Incremental changes

content/develop/interact/search-and-query/best-practices/index-mgmt-best-practices.md

@@ -11,19 +11,43 @@ categories:
 linkTitle: RQE index management
 weight: 3
 ---
+## Introduction to managing Redis Query Engine indexes
 
-#### 1. Plan your indexes strategically
+The Redis Query Engine (RQE) is a powerful tool for executing complex search and query operations on structured, semi-structured, and unstructured data. Indexes are the backbone of this functionality, enabling fast and efficient data retrieval.
+Proper management of these indexes is essential for optimal performance, scalability, and resource utilization.
+
+This guide outlines best practices for managing RQE indexes throughout their lifecycle. It provides recommendations on:
+
+- Planning and creating indexes to suit your query patterns.
+- Using index aliasing to manage schema updates and minimize downtime.
+- Monitoring and verifying index population to ensure query readiness.
+- Optimizing performance through query profiling and memory management.
+- Maintaining and scaling indexes in both standalone and clustered Redis environments.
+- Versioning, testing, and automating index management.
+
+## Why index management matters
+
+Indexes directly impact query speed and resource consumption.
+Poorly managed indexes can lead to increased memory usage, slower query times, and challenges in maintaining data consistency.
+By following the strategies outlined in this guide, you can:
+
+- Reduce operational overhead.
+- Improve application performance.
+- Ensure smooth transitions during schema changes.
+- Scale efficiently with your growing datasets.
+
+## Plan your indexes strategically
    - Understand your query patterns: before creating indexes, analyze your expected query patterns to ensure indexes are optimized for performance.
    - Avoid over-indexing: indexing every field increases memory usage and can slow down updates. Only index fields essential for your queries.
    - Choose appropriate index types: use the correct field types (`TEXT`, `TAG`, `NUMERIC`, `GEO`, or `VECTOR`) for your data to maximize efficiency.
 
-#### 2. Index creation
-   - Atomic creation: use the `FT.CREATE` command to atomically define an index schema.
+## Index creation
+   - Use the [`FT.CREATE`]({{< baseurl >}}/commands/ft.create) command to define an index schema.
    - Field weighting: assign weights to `TEXT` fields to prioritize certain fields in full-text search results.
    - Prefix optimization: leverage the `PREFIX` option to restrict indexing to keys with specific patterns.
    - Data loading strategy: load data into Redis before creating an index when working with large datasets. Use the `ON HASH` or `ON JSON` options to match the data structure.
 
-#### 3. Index aliasing
+## Index aliasing
    - What is index aliasing?
      - Aliases act as abstracted names for indexes, allowing applications to reference the alias instead of the actual index name. This simplifies schema updates and index management.
    - Use cases for index aliasing:
@@ -35,9 +59,9 @@ weight: 3
      - Update an alias: `FT.ALIASUPDATE my_alias new_index`
      - Remove an alias: `FT.ALIASDEL my_alias`
 
-#### 4. Monitoring index population
+## Monitoring index population
    - Check document count:
-     - Use the `FT.INFO` command to monitor the `num_docs` field, ensuring all expected documents are indexed.
+     - Use the `FT.INFO` command to monitor the `num_docs` field, to check that all expected documents are indexed.
      - Example:
        ```bash
        FT.INFO my_new_index
@@ -69,28 +93,28 @@ weight: 3
            print("Index is still populating...")
        ```
 
-#### 5. Monitoring index performance
+## Monitoring index performance
    - Query profiling: use the `FT.PROFILE` command to analyze query performance and identify bottlenecks.
    - Memory usage: regularly monitor memory usage with the `INFO memory` and `FT.INFO` commands to detect growth patterns and optimize resource allocation.
    - Search query logs: enable query logging for better insights into how indexes are utilized.
 
-#### 6. Index maintenance
+## Index maintenance
    - Reindexing: if schema changes are required, create a new index with the updated schema and reassign the alias once the index is ready.
    - Expire old data: use Redis key expiration or TTLs to automatically remove outdated records and keep indexes lean.
 
-#### 7. Scaling and high availability
+## Scaling and high availability
    - Sharding considerations: in a clustered Redis setup, ensure indexes are designed with key distribution in mind to prevent query inefficiencies.
    - Replication: test how indexes behave under replica promotion to ensure consistent query behavior across nodes.
    - Active-Active support: if using Redis in an active-active setup, validate how index updates propagate to avoid inconsistencies.
 
-#### 8. Versioning and testing
+## Versioning and testing
    - Index versioning: when changing schemas, create a new version of the index alongside the old one and migrate data progressively.
    - Staging environment: test index changes in a staging environment before deploying them to production.
 
-#### 9. Cleaning up
+## Cleaning up
    - Index deletion: use the `FT.DROPINDEX` command to remove unused indexes and free up memory. Be cautious with the `DD` (Delete Documents) flag to avoid unintended data deletion.
    - Monitoring orphaned keys: Ensure no keys remain that were previously associated with dropped indexes.
 
-#### 10. Documentation and automation
+## Documentation and automation
    - Maintain clear index schemas: document your index configurations to facilitate future maintenance.
    - Automate index management: use scripts or orchestration tools to automate index creation, monitoring, and cleanup.