By default, Oracle Text uses the cost-based optimizer (CBO) to determine the best execution plan for a query.

To enable the optimizer to better estimate costs, calculate the statistics on the table you queried table:

ANALYZE TABLE <table_name> COMPUTE STATISTICS;

Alternatively, estimate the statistics on a sample of the table:

ANALYZE TABLE <table_name> ESTIMATE STATISTICS 1000 ROWS;

or

ANALYZE TABLE <table_name> ESTIMATE STATISTICS 50 PERCENT;

You can also collect statistics in parallel with the DBMS_STATS.GATHER_TABLE_STATS procedure:

begin
 
DBMS_STATS.GATHER_TABLE_STATS('owner', 'table_name',
                                       estimate_percent=>50,
                                       block_sample=>TRUE,
                                       degree=>4) ;
 
end  ;

These statements collect statistics on all objects associated with table_name, including the table columns and any indexes (b-tree, bitmap, or Text domain) associated with the table.

To re-collect the statistics on a table, enter the ANALYZE statement as many times as necessary or use the DBMS_STATS package.

By collecting statistics on the Text domain index, the CBO in Oracle Database can perform the following tasks:

• Estimate the selectivity of the CONTAINS predicate

• Estimate the I/O and CPU costs of using the Oracle Text index (that is, the cost of processing the CONTAINS predicate by using the domain index)

• Estimate the I/O and CPU costs of each invocation of CONTAINS

Knowing the selectivity of a CONTAINS predicate is useful for queries that contain more than one predicate, such as in structured queries. This way the CBO can better decide whether to use the domain index to evaluate CONTAINS or to apply the CONTAINS predicate as a post filter.

The following structured query provides an example for optimizing statistics:

select score(1) from tab where contains(txt, 'freedom', 1)  > 0 and author = 'King' and year > 1960;

Assume the following:

• The author column is of type VARCHAR2 and the year column is of type NUMBER.

• A b-tree index on the author column.

• The structured author predicate is highly selective with respect to the CONTAINS predicate and the year predicate. That is, the structured predicate (author = 'King') returns a much smaller number of rows with respect to the year and CONTAINS predicates individually, say 5 rows returned versus 1000 and 1500 rows, respectively.

In this situation, Oracle Text can execute this query more efficiently by first scanning a b-tree index range on the structured predicate (author = 'King'), then accessing a table by rowid, and then applying the other two predicates to the rows returned from the b-tree table access.

After synchronizing your index, you can re-collect statistics on a single index to update the cost estimates.

If your base table was reanalyzed before the synchronization, it is sufficient to analyze the index after the synchronization without reanalyzing the entire table.

To re-collect statistics, enter one of the following statements:

ANALYZE INDEX <index_name> COMPUTE STATISTICS;

ANALYZE INDEX <index_name> ESTIMATE STATISTICS SAMPLE 50 PERCENT;

Delete the statistics associated with a table:

ANALYZE TABLE <table_name> DELETE STATISTICS;

Delete statistics on one index:

ANALYZE INDEX <index_name> DELETE STATISTICS;

The following factors can influence query response time:

• Collection of table statistics

• Memory allocation

• Sorting

• Presence of large object (LOB) columns in your base table

• Partitioning

• Parallelism

• The number term expansions in your query

  See Also:

  "Frequently Asked Questions About Query Performance"

When you need the first rows of an ORDER BY query, Oracle recommends that you use the cost-based FIRST_ROWS(n) hint.

You use the FIRST_ROWS(n) hint in cases where you want the first n number of rows in the shortest possible time. For example, consider the following PL/SQL block that uses a cursor to retrieve the first 10 hits of a query and the FIRST_ROWS(n) hint to optimize the response time:

declare
cursor c is 

select /*+ FIRST_ROWS(10) */ article_id from articles_tab
   where contains(article, 'Omophagia')>0 order by pub_date desc;

begin

for i in c
loop
insert into t_s values(i.pk, i.col);
exit when c%rowcount > 11;
end loop;

end;
/

The c cursor is a SELECT statement that returns the rowids that contain the word omophagia in sorted order. The code loops through the cursor to extract the first 10 rows. These rows are stored in the temporary t_s table.

With the FIRST_ROWS(n) hint, the optimizer instructs the Oracle Text index to return rowids in score-sorted order when the cost of returning the top-N hits is lower.

