7 Tuning Oracle Text

7.1.1 Other Factors that Influence Query Response Time

There are other factors that can influence query response time such as:

• Collection of table statistics

• Memory allocation

• Sorting

• Presence of LOB columns in your base table

• Partitioning

• Parallelism

• The number term expansions in your query

  See Also:

  "Frequently Asked Questions About Query Performance"

7.1.2 Improved Response Time with FIRST_ROWS(n) Hint for ORDER BY Queries

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 number (n) 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 uses 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 cursor c 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 table t_s.

With the FIRST_ROWS(n) hint, the optimizer instructs the 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 Text index has returned all the rows in unsorted order that satisfy the CONTAINS predicate. Retrieving the entire result set this way takes time.

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

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

7.1.3 Improved Response Time using Local Partitioned CONTEXT Index

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 from each are combined as necessary to produce the final result set.

You create the CONTEXT index using the LOCAL keyword as follows:

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

• ORDER BY Partition Key Column

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

SELECT * FROM (

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

7.1.4 Improved Response Time with Local Partitioned Index for Order by Score

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

You can work around this by using an inline view when you use the DOMAIN_INDEX_SORT hint. Specifically, you can use the DOMAIN_INDEX_SORT hint to improve query performance on a local partitioned index under the following conditions:

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

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

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

For example, if you have the following text query and local text index created on a partitioned table doc_tab:

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

and you are only interested in fetching top 20 rows, you can rewrite the query to

     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;

7.2.1 CHOOSE and ALL ROWS Modes

By default, queries are optimized for throughput under the CHOOSE and ALL_ROWS modes. When queries are optimized for throughput, Oracle Text returns all rows in the shortest time possible.

7.2.2 FIRST_ROWS(n) Mode

In FIRST_ROWS(n) mode, the optimizer in Oracle Database optimizes for fast response time by having the Text domain index return score-sorted rows, if possible. This is the default behavior when you use the FIRST_ROWS(n) hint.

If you want to optimize for better throughput under FIRST_ROWS(n), you can use the DOMAIN_INDEX_NO_SORT hint. Better throughput means you are interested in getting all the rows to a query in the shortest time.

The following example achieves better throughput by not using the Text domain index to return score-sorted rows. Instead, Oracle Text sorts the rows after all the rows that satisfy the CONTAINS predicate are retrieved from the index:

select /*+ FIRST_ROWS(10) DOMAIN_INDEX_NO_SORT */ pk, score(1), col from ctx_tab 
            where contains(txt_col, 'test', 1) > 0 order by score(1) desc;

7.3.1 Performance Tuning with CDI

Support for mapping a FILTER BY column to MDATA enables query performance to be optimized for equality searches by restricting supported functionality of RANGE and LIKE. However, mapping a FILTER BY column to MDATA is not recommended if the FILTER BY column contains sequential values, or has very high cardinality. Doing so can result in a very long and narrow $I table and reduced $X performance. One example of such a sequential column might be one that uses DATE stamp. For such sequential columns, mapping to SDATA is recommended.

The following hints can be used to push or not push the SORT and FILTER BY predicates into the CDI:

• DOMAIN_INDEX_SORT. The query optimizer will try to push the applicable sorting criteria into the specified composite domain index.

• DOMAIN_INDEX_NO_SORT. The query optimizer will try NOT to push sorting criteria into the specified composite domain index.

• DOMAIN_INDEX_FILTER(table name index name). The query optimizer will try to push the applicable FILTER BY predicate(s) into the specified composite domain index.

• DOMAIN_INDEX_NO_FILTER(table name index name). The query optimizer will not try to push the applicable FILTER BY predicate(s) into the specified composite domain index.

SELECT bookid, pub_date, source FROM
  (SELECT /*+ domain_index_sort domain_index_filter(books books_ctxcdi) */ bookid, pub_date, source
      FROM books
      WHERE CONTAINS(text, 'aaa',1)>0 AND bookid >= 80
      ORDER BY PUB_DATE desc nulls last, SOURCE asc  nulls last, score(1) desc)
 WHERE rownum < 20;

7.5.1 Parallel Queries on a Local Context Index

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 is the default query behavior for local indexes created in parallel.

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

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

will generally perform worse with a parallel query.

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

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

You can also enable or increase the parallel degree by specifying:

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

7.5.2 Parallelizing Queries Across Oracle RAC Nodes

When considering whether to use Oracle Real Application Clusters (Oracle RAC) to improve Oracle Text performance, it is essential to understand which performance issues that you are trying to solve. Oracle RAC is a great 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.

