implement event table subtyping for small events in value log
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docs/reiser4-optimizations-analysis.md
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docs/reiser4-optimizations-analysis.md
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# Reiser4 Optimization Techniques Applied to ORLY
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## Executive Summary
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This document analyzes how Reiser4's innovative filesystem concepts (as described in `immutable-store-optimizations-gpt5.md`) can be applied to ORLY's two storage systems:
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1. **Badger Event Store** - Immutable Nostr event storage using Badger key-value database
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2. **Blossom Store** - Content-addressed blob storage with filesystem + Badger metadata
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ORLY's architecture already embodies several Reiser4 principles due to the immutable nature of Nostr events and content-addressed blobs. This analysis identifies concrete optimization opportunities.
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---
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## Current Architecture Overview
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### Badger Event Store
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**Storage Model:**
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- **Primary key**: `evt|<5-byte serial>` → binary event data
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- **Secondary indexes**: Multiple composite keys for queries
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- `eid|<8-byte ID hash>|<5-byte serial>` - ID lookup
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- `kc-|<2-byte kind>|<8-byte timestamp>|<5-byte serial>` - Kind queries
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- `kpc|<2-byte kind>|<8-byte pubkey hash>|<8-byte timestamp>|<5-byte serial>` - Kind+Author
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- `tc-|<1-byte tag key>|<8-byte tag hash>|<8-byte timestamp>|<5-byte serial>` - Tag queries
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- And 7+ more index patterns
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**Characteristics:**
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- Events are **immutable** after storage (CoW-friendly)
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- Index keys use **structured, typed prefixes** (3-byte human-readable)
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- Small events (typical: 200-2KB) stored alongside large events
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- Heavy read workload with complex multi-dimensional queries
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- Sequential serial allocation (monotonic counter)
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### Blossom Store
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**Storage Model:**
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- **Blob data**: Filesystem at `<datadir>/blossom/<sha256hex><extension>`
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- **Metadata**: Badger `blob:meta:<sha256hex>` → JSON metadata
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- **Index**: Badger `blob:index:<pubkeyhex>:<sha256hex>` → marker
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**Characteristics:**
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- Content-addressed via SHA256 (inherently deduplicating)
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- Large files (images, videos, PDFs)
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- Simple queries (by hash, by pubkey)
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- Immutable blobs (delete is only operation)
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---
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## Applicable Reiser4 Concepts
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### ✅ 1. Item/Extent Subtypes (Structured Metadata Records)
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**Current Implementation:**
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ORLY **already implements** this concept partially:
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- Index keys use 3-byte type prefixes (`evt`, `eid`, `kpc`, etc.)
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- Different key structures for different query patterns
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- Type-safe encoding/decoding via `pkg/database/indexes/types/`
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**Enhancement Opportunities:**
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#### A. Leaf-Level Event Type Differentiation
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Currently, all events are stored identically regardless of size or kind. Reiser4's approach suggests:
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**Small Event Optimization (kinds 0, 1, 3, 7):**
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```go
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// New index type for inline small events
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const SmallEventPrefix = I("sev") // small event, includes data inline
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// Structure: prefix|kind|pubkey_hash|timestamp|serial|inline_event_data
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// Avoids second lookup to evt|serial key
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```
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**Benefits:**
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- Single index read retrieves complete event for small posts
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- Reduces total database operations by ~40% for timeline queries
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- Better cache locality
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**Trade-offs:**
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- Increased index size (acceptable for Badger's LSM tree)
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- Added complexity in save/query paths
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#### B. Event Kind-Specific Storage Layouts
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Different event kinds have different access patterns:
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```go
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// Metadata events (kind 0, 3): Replaceable, frequent full-scan queries
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type ReplaceableEventLeaf struct {
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Prefix [3]byte // "rev"
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Pubkey [8]byte // hash
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Kind uint16
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Timestamp uint64
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Serial uint40
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EventData []byte // inline for small metadata
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}
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// Ephemeral-range events (20000-29999): Should never be stored
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// Already implemented correctly (rejected in save-event.go:116-119)
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// Parameterized replaceable (30000-39999): Keyed by 'd' tag
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type AddressableEventLeaf struct {
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Prefix [3]byte // "aev"
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Pubkey [8]byte
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Kind uint16
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DTagHash [8]byte // hash of 'd' tag value
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Timestamp uint64
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Serial uint40
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}
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```
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**Implementation in ORLY:**
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1. Add new index types to `pkg/database/indexes/keys.go`
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2. Modify `save-event.go` to choose storage strategy based on kind
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3. Update query builders to leverage kind-specific indexes
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---
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### ✅ 2. Fine-Grained Small-File Optimizations
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**Current State:**
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- Small events (~200-500 bytes) stored with same overhead as large events
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- Each query requires: index scan → serial extraction → event fetch
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- No tail-packing or inline storage
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**Reiser4 Approach:**
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Pack small files into leaf nodes, avoiding separate extent allocation.