Without the hint, Oracle Database sorts the rowids after the Oracle Text index returns all rows in unsorted order that satisfy the CONTAINS predicate. Retrieving the entire result set takes time.

Because only the first 10 hits are needed in this query, using the hint results in better performance.

You can also optimize for response time by using the related DOMAIN_INDEX_SORT hint. Like FIRST_ROWS(n), when queries are optimized for response time, Oracle Text returns the first rows in the shortest time possible.

For example, you can use this hint:

select /*+ DOMAIN_INDEX_SORT */ pk, score(1), col from ctx_tab 
            where contains(txt_col, 'test', 1) > 0 order by score(1) desc;

However, this hint is only rule-based. This means that Oracle Text always chooses the index which satisfies the ORDER BY clause. This hint might result in suboptimal performance for queries where the CONTAINS clause is very selective. In these cases, Oracle recommends that you use the FIRST_ROWS(n) hint, which is fully cost-based.

Partitioning your data and creating local partitioned indexes can improve your query performance. On a partitioned table, each partition has its own set of index tables. Effectively, there are multiple indexes, but the results are combined as necessary to produce the final result set.

Create the CONTEXT index with the LOCAL keyword:

CREATE INDEX index_name ON table_name (column_name) 
INDEXTYPE IS ctxsys.context
PARAMETERS ('...')
LOCAL

With partitioned tables and indexes, you can improve performance of the following types of queries:

• Range Search on Partition Key Column: This query restricts the search to a particular range of values on a column that is also the partition key. For example, consider a query on a date range:

  SELECT storyid FROM storytab WHERE CONTAINS(story, 'oliver')>0 and pub_date BETWEEN '1-OCT-93' AND '1-NOV-93';

  If the date range is quite restrictive, it is very likely that the query can be satisfied by only looking in a single partition.

• ORDER BY Partition Key Column: This query requires only the first n hits, and the ORDER BY clause names the partition key. Consider an ORDER BY query on a price column to fetch the first 20 hits:

  SELECT * FROM (

  SELECT itemid FROM item_tab WHERE CONTAINS(item_desc, 'cd player')
    >0 ORDER BY price)
    WHERE ROWNUM < 20;

  In this example, with the table partitioned by price, the query might only need to get hits from the first partition to satisfy the query.

The DOMAIN_INDEX_SORT hint on a local partitioned index might result in poor performance, especially when you order by score. All hits to the query across all partitions must be obtained before the results can be sorted.

Instead, use an inline view when you use the DOMAIN_INDEX_SORT hint. Specifically, use the DOMAIN_INDEX_SORT hint to improve query performance on a local partitioned index under the following conditions:

• The Oracle Text query itself, including the order by SCORE() clause, is expressed as an in-line view.

• The Oracle Text query inside the in-line view contains the DOMAIN_INDEX_SORT hint.

• The query on the in-line view has a ROWNUM predicate that limits the number of rows to fetch from the view.

For example, the following Oracle Text query and local Oracle Text index are created on a partitioned doc_tab table:

     select doc_id, score(1) from doc_tab 
        where contains(doc, 'oracle', 1)>0 
        order by score(1) desc;

If you are interested in fetching only the top 20 rows, you can rewrite the query as follows:

     select * from 
          (select /*+ DOMAIN_INDEX_SORT */ doc_id, score(1) from doc_tab 
              where contains(doc, 'oracle', 1)>0 order by score(1) desc) 
      where rownum < 21;

Oracle Text provides a cache layer called the query filter cache that you can use to cache the query results. The query filter cache is sharable across queries. Multiple queries can reuse cached query results to improve the query response time.

Use the ctxfiltercache operator to specify which query results to cache. The following example uses the operator to store the results of the common_predicate query in the cache:

select * from docs where contains(txt, 'ctxfiltercache((common_predicate), FALSE)')>0;

In this example, the cached results of the common_predicate query are reused by the new_query query, to improve the query response time.

select * from docs where contains(txt, 'new_query & ctxfiltercache((common_predicate), FALSE)')>0; 

Oracle Text provides the BIG_IO option for improving the query performance for the CONTEXT indexes that extensively use IO operations. The query performance improvement is mainly for data stored on rotating disks, not for data stored on solid state disks.

When you enable the BIG_IO option, a CONTEXT index creates token type pairs with one large object (LOB) data type for each unique token text. Tokens with the same text but different token types correspond to different rows in the $I table.

