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Add FINDINGS.md with full benchmark analysis, update README numbers
FINDINGS.md: Deep-dive into all 21 subsystem benchmarks with analysis of why each number is what it is — architecture advantages, trade-offs, partition scaling behavior, cross-engine comparison methodology. README.md: Updated storage engine table to show all 16 subsystems (was 7), added PAR_SCAN column to partition table, refreshed all numbers from latest bench run. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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‎FINDINGS.md‎

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# TurboDB Benchmark Findings
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All benchmarks run on Apple M-series (ARM64), macOS, Zig 0.15.2 ReleaseFast.
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Measured on 2026-03-28. Reproduce with `zig build bench-regression` and `zig build bench-partition`.
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---
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## 1. Regression Benchmark (21 subsystems, in-process)
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No network overhead. These numbers represent the raw speed of each subsystem.
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### Core CRUD
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| Operation | Ops/sec | Latency | Notes |
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|-----------|--------:|--------:|-------|
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| INSERT | 910,100 | 1.1 us | B-tree insert + WAL append |
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| GET | 13,344,009 | 0.1 us | Zero-copy mmap read, no deserialization |
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| UPDATE | 2,346,316 | 0.4 us | In-place mmap write + WAL |
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| DELETE | 3,688,948 | 0.3 us | Tombstone + WAL, epoch GC later |
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**Key insight**: GET at 13.3M ops/s is 100% zero-copy. The `get()` call returns a
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pointer directly into mmap'd memory. No malloc, no memcpy, no deserialization.
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That's why it's 14x faster than insert — reads do no allocation at all.
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### Compression (LZ4)
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| Operation | Ops/sec | Latency | Notes |
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|-----------|--------:|--------:|-------|
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| Compress 4KB | 757,771 | 1.3 us | LZ4 page-level compression |
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| Decompress 4KB | 845,023 | 1.2 us | LZ4 decompression |
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**Key insight**: Compress and decompress are both sub-2us per 4KB page. This means
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compression adds ~1us to each write but saves significant I/O on larger datasets.
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### ART (Adaptive Radix Tree) Index
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| Operation | Ops/sec | Latency | Notes |
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|-----------|--------:|--------:|-------|
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| Insert | 8,712,319 | 0.1 us | Path-compressed trie insert |
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| Search | 19,033,118 | 0.1 us | Trigram-indexed full-text search |
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| Prefix Scan | 2,135,383 | 0.5 us | Trie prefix traversal |
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**Key insight**: ART search at 19M ops/s is the star. This powers TurboDB's full-text
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search. It uses a trigram index (3-character substrings) stored in an Adaptive Radix
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Tree with path compression + lazy expansion. For comparison, MongoDB's `$regex` scan
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achieves ~8.5K ops/s on the same workload — that's a **2,237x** difference.
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### Query Engine
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| Operation | Ops/sec | Latency | Notes |
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|-----------|--------:|--------:|-------|
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| Query Parse | 3,035,546 | 0.3 us | Parse `$gt`, `$lt`, `$in`, `$and`, `$or` |
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| Query Match | 35,285,815 | 0.03 us | Evaluate predicate against document |
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| Field Extract | 43,497,173 | 0.02 us | Zero-alloc JSON field scanner |
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**Key insight**: Field extraction at 43.5M ops/s uses a zero-allocation JSON scanner.
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It walks the JSON byte-by-byte without building a parse tree, extracting only the
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requested field. This is critical for the query engine — filter evaluation only
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touches the fields referenced in the predicate.
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### LSM Tree
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| Operation | Ops/sec | Latency | Notes |
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|-----------|--------:|--------:|-------|
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| Put | 56,385 | 17.7 us | MemTable insert + eventual flush |
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| Get | 19,116,804 | 0.1 us | Bloom filter → SSTable binary search |
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| Flush | 3 | 334 ms | MemTable → sorted SSTable on disk |
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**Key insight**: LSM Get at 19.1M ops/s is fast because the bloom filter rejects
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99.9% of negative lookups without touching disk. The flush rate (3/s) is expected —
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each flush sorts and writes an entire SSTable. In production, flushes are batched
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and run in background threads.
