Databases

ScyllaDB, a high-performance NoSQL database compatible with Apache Cassandra, is known for its low latency and high throughput. However, in large-scale deployments—especially in multi-DC setups or streaming-heavy workloads—teams often encounter the "pending compaction backlog" problem. This manifests as increased read latencies, bloated SSTables, and eventually degraded node performance. Left unchecked, it can lead to cascading failure modes in production systems. This article breaks down the architectural causes, performance implications, and step-by-step troubleshooting techniques to detect, address, and prevent compaction backlog issues in ScyllaDB clusters.

TimescaleDB, built atop PostgreSQL, is optimized for handling time-series data at scale. It introduces hypertables and chunking mechanisms that offer powerful performance benefits. However, in production environments with high-ingestion workloads, developers and DBAs often encounter the persistent issue of "chunk insert contention and autovacuum stalls". This problem can lead to ingestion latency spikes, query planner regressions, and bloated indexes. This article explores TimescaleDB's internal architecture, root causes of contention during inserts and maintenance tasks, and robust strategies for stabilizing write-heavy pipelines in enterprise time-series systems.

Neo4j is a powerful graph database widely used for modeling complex relationships in domains like recommendation systems, fraud detection, and network analysis. However, enterprise deployments frequently encounter the "query performance degradation due to label and relationship explosion" issue. This occurs when unbounded growth in node labels or relationship types leads to poor index utilization, cache thrashing, and suboptimal query plans. As data models evolve, this architectural anti-pattern can severely degrade Cypher execution speed and system responsiveness. This article explores the root causes, diagnostics, and long-term optimization strategies for keeping large-scale Neo4j deployments performant and manageable.

ArangoDB is a multi-model NoSQL database that supports document, key-value, and graph data models with a unified query language (AQL). While it offers flexibility and scalability, many teams operating large ArangoDB clusters encounter the recurring issue of "query performance degradation and memory pressure under complex AQL joins and traversals". These problems often occur in graph-heavy or analytics-driven workloads where multi-hop traversals or deep filter pipelines are executed frequently. This article breaks down ArangoDB’s execution engine, highlights root causes of slow queries, and provides tactical and architectural guidance to optimize performance and reduce memory stress.

Microsoft SQL Server is a powerful and feature-rich relational database system widely used in enterprise environments. However, administrators and architects often encounter a persistent issue: "performance degradation due to parameter sniffing and suboptimal execution plans". This problem can cause queries to perform well for one set of parameters but poorly for others, leading to unpredictable latency and resource contention. This article explores how SQL Server generates and reuses execution plans, why parameter sniffing becomes problematic in large systems, and how to diagnose, fix, and prevent it in high-throughput workloads.

InfluxDB is a high-performance time-series database optimized for storing and analyzing large volumes of timestamped data such as metrics, logs, and events. While it delivers powerful real-time analytics capabilities, teams operating InfluxDB at scale often encounter the persistent issue of "write amplification and retention policy-related storage bloat due to improper shard management and cardinality overload". These issues can lead to high disk I/O, unbounded storage growth, and query slowdowns. This article examines the architecture behind InfluxDB's storage engine, explores the causes of inefficient retention, and provides actionable strategies for maintaining long-term storage and write performance.

Firebase Realtime Database is a cloud-hosted NoSQL database that allows real-time data synchronization across clients. Its event-driven model is excellent for chat apps, collaboration tools, and IoT systems. However, teams often face serious scalability and data consistency issues such as "performance degradation, excessive bandwidth usage, and data overwrite errors due to improper data structuring, client-side over-fetching, and weak security rules". These challenges, if unaddressed, can cause user experience issues, unexpected billing spikes, and security risks. This article explores key architectural pitfalls and proven strategies for troubleshooting Firebase Realtime Database in production environments.

Teradata is a high-performance, MPP (Massively Parallel Processing) relational database platform designed for large-scale analytics and data warehousing. While Teradata provides robust scalability and integration capabilities, enterprise users frequently encounter issues such as "skewed data distribution, slow query performance, spool space errors, and inconsistent load behavior due to improper indexing, statistics management, or session/resource constraints". This article provides an in-depth guide for identifying and resolving operational bottlenecks in Teradata environments, with practical tips for optimizing query design and system configuration.

RethinkDB is a distributed, real-time database designed for web applications that require changefeeds, JSON storage, and querying via ReQL. It supports automatic failover, sharding, and live data push to applications. Despite its ease of use, production deployments can face advanced issues such as "stalled changefeeds, replica lag, cluster instability, write conflicts, and memory bloat due to unbounded queries". This article provides an in-depth troubleshooting guide for resolving common and complex RethinkDB operational issues.

RavenDB is a high-performance NoSQL document database designed for distributed, ACID-compliant data storage. With built-in full-text search, automatic indexing, and support for multi-document transactions, it's ideal for modern enterprise applications. However, teams working at scale may face challenges like "indexing errors, cluster replication delays, excessive memory consumption, stale queries, and certificate/authentication issues". This guide provides advanced troubleshooting strategies to maintain performance, consistency, and availability in RavenDB deployments.

GraphDB by Ontotext is a powerful RDF triple store and semantic graph database designed for linked data and ontology-based applications. It supports SPARQL queries, reasoning, and data integration at scale. While ideal for knowledge graphs and semantic search, advanced deployments often encounter complex issues such as "query performance degradation, reasoning bottlenecks, cluster synchronization failures, SHACL validation errors, and data import inconsistencies". This article provides a comprehensive troubleshooting guide for resolving production-level problems in GraphDB deployments.