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MongoDB to Tacnode Migration Guide

Complete migration guide for moving data and applications from MongoDB to Tacnode, covering environment setup, permissions, export/import, schema transformation, and distributed database best practices.

This comprehensive guide walks you through migrating from MongoDB to Tacnode, covering environment setup, permissions, migration steps, application refactoring, best practices, and troubleshooting. It is adapted from the official Tacnode migration documentation for maximum reliability and clarity.

Overview

MongoDB is a flexible document database, while Tacnode offers distributed, relational, and semi-structured data capabilities. Migrating requires careful planning, data transformation, and application refactoring.

Preparation

Environment Requirements

  • MongoDB instance (version 4.x or newer)
  • Tacnode database instance
  • Network connectivity between MongoDB and Tacnode
  • Data export/import tools (e.g., mongoexport, Tacnode CLI)

Permission Setup

Create a dedicated MongoDB user for migration:

use <your_db>;
db.createUser({
	user: "tacnode_migration",
	pwd: "your_password",
	roles: [ { role: "read", db: "<your_db>" } ]
});

For fine-grained access, create a custom role and assign only necessary privileges.

Network Configuration

Ensure Tacnode can reach MongoDB. If using a migration tool, whitelist its IP in MongoDB and configure network/firewall rules.

Migration Steps

1. Export Data from MongoDB

Use mongoexport to export collections as JSON or CSV files:

mongoexport --uri="mongodb+srv://<username>:<password>@<cluster-url>/<database>" --collection=<collection> --out=<collection>.json

Repeat for each collection you wish to migrate.

2. Prepare Data for Tacnode

  • Remove MongoDB-specific fields (like _id if not needed).
  • Use tools like jq or scripts for transformation.

3. Import Data into Tacnode

Tacnode provides import utilities and APIs for bulk ingestion. CLI Example:

tacnode import --file=<collection>.json --collection=<target-collection>

Or use the Tacnode dashboard or API endpoints for import.

4. Verify Data Integrity

  • Run queries to validate relationships and indexes.

Application Layer Refactoring

Update Connection Settings

Change your application’s database connection from MongoDB to Tacnode (PostgreSQL-compatible):

# Old MongoDB
spring.data.mongodb.uri=mongodb://username:password@host:27017/db
# New Tacnode
spring.datasource.url=jdbc:postgresql://tacnode-host:5432/db
spring.datasource.username=tacnode_user
spring.datasource.password=tacnode_password
spring.datasource.driver-class-name=org.postgresql.Driver

Data Model Transformation

Convert MongoDB documents to Tacnode tables. For nested arrays, use JSONB or normalized tables.

MongoDB Document Example:

{
  "_id": ObjectId("..."),
  "name": "Alice",
  "orders": [ { "orderId": "ORD001", "total": 99.99 } ]
}

Tacnode Table (JSONB):

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(100),
  profile JSONB
);
INSERT INTO users (name, profile) VALUES ('Alice', '{"orders": [{"orderId": "ORD001", "total": 99.99}]}'::JSONB);

Tacnode Tables (Normalized):

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(100)
);
CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  user_id INTEGER REFERENCES users(id),
  order_id VARCHAR(50),
  total NUMERIC(10,2)
);

CRUD Code Examples

Insert Data MongoDB (Java):

MongoCollection<Document> collection = database.getCollection("users");
Document doc = new Document("name", "Alice").append("age", 25).append("city", "New York");
collection.insertOne(doc);

Tacnode (Java, JDBC):

String query = "INSERT INTO users (name, age, city) VALUES (?, ?, ?)";
try (Connection conn = DriverManager.getConnection(url, user, password);
	 PreparedStatement stmt = conn.prepareStatement(query)) {
	stmt.setString(1, "Alice");
	stmt.setInt(2, 25);
	stmt.setString(3, "New York");
	stmt.executeUpdate();
}

Query Data MongoDB (Java):

Document query = new Document("name", "Alice");
Document result = collection.find(query).first();
if (result != null) {
	System.out.println(result.toJson());
}

Tacnode (Java, JDBC):

String query = "SELECT * FROM users WHERE name = ?";
try (Connection conn = DriverManager.getConnection(url, user, password);
	 PreparedStatement stmt = conn.prepareStatement(query)) {
	stmt.setString(1, "Alice");
	try (ResultSet rs = stmt.executeQuery()) {
		while (rs.next()) {
			System.out.println("Name: " + rs.getString("name"));
			System.out.println("Age: " + rs.getInt("age"));
			System.out.println("City: " + rs.getString("city"));
		}
	}
}

Update Data MongoDB (Java):

Document query = new Document("name", "Alice");
Document update = new Document("$set", new Document("age", 26));
collection.updateOne(query, update);

Tacnode (Java, JDBC):

String query = "UPDATE users SET age = ? WHERE name = ?";
try (Connection conn = DriverManager.getConnection(url, user, password);
	 PreparedStatement stmt = conn.prepareStatement(query)) {
	stmt.setInt(1, 26);
	stmt.setString(2, "Alice");
	stmt.executeUpdate();
}

Delete Data MongoDB (Java):

Document query = new Document("name", "Alice");
collection.deleteOne(query);

Tacnode (Java, JDBC):

String query = "DELETE FROM users WHERE name = ?";
try (Connection conn = DriverManager.getConnection(url, user, password);
	 PreparedStatement stmt = conn.prepareStatement(query)) {
	stmt.setString(1, "Alice");
	stmt.executeUpdate();
}

Aggregation Example MongoDB (Java):

List<Bson> pipeline = Arrays.asList(
	Aggregates.group("$city", Accumulators.sum("count", 1))
);
AggregateIterable<Document> results = collection.aggregate(pipeline);
for (Document doc : results) {
	System.out.println(doc.toJson());
}

Tacnode (Java, JDBC):

String query = "SELECT city, COUNT(*) AS count FROM users GROUP BY city";
try (Connection conn = DriverManager.getConnection(url, user, password);
	 PreparedStatement stmt = conn.prepareStatement(query);
	 ResultSet rs = stmt.executeQuery()) {
	while (rs.next()) {
		System.out.println("City: " + rs.getString("city") + ", Count: " + rs.getInt("count"));
	}
}

Best Practices

Pre-Migration

  • Assess MongoDB cluster size and collection count.
  • Analyze document structure and field types.
  • Identify large or complex documents.
  • Plan migration during off-peak hours.
  • Prepare rollback and testing plans.
  • Train your team on Tacnode.

During Migration

  • Migrate non-critical data first.
  • Gradually migrate core business data.
  • Run old and new systems in parallel for validation.
  • Monitor export/import progress and system performance.
  • Document issues and solutions.

Post-Migration

  • Create appropriate indexes based on query patterns.
  • Optimize table storage (row/columnar/hybrid).
  • Tune system parameters for workload.
  • Harden security: IP whitelists, granular permissions, audit logs.

Troubleshooting & FAQ

Data Type Mapping Issues

  • MongoDB’s flexible schema may not directly map to Tacnode’s strong types.
  • Use JSONB for dynamic data.
  • Validate and convert types at the application layer.

Performance Optimization

  • Design indexes carefully.
  • Use proper query optimization techniques.
  • Consider materialized views for complex queries.

Aggregation Differences

  • MongoDB and SQL aggregation functions differ.
  • Adapt aggregation logic in your application.
  • Use window functions for advanced needs.