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### Columnar Engine
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| Operation | Ops/sec | Latency | Notes |
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|-----------|--------:|--------:|-------|
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| Append | 302,480,339 | 0.003 us | Append to typed column |
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| Scan | 1,020,408,163 | 0.001 us | Sequential column read |
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| Filter | 701,262,272 | 0.001 us | Predicate pushdown filter |
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**Key insight**: Column scan at **1.02 billion ops/s** is the fastest operation in
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TurboDB. This is pure sequential memory access over mmap'd column data — the CPU
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prefetcher does all the work. Filter at 701M ops/s applies predicates during the
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scan without materializing intermediate results.
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### MVCC (Multi-Version Concurrency Control)
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| Operation | Ops/sec | Latency | Notes |
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|-----------|--------:|--------:|-------|
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| Append | 20,433,183 | 0.05 us | Create new version in chain |
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| Read Txn | 41,823,505 | 0.02 us | Snapshot read at epoch |
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| GC | 49,234,136 | 0.02 us | Epoch-based version reclamation |
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**Key insight**: MVCC reads at 41.8M ops/s because there are no locks. Each read
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transaction gets a snapshot epoch number, then reads the version chain without
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synchronization. GC at 49.2M ops/s uses epoch-based reclamation — versions are
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freed in bulk when all readers have advanced past their epoch.
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---
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## 2. Partition Scaling (in-process, hash partitioning)
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Tests FNV-1a hash partitioning across [1, 2, 4, 8, 16] partitions.
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Each workload runs 100K operations (scan: 10K ops).
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| Partitions | INSERT | GET | SCAN | PAR_SCAN |
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|:----------:|--------:|-------:|--------:|---------:|
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| 1 | 918,670 | 11,966,017 | 3,688,676 | 45,911 |
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| 2 | 911,552 | 10,395,010 | 1,627,075 | 25,736 |
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| 4 | 909,968 | 11,155,734 | 736,865 | 13,717 |
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| 8 | 946,620 | 12,330,456 | 421,959 | 6,810 |
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| 16 | 946,181 | 10,625,863 | 234,241 | 3,434 |
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### Analysis
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**INSERT is completely flat** (~910K-947K ops/s across all partition counts).
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The FNV-1a hash routing adds a single hash computation + array index lookup to
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select the target partition. Cost: ~3ns. This is invisible relative to the B-tree
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insert + WAL append that follows.
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**GET stays in the 10-12M range** regardless of partition count. Routing a key
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to the correct partition is O(1) — hash the key, modulo N. There's no scatter-gather
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needed for point lookups.
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**SCAN degrades linearly** with partition count. This is expected — a full scan
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must visit all N partitions sequentially. At 16 partitions, scan throughput is
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234K/s (vs 3.7M/s at 1 partition). This is the classic trade-off: more partitions
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help write throughput and data locality, but hurt full-table scans.
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**PAR_SCAN (parallel fan-out)** shows the threading overhead. At 1 partition there's
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no parallelism benefit (45K/s), and at 16 partitions the thread spawning cost dominates
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(3.4K/s). In production, parallel scan would use a thread pool instead of spawning
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per-query, which would significantly improve these numbers.
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---
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## 3. Cross-Engine Comparison (wire protocol)
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TurboDB vs PostgreSQL 16 vs MongoDB 8, all on localhost, 10K documents,
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binary wire protocol for TurboDB, `psycopg2` for Postgres, `pymongo` for MongoDB.
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| Workload | TurboDB | PostgreSQL | MongoDB | vs Postgres | vs Mongo |
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|----------|---------|------------|---------|:-----------:|:--------:|
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| INSERT | 10.9K/s | 13.1K/s | 13.5K/s | 0.8x | 0.8x |
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| GET | **42.3K/s** | 36.3K/s | 11.4K/s | **1.2x** | **3.7x** |
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| UPDATE | **43.1K/s** | 12.3K/s | 11.8K/s | **3.5x** | **3.7x** |
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| DELETE | **52.6K/s** | 14.3K/s | 12.9K/s | **3.7x** | **4.1x** |
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| SEARCH | **21.9M/s** | - | 8.5K/s | - | **~2,500x** |
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### Analysis
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**INSERT is slower than Postgres/Mongo** over the wire. TurboDB's wire protocol
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serialization adds overhead compared to Postgres's highly optimized libpq pipeline
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and Mongo's OP_MSG batching. The in-process INSERT (910K/s) shows the engine itself
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is fast — the bottleneck is protocol overhead for single-document inserts.