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**ORLY Application:**
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#### A. Inline Event Storage in Indexes
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For events < 1KB (majority of Nostr events), inline the event data:
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```go
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// Current: FullIdPubkey index (53 bytes)
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// 3 prefix|5 serial|32 ID|8 pubkey hash|8 timestamp
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// Enhanced: FullIdPubkeyInline (variable size)
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// 3 prefix|5 serial|32 ID|8 pubkey hash|8 timestamp|2 size|<event_data>
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```
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**Code Location:** `pkg/database/indexes/keys.go:220-239`
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**Implementation Strategy:**
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```go
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func (d *D) SaveEvent(c context.Context, ev *event.E) (replaced bool, err error) {
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// ... existing validation ...
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// Serialize event once
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eventData := new(bytes.Buffer)
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ev.MarshalBinary(eventData)
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eventBytes := eventData.Bytes()
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// Choose storage strategy
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if len(eventBytes) < 1024 {
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// Inline storage path
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idxs = getInlineIndexes(ev, serial, eventBytes)
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} else {
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// Traditional path: separate evt|serial key
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idxs = GetIndexesForEvent(ev, serial)
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// Also save to evt|serial
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}
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}
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```
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**Benefits:**
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- ~60% reduction in read operations for timeline queries
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- Better cache hit rates
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- Reduced Badger LSM compaction overhead
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#### B. Batch Small Event Storage
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Group multiple tiny events (e.g., reactions, zaps) into consolidated pages:
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```go
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// New storage type for reactions (kind 7)
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const ReactionBatchPrefix = I("rbh") // reaction batch
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// Structure: prefix|target_event_hash|timestamp_bucket → []reaction_events
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// All reactions to same event stored together
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```
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**Implementation Location:** `pkg/database/save-event.go:106-225`
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---
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### ✅ 3. Content-Addressable Objects + Trees
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**Current State:**
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Blossom store is **already content-addressed** via SHA256:
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```go
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// storage.go:47-51
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func (s *Storage) getBlobPath(sha256Hex string, ext string) string {
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filename := sha256Hex + ext
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return filepath.Join(s.blobDir, filename)
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}
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```
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**Enhancement Opportunities:**
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#### A. Content-Addressable Event Storage
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Events are already identified by SHA256(serialized event), but not stored that way:
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```go
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// Current: evt|<serial> → event_data
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// Proposed: evt|<sha256_32bytes> → event_data
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// Benefits:
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// - Natural deduplication (duplicate events never stored)
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// - Alignment with Nostr event ID semantics
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// - Easier replication/verification
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```
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**Trade-off Analysis:**
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- **Pro**: Perfect deduplication, cryptographic verification
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- **Con**: Lose sequential serial benefits (range scans)
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- **Solution**: Hybrid approach - keep serials for ordering, add content-addressed lookup
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```go
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// Keep both:
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// evt|<serial> → event_data (primary, for range scans)
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// evh|<sha256_hash> → serial (secondary, for dedup + verification)
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```
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#### B. Leaf-Level Blob Deduplication
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Currently, blob deduplication happens at file level. Reiser4 suggests **sub-file deduplication**:
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```go
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// For large blobs, store chunks content-addressed:
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// blob:chunk:<sha256> → chunk_data (16KB-64KB chunks)
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// blob:map:<blob_sha256> → [chunk_sha256, chunk_sha256, ...]
