Test blog for FAQs implimentation

blog/2023-03-29-troubleshooting-common-issues-and-solutions-to-mongodb-etl-errors.mdx

@@ -335,4 +335,49 @@ Are you interested in unlocking the full potential of your data without the need
 
 With features like data ingestion from 150+ sources including MongoDB connectors, data warehousing, data analytics, and data transformation solutions, Datazip can help you make fast, data-driven decisions.
 
+## FAQs
+### Q1. What are the most common MongoDB ETL errors and how do you diagnose them?
+- **Connection timeout errors** — Check network connectivity, firewall/security group rules blocking port 27017, and MongoDB authentication credentials
+- **Schema validation failures** — Caused by polymorphic fields or missing required fields across documents in the same collection
+- **Data type mismatch errors** — Where the source field type differs from the target column type
+- **Socket timeout (`socketTimeoutMS`) exhaustion during large collection scans** — Occurs when MongoDB takes longer than the configured `socketTimeoutMS` to respond to a query, common during unoptimized aggregate queries or large full-collection reads. Increase `socketTimeoutMS` in your connection settings and ensure queries are properly indexed to avoid full collection scans.
+
+### Q2. How should I set up MongoDB for ETL to minimize pipeline errors?
+Best practices for an ETL-ready MongoDB setup include:
+
+- **Enable Read Preference on secondary nodes** to offload ETL reads from the primary and avoid impacting operational performance
+- **Create indexes on user-defined timestamp fields** (such as an application-managed `updated_at` field) that are used for cursor-based incremental sync — note this is not a built-in MongoDB field and must be maintained by your application
+- **Set `socketTimeoutMS` and `serverSelectionTimeoutMS`** appropriately per operation for long-running collection reads, keeping in mind these are per-operation settings, not session-level configurations in most drivers
+- **Configure oplog retention** to cover at least 24–48 hours of changes to ensure CDC consumers do not fall behind the retention window
+- **Ensure a replica set is configured:** this is a hard requirement for change streams and oplog-based CDC; standalone MongoDB instances do not support these features
+
+### Q3. What causes connection timeout errors in MongoDB ETL pipelines and how do I fix them?
+Connection timeouts typically occur due to:
+
+- **Network/firewall issues:** Firewall or security group rules blocking the ETL tool's IP from reaching MongoDB on port 27017
+- **Authentication failures:** Wrong credentials, incorrect `authSource` database, or the user lacking required permissions
+- **Connection pool exhaustion:** Too many concurrent ETL workers exceeding the `maxPoolSize` setting, or connection leaks in application code causing "server selection timed out" errors
+- **SSL/TLS configuration mismatches:** The ETL tool lacking the correct CA certificate to validate the MongoDB server's SSL certificate
+
+**Recommended debug approach:** Test connectivity directly with the MongoDB shell (`mongosh`) using the same connection string first. If that succeeds, the issue is in your ETL tool's configuration — verify credentials, SSL settings, and connection string parameters. If the shell also fails, the issue is at the network or DNS level.
+
+### Q4. How do I handle schema validation errors when MongoDB documents have inconsistent structures?
+Schema validation errors occur because MongoDB allows polymorphic data — documents with varying structures or different data types for the same field — within a single collection. Solutions include:
+
+- **Use schema inference with adequate sampling** — Increase the sample size when inferring the schema so the ETL tool captures the full range of field variations, rather than relying on a small, potentially unrepresentative subset
+- **Mark fields as nullable/optional** for fields that may be absent in some documents
+- **Apply type coercion rules** to handle polymorphic fields by enforcing a consistent target type during ingestion
+- **Filter or quarantine malformed documents** using pre-ingestion validation rules — MongoDB also supports `validationAction: "warn"` mode, which logs invalid documents without rejecting them, making it a useful diagnostic tool during ETL pipeline development
+- **Use a compatible ETL tool** that natively supports MongoDB's BSON types (including `Decimal128`, `ObjectID`) and flexible schema evolution
+
+### Q5. What are best practices for MongoDB ETL setup in production environments?
+For production MongoDB ETL pipelines:
+
+- **Use a dedicated read-only ETL user** with the minimum permissions required — typically `read` on source collections and `clusterMonitor` for oplog access
+- **Connect to a replica set secondary** to avoid adding read load to the primary node
+- **Implement checkpointing using resume tokens** so failed syncs resume from the last successfully processed oplog position rather than restarting from scratch — store the resume token durably and pass it back on reconnection
+- **Monitor oplog lag actively** — a small oplog (e.g., 1GB on a high-throughput cluster) may only retain a few hours of changes; if your CDC consumer falls behind the retention window, you will need to trigger a full resync
+- **Test oplog partial-update handling in staging** before deploying to production — MongoDB's `$set` update operator produces partial update events in the oplog (not full document replacements), and many ETL tools handle these differently; validate that your tool correctly reconstructs the full document from partial oplog events before going live
+
+
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