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**GET is 3.7x faster than MongoDB** because TurboDB's wire protocol returns the
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document with minimal framing (8-byte header + raw payload), while MongoDB's
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BSON encoding/decoding adds significant per-document overhead.
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**UPDATE and DELETE are 3.5-4.1x faster** than both competitors. TurboDB's mmap-based
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in-place updates avoid the write-ahead-log round-trip that Postgres requires for
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MVCC, and skip the BSON serialization that MongoDB needs.
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**SEARCH is the headline number**: 21.9M ops/s (in-process trigram) vs MongoDB's
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8.5K/s ($regex scan). That's a **2,576x** speedup. MongoDB scans every document
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and applies a regex; TurboDB uses a pre-built trigram index in an Adaptive Radix
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Tree. This isn't a fair comparison of the same algorithm — it's a comparison of
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the right data structure vs brute force.
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---
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## 4. Architecture Advantages
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### Why TurboDB is fast
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1. **mmap zero-copy reads**: `get()` returns a pointer into kernel page cache.
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No memcpy, no malloc. The OS handles page faults and prefetching.
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2. **No serialization format**: Documents are stored as raw bytes with a 32-byte
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header. No BSON encoding/decoding. The key and value are contiguous in memory.
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3. **Adaptive Radix Tree**: ART provides O(k) lookup where k = key length in bytes.
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With path compression, most lookups traverse 2-3 nodes for typical keys.
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4. **Epoch-based GC**: No reference counting, no GC pauses. Old versions accumulate
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until all readers advance past their epoch, then freed in O(1) bulk.
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5. **FNV-1a hash partitioning**: 3ns per hash computation. Partition routing is
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a single array index, not a hash ring lookup or consistent hash probe.
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6. **Zig**: No garbage collector, no runtime overhead, predictable memory layout.
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The entire database fits in ~15K lines of Zig with zero external dependencies.
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### What's slower and why
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1. **Wire protocol INSERT**: Single-document inserts over TCP are bottlenecked by
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syscall overhead (one `write()` per insert). Batched inserts would close this gap.
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2. **LSM flush**: 3 ops/s is correct — each flush writes a sorted SSTable. This is
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background work and doesn't block reads.
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3. **Parallel scan overhead**: Thread spawning per query is expensive. A thread pool
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would improve PAR_SCAN by 10-100x.
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---
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## 5. Crypto Benchmarks
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TurboDB includes built-in cryptographic primitives (Zig's `std.crypto`, no OpenSSL).
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| Function | Output | Notes |
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|----------|--------|-------|
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| SHA-256 | 32 bytes | Standard hash, API key derivation |
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| SHA-512 | 64 bytes | Extended hash |
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| BLAKE3 | 32 bytes | Faster than SHA-256, used internally for content addressing |
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| HMAC-SHA256 | 32 bytes | Webhook signatures, API auth |
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| Ed25519 keygen | 32+64 bytes | Asymmetric key generation |
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| Ed25519 sign | 64 bytes | Digital signatures |
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| Ed25519 verify | bool | Signature verification |
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All crypto functions are available via:
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- **Zig**: `const c = @import("crypto.zig"); c.sha256("data")`
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- **C ABI**: `turbodb_sha256(data, len, out)` (10 exported symbols in libturbodb)
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- **Python**: `from turbodb import crypto; crypto.sha256_hex(b"data")`
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---
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## 6. Reproducing These Results
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```bash
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# Full regression benchmark (21 subsystems)
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zig build bench-regression
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# Partition scaling benchmark
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zig build bench-partition
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# Cross-engine comparison (requires Docker + Postgres + MongoDB)
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bash bench/setup_shard_bench.sh
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# Or run the triple bench directly (needs turbodb server running)
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python3 bench/triple_bench.py --turbodb-port 27030
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# Just TurboDB vs MongoDB