Further improvements in Oracle Text performance in an Oracle RAC environment may be achieved by physically partitioning the text data and 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, each Oracle Text index partition must be forced into a separate database file when the index is created. This enables the use of the "re-mastering" feature in Oracle RAC to force database file affinity, in which each node concentrates on a particular database file and, therefore, a particular Oracle Text index partition.

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

While Oracle RAC offers solutions for improving query throughput and performance, is not a "magic bullet," and it will 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 amounts of memory available to the SGA cache, or by partitioning your data in such a way that queries will normally not need to hit all of the partitions of a table in order to provide the required set of query results.

7.7.1 What is Query Performance?

Answer: There are generally two measures of query performance:

• Response time, the time to get an answer to an individual query, and

• Throughput, the number of queries that can be run in any time period; for example, queries each second).

These two are related, but are not the same. In a heavily loaded system, you normally 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 its hits to the user, whereas others might only require the first 20 hits from an ordered set. It is important to distinguish between these two scenarios.

7.7.2 What is the fastest type of text query?

Answer: The fastest type of query will meet the following conditions:

• Single CONTAINS clause

• No other conditions in the WHERE clause

• No ORDER BY clause at all

• Only the first page of results is returned (for example, the first 10 or 20 hits).

7.7.3 Should I collect statistics on my tables?

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.

7.7.4 How does the size of my data affect queries?

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

7.7.5 How does the format of my data affect queries?

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 will make a difference. Spell-checked and sub-edited text for publication tends to have a much smaller total vocabulary (and therefore size of the index table) than informal text such as e-mails, which will contain many spelling errors and abbreviations. For a given index memory setting, the extra text takes up more memory, which can lead to more fragmented rows than in the cleaner text, which can adversely affect query response time.

7.7.6 What is a functional versus an indexed lookup?

Answer: There are two ways the kernel can query the text index. In the first and most common case, the kernel asks the text index for all the rowids that satisfy a particular text search. These rowids are returned in batches. In the second, the kernel passes individual rowids to the text index, and asks whether that particular rowid satisfies a certain text criterion.

The second is known as a functional lookup, and is most commonly done where there is a very selective structured clause, so that only a few rowids must be checked against the text index. An example of a search where a functional lookup may be used:

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 text query ordered by structured column (for example date, price) and text query is unselective.

7.7.7 What tables are involved in queries?

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

The row information is stored as internal DOCID values. These must be translated into external ROWID values. The table used for this depends on the type of lookup: For functional lookups, the $K table, DR$indexname$K, is used. This is a simple Index Organized Table (IOT) which contains a row for each DOCID/ROWID pair.

For indexed lookups, the $R table, DR$indexname$R, is used. This holds the complete list of ROWIDs in a BLOB column.

Hence we 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.

7.7.8 Does sorting the results slow a text-only query?

Answer: Yes, it certainly does.

If there is no sorting, then Oracle Text can return results as it finds them, which is quicker in the common case where the application needs to display only a page of results at a time.

7.7.9 How do I make an ORDER BY score query faster?

Answer: Sorting by relevance (SCORE(n)) can be extremely quick if the FIRST_ROWS(n) hint is used. In this case, Oracle Text performs a high speed internal sort when fetching from the text index tables.

An example of such a query:

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

Note that for this to work efficiently, there must be no other criteria in the WHERE clause other than a single CONTAINS.

7.7.10 Which Memory Settings Affect Querying?

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

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

7.7.11 Does out of line LOB storage of wide base table columns improve performance?

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 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 making it easier for Oracle Text to cache the entire table to memory. This reduces the cost of selecting columns from the base table, and hence speeds up text queries.

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

7.7.12 How can I make a CONTAINS query on more than one column faster?

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

We can obtain the same result with section searching and the WITHIN operator as follows:

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

This query completes more quickly. To use a query like this, we must copy all the data into a single text column for indexing, with section tags around each column's data. This can be done with PL/SQL procedures before indexing, or by making use of the USER_DATASTORE datastore during indexing to synthesize structured columns with the text column into one document.

7.7.13 Is it OK to have many expansions in a query?

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 having large numbers of explansions (80 or 100) in a query. The query feedback mechanism can be used to determine the number of expansions for any particular query expression.

In addition for wildcard and stem queries, you can remove the cost of 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 create 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

You 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;

7.7.14 How can local partition indexes help?

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, there are multiple indexes, but the results from each are combined as necessary to produce the final result set.

The index is created using the LOCAL keyword:

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 is a query that restricts the search to a particular range of values on a column that is also the partition key.

