Marmot v2 is a leaderless, distributed SQLite replication system built on a gossip-based protocol with distributed transactions and eventual consistency.

Key Features:

• Leaderless Architecture: No single point of failure - any node can accept writes
• MySQL Protocol Compatible: Connect with any MySQL client (DBeaver, MySQL Workbench, mysql CLI)
• WordPress Compatible: Full MySQL function support for running distributed WordPress
• Distributed Transactions: Percolator-style write intents with conflict detection
• Multi-Database Support: Create and manage multiple databases per cluster
• DDL Replication: Distributed schema changes with automatic idempotency and cluster-wide locking
• Production-Ready SQL Parser: Powered by rqlite/sql AST parser for MySQL→SQLite transpilation
• CDC-Based Replication: Row-level change data capture for consistent replication

MySQL active-active requires careful setup of replication, conflict avoidance, and monitoring. Failover needs manual intervention. Split-brain scenarios demand operational expertise. This complexity doesn't scale to edge deployments.

• Zero operational overhead: Automatic recovery from split-brain via eventual consistency + anti-entropy
• No leader election: Any node accepts writes, no failover coordination needed
• Direct SQLite access: Clients can read the local SQLite file directly for maximum performance
• Tunable consistency: Choose ONE/QUORUM/ALL per your latency vs durability needs

• Ecosystem compatibility - existing drivers, ORMs, GUI tools work out-of-box
• Battle-tested wire protocol implementations
• Run real applications like WordPress without modification

Marmot excels at read-heavy edge scenarios:

• Strong serializability required → CockroachDB, Spanner
• Single-region high-throughput → PostgreSQL, MySQL directly
• Large datasets (>100GB) → Sharded solutions

# Start a single-node cluster
./marmot-v2

# Connect with MySQL client
mysql -h localhost -P 3306 -u root

# Or use DBeaver, MySQL Workbench, etc.

# Test DDL and DML replication across a 2-node cluster
./scripts/test-ddl-replication.sh

# This script will:
# 1. Start a 2-node cluster
# 2. Create a table on node 1 and verify it replicates to node 2
# 3. Insert data on node 1 and verify it replicates to node 2
# 4. Update data on node 2 and verify it replicates to node 1
# 5. Delete data on node 1 and verify it replicates to node 2

# Manual cluster testing
./examples/start-seed.sh              # Start seed node (port 8081, mysql 3307)
./examples/join-cluster.sh 2 localhost:8081  # Join node 2 (port 8082, mysql 3308)
./examples/join-cluster.sh 3 localhost:8081  # Join node 3 (port 8083, mysql 3309)

# Connect to any node and run queries
mysql --protocol=TCP -h localhost -P 3307 -u root
mysql --protocol=TCP -h localhost -P 3308 -u root

# Cleanup
pkill -f marmot-v2

Marmot can run distributed WordPress with full database replication across nodes. Each WordPress instance connects to its local Marmot node, and all database changes replicate automatically.

Marmot implements MySQL functions required by WordPress:

cd examples/wordpress-cluster
./run.sh up

This starts:

• 3 Marmot nodes with QUORUM write consistency
• 3 WordPress instances each connected to its local Marmot node

┌─────────────┐  ┌─────────────┐  ┌─────────────┐
│ WordPress-1 │  │ WordPress-2 │  │ WordPress-3 │
│ :9101       │  │ :9102       │  │ :9103       │
└──────┬──────┘  └──────┬──────┘  └──────┬──────┘
       ▼                ▼                ▼
┌─────────────┐  ┌─────────────┐  ┌─────────────┐
│  Marmot-1   │◄─┤  Marmot-2   │◄─┤  Marmot-3   │
│ MySQL: 9191 │  │ MySQL: 9192 │  │ MySQL: 9193 │
└─────────────┘  └─────────────┘  └─────────────┘
       └──────────────┴──────────────┘
              QUORUM Replication

Test it:

1. Open http://localhost:9101 - complete WordPress installation
2. Open http://localhost:9102 or http://localhost:9103
3. See your content replicated across all nodes!