The indexes with the BIG_IO option enabled should have the token LOBs created as SecureFile LOBs, so that the data is stored sequentially in multiple blocks. This method improves the response time of the queries, because the queries can now perform longer sequential reads instead of many short reads.

To create a CONTEXT index with the BIG_IO index option, first create a basic storage preference by setting the value of its BIG_IO storage attribute to YES, and then specify this storage preference while creating the CONTEXT index.

The following example creates a basic mystore storage preference and sets the value of its BIG_IO storage attribute to YES:

exec ctx_ddl.create_preference('mystore', 'BASIC_STORAGE');
exec ctx_ddl.set_attribute('mystore', 'BIG_IO', 'YES');

To disable the BIG_IO option, update the existing storage preference (mystore) by setting the value of its BIG_IO storage attribute to NO, and then rebuild the index.

exec ctx_ddl.set_attribute('mystore', 'BIG_IO', 'NO');
alter index idx rebuild('replace storage mystore');

To enable the BIG_IO option for a partitioned index without rebuilding the index, modify the basic storage preference by setting the value of its BIG_IO storage attribute to YES, replace the global index metadata using ctx_ddl.replace_index_metadata, and then call optimize_index in REBUILD mode for each partition of the partitioned index table.

The following example enables the BIG_IO option for the idx partitioned index:

exec ctx_ddl.set_attribute('mystore', 'BIG_IO', 'YES');
exec ctx_ddl.replace_index_metadata('idx', 'replace metadata storage mystore');
exec ctx_ddl.optimize_index('idx', 'rebuild', part_name=>'part1');

Oracle Text provides the SEPARATE_OFFSETS option to improve the query performance for the CONTEXT indexes that use IO operations, and whose queries are mainly single-word or Boolean queries.

The SEPARATE_OFFSETS option creates a different postings list structure for the tokens of type TEXT. Instead of interspersing docids, frequencies, info-length (length of the offsets information), and the offsets in the postings list, the SEPARATE_OFFSETS option stores all docids and frequencies at the beginning of the postings list, and all info-lengths and offsets at the end of the postings list. The header at the beginning of the posting contains the information about the boundary points between docids and offsets. Because separation of docids and offsets reduces the time for the queries to read the data, it improves the query response time.

Performance of the SEPARATE_OFFSETS option is best realized when you use it in conjunction with the BIG_IO option and for tokens with a very long posting.

To create a CONTEXT index with the SEPARATE_OFFSETS option, first create a basic storage preference by setting the value of its SEPARATE_OFFSETS storage attribute to T. Next, specify this storage preference when you create the CONTEXT index.

The following example creates a basic mystore storage preference and sets the value of its SEPARATE_OFFSETS storage attribute to T:

exec ctx_ddl.create_preference('mystore', 'BASIC_STORAGE');
exec ctx_ddl.set_attribute('mystore', 'SEPARATE_OFFSETS', 'T');

To disable the SEPARATE_OFFSETS option, update the existing storage preference (mystore) by setting the value of its SEPARATE_OFFSETS storage attribute to F, and then rebuild the index.

exec ctx_ddl.set_attribute('mystore', 'SEPARATE_OFFSETS', 'F');
alter index idx rebuild('replace storage mystore');

To enable the SEPARATE_OFFSETS option for a partitioned index without rebuilding the index, modify the basic storage preference by setting the value of its SEPARATE_OFFSETS storage attribute to T, replace the global index metadata by using ctx_ddl.replace_index_metadata, and then call optimize_index in REBUILD mode for each partition in the partitioned index table.

The following example enables the SEPARATE_OFFSETS option for the partitioned idx index:

exec ctx_ddl.set_attribute('mystore', 'SEPARATE_OFFSETS', 'T');
exec ctx_ddl.replace_index_metadata('idx', 'replace storage mystore');
exec ctx_ddl.optimize_index('idx', 'rebuild', part_name=>'part1');

Oracle Text provides the STAGE_ITAB option for improving the query performance for the CONTEXT indexes that extensively use insert, update, and delete operations for near real-time indexing.

If you do not use the STAGE_ITAB index option, then when you add a new document to the CONTEXT index, SYNC_INDEX is called to make the documents searchable. This call creates new rows in the $I table, and increases the fragmentation in the $I table. The result is deterioration of the query performance.

