Databases

GraphDB, an industrial-grade RDF triplestore for knowledge graphs, powers search, data integration, and reasoning-heavy applications across regulated industries. At enterprise scale, teams run into subtle production issues: slow SPARQL queries under inference, memory pressure from large joins, write stalls during snapshotting, and inconsistent reasoning after ontology changes. These are not mere tuning glitches—they stem from the interplay of storage indexes, reasoning rules, transaction semantics, and JVM behavior. This article provides a deep, practitioner-focused troubleshooting guide for senior architects and tech leads who operate GraphDB in demanding environments. We will examine root causes, architectural implications, diagnostic workflows, and durable fixes that prevent regressions while keeping throughput, reliability, and semantic correctness in balance.

SAP HANA powers many enterprise-critical workloads with its in-memory columnar architecture, delivering real-time analytics and transaction processing at scale. Yet troubleshooting HANA in production is complex: issues rarely manifest as simple query slowdowns. Instead, architects and DBAs face memory pressure under mixed workloads, plan cache fragmentation, lock contention across distributed services, and unpredictable performance under hybrid transactional/analytical processing (HTAP). Left unresolved, these problems propagate into application outages, SLA breaches, and costly scaling missteps. This article provides a deep dive into diagnosing root causes, understanding architectural ripple effects, and implementing sustainable fixes for SAP HANA in enterprise environments.

RethinkDB, an open-source distributed NoSQL database, is known for its real-time push architecture that streams query results to applications. While it is powerful for event-driven systems, enterprises using RethinkDB often run into advanced challenges that are not widely documented: cluster instability, query performance bottlenecks, resource contention under high concurrency, and schema evolution pitfalls. These issues, if left unresolved, can impact real-time dashboards, streaming APIs, and critical workloads. This article provides senior engineers and architects with a deep dive into diagnosing, troubleshooting, and preventing complex RethinkDB issues in production-scale environments.

MariaDB is a popular open-source relational database engine widely adopted in enterprise environments as a drop-in replacement for MySQL. While it offers performance, scalability, and feature enhancements, troubleshooting MariaDB at scale requires advanced understanding of query optimization, replication topologies, resource contention, and schema evolution. Common issues include slow queries under heavy workloads, replication lag, deadlocks, unexpected crashes, and data corruption risks when storage engines are misconfigured. This article provides senior engineers and architects with deep diagnostics workflows, root cause analysis, and long-term best practices for stabilizing MariaDB in mission-critical environments.

Presto, the distributed SQL query engine, powers mission-critical analytics across modern enterprises. Its ability to query data from heterogeneous sources at interactive speeds makes it invaluable, but troubleshooting at scale introduces significant challenges. Senior architects and tech leads often confront issues like uneven cluster performance, memory pressure from large joins, query skew, connector misconfigurations, and unpredictable latencies. While many teams treat Presto as a black box, diagnosing and resolving these issues requires deep architectural insight. This article explores advanced troubleshooting strategies for Presto in enterprise contexts, covering diagnostics, root causes, and sustainable best practices.

Apache Derby, a lightweight relational database engine, is often embedded within enterprise Java applications and used for development, testing, or lightweight production deployments. While Derby offers simplicity and portability, large-scale or long-lived systems can experience obscure issues such as lock contention, transaction deadlocks, runaway log growth, and unexpected memory pressure. These challenges may remain invisible during small-scale tests but can cripple performance or cause outages when the database is under concurrent enterprise workloads. Understanding Derby's embedded architecture, storage engine, and transaction model is crucial for diagnosing issues and implementing robust, long-term solutions.

Pervasive PSQL, now branded as Actian PSQL, is a high-performance, embedded, and client/server database engine widely used in enterprise software, ISVs, and vertical applications. It is designed for transactional consistency and ease of deployment, but troubleshooting issues in production-scale systems can be extremely challenging. Common problems include database corruption, licensing errors, locking contention, and performance bottlenecks under high transaction volumes. For architects and senior engineers, understanding not only the root causes but also the architectural implications of PSQL's internals is critical for ensuring system stability, compliance, and long-term maintainability.

SingleStore (formerly MemSQL) is designed for ultra-fast, distributed SQL processing, powering real-time analytics and hybrid transactional and analytical workloads. In enterprise-scale deployments, however, administrators and architects often encounter subtle, complex issues that are not well-documented: cluster instability under skewed workloads, memory pressure leading to node evictions, inconsistent query performance due to distributed joins, and challenges with replication in mixed transactional/analytical environments. Troubleshooting these problems requires a deep understanding of SingleStore's distributed architecture, execution engine, and resource management strategies. This article explores root causes, diagnostic approaches, and sustainable solutions for some of the most challenging SingleStore troubleshooting scenarios.

VoltDB, a high-performance in-memory relational database, is designed for extreme throughput and low-latency workloads such as financial trading, IoT ingestion, and telco signaling. While its architecture delivers impressive benchmarks, enterprise deployments often encounter complex operational issues rarely covered in documentation: partition misalignment, stored procedure contention, rejoin/recovery storms, GC stalls in JVM, and cross-datacenter replication lag. These problems can destabilize production systems if not proactively managed. This troubleshooting guide explores root causes, diagnostics, and long-term architectural strategies for stabilizing VoltDB in large-scale enterprise environments.

IBM Db2 is a cornerstone database system for many enterprises, providing high-performance transaction processing, analytics, and integration with legacy and modern applications. Despite its stability, Db2 environments often encounter complex issues that can cripple mission-critical workloads if not properly diagnosed. Common challenges include lock escalation, buffer pool contention, transaction log saturation, and query optimizer anomalies. These problems are rarely trivial—they involve deep architectural understanding and can significantly impact SLAs and long-term scalability. For senior architects and DBAs, mastering advanced troubleshooting in Db2 is essential not only to resolve outages but to prevent recurring systemic risks.

IBM Informix is a robust database platform widely used in industries requiring high availability, embedded deployment, and OLTP performance. While Informix is known for stability, enterprise teams often face nuanced troubleshooting challenges such as memory fragmentation in buffer pools, index corruption on large partitioned tables, unexpected performance degradation under HDR (High Availability Data Replication), and difficulties in diagnosing slow queries due to optimizer plan drift. These problems are rarely encountered in small setups but become critical at enterprise scale where uptime, throughput, and data consistency must be guaranteed.

TimescaleDB blends PostgreSQL's reliability with time-series optimizations, but that fusion also creates unique failure modes and performance traps that don't appear in vanilla PostgreSQL. In real-world enterprise clusters, teams encounter deadlocks around hypertable maintenance, runaway chunk counts, misconfigured compression policies, and continuous aggregate staleness that only surface under production load. The challenge for senior engineers isn't merely fixing a slow query; it's diagnosing systemic issues across storage, planner behavior, background jobs, and retention strategies. This article provides a deep, architecture-level troubleshooting guide for TimescaleDB: how to find root causes, interpret metrics, repair damage safely, and design future-proof configurations that keep data growth and query latency predictable.