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fjall-guide

ECSdb中使用Fjall存储引擎的参考指南

SKILL.md
--- frontmatter
name: fjall-guide
description: Reference guide for using fjall storage engine in ECSdb

fjall Storage Engine Guide

fjall is the embedded LSM-tree storage engine that powers ECSdb. This guide covers its API and best practices.

fjall Overview

fjall is a pure Rust LSM-tree (Log-Structured Merge-tree) storage engine. It provides:

  • •Write-Ahead Log (WAL) for durability
  • •MemTable (skip list) for in-memory buffering
  • •SSTables (Sorted String Tables) for on-disk storage
  • •Background compaction
  • •Snapshots for consistent reads
  • •Partitions (our "column families")

Core Concepts

Keyspace

The top-level container. One keyspace per database.

rust
use fjall::{Config, Keyspace};

let keyspace = Config::new(&data_dir)
    .max_write_buffer_size(64 * 1024 * 1024)  // 64MB memtable
    .open()?;

Partition

Like a column family - a separate key-value namespace.

rust
let partition = keyspace.open_partition("component_Position", Default::default())?;

Basic Operations

rust
// Write
partition.insert(key, value)?;

// Read
let value = partition.get(key)?; // Returns Option<Arc<[u8]>>

// Delete
partition.remove(key)?;

// Persist (sync to disk)
keyspace.persist()?;

Range Scans

rust
// Prefix scan
for result in partition.prefix(b"entity_") {
    let (key, value) = result?;
    // process
}

// Range scan
use std::ops::Bound;
for result in partition.range(start_key..end_key) {
    let (key, value) = result?;
}

Snapshots

For consistent reads (MVCC):

rust
let snapshot = keyspace.snapshot();
let value = partition.get_with_snapshot(key, &snapshot)?;

ECSdb Usage Patterns

Column Family Naming

rust
const METADATA_CF: &str = "_metadata";
const EDGES_CF: &str = "_edges";
const SCHEMAS_CF: &str = "_schemas";

fn component_cf(name: &str) -> String {
    format!("component_{}", name)
}

Key Encoding

Keys must be lexicographically sortable:

rust
// Entity key: [uuid:16]
fn encode_entity_key(id: Uuid) -> [u8; 16] {
    *id.as_bytes()
}

// Component key: [entity_id:16][version:8]
fn encode_component_key(entity_id: Uuid, version: u64) -> [u8; 24] {
    let mut key = [0u8; 24];
    key[0..16].copy_from_slice(entity_id.as_bytes());
    key[16..24].copy_from_slice(&version.to_be_bytes());
    key
}

Batch Writes

For atomicity:

rust
// fjall doesn't have explicit batches like RocksDB
// Use persist() after a group of writes
partition1.insert(key1, value1)?;
partition2.insert(key2, value2)?;
keyspace.persist()?;  // Atomic durability point

Error Handling

rust
use fjall::Result as FjallResult;

fn storage_op() -> Result<(), StorageError> {
    partition.insert(key, value)
        .map_err(|e| StorageError::Backend(e.to_string()))?;
    Ok(())
}

Performance Tuning

MemTable Size

Larger = fewer flushes, more memory:

rust
Config::new(&path)
    .max_write_buffer_size(128 * 1024 * 1024)  // 128MB for write-heavy

Block Cache

For read-heavy workloads:

rust
Config::new(&path)
    .block_cache_capacity(512 * 1024 * 1024)  // 512MB cache

Compression

fjall handles compression internally. Configure via partition config if available.

Common Pitfalls

  1. •Forgetting to persist: Writes are in memory until persist()
  2. •Key ordering: Use big-endian for numeric keys
  3. •Partition lifecycle: Open partitions are cached, don't reopen frequently
  4. •Snapshot scope: Snapshots hold resources, drop when done

Testing with fjall

rust
#[cfg(test)]
mod tests {
    use tempfile::TempDir;

    fn create_test_keyspace() -> Keyspace {
        let temp = TempDir::new().unwrap();
        Config::new(temp.path()).open().unwrap()
    }

    #[test]
    fn test_basic_ops() {
        let ks = create_test_keyspace();
        let part = ks.open_partition("test", Default::default()).unwrap();

        part.insert(b"key", b"value").unwrap();
        assert_eq!(part.get(b"key").unwrap().as_deref(), Some(b"value".as_slice()));
    }
}

Documentation Links