When you enable the STAGE_ITAB index option, the following happens:

• Information about the new documents is stored in the $G staging table, not in the $I table. This storage ensures that the $I table is not fragmented and does not deteriorate the query performance.

• The $H b-tree index is created on the $G table. The $G table and $H b-tree index are equivalent to the $I table and $X b-tree index.

Use the MERGE optimization mode to optimize the rows in the $G table and move them to the $I table.

To create a CONTEXT index with the STAGE_ITAB index option, first create a basic storage preference by setting the value of its STAGE_ITAB storage attribute to YES. Next, specify this storage preference when you create the CONTEXT index.

The following example creates a basic mystore storage preference and sets the value of its STAGE_ITAB storage attribute to YES:

exec ctx_ddl.create_preference('mystore', 'BASIC_STORAGE');
exec ctx_ddl.set_attribute('mystore', 'STAGE_ITAB', 'YES');

You can also enable the STAGE_ITAB index option for an existing nonpartitioned CONTEXT index by using the rebuild option of the ALTER INDEX statement.

alter index IDX rebuild parameters('replace storage mystore');

To disable the STAGE_ITAB option for a nonpartitioned CONTEXT index, update the existing storage preference (mystore) by setting the value of its STAGE_ITAB storage attribute to NO, and then rebuild the index.

exec ctx_ddl.set_attribute('mystore', 'STAGE_ITAB', 'NO');
alter index idx rebuild('replace storage mystore');

This operation runs the optimization process by using the MERGE optimization mode and then drops the $G table.

The rebuild option of the ALTER INDEX statement does not work with the partitioned CONTEXT index for enabling and disabling the STAGE_ITAB option.

The following example enables the STAGE_ITAB option for the partitioned CONTEXT idx index:

alter index idx parameters('add stage_itab');

The following example disables the STAGE_ITAB option for the partitioned CONTEXT idx index:

alter index idx parameters('remove stage_itab');

Starting with Oracle Database 12c Release 2 (12.2), the contents of $G are automatically moved to $I during index synchronization when $G has more than 1 million rows. This value is controlled by the STAGE_ITAB_MAX_ROWS attribute of the STORAGE preference.

The query performance is deteriorated when $G table is too fragmented. To avoid deterioration, starting with Oracle Database Release 18c, Oracle Text provides automatic background optimize merge for every index or partition. To enable automatic background optimize merge, you must set the STAGE_ITAB and STAGE_ITAB_AUTO_OPT storage preference attributes to TRUE, and you must create the index with a storage preference.

By default, if you had enabled STAGE_ITAB in indexes before you upgraded to Oracle Database Release 18c, then STAGE_ITAB_AUTO_OPT is not enabled. If STAGE_ITAB and AUTO_OPTIMIZE are enabled in existing indexes, then you must disable AUTO_OPTIMIZE before you enable STAGE_ITAB_AUTO_OPT.

The following example creates a basic mystore storage preference and sets the value of its STAGE_ITAB_AUTO_OPT storage attribute to TRUE:

exec ctx_ddl.create_preference('mystore', 'basic_storage');
exec ctx_ddl.set_attribute('mystore', 'stage_itab', 'TRUE');
exec ctx_ddl.set_attribute('mystore', 'stage_itab_auto_opt', 'TRUE');
exec ctx_ddl.set_attribute(‘mystore’, 'stage_itab_parallel', 16);

Parallel query refers to the parallelized processing of a local CONTEXT index. Based on the parallel degree of the index and various system attributes, Oracle determines the number of parallel query slaves to be spawned to process the index. Each parallel query slave processes one or more index partitions. This default query behavior applies to local indexes that are created in parallel.

However, for heavily loaded systems with a high number of concurrent users, query throughput is generally worse with parallel query; if the query is run serially, the top-N hits can usually be satisfied by the first few partitions. For example, take the typical top-N text queries with an ORDER BY partition key column:

select * from (
        select story_id from stories_tab where contains(...)>0 order by 
publication_date desc)
    where rownum <= 10;

These text queries generally perform worse with a parallel query.

