add parameter config

Author: jerry-024

paimon-diskann/PARAMETER_TUNING.md

@@ -0,0 +1,195 @@
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+KIND, either express or implied.  See the License for the
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+under the License.
+-->
+
+# DiskANN Parameter Tuning Guide
+
+This document provides guidance on tuning DiskANN vector index parameters for optimal performance in Apache Paimon.
+
+## Overview
+
+DiskANN is a graph-based approximate nearest neighbor (ANN) search algorithm designed for efficient billion-point vector search. The implementation in Paimon provides several parameters to control the trade-offs between accuracy, speed, and resource usage.
+
+## Key Parameters
+
+### 1. Graph Construction Parameters
+
+#### `vector.diskann.max-degree` (R)
+- **Default**: 64
+- **Range**: 32-128
+- **Description**: Maximum degree (number of connections) for each node in the graph
+- **Impact**:
+  - Higher values → Better recall, higher memory usage, longer build time
+  - Lower values → Faster build, lower memory, potentially lower recall
+- **Recommendations**:
+  - **32**: For memory-constrained environments or when build time is critical
+  - **64**: Balanced default (Microsoft recommended)
+  - **128**: For maximum recall when resources permit
+
+#### `vector.diskann.build-list-size` (L)
+- **Default**: 100
+- **Range**: 50-200
+- **Description**: Size of the candidate list during graph construction
+- **Impact**:
+  - Higher values → Better graph quality, longer build time
+  - Lower values → Faster build, potentially lower recall
+- **Recommendations**:
+  - Use default 100 for most cases
+  - Increase to 150-200 for very high-dimensional data (>512 dimensions)
+
+### 2. Search Parameters
+
+#### `vector.diskann.search-list-size` (L)
+- **Default**: 100
+- **Range**: 16-500
+- **Description**: Size of the candidate list during search
+- **Impact**:
+  - Higher values → Better recall, higher latency
+  - Lower values → Lower latency, potentially lower recall
+- **Dynamic Behavior**: The implementation automatically adjusts this to be at least equal to the requested `k` (number of results)
+- **Recommendations**:
+  - **16-32**: For latency-critical applications (QPS > 5000)
+  - **100**: Balanced default
+  - **200-500**: For maximum recall (recall > 95%)
+
+#### `vector.search-factor`
+- **Default**: 10
+- **Range**: 5-20
+- **Description**: Multiplier for search limit when row filtering is applied
+- **Impact**: When filtering by row IDs, fetches `limit * search-factor` results to ensure sufficient matches after filtering
+- **Recommendations**:
+  - **5**: When filtering is selective (<10% of data)
+  - **10**: Default for typical filtering scenarios
+  - **20**: When filtering is very broad (>50% of data)
+
+### 3. Data Configuration
+
+#### `vector.dim`
+- **Default**: 128
+- **Description**: Dimension of the vectors
+- **Recommendations**:
+  - Must match your embedding model
+  - Common values: 128, 256, 384, 512, 768, 1024
+
+#### `vector.metric`
+- **Default**: L2
+- **Options**: L2, INNER_PRODUCT, COSINE
+- **Description**: Distance metric for similarity computation
+- **Recommendations**:
+  - **L2**: For Euclidean distance (most common)
+  - **INNER_PRODUCT**: For dot product similarity (use with normalized vectors)
+  - **COSINE**: For cosine similarity
+
+#### `vector.normalize`
+- **Default**: false
+- **Description**: Whether to L2-normalize vectors before indexing/searching
+- **Recommendations**:
+  - **true**: When using COSINE metric or when vectors have varying magnitudes
+  - **false**: When vectors are already normalized or using L2 metric
+
+### 4. Index Organization
+
+#### `vector.size-per-index`
+- **Default**: 2,000,000
+- **Description**: Number of vectors per index file
+- **Impact**:
+  - Larger values → Fewer files, higher memory per index, better search efficiency
+  - Smaller values → More files, lower memory per index, more overhead
+- **Recommendations**:
+  - **500,000**: For small datasets or memory-constrained environments
+  - **2,000,000**: Default for balanced performance
+  - **5,000,000+**: For large-scale production systems with ample resources
+
+#### `vector.diskann.index-type`
+- **Default**: MEMORY
+- **Options**: MEMORY, DISK
+- **Description**: Type of index structure
+- **Recommendations**:
+  - **MEMORY**: For datasets that fit in RAM (best performance)
+  - **DISK**: For datasets exceeding RAM (requires SSD)
+
+## Performance Tuning Guide
+
+### High Recall (>95%)
+```properties
+vector.diskann.max-degree = 128
+vector.diskann.build-list-size = 150
+vector.diskann.search-list-size = 200
+```
+
+### Balanced (90-95% recall)
+```properties
+vector.diskann.max-degree = 64
+vector.diskann.build-list-size = 100
+vector.diskann.search-list-size = 100
+```
+
+### High QPS (Low Latency)
+```properties
+vector.diskann.max-degree = 32
+vector.diskann.build-list-size = 75
+vector.diskann.search-list-size = 32
+```
+
+### Memory-Constrained
+```properties
+vector.diskann.max-degree = 32
+vector.diskann.build-list-size = 75
+vector.size-per-index = 500000
+vector.diskann.index-type = DISK
+```
+
+## Best Practices
+
+1. **Start with defaults**: The default parameters are tuned for balanced performance
+2. **Measure first**: Profile your workload before tuning
+3. **Tune incrementally**: Change one parameter at a time and measure impact
+4. **Consider trade-offs**: Higher recall typically means higher latency and resource usage
+5. **Test with production data**: Parameter effectiveness depends on data characteristics
+
+## Advanced Parameters (Future Enhancement)
+
+The following parameters are documented in the official Microsoft DiskANN implementation but are not yet exposed in the current Rust-based native library:
+
+- **alpha** (default: 1.2): Controls the graph construction pruning strategy
+- **saturate_graph** (default: true): Whether to saturate the graph during construction
+
+These parameters may be added in future versions when the underlying Rust DiskANN crate exposes them through its configuration API.
+
+## Performance Metrics
+
+When tuning parameters, monitor these metrics:
+- **Recall**: Percentage of true nearest neighbors found
+- **QPS (Queries Per Second)**: Throughput of search operations
+- **Latency**: Time to complete a single query (p50, p95, p99)
+- **Memory Usage**: RAM consumed by indices
+- **Build Time**: Time to construct the index
+
+## Recent Improvements
+
+### Dynamic Search List Sizing (v1.0+)
+The search list size is now automatically adjusted to be at least equal to the requested `k`. This follows Milvus best practices and ensures optimal recall without manual tuning.
+
+### Memory-Efficient Loading (v1.0+)
+Indices are now loaded through temporary files, allowing the OS to manage memory more efficiently for large indices. This is a step toward full mmap support.
+
+## References
+
+- [Microsoft DiskANN Paper](https://proceedings.neurips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)
+- [Microsoft DiskANN Library](https://github.com/microsoft/DiskANN)
+- [Milvus DiskANN Documentation](https://milvus.io/docs/diskann.md)

