Redis Migration Tool: Architecture and Implementation Guide¶

This document outlines the architecture, implementation strategies, and language considerations for building a Redis migration tool with schema change detection capabilities.

Table of Contents¶

Core Architecture
Configurable Migration Strategies
Language Comparison
Implementation Examples
Recommendations

Core Architecture¶

Key Components¶

Schema Registry
Store schema definitions using JSON format
Track schema versions
Map Redis key patterns to schema versions
Change Detection
Deep diff between schema versions
Identify breaking vs. non-breaking changes
Field additions/removals/type changes
Migration Planning
Automatic migration strategy generation
Data transformation templates
Rollback plans
Safe Execution
Atomic migrations where possible
Backup mechanisms
Progress tracking
Validation of migrated data

Architecture Diagram¶

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│ Schema Registry │────▶│ Change Detector │────▶│ Migration       │
└─────────────────┘     └─────────────────┘     │ Planner         │
                                                └─────────────────┘
                                                        │
                                                        ▼
┌─────────────────┐                           ┌─────────────────┐
│ Validation      │◀───────────────────────── │ Migration       │
│ Layer           │                           │ Executor        │
└─────────────────┘                           └─────────────────┘

Schema Definition Example¶

{
  "version": "1.0.0",
  "keyPattern": "user:*",
  "fields": {
    "name": {"type": "string", "required": true},
    "age": {"type": "number"},
    "preferences": {"type": "hash"}
  }
}

Recommended Tools/Libraries¶

Schema Management
JSON Schema for validation
Semver for version management
Redis Streams for change tracking
Data Processing
RediSearch for efficient data scanning
Redis Pipeline for bulk operations
Redis Multi/Exec for atomic operations
Monitoring/Validation
Redis INFO command monitoring
Progress tracking via Redis key scanning
Checksums for data integrity

Configurable Migration Strategies¶

Migration Configuration Structure¶

┌─────────────────┐
│ Migration       │
│ Registry        │
└─────────────────┘
        │
        ▼
┌─────────────────┐
│ Migration       │
│ Config Store    │
└─────────────────┘
        │
        ├─────────────────┐
        │                 │
        ▼                 ▼
┌─────────────────┐ ┌─────────────────┐
│ Version Graph   │ │ Strategy Store  │
└─────────────────┘ └─────────────────┘

Migration Definition Example¶

{
  "migrations": {
    "v1_to_v2": {
      "source": "1.0.0",
      "target": "2.0.0",
      "strategy": "direct",
      "transforms": [
        {
          "type": "renameField",
          "from": "userName",
          "to": "fullName"
        },
        {
          "type": "addField",
          "field": "createdAt",
          "defaultValue": "${NOW}"
        }
      ],
      "validation": {
        "required": ["fullName", "createdAt"]
      }
    },
    "v2_to_v3": {
      "source": "2.0.0",
      "target": "3.0.0",
      "strategy": "direct",
      "transforms": [
        {
          "type": "splitField",
          "from": "fullName",
          "to": ["firstName", "lastName"],
          "separator": " "
        }
      ]
    }
  },
  "paths": {
    "1.0.0": {
      "3.0.0": ["v1_to_v2", "v2_to_v3"]
    }
  }
}

Migration Chain Execution¶

class MigrationExecutor {
  async migrate(sourceVersion, targetVersion, data) {
    const path = this.resolvePath(sourceVersion, targetVersion);
    let currentData = data;

    for (const step of path) {
      currentData = await this.executeStep(step, currentData);
      await this.validate(step, currentData);
    }

    return currentData;
  }
}

Features¶

Flexible Configuration
JSON-based migration definitions
Pluggable transformation strategies
Custom validation rules
Path Management
Automatic path resolution
Manual path override capability
Path validation and optimization
Execution Control
Step-by-step execution
Progress tracking
Partial migration support
Rollback capability
Validation System
Pre-migration validation
Post-step validation
Final state validation

Migration Strategy Types¶

Direct Transformations
Field renaming
Type conversions
Field splitting/merging
Default value injection
Complex Transformations
Custom functions
Async transformations
Batch processing
External service calls
Conditional Migrations
Data-dependent transforms
Feature flags
Environment-specific paths

Language Comparison¶

Node.js/TypeScript¶

Pros¶

Excellent async I/O handling
Rich ecosystem for Redis clients (ioredis, node-redis)
TypeScript provides strong typing and better maintainability
JSON handling is native and efficient
Large community and many utility libraries
Easy to create CLI tools with packages like commander
Great for rapid prototyping

Cons¶

Memory management can be challenging with large datasets
Single-threaded nature (though worker threads are available)
May not be as performant as compiled languages
Type system is not as robust as some other languages

Python¶

Pros¶

Clean, readable syntax
Strong Redis support through redis-py
Excellent data processing libraries (pandas, numpy)
Good async support with asyncio
Rich ecosystem for CLI tools
Great for prototyping and quick iterations

Cons¶

GIL can limit performance in CPU-intensive tasks
Not as performant as compiled languages
Type hints are optional and not as robust
Memory usage can be high