You can disable parallel querying after a parallel index operation with an ALTER INDEX statement:

Alter index <text index name> NOPARALLEL;
Alter index <text index name> PARALLEL 1;

You can also enable or increase the parallel degree:

Alter index <text index name> parallel < parallel degree >;

Oracle Real Application Clusters (Oracle RAC) provides an excellent solution for improving query throughput. If you can get good performance from Oracle Text with a light query load, then you can expect to get excellent scalability from Oracle RAC as the query load increases.

You can achieve further improvements in Oracle Text performance by physically partitioning the text data and Oracle Text indexes (using local partitioned indexes) and ensuring that partitions are handled by separate Oracle RAC nodes. This way, you avoid duplication of the cache contents across multiple nodes and, therefore, maximize the benefit of Oracle RAC cache fusion.

In Oracle 10g Release 1, you must force each Oracle Text index partition into a separate database file when the index is created. This enables the "remastering" feature in Oracle RAC to force database file affinity, where each node concentrates on a particular database file and, therefore, a particular Oracle Text index partition.

In Oracle 10g Release 2 and later, Oracle supports database object-level affinity, which makes it much easier to allocate index objects ($I and $R tables) to particular nodes.

Although Oracle RAC offers solutions for improving query throughput and performance, it does not necessarily enable you to continue to get the same performance improvements as you scale up the data volumes. You are more likely to see improvements by increasing the amount of memory available to the system global area (SGA) cache or by partitioning your data so that queries do not have to hit all table partitions in order to provide the required set of query results.

Answer: There are two measures of query performance:

• Response time: The time to get an answer to an individual query

• Throughput: The number of queries that can be run in any given time period; for example, queries each second

These two measures are related, but they are not the same. In a heavily loaded system, you want maximum throughput, whereas in a relatively lightly loaded system, you probably want minimum response time. Also, some applications require a query to deliver all hits to the user, whereas others only require the first 20 hits from an ordered set. It is important to distinguish between these two scenarios.

Answer: The fastest type of query meets the following conditions:

• Single CONTAINS clause

• No other conditions in the WHERE clause

• No ORDER BY clause

• Returns only the first page of results (for example, the first 10 or 20 hits)

Answer: Yes. Collecting statistics on your tables enables Oracle Text to do cost-based analysis. This helps Oracle Text choose the most efficient execution plan for your queries.

If your queries are always pure text queries (no structured predicate and no joins), you should delete statistics on your Oracle Text index.

Answer: The speed at which the Oracle Text index can deliver rowids is not affected by the actual size of the data. Oracle Text query speed is related to the number of rows that must be fetched from the index table, the number of hits requested, the number of hits produced by the query, and the presence or absence of sorting.

Answer: The format of the documents (plain ASCII text, HTML, or Microsoft Word) should make no difference to query speed. The documents are filtered to plain text at indexing time, not query time.

The cleanliness of the data makes a difference. Spell-checked and subedited text for publication tends to have a much smaller total vocabulary (and therefore size of the index table) than informal text such as email, which contains spelling errors and abbreviations. For a given index memory setting, the extra text takes up memory, creates more fragmented rows, and adversely affects query response time.

Answer: The kernel can query the Oracle Text index with an indexed lookup and a functional lookup. In the indexed lookup, the first and most common case, the kernel asks the Oracle Text index for all rowids that satisfy a particular text search. These rowids are returned in batches.

In the functional lookup, the kernel passes individual rowids to the Oracle Text index and asks whether that particular rowid satisfies a certain text criterion. The functional lookup is most commonly used with a very selective structured clause, so that only a few rowids must be checked against the Oracle Text index. Here is an example of a search where a functional lookup is useful:

SELECT ID, SCORE(1), TEXT FROM MYTABLE

WHERE START_DATE = '21 Oct 1992'         <- highly selective
AND CONTAINS (TEXT, 'commonword') > 0    <- unselective

Functional invocation is also used for an Oracle Text query that is ordered by a structured column (for example date, price) and if the Oracle Text query contains unselective words.

Answer: All queries look at the index token table. The table’s name has the form of DR$indexname$I and contains the list of tokens (TOKEN_TEXT column) and the information about the row and word positions where the token occurs (TOKEN_INFO column).

The row information is stored as internal docid values that must be translated into external rowid values. The table that you use depends on the type of lookup:

• For functional lookups, use the $K table, DR$indexname$K. This simple Index Organized Table (IOT) contains a row for each docid/rowid pair.

• For indexed lookups, use the $R table, DR$indexname$R. This table holds the complete list of rowids in a BLOB column.