Commands:

./run.sh status   # Check cluster health
./run.sh logs-m   # Marmot logs only
./run.sh logs-wp  # WordPress logs only
./run.sh down     # Stop cluster

• Media uploads: Use S3/NFS for shared media storage (files not replicated by Marmot)
• Sessions: Use Redis or database sessions for sticky-session-free load balancing
• Caching: Each node can use local object cache (Redis/Memcached per region)

Marmot v2 uses a fundamentally different architecture from other SQLite replication solutions:

vs. rqlite/dqlite/LiteFS:

• ❌ They require a primary node for all writes
• ✅ Marmot allows writes on any node
• ❌ They use leader election (Raft)
• ✅ Marmot uses gossip protocol (no leader)
• ❌ They require proxy layer or page-level interception
• ✅ Marmot uses MySQL protocol for direct database access

How It Works:

1. Write Coordination: 2PC (Two-Phase Commit) with configurable consistency (ONE, QUORUM, ALL)
2. Conflict Resolution: Last-Write-Wins (LWW) with HLC timestamps
3. Cluster Membership: SWIM-style gossip with failure detection
4. Data Replication: Full database replication - all nodes receive all data
5. DDL Replication: Cluster-wide schema changes with automatic idempotency

Marmot v2 supports distributed DDL (Data Definition Language) replication without requiring master election:

1. Cluster-Wide Locking: Each DDL operation acquires a distributed lock per database (default: 30-second lease)

   • Prevents concurrent schema changes on the same database
   • Locks automatically expire if a node crashes
   • Different databases can have concurrent DDL operations

2. Automatic Idempotency: DDL statements are automatically rewritten for safe replay

   CREATE TABLE users (id INT)
   → CREATE TABLE IF NOT EXISTS users (id INT)
   
   DROP TABLE users
   → DROP TABLE IF EXISTS users

3. Schema Version Tracking: Each database maintains a schema version counter

   • Incremented on every DDL operation
   • Exchanged via gossip protocol for drift detection
   • Used by delta sync to validate transaction applicability

4. Quorum-Based Replication: DDL replicates like DML through the same 2PC mechanism

   • No special master node needed
   • Works with existing consistency levels (QUORUM, ALL, etc.)

[ddl]
# DDL lock lease duration (seconds)
lock_lease_seconds = 30

# Automatically rewrite DDL for idempotency
enable_idempotent = true

• ✅ Do: Execute DDL from a single connection/node at a time
• ✅ Do: Use qualified table names (mydb.users instead of users)
• ⚠️ Caution: ALTER TABLE is less idempotent - avoid replaying failed ALTER operations
• ❌ Don't: Run concurrent DDL on the same database from multiple nodes

Marmot v2 uses Change Data Capture (CDC) for replication instead of SQL statement replay:

1. Row-Level Capture: Instead of replicating SQL statements, Marmot captures the actual row data changes (INSERT/UPDATE/DELETE)
2. Binary Data Format: Row data is serialized as CDC messages with column values, ensuring consistent replication regardless of SQL dialect
3. Deterministic Application: Row data is applied directly to the target database, avoiding parsing ambiguities

• Consistency: Same row data applied everywhere, no SQL parsing differences
• Performance: Binary format is more efficient than SQL text
• Reliability: No issues with SQL syntax variations between MySQL and SQLite

For UPDATE and DELETE operations, Marmot automatically extracts row keys:

• Uses PRIMARY KEY columns when available
• Falls back to ROWID for tables without explicit primary key
• Handles composite primary keys correctly

Marmot can publish CDC events to external messaging systems, enabling real-time data pipelines, analytics, and event-driven architectures. Events follow the Debezium specification for maximum compatibility with existing CDC tooling.

