In-Memory Operation

LiteGraph allows you to run the database entirely in memory for high-performance, low-latency scenarios with advanced memory management and data persistence strategies.

Overview

In-Memory Operation mode enables LiteGraph to run entirely in system memory, providing exceptional performance and ultra-low latency for high-throughput scenarios. This mode is ideal for applications requiring maximum speed, real-time processing, and temporary data storage with optional persistence capabilities.

Key benefits include:

• Ultra-High Performance: Maximum speed with data stored in RAM
• Low Latency: Minimal response times for all operations
• Real-Time Processing: Ideal for streaming and real-time applications
• Temporary Storage: Perfect for caching and session management
• Optional Persistence: Manual control over data persistence to disk
• Memory Management: Efficient memory usage and garbage collection
• Scalability: Enhanced performance for high-concurrency scenarios

Configuration

For high-performance scenarios, run the database entirely in memory by configuring the following settings:

Enable In-Memory Mode

Configure In-Memory Operation: Set InMemory to true in your litegraph.json configuration file:

JSON

{
  "LiteGraph": {
    "InMemory": true,
  }
}

Manual Flush to Disk

Important: When running in-memory mode, data is stored only in RAM by default. You must manually flush data to disk to ensure persistence:

Bash

curl -X POST -H "Authorization: Bearer litegraphadmin" \
     -H "Content-Type: application/json" \
     http://localhost:8701/v1.0/flush

JavaScript

import { LiteGraphSdk } from "litegraphdb";

var api = new LiteGraphSdk(
  "http://localhost:8701/",
  "<Tenant-Guid>",
  "*******"
);

const flushToDisk = async () => {
  try {
    const data = await api.Admin.flush();
    console.log(data, "Data flushed to disk successfully");
  } catch (err) {
    console.log("Error flushing data:", JSON.stringify(err));
  }
};

Python

import litegraph

sdk = litegraph.configure(
    endpoint="http://localhost:8701",
    tenant_guid="Tenant-Guid",
    access_key="******",
)

def flush_db_to_disk():
    backup = litegraph.Admin.flush_db_to_disk()
    print(backup)

flush_db_to_disk()

Performance Considerations

Memory Usage

• RAM Requirements: Ensure sufficient system memory for your dataset
• Memory Monitoring: Monitor memory usage to prevent system overload
• Data Size Limits: Consider dataset size relative to available RAM
• Concurrent Operations: In-memory mode handles high concurrency efficiently

Use Cases

Ideal for:

• Real-time analytics and reporting
• High-frequency trading systems
• Session management and caching
• Temporary data processing
• Development and testing environments
• High-throughput API services

Not recommended for:

• Large datasets exceeding available RAM
• Long-term data storage without persistence
• Systems with limited memory resources
• Production environments requiring automatic persistence

Best Practices

When using in-memory operation mode, consider the following recommendations:

• Memory Monitoring: Implement monitoring to track memory usage and prevent overflow
• Regular Flushing: Establish a schedule for regular data flushing to disk
• Backup Strategy: Create backups before critical operations or system restarts
• Error Handling: Implement proper error handling for memory-related issues
• Resource Planning: Plan memory allocation based on expected data volumes
• Testing: Thoroughly test in-memory operations in your specific environment
• Documentation: Document flush procedures and memory management strategies

Next Steps

After configuring in-memory operation, you can:

• Implement automated flush scheduling and management
• Set up memory monitoring and alerting systems
• Create data persistence strategies and procedures
• Optimize memory usage and garbage collection settings
• Develop backup and recovery procedures for in-memory data
• Implement performance monitoring and optimization
• Create comprehensive memory management documentation