Starting with Oracle Database 12c Release 2 (12.2), a new storage attribute, SMALL_R_ROW, was introduced to reduce the size of the $R row. It populates $R rows on demand instead of creating 22 static rows, thereby reducing the Data Manipulation Language contention. The contention happens when parallel insert, update, and delete operations try to lock the same $R row.

You can easily find out whether a functional or indexed lookup is being used by examining a SQL trace and looking for the $K or $R tables.

Answer: Yes, it certainly does.

If Oracle Text does not sort, then it can return results as it finds them. This approach is quicker when the application needs to display only a page of results at a time.

Answer: Sorting by relevance (SCORE(n)) can be fast if you use the FIRST_ROWS(n) hint. In this case, Oracle Text performs a high-speed internal sort when fetching from the Oracle Text index tables.

Here is an example of this query:

              SELECT /*+ FIRST_ROWS(10) */ ID, SCORE(1), TEXT FROM mytable
                WHERE CONTAINS (TEXT, 'searchterm', 1) > 0
                ORDER BY SCORE(1) DESC;

It is important to note that, there must be no other criteria in the WHERE clause, other than a single CONTAINS.

Answer: For querying, you want to strive for a large system global area (SGA). You can set these SGA parameters in your Oracle Database initialization file. You can also set these parameters dynamically.

The SORT_AREA_SIZE parameter controls the memory that is available for sorting ORDER BY queries. You should increase the size of this parameter if you frequently order by structured columns.

Answer: Yes. Typically, a SELECT statement selects more than one column from your base table. Because Oracle Text fetches columns to memory, it is more efficient to store wide base table columns such as large objects (LOBs) out of line, especially when these columns are rarely updated but frequently selected.

When LOBs are stored out of line, only the LOB locators need to be fetched to memory during querying. Out-of-line storage reduces the effective size of the base table. It makes it easier for Oracle Text to cache the entire table to memory, and so reduces the cost of selecting columns from the base table, and speeds up text queries.

In addition, smaller base tables cached in memory enables more index table data to be cached during querying, which improves performance.

Answer: The fastest type of query is one where there is only a single CONTAINS clause and no other conditions in the WHERE clause.

Consider the following multiple CONTAINS query:

              SELECT title, isbn FROM booklist
                WHERE CONTAINS (title, 'horse') > 0
                  AND CONTAINS (abstract, 'racing') > 0

You can get the same result with section searching and the WITHIN operator:

              SELECT title, isbn FROM booklist
                WHERE CONTAINS (alltext, 
                  'horse WITHIN title AND racing WITHIN abstract')>0

This query is completed more quickly than the single CONTAINS clause. To use a query like this, you must copy all data into a single text column for indexing, with section tags around each column's data. You can do that with PL/SQL procedures before indexing, or you can use the USER_DATASTORE datastore during indexing to synthesize structured columns with the text column into one document.

Answer: Each distinct word used in a query requires at least one row to be fetched from the index table. It is therefore best to keep the number of expansions down as much as possible.

You should not use expansions such as wild cards, thesaurus, stemming, and fuzzy matching unless they are necessary to the task. In general, a few expansions (for example, 10 to 20) does not cause difficulty, but avoid a large number of expansions (80 or 100) in a query. Use the query feedback mechanism to determine the number of expansions for any particular query expression.

For wildcard and stem queries, you can avoid term expansion from query time to index time by creating prefix, substring, or stem indexes. Query performance increases at the cost of longer indexing time and added disk space.

Prefix and substring indexes can improve wildcard performance. You enable prefix and substring indexing with the BASIC_WORDLIST preference. The following example sets the wordlist preference for prefix and substring indexing. For prefix indexing, it specifies that Oracle Text creates token prefixes between 3 and 4 characters long:

begin 

ctx_ddl.create_preference('mywordlist', 'BASIC_WORDLIST'); 
ctx_ddl.set_attribute('mywordlist','PREFIX_INDEX','TRUE');
ctx_ddl.set_attribute('mywordlist','PREFIX_MIN_LENGTH', '3');
ctx_ddl.set_attribute('mywordlist','PREFIX_MAX_LENGTH', '4');
ctx_ddl.set_attribute('mywordlist','SUBSTRING_INDEX', 'YES');

end

Enable stem indexing with the BASIC_LEXER preference:

begin

ctx_ddl.create_preference('mylex', 'BASIC_LEXER');
ctx_ddl.set_attribute ( 'mylex', 'index_stems', 'ENGLISH');

end;

Answer: You can create local partitioned CONTEXT indexes on partitioned tables. This means that, on a partitioned table, each partition has its own set of index tables. Effectively, the results from the multiple indexes are combined as necessary to produce the final result set.

