Marmot v2

What & Why?

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)
• 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

Quick Start

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

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

# Or use DBeaver, MySQL Workbench, etc.

Testing Replication

# 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

Stargazers over time

Architecture

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

DDL Replication

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

How It Works

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

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

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

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.)

Configuration

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

# Automatically rewrite DDL for idempotency
enable_idempotent = true

Best Practices

• ✅ 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

CDC-Based Replication

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

How It Works

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

Benefits

• 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

Row Key Extraction

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

CDC Publisher

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.

Features

• 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

Configuration

[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 = ["*"]

Event Format

Events follow the Debezium envelope structure:

{
"schema": { ... },
"payload": {
"before": null,
"after": {"id": 1, "name": "alice", "email": "alice@example.com"},
"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):

Operation	op	before	after
INSERT	c (create)	null	row data
UPDATE	u (update)	old row	new row
DELETE	d (delete)	old row	null

Topic Naming

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

Examples:

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

Use Cases

• 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.

SQL Statement Compatibility

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

Statement Type	Support	Replication	Notes
DML - Data Manipulation
INSERT / REPLACE	✅ Full	✅ Yes	Includes qualified table names (db.table)
UPDATE	✅ Full	✅ Yes	Includes qualified table names
DELETE	✅ Full	✅ Yes	Includes qualified table names
SELECT	✅ Full	N/A	Read operations
LOAD DATA	✅ Full	✅ Yes	Bulk data loading
DDL - Data Definition
CREATE TABLE	✅ Full	✅ Yes	Replicated with cluster-wide locking
ALTER TABLE	✅ Full	✅ Yes	Replicated with cluster-wide locking
DROP TABLE	✅ Full	✅ Yes	Replicated with cluster-wide locking
TRUNCATE TABLE	✅ Full	✅ Yes
RENAME TABLE	✅ Full	✅ Yes	Replicated with cluster-wide locking
CREATE/DROP INDEX	✅ Full	✅ Yes	Replicated with cluster-wide locking
CREATE/DROP VIEW	✅ Full	✅ Yes	Replicated with cluster-wide locking
CREATE/DROP TRIGGER	✅ Full	✅ Yes	Replicated with cluster-wide locking
Database Management
CREATE DATABASE	✅ Full	✅ Yes	Replicated with cluster-wide locking
DROP DATABASE	✅ Full	✅ Yes	Replicated with cluster-wide locking
ALTER DATABASE	✅ Full	✅ Yes	Replicated with cluster-wide locking
SHOW DATABASES	✅ Full	N/A	Metadata query
SHOW TABLES	✅ Full	N/A	Metadata query
USE database	✅ Full	N/A	Session state
Transaction Control
BEGIN / START TRANSACTION	✅ Full	N/A	Transaction boundary
COMMIT	✅ Full	✅ Yes	Commits distributed transaction
ROLLBACK	✅ Full	✅ Yes	Aborts distributed transaction
SAVEPOINT	✅ Full	✅ Yes	Nested transaction support
Locking
LOCK TABLES	✅ Parsed	❌ No	Requires distributed locking coordination
UNLOCK TABLES	✅ Parsed	❌ No	Requires distributed locking coordination
Session Configuration
SET statements	✅ Parsed	❌ No	Session-local, not replicated
XA Transactions
XA START/END/PREPARE	✅ Parsed	❌ No	Marmot uses its own 2PC protocol
XA COMMIT/ROLLBACK	✅ Parsed	❌ No	Not compatible with Marmot’s model
DCL - Data Control
GRANT / REVOKE	✅ Parsed	❌ No	User management not replicated
CREATE/DROP USER	✅ Parsed	❌ No	User management not replicated
ALTER USER	✅ Parsed	❌ No	User management not replicated
Administrative
OPTIMIZE TABLE	✅ Parsed	❌ No	Node-local administrative command
REPAIR TABLE	✅ Parsed	❌ No	Node-local administrative command

Legend

• ✅ 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)

Important Notes

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

XA Transactions: Marmot has its own distributed transaction protocol based on 2PC. MySQL XA transactions are not compatible with Marmot’s replication model. 1.

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. 1.

Table Locking: LOCK TABLES statements are recognized but not enforced across the cluster. Use application-level coordination for distributed locking needs. 1.

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

MySQL Protocol & Metadata Queries

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

Metadata Query Support

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.