Go¶

Pros¶

Excellent performance
Built-in concurrency support
Strong type system
Efficient memory management
Single binary deployment
Great Redis client (go-redis)
Good handling of large datasets

Cons¶

More verbose than Python or Node.js
Less flexible than dynamic languages
Steeper learning curve
Slower development cycle
Fewer high-level data processing libraries

Rust¶

Pros¶

Exceptional performance
Memory safety guarantees
Zero-cost abstractions
Excellent concurrency model
Strong type system
Single binary deployment
Growing Redis ecosystem

Cons¶

Steepest learning curve
Longer development time
Stricter compiler rules
Smaller ecosystem compared to others
More complex error handling

Language Comparison Matrix¶

                | Performance | Development Speed | Memory Efficiency | Ecosystem |
----------------+-------------+-------------------+-------------------+-----------+
Node.js/TS      |     ★★☆☆☆   |       ★★★★★       |       ★★☆☆☆       |   ★★★★★   |
Python          |     ★★☆☆☆   |       ★★★★☆       |       ★★☆☆☆       |   ★★★★☆   |
Go              |     ★★★★☆   |       ★★★☆☆       |       ★★★★☆       |   ★★★☆☆   |
Rust            |     ★★★★★   |       ★★☆☆☆       |       ★★★★★       |   ★★☆☆☆   |

Implementation Examples¶

Node.js/TypeScript Implementation¶

import { Redis } from 'ioredis';
import { Command } from 'commander';

async function migrateBatch(
  redis: Redis,
  keys: string[],
  strategy: MigrationStrategy
): Promise<void> {
  const pipeline = redis.pipeline();

  for (const key of keys) {
    const data = await redis.get(key);
    const parsedData = JSON.parse(data);
    const migratedData = applyTransforms(parsedData, strategy.transforms);
    pipeline.set(key, JSON.stringify(migratedData));
  }

  await pipeline.exec();
}

Python Implementation¶

import redis
import asyncio
from typing import List, Dict, Any

async def migrate_batch(
    redis_client: redis.Redis,
    keys: List[str],
    strategy: Dict[str, Any]
) -> None:
    pipeline = redis_client.pipeline()

    for key in keys:
        data = redis_client.get(key)
        parsed_data = json.loads(data)
        migrated_data = apply_transforms(parsed_data, strategy["transforms"])
        pipeline.set(key, json.dumps(migrated_data))

    pipeline.execute()

Go Implementation¶

func migrateBatch(
    ctx context.Context,
    rdb *redis.Client,
    keys []string,
    strategy MigrationStrategy,
) error {
    pipe := rdb.Pipeline()

    for _, key := range keys {
        data, err := rdb.Get(ctx, key).Result()
        if err != nil {
            return err
        }

        var parsedData map[string]interface{}
        if err := json.Unmarshal([]byte(data), &parsedData); err != nil {
            return err
        }

        migratedData, err := applyTransforms(parsedData, strategy.Transforms)
        if err != nil {
            return err
        }

        migratedJSON, err := json.Marshal(migratedData)
        if err != nil {
            return err
        }

        pipe.Set(ctx, key, migratedJSON, 0)
    }

    _, err := pipe.Exec(ctx)
    return err
}

Rust Implementation¶

async fn migrate_batch(
    redis: &Redis,
    keys: Vec<String>,
    strategy: MigrationStrategy,
) -> Result<(), Error> {
    let mut pipe = redis.pipeline();

    for key in keys {
        let data: String = redis.get(&key).await?;
        let parsed_data: Value = serde_json::from_str(&data)?;
        let migrated_data = apply_transforms(parsed_data, &strategy.transforms)?;
        let migrated_json = serde_json::to_string(&migrated_data)?;

        pipe.set(&key, migrated_json);
    }

    pipe.execute().await?;
    Ok(())
}

Recommendations¶

Decision Factors to Consider¶

Team Expertise
Existing language knowledge
Learning curve tolerance
Maintenance requirements
Project Requirements
Data volume
Performance needs
Deployment constraints
Integration requirements
Development Timeline
Time to market
Prototype vs production
Long-term maintenance
Operational Concerns
Deployment environment
Memory constraints
CPU constraints
Monitoring needs

Recommendations Based on Requirements¶

For Rapid Development & Prototyping
Node.js/TypeScript
Python
For Enterprise-Scale & Performance
Go
Rust
For Balance of Performance & Development Speed
Go

Best Practices¶

Schema Management
Use semantic versioning for schemas
Store schema definitions in Redis itself
Implement schema validation
Migration Safety
Always create backups before migrations
Implement dry-run mode
Use progressive migrations for large datasets
Validate data after each migration step
Performance Optimization
Use Redis pipelines for batch operations
Implement key scanning with cursor for large datasets
Consider using RediSearch for efficient data filtering
Implement parallel processing where appropriate
Monitoring & Observability
Track migration progress
Implement detailed logging
Create metrics for migration performance
Set up alerts for migration failures