Databases

FaunaDB is a globally distributed, serverless database designed for modern applications, offering strong consistency and native GraphQL support. However, despite its scalability and flexibility, troubleshooting FaunaDB in production environments can present unique challenges. Issues such as query performance bottlenecks, consistency anomalies, rate-limiting, and index misuse can severely impact application behavior. This article addresses these problems in depth, helping architects and tech leads to diagnose and optimize FaunaDB deployments across various use cases.

IBM Db2 is a powerful enterprise-grade relational database used in mission-critical systems across finance, healthcare, and logistics. While Db2 is known for its stability and performance, complex production environments introduce nuanced issues that are difficult to detect and troubleshoot. These include lock contention, buffer pool misconfiguration, slow query performance, HADR (High Availability Disaster Recovery) synchronization delays, and problematic utilities. This article provides advanced diagnostics, root cause analysis, and long-term architectural guidance to address these challenges.

ArangoDB is a multi-model database designed to unify graph, document, and key/value data with a single query language—AQL. Despite its versatility, enterprises using ArangoDB in production often face non-obvious challenges such as cluster synchronization issues, performance regressions in distributed joins, query planner inefficiencies, or replication drift. These problems, if left unresolved, can lead to data inconsistency, query timeouts, or even systemic failure in microservices architectures. This article provides a senior-level troubleshooting framework for diagnosing and fixing critical issues in ArangoDB deployments, with a focus on architectural implications and resilient remediation techniques.

QuestDB is a high-performance, open-source time-series database designed for real-time analytics, offering ultra-low-latency ingestion and SQL-like querying over millions of rows per second. However, teams working at scale often encounter issues with query timeouts, memory pressure, or ingestion bottlenecks—especially when dealing with massive volumes of time-stamped data from IoT, finance, or observability pipelines. These challenges are rarely surface-level; they involve deep architectural nuances in how QuestDB handles partitioning, memory mapping, WAL (Write-Ahead Logging), and concurrent workloads. This article provides a structured approach to diagnosing and resolving real-time performance degradation in QuestDB deployments.

Oracle Database remains a cornerstone of enterprise systems, powering mission-critical applications across finance, healthcare, manufacturing, and government sectors. While its robustness and feature set are unparalleled, complex issues can arise in large-scale implementations. One particularly intricate and often misunderstood problem is the gradual buildup of performance degradation due to unmonitored cursor sharing and execution plan instability. These issues are subtle, often eluding detection in traditional monitoring, and can wreak havoc on system responsiveness. This article explores the root causes, diagnostic techniques, architectural considerations, and long-term strategies for resolving cursor-related performance issues in Oracle Database environments.

Apache Cassandra is a highly scalable, distributed NoSQL database designed for high availability and linear scalability. While it performs exceptionally well in write-heavy, fault-tolerant applications, operating Cassandra clusters at scale introduces several complex challenges. Issues like inconsistent read latencies, hinted handoff overload, GC pressure, and tombstone accumulation can silently degrade performance or cause outages. This article provides an in-depth troubleshooting guide for Cassandra in enterprise environments, focusing on identifying root causes, performance tuning, and architectural best practices.

Pervasive PSQL (now Actian Zen) is a high-performance embedded database engine used extensively in legacy and vertical-market applications. Known for its zero-administration model and tight application integration, it powers many industrial, financial, and healthcare solutions. However, as systems modernize and scale—especially in environments involving virtualization, cloud migration, or Windows service orchestration—rare yet impactful issues can arise. This article explores intricate troubleshooting scenarios involving Pervasive PSQL, with a focus on diagnostics, root causes, and long-term architectural mitigation.

PostgreSQL is a robust open-source relational database, widely used across industries for transactional systems and analytics workloads alike. In large-scale enterprise environments, one particularly tricky and under-reported issue is "autovacuum-induced bloat and query latency." Autovacuum is a critical background process responsible for cleaning up dead tuples, but when misconfigured or overwhelmed, it can silently degrade performance, inflate table sizes, and cause erratic query response times. This article provides a deep dive into the mechanics of autovacuum, explores architectural considerations, and outlines strategies for diagnosing and resolving performance bottlenecks caused by autovacuum lag in PostgreSQL production systems.

Apache HBase, as a distributed, scalable, and column-oriented NoSQL database, is a critical component in many enterprise big data architectures. However, in production environments with high data volume and strict latency SLAs, HBase can suffer from performance bottlenecks, region server instability, and data inconsistency issues. These problems often stem from complex interactions between HBase, HDFS, ZooKeeper, and the network layer. This article provides senior engineers, architects, and technical leads with a deep dive into diagnosing and resolving advanced HBase operational problems in large-scale deployments.

Apache HBase is a distributed, column-oriented NoSQL database built on top of Hadoop's HDFS, widely used for real-time read/write access to large datasets. While HBase is designed for scalability and fault tolerance, enterprise deployments can encounter complex issues such as region server hotspots, write amplification, compaction stalls, and GC pauses under heavy load. These problems are particularly challenging in large clusters where latency-sensitive applications depend on predictable performance. Troubleshooting HBase effectively requires understanding its architecture, monitoring key metrics, and applying tuning strategies to maintain throughput and consistency.

IBM Db2 is a robust enterprise-grade relational database that powers critical systems in finance, government, retail, and manufacturing. While it offers high availability, sophisticated query optimization, and deep integration with enterprise tooling, large-scale deployments can encounter complex operational issues that are rarely seen in smaller environments. These include lock contention storms, query plan instability under varying workloads, unexpected buffer pool saturation, HADR synchronization lag, and package cache bloating. Such problems often manifest under peak transactional load or after schema changes, and they can have severe business impact if not diagnosed and resolved quickly. This article provides a senior-level troubleshooting guide for diagnosing and fixing advanced Db2 issues, with a focus on root cause analysis, architectural implications, and long-term prevention strategies.

RethinkDB, known for its real-time changefeeds and JSON-native query language, still powers critical systems in IoT, collaborative platforms, and event-driven applications. However, in large-scale deployments, troubleshooting performance degradation, cluster instability, and changefeed stalls can be complex. Senior engineers face challenges stemming from RethinkDB's distributed architecture, sharding model, and interplay between Raft consensus and query execution. While many issues appear as transient slowdowns, they often signal deeper architectural or operational faults. This article dissects these scenarios, provides step-by-step diagnostics, and outlines durable fixes to keep RethinkDB clusters performant and resilient in production.