The JusDB Database Engineering Blog
Deep dives on PostgreSQL, MySQL, MongoDB and more — performance tuning, high availability, migrations and production war stories from our DBAs.
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StarRocks Monitoring & Alerting: The Complete Production Guide
Complete guide to StarRocks monitoring with Prometheus and Grafana. Covers resource saturation, cluster health, and application availability alerts with PromQL expressions and runbooks.
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Migrating from Oracle to SQL Server: A Practical Checklist
Migrate Oracle workloads to SQL Server — data type mapping, PL/SQL conversion, SSMA tooling, and validation strategies
SQL Server Query Store: Identifying and Fixing Plan Regressions
Use SQL Server Query Store to catch bad execution plans, force stable plans, and automate query performance regression alerts
SQL Server Always On Availability Groups: Setup and Tuning
Configure SQL Server Always On AG — listener routing, synchronous vs asynchronous commit, failover policy, and monitoring
Apache Pinot vs ClickHouse vs Druid: Real-Time OLAP Comparison
Compare Pinot, ClickHouse, and Druid for real-time analytics — ingestion latency, query patterns, and operational cost
Apache Pinot Architecture Deep Dive: Segments, Indexes, and Brokers
Understand Apache Pinot internals — segment lifecycle, StarTree indexes, broker routing, and controller operations
SeaTunnel vs Debezium vs Flink CDC: Choosing a CDC Platform
Compare SeaTunnel, Debezium, and Apache Flink CDC — connector coverage, fault tolerance, latency, and operational complexity
Apache SeaTunnel: Unified CDC and Batch Pipelines at Scale
Use Apache SeaTunnel for CDC and batch ingestion — Zeta engine, 100+ connectors, exactly-once semantics, and Kubernetes deployment
Valkey Performance Tuning: Memory, Persistence, and Cluster Config
Tune Valkey for production — memory policies, AOF/RDB persistence, cluster rebalancing, and latency monitoring
Database Schema Design for LLM Applications
Design schemas for LLM apps — conversation history, embedding metadata, prompt versioning, and feedback loops
Time-Series Databases for AI and ML Pipelines
Store model metrics, feature stores, and inference logs with TimescaleDB, InfluxDB, and QuestDB
Knowledge Graphs and Graph Databases for AI Applications
Use Neo4j and graph databases for AI knowledge graphs — entity extraction, relationship storage, and RAG augmentation
Database Observability for AI Workloads: Monitoring pgvector Queries
Monitor pgvector query performance — index hit rates, slow similarity searches, HNSW vs IVFFlat trade-offs
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