Databases

IBM Db2 remains a foundational RDBMS for large-scale enterprise environments, especially within financial services, healthcare, and government sectors. However, in high-concurrency workloads or legacy-integrated systems, teams often face performance degradation and unexpected locking behavior. One elusive issue is phantom locking or unpredictable escalation of row locks to table locks, which severely impacts OLTP responsiveness. This article provides deep insights into diagnosing and permanently mitigating such locking anomalies in Db2 production systems.

PostgreSQL is a robust and feature-rich open-source relational database, widely adopted in both startups and large enterprises. However, as systems scale, performance degradation caused by bloated tables and indexes due to MVCC (Multi-Version Concurrency Control) becomes a subtle yet critical problem. The issue often manifests as unexpected disk usage growth, vacuum inefficiencies, or degraded query performance despite low row counts. This article dives into diagnosing and resolving PostgreSQL table and index bloat in production environments, offering insights valuable to senior engineers and database architects.

VoltDB, a high-performance in-memory distributed database designed for real-time analytics and fast transactional workloads, is widely used in telecom, finance, and IoT platforms. Despite its throughput and low-latency strengths, teams operating VoltDB at scale often encounter a subtle yet debilitating issue: cluster rejoin failures and data inconsistency during node recovery or rolling upgrades. This problem typically arises in multi-site, HA configurations or when mismanaging snapshots and command logs during failover scenarios. The underlying challenges include synchronization gaps, improperly ordered replay logs, and stale replication buffers. For architects and DBAs, resolving these failures requires a deep understanding of VoltDB's consensus model, snapshot protocol, and real-time fault tolerance mechanisms.

SAP HANA is a high-performance in-memory database widely used in enterprise environments for real-time analytics and transactional processing. While it's architecturally robust, it presents a unique set of operational challenges when deployed at scale—particularly around memory management, delta merges, thread contention, and index fragmentation. These issues often surface subtly in production environments, causing performance degradation or unplanned downtime. This article addresses rarely discussed yet critical troubleshooting scenarios in SAP HANA, offering actionable insights for database architects and system leads managing mission-critical workloads.

MySQL remains one of the most widely deployed open-source relational databases, powering critical workloads from startups to Fortune 500 enterprises. While MySQL is known for its simplicity and performance, production environments—especially those with high concurrency, replication, and complex queries—face persistent, hard-to-diagnose issues that standard documentation rarely covers. This article targets senior-level DBAs, architects, and backend leads with a deep dive into rarely discussed MySQL issues such as lock contention, replication drift, query plan regressions, and I/O saturation, along with actionable, long-term resolutions.

ClickHouse is a high-performance columnar database designed for OLAP workloads at scale. Despite its speed and efficiency, engineers working with complex distributed ClickHouse clusters often face elusive problems that only manifest under real-world production loads. One such problem involves memory spikes and unexpected replica lag, leading to inconsistent query results or stalled ingestion pipelines. These issues can be difficult to pinpoint due to ClickHouse's asynchronous architecture and performance-centric configuration defaults. This article provides a deep dive into diagnosing and resolving such memory bottlenecks and replica synchronization issues in large-scale ClickHouse deployments.

RethinkDB is an open-source, real-time database designed for modern web applications, enabling real-time updates and distributed data handling. While its changefeeds and horizontal scalability are powerful, large-scale enterprise deployments often face complex operational challenges. A particularly intricate problem is "Unstable Changefeeds and Performance Degradation Under High Concurrency." This article provides senior architects, DB engineers, and tech leads with an in-depth exploration of the root causes, diagnostics, and long-term solutions for this issue in production-grade RethinkDB environments.

FaunaDB is a globally distributed, serverless database optimized for low-latency transactional workloads. Its unique architecture—based on temporal data, native GraphQL support, and multi-region consistency—makes it ideal for modern applications. However, teams operating FaunaDB at scale often encounter elusive issues such as unanticipated query timeouts, write contention across regions, and cryptic authorization errors. These issues, while rare in smaller setups, can bring down entire microservice interactions or front-end data access pipelines. Troubleshooting FaunaDB effectively requires deep knowledge of its FQL/GraphQL engine, security model, and temporal consistency mechanisms.

TimescaleDB, built as an extension to PostgreSQL, provides high-performance time-series data storage and analytics. While praised for its scalability and PostgreSQL compatibility, large-scale deployments often encounter subtle performance degradation issues, especially during long-range queries. One commonly overlooked problem is hypertable chunk explosion—when excessive chunk creation leads to planner inefficiencies, bloated metadata, and degraded query performance. This issue can silently undermine real-time analytics, monitoring dashboards, and alerting systems. This article explores the root causes, architectural consequences, and actionable fixes for hypertable chunk over-fragmentation in TimescaleDB.

Altibase, a high-performance in-memory relational database, is widely used in mission-critical systems across telecom, finance, and defense sectors. While its hybrid architecture (memory and disk) offers immense throughput, teams often face challenging troubleshooting scenarios related to session contention and lock escalation—particularly under high concurrency. These issues often manifest as random query timeouts, deadlocks, or cascading transaction failures. This article dives into the root causes of session locking anomalies in Altibase, outlines the architectural dynamics behind them, and provides robust diagnostics and resolution strategies.

Apache Derby is a lightweight, embeddable relational database that sees frequent use in embedded systems, development environments, and legacy enterprise software. Despite its simplicity and portability, Derby introduces a class of issues under concurrent, long-running, or transactional workloads. One rarely discussed but impactful issue is transaction log corruption or lock contention under heavy multi-threaded access. This can result in persistent deadlocks, phantom reads, or database startup failures—posing serious reliability risks for systems assuming Derby's default configurations are safe for production.

Elasticsearch is a powerful distributed search engine, but in large-scale enterprise environments, it often suffers from subtle performance degradation, unexplained query latency spikes, or persistent cluster instability. One particularly elusive issue is the 'unassigned shards' problem, which affects availability and scalability. When shards remain unassigned, searches return incomplete results and indexing fails silently—damaging data integrity. This article dissects the root causes, architectural implications, and permanent fixes for unassigned shards in Elasticsearch clusters under production-grade loads.