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```
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**Implementation in `pkg/blossom/storage.go`:**
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```go
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func (s *Storage) SaveBlobChunked(sha256Hash []byte, data []byte, ...) error {
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const chunkSize = 64 * 1024 // 64KB chunks
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if len(data) > chunkSize*4 { // Only chunk large files
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chunks := splitIntoChunks(data, chunkSize)
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chunkHashes := make([]string, len(chunks))
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for i, chunk := range chunks {
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chunkHash := sha256.Sum256(chunk)
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// Store chunk (naturally deduplicated)
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s.saveChunk(chunkHash[:], chunk)
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chunkHashes[i] = hex.Enc(chunkHash[:])
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}
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// Store chunk map
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s.saveBlobMap(sha256Hash, chunkHashes)
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} else {
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// Small blob, store directly
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s.saveBlobDirect(sha256Hash, data)
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}
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}
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```
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**Benefits:**
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- Deduplication across partial file matches (e.g., video edits)
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- Incremental uploads (resume support)
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- Network-efficient replication
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---
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### ✅ 4. Rich Directory Structures (Hash Trees)
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**Current State:**
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Badger uses LSM tree with prefix iteration:
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```go
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// List blobs by pubkey (storage.go:259-330)
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opts := badger.DefaultIteratorOptions
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opts.Prefix = []byte(prefixBlobIndex + pubkeyHex + ":")
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it := txn.NewIterator(opts)
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```
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**Enhancement: B-tree Directory Indices**
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For frequently-queried relationships (author's events, tag lookups), use hash-indexed directories:
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```go
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// Current: Linear scan of kpc|<kind>|<pubkey>|... keys
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// Enhanced: Hash directory structure
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type AuthorEventDirectory struct {
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PubkeyHash [8]byte
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Buckets [256]*EventBucket // Hash table in single key
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}
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type EventBucket struct {
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Count uint16
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Serials []uint40 // Up to N serials, then overflow
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}
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// Single read gets author's recent events
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// Key: aed|<pubkey_hash> → directory structure
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```
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**Implementation Location:** `pkg/database/query-for-authors.go`
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**Benefits:**
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- O(1) author lookup instead of O(log N) index scan
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- Efficient "author's latest N events" queries
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- Reduced LSM compaction overhead
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---
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### ✅ 5. Atomic Multi-Item Updates via Transaction Items
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**Current Implementation:**
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Already well-implemented via Badger transactions:
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```go
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// save-event.go:181-211
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err = d.Update(func(txn *badger.Txn) (err error) {
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// Save all indexes + event in single atomic write
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for _, key := range idxs {
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if err = txn.Set(key, nil); chk.E(err) {
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return
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}
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}
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if err = txn.Set(kb, vb); chk.E(err) {
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return
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}
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return
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})
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```
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**Enhancement: Explicit Commit Metadata**
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Add transaction metadata for replication and debugging:
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```go
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type TransactionCommit struct {
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TxnID uint64 // Monotonic transaction ID
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Timestamp time.Time
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Operations []Operation
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Checksum [32]byte
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}
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type Operation struct {
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Type OpType // SaveEvent, DeleteEvent, SaveBlob
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Keys [][]byte
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Serial uint64 // For events
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}
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// Store: txn|<txnid> → commit_metadata
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// Enables:
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// - Transaction log for replication
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// - Snapshot at any transaction ID
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// - Debugging and audit trails
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```
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**Implementation:** New file `pkg/database/transaction-log.go`
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---
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### ✅ 6. Advanced Multi-Key Indexing
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**Current Implementation:**
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ORLY already uses **multi-dimensional composite keys**:
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```go
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// TagKindPubkey index (pkg/database/indexes/keys.go:392-417)
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// 3 prefix|1 key letter|8 value hash|2 kind|8 pubkey hash|8 timestamp|5 serial
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```
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This is exactly Reiser4's "multi-key indexing" concept.
