Xbase++ developers often use ISAM (Indexed Sequential Access Method) for data management because of its efficiency and straightforward approach to handling database operations. Moving to PostgreSQL, a robust SQL database, offers enhanced capabilities but requires an understanding of new concepts, one of which is the use of remote filters. In the context of PostgreSQL's ISAM emulation, remote filters play a fundamental role in ISAM-based data query optimization and performance.

The basic premise of ISAM emulation in PostgreSQL is to treat SQL tables as navigational database files, similar to traditional Xbase++ operations. This allows developers to use their existing knowledge of navigational commands while benefiting from the advanced features of a SQL database. A key component of this setup is the distinction between local filters, which are applied after data retrieval, and remote filters, which are applied at the database server level.

Benefits of Using Remote Filters​

Remote filters are predicates (conditions) applied directly on the PostgreSQL server, allowing the server to filter records before sending them to the client. This method has several advantages:

1. Reduced network traffic: By filtering data at the server, only relevant records are transmitted over the network, reducing the amount of data transferred and potentially lowering network costs.

2. Improved performance: Applying filters on the server leverages the database's optimized query processing capabilities, resulting in faster retrieval times compared to fetching large data sets and filtering them on client-side.

3. Better resource utilization: Server-side filtering helps to better utilize database server resources (such as CPU and memory), freeing client resources for other operations.

4. Scalability: As data grows, maintaining performance becomes a challenge. Remote filters help scale applications by minimizing client-side processing and leveraging powerful server-side capabilities.

Performance Optimization through Proper Index Usage​

Optimizing query performance in a PostgreSQL-based Xbase++ application requires careful use of SQL indexes. SQL indexes are structures that allow PostgreSQL to quickly locate data without scanning every row in a table, a process that becomes increasingly inefficient as datasets grow.

SQL indexes should not be confused with ISAM indexes. Remember that an ISAM index is part of the data model, while a SQL index is unknown to your application and code. You can even create/destroy SQL indexes on the fly while your application is running.

Note that the Xbase++ ISAM emulation automatically creates special SQL indexes for each ISAM order based on its order columns or surrogate keys. This article strictly applies to columns that are a part of the filter or SQL WHERE expressions.

When to Use Indexes​

Indexes are particularly useful when:
- The data volume is large, and queries access only a fraction of the total dataset.
- Queries involve sorting or grouping operations on large datasets.
- Frequent use of specific columns in query conditions (filters or SQL WHERE clauses).

Creating Effective Indexes​

To create an index in PostgreSQL that enhances performance with ISAM emulation:
1. Identify Frequently Queried Columns: Analyze your application to determine which columns are most commonly used in queries/filters.
2. Use the `CREATE INDEX` Statement: Create an index using SQL commands. For example:
SQL: 
   CREATE INDEX idx_column ON table_name (column_name);
Replace `table_name` and `column_name` with the actual table and column names used in your application.

Special Considerations for LIKE Clauses​

In SQL, the `LIKE` operator is used for pattern matching. However, using `LIKE` can be very inefficient without proper indexing, especially with patterns that do not start with a wildcard. For optimizing `LIKE 'pattern%'` searches, consider using special index operators such as `bpchar_pattern_ops`:
- Creating a Special Index for LIKE Queries:
SQL: 
  CREATE INDEX idx_column_like ON table_name (column_name bpchar_pattern_ops);
This index type is optimized for pattern-matching queries that benefit from the specific pattern operations, improving the performance of `LIKE` queries significantly.

Conclusion​

Incorporating Remote Filters within Xbase++ applications using PostgreSQL’s ISAM emulation provides substantial benefits in terms of performance, scalability, and efficiency. By understanding and implementing proper indexing strategies, especially for operations like `LIKE` searches, developers can significantly enhance the responsiveness and speed of their applications. Remember, the key to performance optimization lies in effectively leveraging the database server’s capabilities to minimize unnecessary data processing and transport.