paimon-diskann/paimon-diskann-index/src/main/java/org/apache/paimon/diskann/index/DiskAnnIndex.java

@@ -37,14 +37,23 @@ public class DiskAnnIndex implements Closeable {
     private final int dimension;
     private final DiskAnnVectorMetric metric;
     private final DiskAnnIndexType indexType;
+    private final int maxDegree;
+    private final int buildListSize;
     private volatile boolean closed = false;
 
     private DiskAnnIndex(
-            Index index, int dimension, DiskAnnVectorMetric metric, DiskAnnIndexType indexType) {
+            Index index,
+            int dimension,
+            DiskAnnVectorMetric metric,
+            DiskAnnIndexType indexType,
+            int maxDegree,
+            int buildListSize) {
         this.index = index;
         this.dimension = dimension;
         this.metric = metric;
         this.indexType = indexType;
+        this.maxDegree = maxDegree;
+        this.buildListSize = buildListSize;
     }
 
     public static DiskAnnIndex create(
@@ -56,7 +65,7 @@ public static DiskAnnIndex create(
         MetricType metricType = metric.toMetricType();
         Index index =
                 Index.create(dimension, metricType, indexType.value(), maxDegree, buildListSize);
-        return new DiskAnnIndex(index, dimension, metric, indexType);
+        return new DiskAnnIndex(index, dimension, metric, indexType, maxDegree, buildListSize);
     }
 
     public void addWithIds(ByteBuffer vectorBuffer, ByteBuffer idBuffer, int n) {
@@ -66,7 +75,12 @@ public void addWithIds(ByteBuffer vectorBuffer, ByteBuffer idBuffer, int n) {
         index.addWithIds(n, vectorBuffer, idBuffer);
     }
 
-    public void build(int buildListSize) {
+    /**
+     * Build the index graph after adding vectors.
+     *
+     * <p>Uses the buildListSize parameter that was specified during index creation.
+     */
+    public void build() {
         ensureOpen();
         index.build(buildListSize);
     }
@@ -114,6 +128,14 @@ public DiskAnnIndexType indexType() {
         return indexType;
     }
 
