable values to catalog statistics and compute a better selectivity estimate for predicates.

Using this information, the DB2 Optimizer will determine the cost of using each index.

As shown in Figure 1, the DB2 optimizer chooses the best access path.

The ACCESS guideline (any access request) allows the optimizer to choose the access method.

We also propose to use a hybrid version of PSO embedding a local optimizer to enhance the performance.

Goal directives that force the optimizer to use the designated rules to return query results.

Use Vista Services Optimizer to disable unnecessary services in Vista safely.

The optimizer begins selecting for the choices available using efficient, and in some cases proprietary, algorithms.

Under-estimating the cardinality could lead the optimizer to choose a sub-optimal access plan.

Additionally, a tight integration into the DB2 optimizer provides for strong run time performance.

Propagation role blocks are not modified by the Optimizer, by default.

In our experience, using the website optimizer tool does not affect your rankings and does not cause duplicate content issues.

This can cause the query optimizer to choose a less-efficient query plan than otherwise expected.

The Optimizer's Curse: Skepticism and Postdecision Surprise in Decision Analysis By: Smith, James E. ; Winkler, Robert L.

Node IDs in Showplans identify each operation the query optimizer performs when it generates a query execution plan.

In other words, the optimizer overestimated the selectivity of the local predicates on the table.

In this case, the optimizer creates a different access plan, which may not be optimal if the user input is 0.

The optimizer will return the best plan using a cost-based decision.