• Debezium-Compatible Format: Events conform to the Debezium event structure, compatible with Kafka Connect, Flink, Spark, and other CDC consumers
• Multi-Sink Support: Publish to multiple destinations simultaneously (Kafka, NATS)
• Glob-Based Filtering: Filter which tables and databases to publish
• Automatic Retry: Exponential backoff with configurable limits
• Persistent Cursors: Survives restarts without losing position

[publisher]
enabled = true

[[publisher.sinks]]
name = "kafka-main"
type = "kafka"                    # "kafka" or "nats"
format = "debezium"               # Debezium-compatible JSON format
brokers = ["localhost:9092"]      # Kafka broker addresses
topic_prefix = "marmot.cdc"       # Topics: {prefix}.{database}.{table}
filter_tables = ["*"]             # Glob patterns (e.g., "users", "order_*")
filter_databases = ["*"]          # Glob patterns (e.g., "prod_*")
batch_size = 100                  # Events per poll cycle
poll_interval_ms = 10             # Polling interval

# NATS sink example
[[publisher.sinks]]
name = "nats-events"
type = "nats"
format = "debezium"
nats_url = "nats://localhost:4222"
topic_prefix = "marmot.cdc"
filter_tables = ["*"]
filter_databases = ["*"]

Events follow the Debezium envelope structure:

{
  "schema": { ... },
  "payload": {
    "before": null,
    "after": {"id": 1, "name": "alice", "email": "[email protected]"},
    "source": {
      "version": "2.0.0",
      "connector": "marmot",
      "name": "marmot",
      "ts_ms": 1702500000000,
      "db": "myapp",
      "table": "users"
    },
    "op": "c",
    "ts_ms": 1702500000000
  }
}

Operation Types (per Debezium spec):

Topics follow the pattern: {topic_prefix}.{database}.{table}

Examples:

• marmot.cdc.myapp.users
• marmot.cdc.myapp.orders
• marmot.cdc.analytics.events

• Real-Time Analytics: Stream changes to data warehouses (Snowflake, BigQuery, ClickHouse)
• Event-Driven Microservices: Trigger actions on data changes
• Cache Invalidation: Keep caches in sync with database changes
• Audit Logging: Capture all changes for compliance
• Search Indexing: Keep Elasticsearch/Algolia in sync

For more details, see the Integrations documentation.

Deploy Marmot as a lightweight regional replica alongside Lambda functions:

• Local SQLite reads (sub-ms latency)
• Writes replicate to cluster
• No cold-start database connections

Scale reads globally with replica mode and transparent failover:

[replica]
enabled = true
follow_addresses = ["central-cluster-1:8080", "central-cluster-2:8080", "central-cluster-3:8080"]
replicate_databases = []                    # Filter databases (empty = all, supports glob patterns)
database_discovery_interval_seconds = 10    # Poll for new databases (default: 10)
discovery_interval_seconds = 30             # Poll for cluster membership (default: 30)
failover_timeout_seconds = 60               # Failover timeout (default: 60)
snapshot_concurrency = 3                    # Parallel snapshot downloads (default: 3)
snapshot_cache_ttl_seconds = 30             # Snapshot cache TTL (default: 30)

• Discovers cluster nodes automatically via GetClusterNodes RPC
• Transparent failover when current source becomes unavailable
• Automatic discovery of new databases with configurable polling
• Per-database selective replication with glob pattern support
• Parallel snapshot downloads with configurable concurrency
• Snapshot caching for performance optimization
• Zero cluster participation overhead
• Auto-reconnect with exponential backoff

• Deploy full cluster in central region
• Deploy read replicas at edge locations
• Application routes writes to central, reads to local replica

Marmot supports a wide range of MySQL/SQLite statements through its MySQL protocol server. The following table shows compatibility for different statement types:

• ✅ Full: Fully supported and working
• ✅ Parsed: Statement is parsed and recognized
• ⚠️ Limited: Works but has limitations in distributed context
• ❌ No: Not supported or not replicated
• N/A: Not applicable (read-only or session-local)

1. Schema Changes (DDL): DDL statements are fully replicated with cluster-wide locking and automatic idempotency. See the DDL Replication section for details.

2. XA Transactions: Marmot has its own distributed transaction protocol based on 2PC. MySQL XA transactions are not compatible with Marmot's replication model.

3. User Management (DCL): User and privilege management statements are local to each node. For production deployments, consider handling authentication at the application or proxy level.

4. Table Locking: LOCK TABLES statements are recognized but not enforced across the cluster. Use application-level coordination for distributed locking needs.