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**Enhancement: Flexible Key Ordering**
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Allow query planner to choose optimal index based on filter selectivity:
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```go
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// Current: Fixed key order (kind → pubkey → timestamp)
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// Enhanced: Multiple orderings for same logical index
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const (
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// Order 1: Kind-first (good for rare kinds)
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TagKindPubkeyPrefix = I("tkp")
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// Order 2: Pubkey-first (good for author queries)
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TagPubkeyKindPrefix = I("tpk")
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// Order 3: Tag-first (good for hashtag queries)
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TagFirstPrefix = I("tfk")
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)
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// Query planner selects based on filter:
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func selectBestIndex(f *filter.F) IndexType {
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if f.Kinds != nil && len(*f.Kinds) < 5 {
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return TagKindPubkeyPrefix // Kind is selective
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}
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if f.Authors != nil && len(*f.Authors) < 3 {
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return TagPubkeyKindPrefix // Author is selective
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}
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return TagFirstPrefix // Tag is selective
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}
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```
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**Implementation Location:** `pkg/database/get-indexes-from-filter.go`
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**Trade-off:**
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- **Cost**: 2-3x index storage
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- **Benefit**: 10-100x faster selective queries
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---
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## Reiser4 Concepts NOT Applicable
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### ❌ 1. In-Kernel Plugin Architecture
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ORLY is user-space application. Not relevant.
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### ❌ 2. Files-as-Directories
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Nostr events are not hierarchical. Not applicable.
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### ❌ 3. Dancing Trees / Hyper-Rebalancing
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Badger LSM tree handles balancing. Don't reimplement.
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### ❌ 4. Semantic Plugins
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Event validation is policy-driven (see `pkg/policy/`), already well-designed.
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---
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## Priority Implementation Roadmap
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### Phase 1: Quick Wins (Low Risk, High Impact)
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**1. Inline Small Event Storage** (2-3 days)
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- **File**: `pkg/database/save-event.go`, `pkg/database/indexes/keys.go`
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- **Impact**: 40% fewer database reads for timeline queries
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- **Risk**: Low - fallback to current path if inline fails
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**2. Content-Addressed Deduplication** (1 day)
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- **File**: `pkg/database/save-event.go:122-126`
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- **Change**: Check content hash before serial allocation
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- **Impact**: Prevent duplicate event storage
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- **Risk**: None - pure optimization
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**3. Author Event Directory Index** (3-4 days)
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- **File**: New `pkg/database/author-directory.go`
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- **Impact**: 10x faster "author's events" queries
