10 Elasticsearch Production Issues (and How Postgres Avoids Them)

Elasticsearch, while powerful, presents significant operational challenges when used in production environments, including JVM garbage collection pauses, mapping explosions, oversharding or undersharding, deep pagination performance issues, split-brain scenarios, eventual consistency surprises, security misconfigurations, monitoring complexities, data pipeline synchronization issues, and high infrastructure costs. Each of these issues stems from Elasticsearch's nature as a separate system that requires independent infrastructure, expertise, and maintenance, which can lead to complications and inefficiencies. In contrast, PostgreSQL offers a more streamlined alternative by integrating search capabilities directly into its database system, eliminating the need for separate search infrastructure. With extensions like pg_textsearch for BM25 ranking and pgvectorscale for vector search, Postgres can handle many search workloads effectively, avoiding the complexities associated with running Elasticsearch.