Optimization

Practical tips for optimizing OpenMLS performance

OpenMLS provides a high-performance implementation of the MLS protocol, but optimal performance depends on how you configure and use the library. This guide covers key optimization strategies.

Choose the right ciphersuite

Ciphersuite selection significantly impacts performance. OpenMLS supports multiple ciphersuites with different performance characteristics:

Performance-optimized

use openmls::prelude::*;

// ChaCha20Poly1305 with X25519 offers excellent performance
let ciphersuite = Ciphersuite::MLS_128_DHKEMX25519_CHACHA20POLY1305_SHA256_Ed25519;

let config = MlsGroupCreateConfig::builder()
    .ciphersuite(ciphersuite)
    .build();

ChaCha20Poly1305 ciphersuites typically perform better on systems without AES hardware acceleration, while AES-GCM ciphersuites excel when hardware support is available.

Optimize key package generation

Key package creation is a common operation that can be optimized:

use openmls::prelude::*;
use openmls_basic_credential::SignatureKeyPair;

// Pre-generate key packages in batches
let mut key_packages = Vec::new();
for _ in 0..10 {
    let key_package = KeyPackage::builder()
        .build(
            ciphersuite,
            provider,
            &signer,
            credential_with_key.clone(),
        )?;
    key_packages.push(key_package);
}

Pre-generating key packages allows you to respond quickly to new group invitations without blocking on cryptographic operations.

Group size considerations

Performance characteristics change with group size. Consider these factors:

Small groups (2-10 members)

• Commit operations are fast (typically < 10ms)
• Message encryption/decryption is minimal overhead
• Updates propagate quickly

Medium groups (10-100 members)

• Commit creation time scales with group size
• Consider batching member additions when creating groups
• Monitor ratchet tree depth and balance

Large groups (100+ members)

• Use PURE_PLAINTEXT_WIRE_FORMAT_POLICY for better performance if your transport provides encryption
• Batch operations when possible
• Consider the tree state when optimizing update frequency

use openmls::group::PURE_PLAINTEXT_WIRE_FORMAT_POLICY;

let config = MlsGroupCreateConfig::builder()
    .wire_format_policy(PURE_PLAINTEXT_WIRE_FORMAT_POLICY)
    .ciphersuite(ciphersuite)
    .build();

Tree state optimization

The ratchet tree’s state affects performance:

Fresh tree after creation

Operations are fastest immediately after group creation when the tree is fully balanced and all members have just joined.

After many updates

A tree where many members have sent updates may have different performance characteristics. Consider the update frequency in your application.

Blank nodes

Trees with many blank nodes (after member removals) may impact performance. Large-scale removals followed by additions can help rebalance the tree.

Storage provider selection

Your choice of storage provider impacts I/O performance:

Memory storage

Fastest for testing and development. No persistence.

openmls_memory_storage = "0.5.0"

SQLite storage

Good balance of performance and persistence for most applications.

openmls_sqlite_storage = "0.2.0"

Commit and update strategies

Batching operations

When adding multiple members, batch them in a single commit:

// Efficient: Single commit for multiple members
let (commit, welcome, _) = group.add_members(
    provider,
    &signer,
    &[key_package1, key_package2, key_package3],
)?;

group.merge_pending_commit(provider)?;

Update frequency

Balance security and performance when deciding update frequency:

1. Evaluate your threat model

   Determine how often you need forward secrecy updates based on your security requirements.

2. Monitor group activity

   Send updates based on message count or time intervals that make sense for your application.

3. Consider group size

   Larger groups have higher costs for processing updates, so adjust frequency accordingly.

Message processing

Optimize how you process incoming messages:

// Process messages efficiently
let processed = group.process_message(provider, message)?;

match processed.into_content() {
    ProcessedMessageContent::ApplicationMessage(app_msg) => {
        // Handle application message
    }
    ProcessedMessageContent::StagedCommitMessage(staged_commit) => {
        // Merge commit immediately or defer based on your needs
        group.merge_staged_commit(provider, *staged_commit)?;
    }
    _ => {}
}

Don’t forget to call merge_pending_commit() after creating commits or merge_staged_commit() after processing commits. Delayed merging can impact subsequent operations.

Crypto provider selection

OpenMLS supports multiple crypto providers with different performance profiles:

# In Cargo.toml
[dependencies]
openmls = "0.8.1"

# Choose one crypto provider:
openmls_rust_crypto = "0.5.1"      # Pure Rust, good portability
openmls_libcrux_crypto = "0.3.1"   # Optimized implementation

Run benchmarks with your specific workload to determine which crypto provider performs best for your use case.

Monitoring and profiling

Measure performance in your specific context:

use std::time::Instant;

let start = Instant::now();
let (commit, welcome, _) = group.add_members(provider, &signer, &[key_package])?;
let duration = start.elapsed();

println!("Add member took: {:?}", duration);

Use Rust’s built-in benchmarking tools or Criterion.rs to establish performance baselines for your application.

Common scenarios

Stable groups

For groups with infrequent membership changes:

• Optimize for message encryption/decryption speed
• Send periodic updates based on message count (e.g., every 1000 messages)
• Pre-generate key packages for potential new members

High-fluctuation groups

For groups with frequent joins and leaves (e.g., public channels):

• Batch membership changes when possible
• Consider shorter update intervals to keep tree balanced
• Monitor tree blank node ratio
• Use efficient storage backend for high write volume

Long-offline devices

When devices may process many messages after being offline:

• Process messages in batches if your protocol allows
• Consider maximum catchup limits
• Implement efficient message queuing

Next steps

Benchmarks

See detailed benchmark results and methodology.

API reference

Explore the complete OpenMLS API.