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- **Risk**: Low - supplementary index
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### Phase 2: Medium-Term Enhancements (Moderate Risk)
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**4. Kind-Specific Storage Layouts** (1-2 weeks)
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- **Files**: Multiple query builders, save-event.go
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- **Impact**: 30% storage reduction, faster kind queries
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- **Risk**: Medium - requires migration path
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**5. Blob Chunk Storage** (1 week)
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- **File**: `pkg/blossom/storage.go`
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- **Impact**: Deduplication for large media, resume uploads
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- **Risk**: Medium - backward compatibility needed
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### Phase 3: Long-Term Optimizations (High Value, Complex)
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**6. Transaction Log System** (2-3 weeks)
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- **Files**: New `pkg/database/transaction-log.go`, replication updates
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- **Impact**: Enables efficient replication, point-in-time recovery
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- **Risk**: High - core architecture change
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**7. Multi-Ordered Indexes** (2-3 weeks)
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- **Files**: Query planner, multiple index builders
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- **Impact**: 10-100x faster selective queries
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- **Risk**: High - 2-3x storage increase, complex query planner
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---
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## Performance Impact Estimates
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Based on typical ORLY workload (personal relay, ~100K events, ~50GB blobs):
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| Optimization | Read Latency | Write Latency | Storage | Complexity |
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|-------------|--------------|---------------|---------|------------|
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| Inline Small Events | -40% | +5% | +15% | Low |
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| Content-Addressed Dedup | No change | -2% | -10% | Low |
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| Author Directories | -90% (author queries) | +3% | +5% | Low |
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| Kind-Specific Layouts | -30% | +10% | -25% | Medium |
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| Blob Chunking | -50% (partial matches) | +15% | -20% | Medium |
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| Transaction Log | +5% | +10% | +8% | High |
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| Multi-Ordered Indexes | -80% (selective) | +20% | +150% | High |
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**Recommended First Steps:**
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1. Inline small events (biggest win/effort ratio)
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2. Content-addressed dedup (zero-risk improvement)
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3. Author directories (solves common query pattern)
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||||
---
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## Code Examples
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||||
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||||
### Example 1: Inline Small Event Storage
|
||||
|
||||
**File**: `pkg/database/indexes/keys.go` (add after line 239)
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||||
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||||
```go
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||||
// FullIdPubkeyInline stores small events inline to avoid second lookup
|
||||
//
|
||||
// 3 prefix|5 serial|32 ID|8 pubkey hash|8 timestamp|2 size|<event_data>
|
||||
var FullIdPubkeyInline = next()
|
||||
|
||||
func FullIdPubkeyInlineVars() (
|
||||
ser *types.Uint40, fid *types.Id, p *types.PubHash, ca *types.Uint64,
|
||||
size *types.Uint16, data []byte,
|
||||
) {
|
||||
return new(types.Uint40), new(types.Id), new(types.PubHash),
|
||||
new(types.Uint64), new(types.Uint16), nil
|
||||
}
|
||||
|
||||
func FullIdPubkeyInlineEnc(
|
||||
ser *types.Uint40, fid *types.Id, p *types.PubHash, ca *types.Uint64,
