Databases

HSQLDB (HyperSQL Database) is a lightweight, 100% Java-based relational database commonly used in embedded systems, unit testing, and Java applications requiring an in-process database. Despite its convenience, developers working in large or persistent application contexts often encounter difficult-to-trace issues, particularly related to data persistence, concurrency, and unexpected shutdown behavior. This article focuses on diagnosing and resolving complex HSQLDB issues in production-like environments, highlighting root causes, architectural impacts, and long-term fixes for scenarios where HSQLDB's default configuration can fail silently or introduce data corruption risks.

RavenDB is a powerful NoSQL document database designed for performance, ACID compliance, and distributed scalability. While it simplifies data modeling and supports features like automatic indexing, revisions, and multi-node replication, enterprise-level deployments often encounter nuanced issues. These include index staleness, replication lag, memory pressure under high throughput, and cluster topology inconsistencies. Such challenges are rarely asked about but critically impact system reliability and query latency if not addressed with architectural foresight.

Elasticsearch is a powerful distributed search engine commonly used in large-scale logging, analytics, and full-text search scenarios. However, in enterprise deployments, it often becomes a bottleneck when query latency spikes, indexing stalls, or nodes drop out of the cluster unexpectedly. These issues are rarely caused by a single component, but rather a combination of configuration mismatches, JVM tuning pitfalls, improper shard allocation, and unbounded indexing rates. Troubleshooting these issues requires a holistic understanding of Elasticsearch's internal architecture and careful observability of cluster metrics and logs.

Presto, now known as Trino in its community-driven fork, is a distributed SQL query engine designed for interactive analytics over large-scale datasets. While its performance and flexibility make it ideal for federated querying across heterogeneous data sources, enterprises often struggle with complex, intermittent issues in production environments. These range from memory exhaustion and query skew to metadata inconsistencies and security integration problems. This article dives into the rarely discussed, high-impact issues encountered when operating Presto at scale, offering architecture-aware diagnostics and long-term resolution strategies.

IBM Db2 remains a cornerstone in many enterprise database architectures, particularly in finance, insurance, and government sectors. However, teams managing large-scale Db2 deployments often encounter complex issues—ranging from locking anomalies and query optimizer misbehaviors to memory exhaustion and unpredictable performance under load. This article delivers a deep-dive into diagnosing and resolving these elusive problems, helping architects and DBAs build more predictable, high-performing Db2 environments.

Greenplum is a powerful MPP (Massively Parallel Processing) data warehouse solution widely used in enterprise environments for large-scale analytics. While it delivers excellent performance for parallel queries and big data workloads, production teams often encounter non-trivial issues—ranging from skewed query performance and interconnect congestion to catalog bloat and data distribution mismatches. These challenges can degrade performance or even destabilize clusters in high-concurrency settings. This article addresses the less-discussed, complex issues of Greenplum troubleshooting and provides systematic solutions rooted in architectural understanding and operational best practices.

Apache Druid is a high-performance real-time analytics database designed for fast slice-and-dice analytics on large data sets. It powers interactive dashboards and data applications at scale. However, enterprises using Druid often encounter advanced operational and performance issues that are difficult to diagnose—such as segment imbalance, query latency spikes, memory pressure, and ingestion delays. These problems typically stem from architectural misconfigurations, unoptimized ingestion specs, or underutilized cluster roles. This article explores in-depth troubleshooting techniques and best practices to maintain Druid's performance and reliability at enterprise scale.

Apache Cassandra is a highly scalable, distributed NoSQL database trusted for handling high-throughput workloads across multiple nodes and datacenters. Despite its strengths, teams operating Cassandra at scale often encounter subtle and complex issues—particularly around **read inconsistencies and tombstone overload**. These problems can degrade performance, mislead business logic, and increase operational costs due to excessive repair operations and failed consistency guarantees. This article delves into diagnosing and resolving these challenges, providing actionable steps and architectural insights tailored for enterprise-grade deployments.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that is widely used for analytical workloads. While it offers powerful performance capabilities via columnar storage, parallel processing, and seamless integration with AWS services, enterprises often encounter complex issues related to query optimization, concurrency bottlenecks, vacuum maintenance, and spectrum integration. This article addresses these advanced troubleshooting challenges in large-scale Redshift deployments and provides architectural guidance, diagnostics, and sustainable fixes to help data architects and engineers ensure optimal performance and reliability.

Enterprise systems often demand embedded databases that are lightweight yet highly performant. Raima Database Manager (RDM), with its deterministic response times and minimal footprint, is a prime candidate. However, diagnosing rare transactional anomalies in RDM—such as phantom reads or unexpected deadlocks under concurrent access—can be a nightmare for architects and engineers unfamiliar with its internal memory model and transaction isolation mechanisms. These issues, though infrequent, can compromise system integrity and performance in mission-critical applications if left unresolved.

Teradata remains a cornerstone in enterprise data warehousing and analytics due to its high-performance parallel architecture. However, troubleshooting Teradata performance issues can be deceptively complex, especially in large-scale environments dealing with billions of rows, skewed data distribution, or ETL pipeline bottlenecks. A poorly tuned query or incorrect join strategy can cause severe system degradation, leading to increased query times or even aborted sessions. This article provides an in-depth guide to diagnosing and resolving such issues with precision, catering to architects and DBAs who manage critical data platforms.

Pervasive PSQL, a robust embedded database engine, is widely used in legacy and mid-market enterprise systems for its performance and reliability. However, as systems grow in complexity and scale, subtle issues can arise that degrade performance or lead to unpredictable behavior. Troubleshooting these issues is particularly challenging due to limited observability, tight application coupling, and outdated tooling. Senior engineers and architects often face complex problems involving locking contention, transaction anomalies, ODBC driver quirks, and index corruption that are rarely discussed publicly. This article offers a deep dive into these lesser-known, high-impact issues, providing technical root cause analysis and sustainable solutions tailored to Pervasive PSQL.