5. Qualified Names: Marmot fully supports qualified table names (e.g., db.table) in DML and DDL operations.

Marmot includes a MySQL-compatible protocol server, allowing you to connect using any MySQL client (DBeaver, MySQL Workbench, mysql CLI, etc.). The server supports:

Marmot provides full support for MySQL metadata queries, enabling GUI tools like DBeaver to browse databases, tables, and columns:

• SHOW Commands: SHOW DATABASES, SHOW TABLES, SHOW COLUMNS FROM table, SHOW CREATE TABLE, SHOW INDEXES
• INFORMATION_SCHEMA: Queries against INFORMATION_SCHEMA.TABLES, INFORMATION_SCHEMA.COLUMNS, INFORMATION_SCHEMA.SCHEMATA, and INFORMATION_SCHEMA.STATISTICS
• Type Conversion: Automatic SQLite-to-MySQL type mapping for compatibility

These metadata queries are powered by the rqlite/sql AST parser, providing production-grade MySQL query compatibility.

# Using mysql CLI
mysql -h localhost -P 3306 -u root

# Connection string for applications
mysql://root@localhost:3306/marmot

Marmot handles various failure and recovery scenarios automatically:

How it works:

1. During partition, only the majority side can commit writes (quorum enforcement)
2. When partition heals, nodes exchange transaction logs via StreamChanges RPC
3. Conflicts resolved using Last-Writer-Wins (LWW) with HLC timestamps
4. Higher node ID breaks ties for simultaneous writes

Anti-Entropy Background Process:

Marmot v2 includes an automatic anti-entropy system that continuously monitors and repairs replication lag across the cluster:

1. Lag Detection: Every 60 seconds (configurable), each node queries peers for their replication state
2. Smart Recovery Decision:
   • Delta Sync if lag < 10,000 transactions AND < 1 hour: Streams missed transactions incrementally
   • Snapshot Transfer if lag exceeds thresholds: Full database file transfer for efficiency
3. Gap Detection: Detects when transaction logs have been GC'd and automatically falls back to snapshot
4. Multi-Database Support: Tracks and syncs each database independently
5. GC Coordination: Garbage collection respects peer replication state - logs aren't deleted until all peers have applied them

Delta Sync Process:

1. Lagging node queries last_applied_txn_id for each peer/database
2. Requests transactions since that ID via StreamChanges RPC
3. Gap Detection: Checks if first received txn_id has a large gap from requested ID
   • If gap > delta_sync_threshold_txns, indicates missing (GC'd) transactions
   • Automatically falls back to snapshot transfer to prevent data loss
4. Applies changes using LWW conflict resolution
5. Updates replication state tracking (per-database)
6. Progress logged every 100 transactions

GC Coordination with Anti-Entropy:

• Transaction logs are retained with a two-tier policy:
  • Min retention (2 hours): Must be >= delta sync threshold, respects peer lag
  • Max retention (24 hours): Force delete after this time to prevent unbounded growth
• Config validation enforces: gc_min >= delta_threshold and gc_max >= 2x delta_threshold
• Each database tracks replication progress per peer
• GC queries minimum applied txn_id across all peers before cleanup
• Gap detection prevents data loss if GC runs while nodes are offline

Conflict Resolution:

• All conflicts resolved via LWW using HLC timestamps
• No data loss - later write always wins deterministically
• Tie-breaker: higher node ID wins for equal timestamps

• Selective Table Watching: All tables in a database are replicated. Selective table replication is not supported.
• WAL Mode Required: SQLite must use WAL mode for reliable multi-process changes.
• Eventually Consistent: Rows may sync out of order. SERIALIZABLE transaction assumptions may not hold across nodes.
• Concurrent DDL: Avoid running concurrent DDL operations on the same database from multiple nodes (protected by cluster-wide lock with 30s lease).

Distributed databases need globally unique IDs, but traditional solutions cause problems:

The JavaScript Problem:

// 64-bit ID from TiDB or other distributed DBs
const id = 7318624812345678901;
console.log(id);  // 7318624812345679000 - WRONG! Precision lost!

// JSON parsing also breaks
JSON.parse('{"id": 7318624812345678901}');  // {id: 7318624812345679000}

TiDB's answer? "Use strings." But that breaks ORMs, existing application code, and type safety.

