PostgreSQL-to-Elasticsearch synchronization: Logstash with JDBC input plugin Vs. PGSync

After hours of implementing the PostgreSQL-to-Elasticsearch synchronization service in many different ways, my team and I at GT finally decided to use PGSync. Here's a comparison of Logstash with the JDBC input plugin and PGSync for PostgreSQL-to-Elasticsearch synchronization:
Factor | Logstash with JDBC Input Plugin | PGSync |
Overview | General-purpose data pipeline tool with a JDBC plugin for database integration | Specialized tool for PostgreSQL replication to Elasticsearch |
Integration | Connects to various databases, not PostgreSQL-specific | Optimized for PostgreSQL, leveraging its logical decoding feature |
Configuration | Requires defining pipelines and filters for data processing | Configuration is simpler, focused on replication settings |
Data Transformation | Offers extensive filtering and transformation capabilities within pipelines | Limited to basic filtering and mapping |
Performance | Can be slower for large-scale replication due to overhead of pipeline processing | Generally faster due to direct replication without intermediate processing |
Scalability | Can be horizontally scaled by adding more Logstash nodes | Limited to vertical scaling of a single PGSync instance |
Error Handling | Provides mechanisms for retrying failed events and handling errors | Less robust error handling mechanisms |
Monitoring | Integrates with monitoring tools for pipeline visibility | Limited monitoring capabilities |
Maintenance | Requires managing Logstash and its dependencies | Simpler setup and maintenance |
Choosing the right tool depends on your specific needs:
Additional considerations:
Recommendations: