自定义Lucene索引的Ignite标准查询实现及持久内存存储问询

Great questions! Let’s tackle them one by one since you’re already deep into implementing a custom IndexingSpi for Lucene in Ignite.

1. Querying Custom Lucene Indexes Like a Table View #

To make your custom Lucene index work seamlessly with Ignite’s standard interfaces (and feel like querying a regular table), here are the most practical approaches:

Option 1: Integrate with Ignite’s SQL Engine #

If you want to support SQL-like queries, bridge your custom Lucene index with Ignite’s SQL pipeline:

• Define a QueryEntity in your cache configuration to represent your "virtual table"—map fields from your Lucene index to SQL-compatible types, specify key/value classes, and define indexes.
• In your custom IndexingSpi, override methods like createSqlQuery to translate Ignite’s SQL clauses into Lucene Query objects (e.g., convert a SQL WHERE name = 'foo' to a Lucene TermQuery).
• Use Ignite’s standard SqlQuery or FieldsQuery API to execute queries—your SPI will handle routing the query to Lucene and mapping results back to your entity type, just like a regular table.

Option 2: Wrap Custom Lucene Queries in Ignite’s CacheQuery Interface #

For more control over Lucene’s native syntax while using Ignite’s standard query API:

• Create a custom CacheQuery subclass (e.g., LuceneCacheQuery) that accepts Lucene Query instances as input.
• In your IndexingSpi, implement logic to run this query against your Lucene index and return results as an Iterable or QueryCursor.
• Your app code can call igniteCache.query(new LuceneCacheQuery(luceneQuery)) and process results exactly like any other Ignite query result set.

Option 3: Expose Results as a Virtual Cache #

For a true "table view" experience:

• Set up a read-only Ignite cache that acts as a proxy for your Lucene index. Populate it lazily on query or preload results from the index.
• Users can run standard SQL, scan, or key-based queries against this proxy cache without knowing they’re interacting with a custom Lucene index under the hood.

2. Storing Custom Lucene Indexes in Ignite’s Durable Memory #

Yes, you absolutely can store your custom Lucene index in Ignite’s Durable Memory. Here’s how it works, both for your custom implementation and Ignite’s native Lucene indexing:

How to Enable Durable Memory for Custom Lucene Indexes #

Ignite’s native Lucene integration uses a custom Directory implementation (IgniteDirectory) that stores index data directly in Durable Memory. To reuse this for your custom SPI:

• Replace Lucene’s default FSDirectory with IgniteDirectory in your index setup. This directory maps Lucene’s index segments to Ignite’s page-based memory storage.
• Configure a DataRegion in your Ignite config with persistenceEnabled=true—this ensures your index data is persisted to disk and survives node restarts.
• Ensure your IndexingSpi uses Ignite’s PageMemory API to manage index pages instead of writing directly to the filesystem.

How Ignite Stores Lucene Indexes in Durable Memory #

Ignite’s native Lucene indexing handles storage through these core mechanisms:

• Page-Based Segmentation: Lucene’s index segments are split into fixed-size pages (configurable via DataRegionConfiguration.setPageSize(), default 4KB), managed by Ignite’s PageMemory layer.
• Asynchronous Flushing: Dirty pages (modified index data) are flushed to disk-based page stores in the background, balancing durability and performance.
• Hot Data Caching: Frequently accessed index pages stay in memory for fast queries, while less used pages are evicted to disk as needed.
• Metadata Persistence: Lucene’s index metadata (segment info, field mappings) is stored alongside data pages in Durable Memory, ensuring full recovery on node restart.

Key Implementation Tips #

• Reuse Native Components: Lean on IgniteDirectory and existing SPI abstractions to avoid reinventing the wheel—this leverages Ignite’s optimized Durable Memory handling.
• Validate Persistence: After setup, restart your cluster to confirm your custom Lucene index is fully recovered from disk.
• Tune for Performance: Use Ignite’s built-in metrics to track index read/write speeds, and adjust DataRegion settings (like eviction policies) to match your workload.

内容的提问来源于stack exchange，提问作者user9532707