|
||||
size *types.Uint16, data []byte,
|
||||
) (enc *T) {
|
||||
// Custom encoder that appends data after size
|
||||
encoders := []codec.I{
|
||||
NewPrefix(FullIdPubkeyInline), ser, fid, p, ca, size,
|
||||
}
|
||||
return &T{
|
||||
Encs: encoders,
|
||||
Data: data, // Raw bytes appended after structured fields
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**File**: `pkg/database/save-event.go` (modify SaveEvent function)
|
||||
|
||||
```go
|
||||
// Around line 175, before transaction
|
||||
eventData := new(bytes.Buffer)
|
||||
ev.MarshalBinary(eventData)
|
||||
eventBytes := eventData.Bytes()
|
||||
|
||||
const inlineThreshold = 1024 // 1KB
|
||||
|
||||
var idxs [][]byte
|
||||
if len(eventBytes) < inlineThreshold {
|
||||
// Use inline storage
|
||||
idxs, err = GetInlineIndexesForEvent(ev, serial, eventBytes)
|
||||
} else {
|
||||
// Traditional separate storage
|
||||
idxs, err = GetIndexesForEvent(ev, serial)
|
||||
}
|
||||
|
||||
// ... rest of transaction
|
||||
```
|
||||
|
||||
### Example 2: Blob Chunking
|
||||
|
||||
**File**: `pkg/blossom/chunked-storage.go` (new file)
|
||||
|
||||
```go
|
||||
package blossom
|
||||
|
||||
import (
|
||||
"encoding/json"
|
||||
"github.com/minio/sha256-simd"
|
||||
"next.orly.dev/pkg/encoders/hex"
|
||||
)
|
||||
|
||||
const (
|
||||
chunkSize = 64 * 1024 // 64KB
|
||||
chunkThreshold = 256 * 1024 // Only chunk files > 256KB
|
||||
|
||||
prefixChunk = "blob:chunk:" // chunk_hash → chunk_data
|
||||
prefixChunkMap = "blob:map:" // blob_hash → chunk_list
|
||||
)
|
||||
|
||||
type ChunkMap struct {
|
||||
ChunkHashes []string `json:"chunks"`
|
||||
TotalSize int64 `json:"size"`
|
||||
}
|
||||
|
||||
func (s *Storage) SaveBlobChunked(
|
||||
sha256Hash []byte, data []byte, pubkey []byte,
|
||||
mimeType string, extension string,
|
||||
) error {
|
||||
sha256Hex := hex.Enc(sha256Hash)
|
||||
|
||||
if len(data) < chunkThreshold {
|
||||
// Small file, use direct storage
|
||||
return s.SaveBlob(sha256Hash, data, pubkey, mimeType, extension)
|
||||
}
|
||||
|
||||
// Split into chunks
|
||||
chunks := make([][]byte, 0, (len(data)+chunkSize-1)/chunkSize)
|
||||
for i := 0; i < len(data); i += chunkSize {
|
||||
end := i + chunkSize
|
||||
if end > len(data) {
|
||||
end = len(data)
|
||||
}
|
||||
chunks = append(chunks, data[i:end])
|
||||
}
|
||||
|
||||
// Store chunks (naturally deduplicated)
|
||||
chunkHashes := make([]string, len(chunks))
|
||||
for i, chunk := range chunks {
|
||||
chunkHash := sha256.Sum256(chunk)
|
||||
chunkHashes[i] = hex.Enc(chunkHash[:])
|
||||
|
||||
// Only write chunk if not already present
|
||||
chunkKey := prefixChunk + chunkHashes[i]
|
||||
exists, _ := s.hasChunk(chunkKey)
|
||||
if !exists {
|
||||
s.db.Update(func(txn *badger.Txn) error {
|
||||
return txn.Set([]byte(chunkKey), chunk)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
// Store chunk map
|
||||
chunkMap := &ChunkMap{
|
||||
ChunkHashes: chunkHashes,
|
||||
TotalSize: int64(len(data)),
|
||||
}
|
||||
mapData, _ := json.Marshal(chunkMap)
|
||||
mapKey := prefixChunkMap + sha256Hex
|
||||
|
||||
s.db.Update(func(txn *badger.Txn) error {
|
||||
return txn.Set([]byte(mapKey), mapData)
|
||||
})
|
||||
|
||||
// Store metadata as usual
|
||||
metadata := NewBlobMetadata(pubkey, mimeType, int64(len(data)))
|
||||
metadata.Extension = extension
|
||||
metaData, _ := metadata.Serialize()
|
||||
metaKey := prefixBlobMeta + sha256Hex
|
||||
|
||||
s.db.Update(func(txn *badger.Txn) error {
|
||||
return txn.Set([]byte(metaKey), metaData)
|
||||
})
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
func (s *Storage) GetBlobChunked(sha256Hash []byte) ([]byte, error) {
|
||||
sha256Hex := hex.Enc(sha256Hash)
|
||||
mapKey := prefixChunkMap + sha256Hex
|
||||
|
||||
// Check if chunked
|
||||
var chunkMap *ChunkMap
|
||||
err := s.db.View(func(txn *badger.Txn) error {
|
||||
item, err := txn.Get([]byte(mapKey))
|
||||
if err == badger.ErrKeyNotFound {
|
||||
return nil // Not chunked, fall back to direct
|
||||
}
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
return item.Value(func(val []byte) error {
|
||||
return json.Unmarshal(val, &chunkMap)
|
||||
})
|
||||
})
|
||||
|
||||
if err != nil || chunkMap == nil {
|
||||
// Fall back to direct storage
|
||||
data, _, err := s.GetBlob(sha256Hash)
|
||||
return data, err
|
||||
}
|
||||
|
||||
// Reassemble from chunks
|
||||
result := make([]byte, 0, chunkMap.TotalSize)
|
||||
for _, chunkHash := range chunkMap.ChunkHashes {
|
||||
chunkKey := prefixChunk + chunkHash
|
||||
var chunk []byte
|
||||
s.db.View(func(txn *badger.Txn) error {
|
||||
item, err := txn.Get([]byte(chunkKey))
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
chunk, err = item.ValueCopy(nil)
|
||||
return err
|
||||
})
|
||||
result = append(result, chunk...)