Marmot offers two ID generation modes to solve this:

[mysql]
auto_id_mode = "compact"  # Safe for JavaScript (default)
# auto_id_mode = "extended"  # Full 64-bit for new systems

Compact Mode Guarantees:

• IDs stay under Number.MAX_SAFE_INTEGER (9,007,199,254,740,991)
• Still globally unique across all nodes
• Still monotonically increasing (per node)
• No silent precision loss in JSON/JavaScript
• Works with existing ORMs expecting integer IDs

With Marmot compact mode:

const id = 4503599627370496;
console.log(id);  // 4503599627370496 - Correct!
JSON.parse('{"id": 4503599627370496}');  // {id: 4503599627370496} - Correct!

Note: Marmot automatically converts INT AUTO_INCREMENT to BIGINT to support distributed ID generation.

1. DDL Transformation: When you create a table with AUTO_INCREMENT:

   CREATE TABLE users (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(100))
   -- Becomes internally:
   CREATE TABLE users (id BIGINT PRIMARY KEY, name TEXT)

2. DML ID Injection: When inserting with 0 or NULL for an auto-increment column:

   INSERT INTO users (id, name) VALUES (0, 'alice')
   -- Becomes internally (compact mode):
   INSERT INTO users (id, name) VALUES (4503599627370496, 'alice')

3. Explicit IDs Preserved: If you provide an explicit non-zero ID, it is used as-is.

Schema-Based Detection:

Marmot automatically detects auto-increment columns by querying SQLite schema directly:

• Single-column INTEGER PRIMARY KEY (SQLite rowid alias)
• Single-column BIGINT PRIMARY KEY (Marmot's transformed columns)

No registration required - columns are detected from schema at runtime, works across restarts, and works with existing databases.

Marmot v2 uses a TOML configuration file (default: config.toml). All settings have sensible defaults.

node_id = 0  # 0 = auto-generate
data_dir = "./marmot-data"

[transaction]
heartbeat_timeout_seconds = 10  # Transaction timeout without heartbeat
conflict_window_seconds = 10    # Conflict resolution window
lock_wait_timeout_seconds = 50  # Lock wait timeout (MySQL: innodb_lock_wait_timeout)

Note: Transaction log garbage collection is managed by the replication configuration to coordinate with anti-entropy. See replication.gc_min_retention_hours and replication.gc_max_retention_hours.

[connection_pool]
pool_size = 4              # Number of SQLite connections
max_idle_time_seconds = 10 # Max idle time before closing
max_lifetime_seconds = 300 # Max connection lifetime (0 = unlimited)

[grpc_client]
keepalive_time_seconds = 10    # Keepalive ping interval
keepalive_timeout_seconds = 3  # Keepalive ping timeout
max_retries = 3                # Max retry attempts
retry_backoff_ms = 100         # Retry backoff duration

[coordinator]
prepare_timeout_ms = 2000 # Prepare phase timeout
commit_timeout_ms = 2000  # Commit phase timeout
abort_timeout_ms = 2000   # Abort phase timeout

[cluster]
grpc_bind_address = "0.0.0.0"
grpc_port = 8080
seed_nodes = []                # List of seed node addresses
cluster_secret = ""            # PSK for cluster authentication (see Security section)
gossip_interval_ms = 1000      # Gossip interval
gossip_fanout = 3              # Number of peers to gossip to
suspect_timeout_ms = 5000      # Suspect timeout
dead_timeout_ms = 10000        # Dead timeout

Marmot supports Pre-Shared Key (PSK) authentication for cluster communication. This is strongly recommended for production deployments.

[cluster]
# All nodes in the cluster must use the same secret
cluster_secret = "your-secret-key-here"

Environment Variable (Recommended):

For production, use the environment variable to avoid storing secrets in config files:

export MARMOT_CLUSTER_SECRET="your-secret-key-here"
./marmot

The environment variable takes precedence over the config file.