Connecting with MySQL Clients

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

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

Recovery Scenarios

Marmot handles various failure and recovery scenarios automatically:

Network Partition (Split-Brain)

Scenario	Behavior
Minority partition	Writes fail - cannot achieve quorum
Majority partition	Writes succeed - quorum achieved
Partition heals	Delta sync + LWW merges divergent data

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

Node Failure & Recovery

Scenario	Recovery Method
Brief outage	Delta sync - replay missed transactions
Extended outage	Snapshot transfer + delta sync
New node joining	Full snapshot from existing node

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

1. Gap Detection: Detects when transaction logs have been GC’d and automatically falls back to snapshot
2. Multi-Database Support: Tracks and syncs each database independently
3. 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

1. Applies changes using LWW conflict resolution
2. Updates replication state tracking (per-database)
3. 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

Consistency Guarantees

Write Consistency	Behavior
ONE	Returns after 1 node ACK (fast, less durable)
QUORUM	Returns after majority ACK (default, balanced)
ALL	Returns after all nodes ACK (slow, most durable)

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

Limitations

• 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).

Compatibility

AUTO_INCREMENT and Integer Types

IMPORTANT: Marmot automatically converts INT AUTO_INCREMENT to BIGINT

This is a breaking change from standard MySQL/SQLite behavior. Marmot does not respect 32-bit INT for auto-increment columns - they are automatically promoted to BIGINT to support distributed ID generation.

Why?

In a distributed, leaderless system, each node must generate unique IDs independently without coordination. Marmot uses HLC-based (Hybrid Logical Clock) 64-bit IDs to ensure:

1. Global Uniqueness: IDs are unique across all nodes without central coordination
2. Monotonicity: IDs increase over time (within each node)
3. No Collisions: Unlike auto-increment sequences, HLC IDs cannot collide between nodes

How It Works:

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)

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

INSERT INTO users (id, name) VALUES (0, 'alice')
-- Becomes internally:
INSERT INTO users (id, name) VALUES (7318624812345678901, 'alice')

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

INSERT INTO users (id, name) VALUES (12345, 'bob')
-- Remains:
INSERT INTO users (id, name) VALUES (12345, 'bob')

Important Considerations:

Aspect	Behavior
ID Range	64-bit (up to 9.2 quintillion) instead of 32-bit (4.2 billion)
ID Format	HLC-based, not sequential integers
SQLite ROWID	Not used - Marmot manages IDs explicitly
Client Libraries	Ensure your client handles BIGINT correctly (some JSON serializers may lose precision)
Existing Data	Migrate existing INT columns to BIGINT before enabling Marmot

Schema-Based Detection:

Marmot automatically detects auto-increment columns by querying SQLite schema directly. A column is considered auto-increment if:

• It is a single-column INTEGER PRIMARY KEY (SQLite rowid alias), or
• It is a single-column BIGINT PRIMARY KEY (Marmot’s transformed columns)

This means:

• No registration required - columns are detected from schema at runtime
• Works across restarts - no need to re-execute DDL statements
• Works with existing databases - tables created directly on SQLite work too

Configuration

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

Core Configuration

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

Transaction Manager

[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

[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

[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

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

Cluster

[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

Security

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")

Cluster Membership Management

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.

Replication

[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

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

MySQL Protocol Server

[mysql]
enabled = true
bind_address = "0.0.0.0"
port = 3306
max_connections = 1000

CDC Publisher

[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

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

Prometheus Metrics

[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.

Benchmarks

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

Test Configuration

Parameter	Value
Nodes	3 (ports 3307, 3308, 3309)
Threads	16
Batch Size	10 ops/transaction
Consistency	QUORUM

Load Phase (INSERT-only)

Metric	Value
Throughput	4,175 ops/sec
TX Throughput	417 tx/sec
Records Loaded	200,000
Errors	0

Mixed Workload

Metric	Value
Throughput	3,370 ops/sec
TX Throughput	337 tx/sec
Duration	120 seconds
Total Operations	404,930
Errors	0
Retries	37 (0.09%)

Operation Distribution:

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

Latency (Mixed Workload)

Percentile	Latency
P50	4.3ms
P90	14.0ms
P95	36.8ms
P99	85.1ms

Replication Verification

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.

FAQs & Community

• For FAQs visit this page
• For community visit our discord or discussions on GitHub