Use the LOCAL keyword to create the index:

CREATE INDEX index_name ON table_name (column_name) 
INDEXTYPE IS ctxsys.context
PARAMETERS ('...')
LOCAL

With partitioned tables and local indexes, you can improve performance of the following types of CONTAINS queries:

• Range Search on Partition Key Column: This query restricts the search to a particular range of values on a column that is also the partition key.

• ORDER BY Partition Key Column: This query requires only the first n hits, and the ORDER BY clause names the partition key.

  See Also:

  "Improved Response Time using Local Partitioned CONTEXT Index"

Answer: It depends on system load and server capacity. Even though parallel querying is the default behavior for indexes created in parallel, it usually degrades the overall query throughput on heavily loaded systems.

Parallel queries are optimal for Decision Support System (DSS) and Online Analysis Processing (OLAP). They are also optimal for analytical systems that have large data collections, multiple CPUs with a low number of concurrent users, or Oracle Real Application Clusters (Oracle RAC) nodes.

Answer: Indexing theme information with a CONTEXT index takes longer and also increases the size of your index. However, theme indexes enable ABOUT queries to be more precise by using the knowledge base. If your application uses many ABOUT queries, it might be worthwhile to create a theme component to the index, despite the extra indexing time and extra storage space required.

Answer: CTXCAT indexes work best when the text is in small chunks (just a few lines), and you want searches to restrict or sort the result set according to certain structured criteria, such as numbers or dates.

For example, consider an online auction site. Each item for sale has a short description, a current bid price, and start and end dates for the auction. A user might want to see all records with antique cabinet in the description, with a current bid price less than $500. Because he is particularly interested in newly posted items, he wants the results sorted by auction start time.

This search is not always efficient with a CONTAINS structured query on a CONTEXT index. The response time can vary significantly depending on the structured and CONTAINS clauses, because the intersection of structured and CONTAINS clauses or the Oracle Text query ordering is computed during query time.

By including structured information within the CTXCAT index, you ensure that the query response time is always in an optimal range regardless of search criteria. Because the interaction between text and structured query is precomputed during indexing, query response time is optimum.

Answer: There are differences in the time and space needed to create the index. CTXCAT indexes take a bit longer to create, and they use considerably more disk space than CONTEXT indexes. If you are tight on disk space, consider carefully whether CTXCAT indexes are appropriate for you.

With query operators, you can use the richer CONTEXT grammar in CATSEARCH queries with query templates. The older restriction of a single CATSEARCH query grammar no longer holds.

Answer: To drive the query with a text or b-tree index, you can use the INDEX(table column) optimizer hint in the usual way.

You can also use the NO_INDEX(table column) hint to disable a specific index.

The FIRST_ROWS(n) hint has a special meaning for text queries. Use it when you need the first n hits to a query. When you use the DOMAIN_INDEX_SORT hint in conjunction with ORDER BY SCORE(n) DESC, you tell the Oracle optimizer to accept a sorted set from the Oracle Text index and to sort no farther.

Answer: Indexing text is a resource-intensive process. The speed of indexing depends on the power of your hardware. Indexing speed depends on CPU and I/O capacity. With sufficient I/O capacity to read in the original data and write out index entries, the CPU is the limiting factor.

Tests with Intel x86 (Core 2 architecture, 2.5GHz) CPUs have shown that Oracle Text can index around 100 GB of text per CPU core, per day. This speed would be expected to increase as CPU clock speeds increase and CPU architectures become more efficient.

Other factors, such as your document format, location of your data, and the calls to user-defined datastores, filters, and lexers, can affect your indexing speed.

Answer: You can set your index memory with the DEFAULT_INDEX_MEMORY and MAX_INDEX_MEMORY system parameters. You can also set your index memory at runtime with the CREATE INDEX memory parameter in the parameter string.

You should aim to set the DEFAULT_INDEX_MEMORY value as high as possible, without causing paging.

You can also improve indexing performance by increasing the SORT_AREA_SIZE system parameter.