Generating a Secret:

# Generate a secure random secret
openssl rand -base64 32

Behavior:

• If cluster_secret is empty and MARMOT_CLUSTER_SECRET is not set, authentication is disabled
• A warning is logged at startup when authentication is disabled
• All gRPC endpoints (gossip, replication, snapshots) are protected when authentication is enabled
• Nodes with mismatched secrets will fail to communicate (connection rejected with "invalid cluster secret")

Marmot provides admin HTTP endpoints for managing cluster membership (requires cluster_secret to be configured):

Node Lifecycle:

• New/restarted nodes auto-join via gossip - no manual intervention needed
• Nodes marked REMOVED via admin API cannot auto-rejoin - must be explicitly allowed
• This prevents decommissioned nodes from accidentally rejoining the cluster

# View cluster members and quorum info
curl -H "X-Marmot-Secret: your-secret" http://localhost:8080/admin/cluster/members

# Remove a node from the cluster (excludes from quorum, blocks auto-rejoin)
curl -X POST -H "X-Marmot-Secret: your-secret" http://localhost:8080/admin/cluster/remove/2

# Allow a removed node to rejoin (node must then restart to join)
curl -X POST -H "X-Marmot-Secret: your-secret" http://localhost:8080/admin/cluster/allow/2

See the Operations documentation for detailed usage and examples.

For read-only replicas that follow cluster nodes with transparent failover:

[replica]
enabled = true                                   # Enable read-only replica mode
follow_addresses = ["node1:8080", "node2:8080", "node3:8080"]  # Seed nodes for discovery
secret = "replica-secret"                        # PSK for authentication (required)
replicate_databases = []                         # Filter databases (empty = all, supports glob patterns like "prod_*")
database_discovery_interval_seconds = 10         # Poll for new databases (default: 10)
discovery_interval_seconds = 30                  # Poll for cluster membership (default: 30)
failover_timeout_seconds = 60                    # Max time to find alive node during failover (default: 60)
reconnect_interval_seconds = 5                   # Reconnect delay on disconnect (default: 5)
reconnect_max_backoff_seconds = 30               # Max reconnect backoff (default: 30)
initial_sync_timeout_minutes = 30                # Timeout for initial snapshot (default: 30)
snapshot_concurrency = 3                         # Parallel snapshot downloads (default: 3)
snapshot_cache_ttl_seconds = 30                  # Snapshot cache TTL in seconds (default: 30)

You can also specify follow addresses via CLI:

./marmot --config=replica.toml --follow-addresses=node1:8080,node2:8080,node3:8080

Per-Database Selective Replication:

Control which databases are replicated using the replicate_databases filter:

[replica]
# Replicate only specific databases
replicate_databases = ["myapp", "analytics"]

# Replicate databases matching glob patterns
replicate_databases = ["prod_*", "staging_*"]

# Replicate all databases (default)
replicate_databases = []

The system database (__marmot_system) is never replicated - each replica maintains its own independent system database.

Snapshot Caching:

Replicas use an LRU cache to avoid redundant snapshot creation:

• Cache TTL controlled by snapshot_cache_ttl_seconds (default: 30 seconds)
• Cached snapshots served from temp storage until expiration
• Background cleanup runs automatically
• Improves performance when multiple replicas bootstrap simultaneously

Parallel Snapshot Downloads:

Control download concurrency with snapshot_concurrency:

• Downloads multiple database snapshots in parallel (default: 3)
• Uses worker pool pattern to limit resource usage
• Partial failure handling: continues even if some databases fail
• Failed databases retry in background with exponential backoff

Note: Replica mode is mutually exclusive with cluster mode. A replica receives all data via streaming replication but cannot accept writes. It automatically discovers cluster nodes and fails over to another node if the current source becomes unavailable.

[replication]
default_write_consistency = "QUORUM"      # Write consistency level: ONE, QUORUM, ALL
default_read_consistency = "LOCAL_ONE"    # Read consistency level
write_timeout_ms = 5000                   # Write operation timeout
read_timeout_ms = 2000                    # Read operation timeout

# Anti-Entropy: Background healing for eventual consistency
# - Detects and repairs divergence between replicas
# - Uses delta sync for small lags, snapshot for large lags
# - Includes gap detection to prevent incomplete data after GC
enable_anti_entropy = true                 # Enable automatic catch-up for lagging nodes
anti_entropy_interval_seconds = 60         # How often to check for lag (default: 60s)
delta_sync_threshold_transactions = 10000  # Delta sync if lag < 10K txns
delta_sync_threshold_seconds = 3600        # Snapshot if lag > 1 hour