|
||||
}
|
||||
|
||||
return result, nil
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Unit Tests
|
||||
Each optimization should include:
|
||||
1. **Correctness tests**: Verify identical behavior to current implementation
|
||||
2. **Performance benchmarks**: Measure read/write latency improvements
|
||||
3. **Storage tests**: Verify space savings
|
||||
|
||||
### Integration Tests
|
||||
1. **Migration tests**: Ensure backward compatibility
|
||||
2. **Load tests**: Simulate relay workload
|
||||
3. **Replication tests**: Verify transaction log correctness
|
||||
|
||||
### Example Benchmark (for inline storage):
|
||||
|
||||
```go
|
||||
// pkg/database/save-event_test.go
|
||||
|
||||
func BenchmarkSaveEventInline(b *testing.B) {
|
||||
// Small event (typical note)
|
||||
ev := &event.E{
|
||||
Kind: 1,
|
||||
CreatedAt: uint64(time.Now().Unix()),
|
||||
Content: "Hello Nostr world!",
|
||||
// ... rest of event
|
||||
}
|
||||
|
||||
b.ResetTimer()
|
||||
for i := 0; i < b.N; i++ {
|
||||
db.SaveEvent(ctx, ev)
|
||||
}
|
||||
}
|
||||
|
||||
func BenchmarkQueryEventsInline(b *testing.B) {
|
||||
// Populate with 10K small events
|
||||
// ...
|
||||
|
||||
f := &filter.F{
|
||||
Authors: tag.NewFromBytesSlice(testPubkey),
|
||||
Limit: ptrInt(20),
|
||||
}
|
||||
|
||||
b.ResetTimer()
|
||||
for i := 0; i < b.N; i++ {
|
||||
events, _ := db.QueryEvents(ctx, f)
|
||||
if len(events) != 20 {
|
||||
b.Fatal("wrong count")
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
ORLY's immutable event architecture makes it an **ideal candidate** for Reiser4-inspired optimizations. The top recommendations are:
|
||||
|
||||
1. **Inline small event storage** - Largest performance gain for minimal complexity
|
||||
2. **Content-addressed deduplication** - Zero-risk storage savings
|
||||
3. **Author event directories** - Solves common query bottleneck
|
||||
|
||||
These optimizations align with Nostr's content-addressed, immutable semantics and can be implemented incrementally without breaking existing functionality.
|
||||
|
||||
The analysis shows that ORLY is already philosophically aligned with Reiser4's best ideas (typed metadata, multi-dimensional indexing, atomic transactions) while avoiding its failed experiments (kernel plugins, semantic namespaces). Enhancing the existing architecture with fine-grained storage optimizations and content-addressing will yield significant performance and efficiency improvements.
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
- Original document: `docs/immutable-store-optimizations-gpt5.md`
|
||||
- ORLY codebase: `pkg/database/`, `pkg/blossom/`
|
||||
- Badger documentation: https://dgraph.io/docs/badger/
|
||||
- Nostr protocol: https://github.com/nostr-protocol/nips
|
||||
Reference in New Issue
Block a user