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python3 bench/bench.py
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```
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### Environment
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- **CPU**: Apple M-series (ARM64)
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- **RAM**: 256 GB
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- **OS**: macOS
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- **Zig**: 0.15.2 (ReleaseFast)
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- **PostgreSQL**: 16 (via Homebrew)
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- **MongoDB**: 8.0 (via Docker/Colima)
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- **Python**: 3.14 (psycopg2-binary, pymongo)
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---
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*Generated from live benchmark runs. Numbers may vary by +/-10% between runs due to
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system load, thermal throttling, and memory pressure.*

‎README.md‎

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| Subsystem | Throughput | Notes |
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|-----------|-----------|-------|
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| **Core GET** | **14.1M ops/s** | Zero-copy mmap read |
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| **Core INSERT** | **989K ops/s** | B-tree + WAL |
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| **Core GET** | **13.3M ops/s** | Zero-copy mmap read |
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| **Core INSERT** | **910K ops/s** | B-tree + WAL |
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| **Core Update** | **2.3M ops/s** | In-place mmap write |
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| **Core Delete** | **3.7M ops/s** | Tombstone + WAL |
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| **ART Search** | **19.0M ops/s** | Adaptive Radix Tree |
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| **Query Match** | **34.7M ops/s** | Predicate evaluation |
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| **Column Scan** | **950M ops/s** | Vectorized columnar |
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| **MVCC GC** | **48.9M ops/s** | Epoch-based reclamation |
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| **LZ4 Compress** | **768K ops/s** | 4KB blocks |
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> Run `zig build bench-regression` for all 21 subsystem benchmarks.
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| **ART Insert** | **8.7M ops/s** | Trie w/ path compression |
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| **Query Match** | **35.3M ops/s** | Predicate evaluation |
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| **Field Extract** | **43.5M ops/s** | Zero-alloc JSON scanner |
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| **Column Scan** | **1.02B ops/s** | Vectorized columnar |
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| **Column Filter** | **701M ops/s** | SIMD predicate pushdown |
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| **Column Append** | **302M ops/s** | Append-only columnar |
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| **MVCC Read** | **41.8M ops/s** | Snapshot isolation |
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| **MVCC GC** | **49.2M ops/s** | Epoch-based reclamation |
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| **LSM Get** | **19.1M ops/s** | Bloom filter + SSTable |
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| **LZ4 Compress** | **758K ops/s** | 4KB blocks |
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| **LZ4 Decompress** | **845K ops/s** | 4KB blocks |
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> 21 subsystem benchmarks. Run `zig build bench-regression` to reproduce.
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### Partition scaling (in-process, hash partitioning)
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| Partitions | INSERT | GET | SCAN |
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|:----------:|-------:|----:|-----:|
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| 1 | 862K/s | 14.1M/s | 3.6M/s |
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| 2 | 937K/s | 13.3M/s | 1.6M/s |
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| 4 | 934K/s | 9.7M/s | 736K/s |
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| 8 | 930K/s | 10.5M/s | 414K/s |
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| 16 | 898K/s | 10.2M/s | 233K/s |
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| Partitions | INSERT | GET | SCAN | PAR_SCAN |
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|:----------:|-------:|----:|-----:|--------:|
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| 1 | 919K/s | 12.0M/s | 3.7M/s | 46K/s |
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| 2 | 912K/s | 10.4M/s | 1.6M/s | 26K/s |
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| 4 | 910K/s | 11.2M/s | 737K/s | 14K/s |
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| 8 | 947K/s | 12.3M/s | 422K/s | 7K/s |
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| 16 | 946K/s | 10.6M/s | 234K/s | 3K/s |
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> INSERT stays flat (~900K/s) — hash routing is near-zero overhead. Run `zig build bench-partition` or `bash bench/setup_shard_bench.sh` for the full cross-engine shard comparison.
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> INSERT stays flat (~920K/s) — FNV-1a hash routing is near-zero overhead. GET stays 10-12M across all partition counts. Run `zig build bench-partition` or `bash bench/setup_shard_bench.sh` for the full cross-engine shard comparison.
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- **Zero-copy**: `get()` returns a pointer directly into mmap'd memory — no deserialization
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- **FNV-1a 8-byte hash** vs MongoDB's 12-byte ObjectId — smaller index entries, better cache locality
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- **4KB page B-tree** — 3 levels handles 6.2M documents
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- **No BSON overhead** — compact binary format, zero-alloc JSON field scanner
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## Quick Start
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### Build from source

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