# Garbage Collection: Reclaim disk space by deleting old transaction records
# - gc_min must be >= delta_sync_threshold (validated at startup)
# - gc_max should be >= 2x delta_sync_threshold (recommended)
# - Set gc_max = 0 for unlimited retention
gc_min_retention_hours = 2   # Keep at least 2 hours (>= 1 hour delta threshold)
gc_max_retention_hours = 24  # Force delete after 24 hours

Anti-Entropy Tuning:

• Small clusters (2-3 nodes): Use default settings (60s interval)
• Large clusters (5+ nodes): Consider increasing interval to 120-180s to reduce network overhead
• High write throughput: Increase delta_sync_threshold_transactions to 50000+
• Long-running clusters: Keep gc_max_retention_hours at 24+ to handle extended outages

GC Configuration Rules (Validated at Startup):

• gc_min_retention_hours must be >= delta_sync_threshold_seconds (in hours)
• gc_max_retention_hours should be >= 2x delta_sync_threshold_seconds
• Violating these rules will cause startup failure with helpful error messages

[query_pipeline]
transpiler_cache_size = 10000  # LRU cache for MySQL→SQLite transpilation
validator_pool_size = 8        # SQLite connection pool for validation

[mysql]
enabled = true
bind_address = "0.0.0.0"
port = 3306
max_connections = 1000
unix_socket = ""              # Unix socket path (empty = disabled)
unix_socket_perm = 0660       # Socket file permissions
auto_id_mode = "compact"      # "compact" (53-bit, JS-safe) or "extended" (64-bit)

Unix Socket Connection (lower latency than TCP):

mysql --socket=/tmp/marmot/mysql.sock -u root

[publisher]
enabled = false  # Enable CDC publishing to external systems

[[publisher.sinks]]
name = "kafka-main"              # Unique sink name
type = "kafka"                   # "kafka" or "nats"
format = "debezium"              # Debezium-compatible JSON (only option)
brokers = ["localhost:9092"]     # Kafka broker addresses
topic_prefix = "marmot.cdc"      # Topic pattern: {prefix}.{db}.{table}
filter_tables = ["*"]            # Glob patterns for table filtering
filter_databases = ["*"]         # Glob patterns for database filtering
batch_size = 100                 # Events to read per poll cycle
poll_interval_ms = 10            # Polling interval (default: 10ms)
retry_initial_ms = 100           # Initial retry delay on failure
retry_max_ms = 30000             # Max retry delay (30 seconds)
retry_multiplier = 2.0           # Exponential backoff multiplier

See the Integrations documentation for details on event format, Kafka/NATS configuration, and use cases.

[logging]
verbose = false          # Enable verbose logging
format = "console"       # Log format: console or json

[prometheus]
enabled = true  # Metrics served on gRPC port at /metrics endpoint

Accessing Metrics:

# Metrics are multiplexed with gRPC on the same port
curl http://localhost:8080/metrics

# Prometheus scrape config
scrape_configs:
  - job_name: 'marmot'
    static_configs:
      - targets: ['node1:8080', 'node2:8080', 'node3:8080']

See config.toml for complete configuration reference with detailed comments.

Performance benchmarks on a local development machine (Apple M-series, 3-node cluster, single machine):

Operation Distribution:

• READ: 20%
• UPDATE: 30%
• INSERT: 35%
• DELETE: 5%
• UPSERT: 10%

All 3 nodes maintained identical row counts (346,684 rows) throughout the test, confirming consistent replication.

Note: These benchmarks are from a local development machine with all nodes on the same host. Production deployments across multiple machines will have different characteristics based on network latency. Expect P99 latencies of 50-200ms for cross-region QUORUM writes.

Marmot's SQLite files are standard WAL-mode databases, compatible with Litestream:

litestream replicate /path/to/marmot-data/*.db s3://bucket/backup

Enable CDC publisher to stream changes to Kafka/NATS, then archive to your preferred storage.

Since Marmot uses SQLite with WAL mode, you can safely snapshot the data directory during operation.

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