• ORDER BY Partition Key Column

  This is a query that 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"

7.7.15 Should I query in parallel?

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

In general, parallel queries are particularly appropriate for DSS or analytical systems with large data collections, multiple CPUs, and low number of concurrent users.

7.7.16 Should I index themes?

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 available. If your application uses ABOUT queries heavily, it might be worthwhile to create a theme component to the index, despite the extra indexing time and extra storage space required.

7.7.17 When should I use a CTXCAT index?

Answer: CTXCAT indexes work best when text is in small chunks, maybe a few lines maximum, and searches need to restrict or sort the result set according to certain structured criteria, usually numbers or dates.

For example, consider an on-line auction site. Each item for sale has a short description, a current bid price, and dates for the start and end of the auction. A user might want to see all the 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.

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

By including structured information such as price and date within the CTXCAT index, query response time is always in an optimal range regardless of search criteria. This is because the interaction between text and structured query is pre-computed during indexing. Consequently query response time is optimum.

7.7.18 When is a CTXCAT index NOT suitable?

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

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

7.7.19 What optimizer hints are available, and what do they do?

Answer: The optimizer hint INDEX(table column) can be used in the usual way to drive the query with a text or b-tree index.

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

Additionally, the FIRST_ROWS(n) hint has a special meaning for text queries and should be used when you need the first n hits to a query. Use of the DOMAIN_INDEX_SORT hint in conjunction with ORDER BY SCORE(n) DESC tells the Oracle optimizer to accept a sorted set from the text index, and not to do a further sort.

7.8.1 How long should indexing take?

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

Tests with Intel x86 (Core 2 architecture, 2.5GHz) CPUs have shown that Oracle Text can index around 100GB of text per CPU core, per day. This would be expected to increase as CPU clock speeds increase and/or 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 have an impact on your indexing speed.

7.8.2 Which index memory settings should I use?

Answer: You can set your index memory with the system parameters DEFAULT_INDEX_MEMORY and MAX_INDEX_MEMORY. You can also set your index memory at run time 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 the fragmentation of the final indexes. However, if you set the index memory setting too high, then memory paging can occur that will reduce 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 system global area's (SGA) block buffer cache as possible. So you should allocate a large amount of memory to the block buffer cache. But this will not make any difference to indexing, so you would be better off reducing the size of the SGA to make more room for a large index memory settings during indexing.

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

7.8.3 How much disk overhead will indexing require?

Answer: The overhead, the amount of space needed for the index tables, varies between about 50% of the original text volume and 200%. Generally, the larger the total amount of text, the smaller the overhead, but many small records will use more overhead than fewer large records. Also, clean data (such as published text) will require less overhead than dirty data such as e-mails or discussion notes, because the dirty data is likely to include many unique words from mis-spellings and abbreviations.

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

7.8.4 How does the format of my data affect indexing?

Answer: You can expect much lower storage overhead for formatted documents such as Microsoft Word files because such documents tend to be very large compared to the actual text held in them. So 1GB of Word documents might only require 50MB of index space, whereas 1GB of plain text might require 500MB, 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 will have an obvious effect, you must balance this out against the cost of filtering the documents with the AUTO_FILTER filter or other user-defined filters.

7.8.5 Can parallel indexing improve performance?

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

You use the PARALLEL keyword when creating the index:

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

This will create the index with up to three separate indexing processes depending on your resources.

Parallel indexing can also be used to create local partitioned indexes on partitioned tables. However, indexing performance only improves when you have multiple CPUs.

7.8.6 How can I improve index performance for creating local partitioned index?

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

You can create a local partitioned index in parallel in two ways:

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

• Create an unusable index first, 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 for the second method. In this example, the base table has three partitions. We create a local partitioned unusable index first, the run the DBMS_PCLUTIL.BUILD_PART_INDEX, which builds the 3 partitions in parallel (inter-partition parallelism). Also inside each partition, index creation is done 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); 

7.8.7 How can I tell how much indexing has completed?

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

7.9.1 How often should I index new or updated records?

Answer: The less often you run reindexing with CTX_DLL.SYNC_INDEX, the less fragmented your indexes will be, and the less you will need to optimize them.

However, this means that your data will become progressively more out of date, which may be unacceptable for 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, ten minute, or five minute updates.

7.9.2 How can I tell when my indexes are getting fragmented?

Answer: The best way is to time some queries, run index optimization, 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 don't, you can wait longer next time.

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

7.9.3 Does memory allocation affect index synchronization?

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.