Databases

Amazon Aurora, a high-performance and highly available relational database service on AWS, powers many mission-critical enterprise applications. While it offers exceptional scalability and resilience, troubleshooting Aurora issues in production can be challenging due to its distributed architecture, replication mechanics, and integration with other AWS services. Problems can range from replication lag and failover delays to transaction lock contention and unexpected performance degradation. In large-scale systems, these issues can have cascading effects across microservices and analytics pipelines. Understanding Aurora's internals, diagnosing problems systematically, and applying architectural best practices are essential to maintaining predictable, reliable performance in enterprise deployments.

Apache Druid is a high-performance, real-time analytics database designed for sub-second OLAP queries on massive datasets. While it excels at ingesting and querying high-velocity event streams, enterprise-scale deployments often encounter complex issues that go beyond basic cluster setup: memory pressure from misconfigured JVM parameters, query performance degradation from skewed segments, ingestion failures due to schema drift, coordination bottlenecks in the metadata store, and under-documented scaling behaviors in multi-tier deployments. This article examines these challenges in depth, providing senior engineers and architects with diagnostic workflows, architectural considerations, and durable fixes for sustained stability.

Neo4j, as a leading graph database platform, excels at modeling and querying complex relationships at scale. In enterprise environments, however, its performance and stability can be challenged by massive datasets, complex Cypher queries, and distributed cluster configurations. Problems like slow traversal speeds, memory pressure under large graph workloads, inconsistent results in clustered deployments, and query planner missteps often surface only in high-throughput scenarios. This article explores advanced troubleshooting techniques for Neo4j in production, focusing on diagnosing deep-rooted performance bottlenecks, ensuring data consistency in HA setups, and establishing best practices for sustainable graph database operations.

VoltDB, an in-memory, distributed SQL database designed for high-throughput, low-latency transactions, is widely used in real-time analytics, telco policy enforcement, and financial systems. While its architecture enables exceptional performance, it can also present complex operational challenges at enterprise scale—particularly when dealing with cluster synchronization issues, latency spikes, or data consistency anomalies under heavy load. Troubleshooting VoltDB problems requires not only understanding the database’s runtime internals, but also the interplay between partitioning, stored procedures, and cluster fault recovery mechanisms. Neglecting these factors can lead to performance degradation, inconsistent query results, and costly downtime.

MariaDB, a high-performance open-source relational database, powers many enterprise systems due to its MySQL compatibility, scalability features, and robust replication capabilities. While small deployments rarely encounter deep architectural issues, large-scale MariaDB clusters face subtle problems—deadlocks that evade logs, replication lag under mixed workloads, erratic query performance due to statistics drift, or failovers that introduce silent data divergence. In production environments with mission-critical SLAs, these incidents cause cascading service degradation, financial losses, and reputational harm. This article provides a comprehensive troubleshooting guide aimed at senior engineers and architects, covering advanced diagnostics, root cause analysis, and long-term remediation strategies for complex MariaDB issues in enterprise deployments.

Raima Database Manager (RDM) is a high-performance, embedded database designed for real-time and edge computing environments. While it offers predictable low-latency performance and small footprint advantages, enterprise deployments—especially in IoT, industrial control, or embedded analytics—face complex challenges that rarely appear in small prototypes. Issues like transaction deadlocks under concurrent sensor writes, data corruption from power loss, schema evolution in resource-constrained devices, and replication desynchronization in intermittent connectivity scenarios require deep architectural troubleshooting. This article provides a senior-level, systematic approach to diagnosing and resolving these advanced RDM issues, with an emphasis on long-term resilience and predictable performance in mission-critical deployments.

Pervasive PSQL, a high-performance transactional database often embedded in business applications, is renowned for its stability and backward compatibility. However, in enterprise environments with complex integrations and heavy concurrent workloads, administrators encounter rare but severe issues—such as index corruption under concurrent writes, unexpected transaction deadlocks, and slowdowns from suboptimal Btrieve API usage. These are not beginner problems; they arise in large-scale deployments where Pervasive PSQL is a critical back-end to ERP, accounting, or manufacturing systems. For senior architects and DBAs, the challenge lies in diagnosing root causes that span database configuration, application-level logic, and OS-level tuning. This guide provides in-depth troubleshooting strategies and long-term best practices for maintaining optimal Pervasive PSQL performance.

In enterprise environments running IBM Informix, one of the more complex and high-impact operational issues is long transaction table overflow and checkpoint stalls. While Informix is known for its resilience and minimal DBA overhead, under certain conditions—particularly in high-throughput OLTP systems—long-running or uncommitted transactions can consume transaction log space, trigger long transaction alarms, and stall checkpoints. These stalls not only slow down database performance but can block new connections, delay replication, and threaten data consistency in HDR or RSS configurations. Troubleshooting this requires deep understanding of Informix's transaction logging, checkpointing process, and how long transaction tables interact with active workloads.

ClickHouse is an open-source columnar database management system designed for real-time analytics and large-scale data workloads. While it is known for exceptional query performance, enterprise-scale deployments can encounter rare but complex issues involving cluster synchronization, query planner inefficiencies, and unexpected disk I/O bottlenecks. One of the most challenging and disruptive problems is uneven data distribution across shards in a distributed ClickHouse cluster, leading to unbalanced load, skewed query execution times, and unpredictable system performance. This article provides senior-level troubleshooting guidance, covering root cause analysis, deep architectural considerations, diagnostic techniques, and preventive strategies for ensuring consistent performance across distributed ClickHouse environments.

QuestDB is a high-performance time-series database built for real-time analytics, known for its column-oriented storage and SQL compatibility. Its ability to handle billions of rows per second makes it ideal for financial data, IoT telemetry, and event monitoring. However, when deployed in enterprise environments with sustained high-ingest rates, developers can encounter complex issues such as ingestion stalls, out-of-memory errors, slow queries over large partitions, or unexpected data gaps. These issues are often subtle, only emerging under prolonged production loads, and require a precise understanding of QuestDB’s internal architecture to resolve effectively.

MongoDB is a leading NoSQL database known for its flexibility, scalability, and developer-friendly query model. In enterprise-scale deployments, however, teams often encounter elusive issues: unexplained performance drops, memory spikes, replication lag, and data inconsistencies. These problems are rarely the result of a single misstep; they emerge from the interplay of schema design, query patterns, cluster topology, and operational tuning. This guide targets architects, tech leads, and senior engineers who must diagnose and resolve MongoDB issues in large-scale, mission-critical environments. We will examine root causes, walk through advanced diagnostic steps, explore architecture-level implications, and propose sustainable fixes for long-term resilience.

ScyllaDB is a high-performance, Cassandra-compatible database built on the Seastar framework with a shard-per-core architecture. In enterprise settings, teams adopt ScyllaDB for ultra-low latency and predictable throughput, yet subtle production issues can appear months after launch: p99 latency spikes, compaction stalls, unbalanced shards, tombstone storms, streaming bottlenecks during repair, or cross-DC consistency anomalies. These failures are rarely caused by a single misconfiguration. Instead, they emerge from the interaction of data modeling, driver behavior, resource isolation, compaction strategy, and network topology. This article provides senior architects and tech leads with a rigorous troubleshooting playbook that connects symptoms to root causes, explains architectural implications, and delivers durable fixes suitable for large-scale, mission-critical ScyllaDB clusters.