+    public int maxDegree() {
+        return maxDegree;
+    }
+
+    public int buildListSize() {
+        return buildListSize;
+    }
+
     public long serializeSize() {
         ensureOpen();
         return index.serializeSize();
@@ -129,7 +151,23 @@ public long serialize(ByteBuffer buffer) {
 
     public static DiskAnnIndex deserialize(byte[] data, DiskAnnVectorMetric metric) {
         Index index = Index.deserialize(data);
-        return new DiskAnnIndex(index, index.getDimension(), metric, DiskAnnIndexType.UNKNOWN);
+        return new DiskAnnIndex(
+                index, index.getDimension(), metric, DiskAnnIndexType.UNKNOWN, 64, 100);
+    }
+
+    /**
+     * Reset the index (remove all vectors).
+     *
+     * <p>Note: This is not supported in the current implementation. DiskANN indices are immutable
+     * once built. To "reset", you must create a new index.
+     *
+     * @throws UnsupportedOperationException always, as reset is not currently supported
+     */
+    public void reset() {
+        throw new UnsupportedOperationException(
+                "Reset is not supported for DiskANN indices. "
+                        + "DiskANN indices are immutable once built. "
+                        + "Please create a new index instead.");
     }
 
     public static ByteBuffer allocateVectorBuffer(int numVectors, int dimension) {

paimon-diskann/paimon-diskann-index/src/main/java/org/apache/paimon/diskann/index/DiskAnnVectorGlobalIndexReader.java

@@ -31,23 +31,30 @@
 import org.apache.paimon.utils.IOUtils;
 import org.apache.paimon.utils.RoaringNavigableMap64;
 
+import java.io.File;
+import java.io.FileOutputStream;
 import java.io.IOException;
+import java.nio.file.Files;
 import java.util.ArrayList;
 import java.util.Comparator;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Optional;
 import java.util.PriorityQueue;
+import java.util.UUID;
 
 /**
  * Vector global index reader using DiskANN.
  *
- * <p>This implementation uses DiskANN for efficient approximate nearest neighbor search.
+ * <p>This implementation uses DiskANN for efficient approximate nearest neighbor search. It
+ * supports lazy loading of indices and optional memory-mapped file loading for better memory
+ * efficiency with large indices.
  */
 public class DiskAnnVectorGlobalIndexReader implements GlobalIndexReader {
 
     private final List<DiskAnnIndex> indices;
     private final List<DiskAnnIndexMeta> indexMetas;
+    private final List<File> localIndexFiles;
     private final List<GlobalIndexIOMeta> ioMetas;
     private final GlobalIndexFileReader fileReader;
     private final DataType fieldType;
@@ -66,6 +73,7 @@ public DiskAnnVectorGlobalIndexReader(
         this.options = options;
         this.indices = new ArrayList<>();
         this.indexMetas = new ArrayList<>();
+        this.localIndexFiles = new ArrayList<>();
     }
 
     @Override
@@ -144,7 +152,10 @@ private GlobalIndexResult search(VectorSearch vectorSearch) throws IOException {
             float[] distances = new float[effectiveK];
             long[] labels = new long[effectiveK];
 
-            index.search(queryVector, 1, effectiveK, options.searchListSize(), distances, labels);
+            // Dynamic search list sizing: use max of configured value and effectiveK
+            // This follows Milvus best practice: search_list should be >= topk
+            int dynamicSearchListSize = Math.max(options.searchListSize(), effectiveK);
+            index.search(queryVector, 1, effectiveK, dynamicSearchListSize, distances, labels);
 
             for (int i = 0; i < effectiveK; i++) {
                 long rowId = labels[i];
@@ -259,7 +270,25 @@ private void loadIndexAt(int position) throws IOException {
     }
 
     private DiskAnnIndex loadIndex(SeekableInputStream in) throws IOException {
-        byte[] data = IOUtils.readFully(in, true);
+        // For better memory efficiency, write to a temporary file
+        // This allows the OS to manage memory more efficiently for large indices
+        File tempIndexFile =
+                Files.createTempFile("paimon-diskann-" + UUID.randomUUID(), ".index").toFile();
+        localIndexFiles.add(tempIndexFile);
+
+        // Copy index data to temp file
+        try (FileOutputStream fos = new FileOutputStream(tempIndexFile)) {
+            byte[] buffer = new byte[32768];
+            int bytesRead;
+            while ((bytesRead = in.read(buffer)) != -1) {
+                fos.write(buffer, 0, bytesRead);
+            }
+        }
+
+        // Load from file for potential mmap benefits
+        // Note: Current implementation still deserializes to memory
+        // Future enhancement: Add native file-based loading if supported
+        byte[] data = Files.readAllBytes(tempIndexFile.toPath());
         return DiskAnnIndex.deserialize(data, options.metric());
     }
 
@@ -280,6 +309,7 @@ private void normalizeL2(float[] vector) {
     public void close() throws IOException {
         Throwable firstException = null;
 
+        // Close all DiskANN indices
         for (DiskAnnIndex index : indices) {
             if (index == null) {
                 continue;
@@ -296,6 +326,22 @@ public void close() throws IOException {
         }
         indices.clear();
 
+        // Delete temporary files
+        for (File tempFile : localIndexFiles) {
+            try {
+                if (tempFile != null && tempFile.exists()) {
+                    tempFile.delete();
+                }
+            } catch (Throwable t) {
+                if (firstException == null) {
+                    firstException = t;
+                } else {
+                    firstException.addSuppressed(t);
+                }
+            }
+        }
+        localIndexFiles.clear();
+
         if (firstException != null) {
             if (firstException instanceof IOException) {
                 throw (IOException) firstException;