Oracle recommends that you use a large index memory setting. Large settings, even up to hundreds of megabytes, can improve the speed of indexing and reduce fragmentation of the final indexes. However, if you set the index memory setting too high, then memory paging reduces indexing speed.

With parallel indexing, each stream requires its own index memory. When dealing with very large tables, you can tune your database system global area (SGA) differently for indexing and retrieval. For querying, you want to get as much information cached in the SGA block buffer cache as possible. So you should allocate a large amount of memory to the block buffer cache. Because this approach does not make any difference to indexing, you would be better off reducing the size of the SGA to make more room for large index memory settings during indexing.

You set the size of SGA in your Oracle Database initialization file.

Answer: The overhead, the amount of space needed for the index tables, varies between about 50 and 200 percent of the original text volume. Generally, larger amounts of text result in smaller overhead, but many small records use more overhead than fewer large records. Also, clean data (such as published text) requires less overhead than dirty data such as emails or discussion notes, because the dirty data is likely to include many misspelled and abbreviated words.

A text-only index is smaller than a combined text and theme index. A prefix and substring index makes the index significantly larger.

Answer: You can expect much lower storage overhead for formatted documents such as Microsoft Word files because the documents tend to be very large compared to the actual text held in them. So 1 GB of Word documents might only require 50 MB of index space, whereas 1 GB of plain text might require 500 MB, because there is ten times as much plain text in the latter set.

Indexing time is less clear-cut. Although the reduction in the amount of text to be indexed has an obvious effect, you must balance this against the cost of filtering the documents with the AUTO_FILTER filter or other user-defined filters.

Answer: Parallel indexing can improve index performance when you have a large amount of data and multiple CPUs.

Use the PARALLEL keyword to create an index with up to three separate indexing processes, depending on your resources.

CREATE INDEX index_name ON table_name (column_name) 
INDEXTYPE IS ctxsys.context PARAMETERS ('...') PARALLEL 3;

You can also use parallel indexing to create local partitioned indexes on partitioned tables. However, indexing performance improves only with multiple CPUs.

Answer: When you have multiple CPUs, you can improve indexing performance by creating a local index in parallel.

You can create a local partitioned index in parallel in the following ways:

• Use the PARALLEL clause with the LOCAL clause in the CREATE INDEX statement. In this case, the maximum parallel degree is limited to the number of partitions.

• Create an unusable index, and then run the DBMS_PCLXUTIL.BUILD_PART_INDEX utility. This method can result in a higher degree of parallelism, especially if you have more CPUs than partitions.

The following is an example of the second method. The base table has three partitions. You create a local partitioned unusable index first, and then run the DBMS_PCLUTIL.BUILD_PART_INDEX, to build the three partitions in parallel (inter-partition parallelism). Inside each partition, index creation occurs in parallel (intra-partition parallelism) with a parallel degree of 2.

create index tdrbip02bx on tdrbip02b(text) 
indextype is ctxsys.context local (partition tdrbip02bx1, 
                                   partition tdrbip02bx2, 
                                   partition tdrbip02bx3) 
unusable; 

exec dbms_pclxutil.build_part_index(3,2,'TDRBIP02B','TDRBIP02BX',TRUE); 

Answer: You can use the CTX_OUTPUT.START_LOG procedure to log output from the indexing process. The filename is normally written to $ORACLE_HOME/ctx/log, but you can change the directory by using the LOG_DIRECTORY parameter in CTX_ADM.SET_PARAMETER.

Answer: If you run reindexing with CTX_DDL.SYNC_INDEX less often, your indexes will be less fragmented, and you will not have to optimize them as often.

However, your data becomes progressively more out-of-date, and that may be unacceptable to your users.

Overnight indexing is acceptable for many systems. In this case, data that is less than a day old is not searchable. Other systems use hourly, 10-minute, or 5-minute updates.

Answer: The best way is to time some queries, run index optimization, and then time the same queries (restarting the database to clear the SGA each time, of course). If the queries speed up significantly, then optimization was worthwhile. If they do not, then you can wait longer next time.

You can also use CTX_REPORT.INDEX_STATS to analyze index fragmentation.

Answer: Yes, the same way as for normal indexing. There are often far fewer records to be indexed during a synchronize operation, so it is not usually necessary to provide hundreds of megabytes of indexing memory.