MongoDB Operational Patterns#

MongoDB operations center on three areas: keeping the cluster healthy (replica sets and sharding), protecting data (backups), and keeping queries fast (indexes and explain plans). This reference covers the practical commands and patterns for each.

Replica Set Setup#

A replica set is the minimum production deployment – three data-bearing members that elect a primary and maintain identical copies of the data.

Launching Members#

Each member runs mongod with the same --replSet name:

MySQL Performance Tuning#

Performance tuning comes down to three things: making queries touch fewer rows (indexes), keeping hot data in memory (buffer pool), and finding the slow queries (slow query log, Performance Schema).

Reading EXPLAIN Output#

EXPLAIN shows MySQL’s query execution plan. Always use EXPLAIN ANALYZE (MySQL 8.0.18+) for actual runtime stats, not just estimates.

EXPLAIN ANALYZE
SELECT u.name, COUNT(o.id) as order_count
FROM users u
JOIN orders o ON o.user_id = u.id
WHERE u.created_at > '2025-01-01'
GROUP BY u.id;

Key columns:

PostgreSQL Performance Tuning#

Most PostgreSQL performance problems come from missing indexes, bad query plans, connection overhead, or table bloat. This covers how to diagnose each one.

Reading EXPLAIN ANALYZE#

EXPLAIN shows the query plan. EXPLAIN ANALYZE actually executes the query and shows real timings.

EXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 42 AND status = 'pending';

Index Scan using idx_orders_customer on orders  (cost=0.43..8.45 rows=1 width=120) (actual time=0.023..0.025 rows=3 loops=1)
  Index Cond: (customer_id = 42)
  Filter: (status = 'pending'::text)
  Rows Removed by Filter: 12
Planning Time: 0.152 ms
Execution Time: 0.048 ms

What to look for: Seq Scan on large tables means a missing index. Rows Removed by Filter means the index fetched extra rows that a composite index would eliminate. actual rows far from estimated rows means stale statistics – run ANALYZE tablename;. Nested Loop with high loops count usually wants a hash join; check the inner table’s indexes.

Elasticsearch and OpenSearch: Indexing, Queries, Cluster Management, and Performance#

Elasticsearch and OpenSearch are distributed search and analytics engines built on Apache Lucene. They excel at full-text search, log aggregation, metrics storage, and any workload that benefits from inverted indices. Understanding index design, mappings, query mechanics, and cluster management separates a working setup from one that collapses under production load.

Elasticsearch vs OpenSearch#

OpenSearch is the AWS-maintained fork of Elasticsearch, created after Elastic changed its license from Apache 2.0 to the Server Side Public License (SSPL) in early 2021. For the vast majority of use cases, the two are interchangeable. APIs are compatible, concepts are identical, and most tooling works with both. OpenSearch Dashboards replaces Kibana. This guide applies to both unless explicitly noted.