Databases

Vertica is a high-performance, columnar database designed for large-scale analytics workloads. While known for its speed and scalability, enterprise deployments frequently encounter hard-to-diagnose problems related to data skew, query plan regressions, suboptimal projection design, and underperforming UDx or external procedures. These issues rarely occur in small-scale or lab environments, but at petabyte scale or with real-time ingestion pipelines, they manifest with production-level consequences. This article targets senior engineers and architects tasked with maintaining high-performance Vertica clusters under demanding SLAs, offering in-depth diagnostics, architectural strategies, and actionable best practices.

Presto, now widely adopted as Trino, is an open-source distributed SQL query engine designed for running interactive analytic queries against large-scale data sources. Its performance, scalability, and support for heterogeneous backends make it ideal for modern data lakes. However, enterprises often encounter nuanced issues with query timeouts, inconsistent performance, memory exhaustion, and connector-specific failures. These problems can affect SLA guarantees, dashboard reliability, and even bring down entire clusters. Troubleshooting Presto requires understanding its multi-stage architecture, distributed execution model, and configuration dependencies across workers, coordinators, and connectors.

Elasticsearch is widely used in enterprise systems for full-text search, real-time analytics, and log aggregation. However, as data volumes grow and query complexity increases, organizations often encounter hidden operational challenges that impact stability, accuracy, and performance. From index bloat and cluster instability to subtle mapping mismatches and slow queries, these issues require deep technical insight to diagnose and resolve. This guide provides a comprehensive troubleshooting framework tailored for architects, SREs, and senior engineers operating Elasticsearch clusters in production environments.

InfluxDB is a powerful time-series database optimized for metrics and event data. However, enterprise-scale deployments often experience challenges such as write throughput bottlenecks, shard compaction stalls, unexpected memory usage spikes, and data loss during retention policy enforcement. These issues are subtle, require deep observability, and can cause serious performance degradation or availability risks if not addressed. This article explores the advanced troubleshooting of InfluxDB in production environments, focusing on root causes, architectural implications, and sustainable solutions.

Exasol, known for its blazing-fast in-memory analytical processing, is a favorite among enterprises for large-scale data warehousing. However, even high-performance systems encounter nuanced challenges that only surface under heavy operational stress or architectural complexity. One particularly thorny issue is unexplained query degradation over time, especially in ELT-intensive environments. These performance issues are often hard to diagnose, as they stem not from traditional tuning problems, but from intricate interactions between resource allocation, metadata statistics, caching strategies, and external integration patterns. This article dissects this rare but impactful problem in Exasol and offers a comprehensive approach to root cause analysis and sustainable resolution.

MySQL is the backbone of countless enterprise and SaaS applications, prized for its simplicity, stability, and vast ecosystem. However, as applications scale and evolve, teams frequently encounter elusive performance degradation — particularly from "invisible" issues like inefficient query plans, suboptimal index usage, or table-level contention under high concurrency. Unlike overt failures, these problems manifest subtly and often escalate only in production. This article dives deep into diagnosing and resolving MySQL query bottlenecks that stem from internal metadata locks, flawed execution plans, and architectural oversights in large-scale systems.

Apache Cassandra is a highly scalable, distributed NoSQL database favored in systems demanding high availability and fault tolerance. However, it presents a unique set of operational challenges—especially under high throughput, inconsistent schema practices, or improper data modeling. One of the most complex and misunderstood issues in enterprise-scale Cassandra deployments is unexplained read latency spikes and consistency anomalies. These issues often escape simple metrics dashboards, impacting SLA-bound services and analytics pipelines. This article dives into diagnosing and resolving persistent Cassandra read performance issues, focusing on architectural implications and sustainable long-term solutions.

PostgreSQL, while renowned for its reliability and performance, often poses complex troubleshooting challenges in enterprise environments. One such elusive issue is transaction ID (XID) wraparound, which can silently disrupt operations and cause downtime if not proactively addressed. This article dives deep into diagnosing and preventing PostgreSQL wraparound failures—an advanced topic rarely discussed until it's too late. We explore why it happens, how to detect early warning signs, and the long-term architectural implications for large-scale systems relying heavily on ACID compliance and high write throughput.

MarkLogic is an enterprise-grade, multi-model NoSQL database known for handling complex data integration, semantic search, and large-scale content repositories. Despite its capabilities, real-world production environments often encounter challenges around query performance degradation, stale indexes, or misconfigured clusters. These issues are rarely straightforward, especially in deployments involving heavy ingestion pipelines or mixed workloads. This article focuses on diagnosing and resolving advanced operational problems in MarkLogic, equipping architects and senior developers with actionable insights and preventative strategies.

ClickHouse is a high-performance, column-oriented database used for analytical workloads at scale. Its architecture is optimized for fast reads and compression, but as production environments grow, certain operational challenges surface—especially around replication lag, data inconsistency, and merging delays in distributed setups. A particularly frustrating issue arises when replicated tables report out-of-sync parts or fail to complete merges, resulting in stale query results or read amplification. Understanding the storage engine internals and replication mechanics is essential to troubleshoot these anomalies.

NuoDB is a distributed SQL database designed for cloud-native applications, offering elasticity, ACID compliance, and high availability. However, in enterprise-scale deployments, teams often encounter elusive issues such as transaction inconsistencies, cache synchronization delays, and node role conflicts. These problems can severely impact application reliability and performance if not addressed with a deep architectural understanding. This article offers an advanced troubleshooting guide tailored for architects and database engineers maintaining NuoDB in production.

ArangoDB is a multi-model database that combines key/value, document, and graph data models, making it attractive for modern, complex applications. However, as systems scale and data models evolve, ArangoDB can present unique operational challenges—particularly in distributed or enterprise-grade deployments. These challenges often go beyond what standard documentation covers, affecting query performance, cluster consistency, failover behavior, and data replication. This article aims to help senior engineers, architects, and DBAs diagnose and resolve critical ArangoDB issues that emerge in production environments, focusing on architectural root causes, diagnostic tooling, and long-term resilience strategies.