This article walks through designing and implementing a complete JMeter load‑testing solution for an e‑commerce platform's big‑sale scenario, covering business‑flow mapping, request correlation, multi‑stage stress testing, real‑time monitoring with InfluxDB + Grafana, bottleneck identification, and practical optimization tips.
The article chronicles the evolution of database technology from early hierarchical and network models through relational, column‑store, document, key‑value, graph, time‑series, HTAP, and finally vector databases, detailing each system’s architecture, strengths, limitations, typical uses, and future trends toward specialization, distributed cloud‑native designs, and AI‑driven applications.
This article explains why InfluxDB stands out among time‑series databases by covering its performance, storage model, use cases, comparison with relational databases, line protocol fundamentals, Docker installation steps, and PHP client integration, providing a practical guide for developers.
The article explains how Corona leverages three time‑series databases—InfluxDB for storing pre‑aggregated user metrics and platform health data, ClickHouse for real‑time multidimensional log analysis with aggregations, and ElasticSearch for full‑text searchable log monitoring—detailing their schema designs and query examples.
This article details the background, architecture, challenges, and iterative improvements of Bilibili's real-time data quality monitoring platform, covering offline and streaming DQC, resource-efficient Flink designs, InfluxDB proxy integration, CQ table handling, operational safeguards, and future engineering plans.
This article compares three leading time‑series databases—InfluxDB, Kdb+, and Prometheus—detailing their origins, core features, strengths, and drawbacks, and helps readers decide which solution best fits specific monitoring, IoT, or financial data workloads.
This tutorial explains how to gather build data from Jenkins pipelines, store the metrics in InfluxDB deployed on Kubernetes, and visualize the information using Grafana dashboards, providing a complete end‑to‑end solution for continuous delivery measurement and feedback.
This guide explains InfluxDB's architecture, data model, CRUD commands, measurement handling, retention policies, query syntax, time‑zone considerations, and service management, providing a comprehensive tutorial for developers working with time‑series databases.
Bilibili redesigned its real-time data-quality control platform by replacing per-rule Flink jobs with a unified, dynamically-configured architecture that classifies Kafka topics, aggregates via InfluxDB full-table and continuous queries, mitigates data inflation, adds a high-performance proxy, and implements robust monitoring and recovery to ensure scalable, reliable data quality for its big-data services.
This article introduces MyPerf4J, a high‑performance, low‑latency Java monitoring tool, outlines its key features, provides step‑by‑step installation and configuration instructions—including JavaAgent setup, InfluxDB and Grafana integration—and demonstrates its monitoring results, helping developers efficiently track JVM metrics.
This guide explains how to build a real‑time monitoring system for JMeter performance tests by integrating InfluxDB, Prometheus, and Grafana, deploying the stack with Docker‑Compose, and configuring JMeter’s Backend Listener to collect and visualize metrics continuously.
This article walks through setting up real‑time performance monitoring by sending JMeter metrics to InfluxDB via Backend Listener, visualizing them in Grafana, and extending the approach to system metrics with node_exporter, Prometheus, and Grafana, covering configuration steps, code snippets, and query examples.
Learn how to enable Nginx's stub_status module, collect metrics with Telegraf, store them in InfluxDB, and visualize real‑time performance dashboards in Grafana, covering installation, configuration, and interpretation of key Nginx statistics for effective server monitoring.
This tutorial walks through the end‑to‑end setup of JMeter performance testing data collection using Backend Listener, sending metrics to InfluxDB, and visualizing real‑time TPS, response time, and error rates in Grafana, as well as monitoring OS metrics with node_exporter, Prometheus, and Grafana.
This article walks through the most common performance‑monitoring stack—JMeter, node_exporter, Prometheus, InfluxDB, and Grafana—explaining how to configure backend listeners, send metrics, store them, and build real‑time dashboards while highlighting code snippets and query examples.
This guide explains how to enable the nginx‑status module, configure Nginx to expose metrics, collect them with Telegraf, store them in InfluxDB, and visualize the data in Grafana, providing a complete end‑to‑end monitoring solution for Nginx servers.
After setting up an Nginx cluster, this guide explains how to enable the nginx-status module, collect metrics with Telegraf, store them in InfluxDB, and visualize the data using Grafana, providing a complete solution for real-time Nginx monitoring.
Learn how to set up a comprehensive container monitoring solution using CAdvisor for metrics collection, InfluxDB for time‑series storage, and Grafana for visualization, including deployment steps, integration details, common issues, and best‑practice configurations for reliable Docker‑based environments.
This article explains how to set up end‑to‑end performance monitoring by sending JMeter metrics to InfluxDB via Backend Listener, visualizing them in Grafana, and similarly collecting system metrics with node_exporter and Prometheus, covering configuration, data storage, query examples, and practical visualization techniques.
This article explains step‑by‑step how to collect JMeter test metrics via Backend Listener, store them in InfluxDB, and display real‑time performance charts—including TPS, response time, and error rates—in Grafana, while also covering node_exporter integration with Prometheus for system‑level monitoring.
This guide walks through the end‑to‑end workflow of sending JMeter test metrics to InfluxDB via Backend Listener, storing them alongside node_exporter data in Prometheus, and visualizing TPS, response time, and resource usage in Grafana dashboards, complete with configuration steps and code examples.
This guide explains step‑by‑step how to configure JMeter’s Backend Listener to send metrics to InfluxDB, set up Prometheus and node_exporter, and create Grafana dashboards for real‑time TPS, response time, and system resource monitoring.
The article presents DB‑Engines' September 2021 ranking of 378 databases, analyzes trends such as the rise of domestic systems, highlights leading time‑series and key‑value databases, and announces a community book‑giving event with ten recommended database titles.
The September 2021 DB‑Engines ranking shows longtime leaders Oracle, MySQL and Microsoft SQL Server losing hundreds of points, while MongoDB, Snowflake and ClickHouse surge in popularity, Redis and InfluxDB dominate their niches, and the report stresses that selecting a database should prioritize business needs over mere ranking.
This tutorial walks through the end‑to‑end monitoring pipeline—from JMeter’s Backend Listener sending metrics to InfluxDB, through storing and querying those metrics, to configuring Grafana dashboards for real‑time performance visualization, and also covers OS monitoring with node_exporter, Prometheus, and Grafana.
This article explains what time series databases are, introduces InfluxDB as the leading TSDB, describes its TICK architecture and storage engine, provides step‑by‑step installation and configuration of InfluxDB and Telegraf, demonstrates visualization, JMeter integration, and Flux queries in Python, and highlights the rapid market growth of TSDBs.
Learn how to configure and use the open‑source time‑series database InfluxDB in a Spring Boot application, covering essential concepts, dependency setup, property configuration, scheduled data writing, and verification via InfluxDB CLI, with complete code examples.
This article explores the evolution and current landscape of time‑series databases, detailing design principles, storage structures such as B‑Tree, B+Tree, and LSM‑Tree, and providing an in‑depth comparison of OpenTSDB, InfluxDB, and the emerging Apache IoTDB, while also discussing practical deployment considerations and industry use cases.
This article details the design and implementation of a comprehensive database monitoring platform that automatically adapts to cluster changes, aggregates host and DB metrics, offers flexible alert templates and strategies, stores data in InfluxDB, and provides customizable dashboards for real‑time insight and incident response.
This article examines InfluxDB's in‑memory and TSI indexing, investigates queue buildup caused by asynchronous batch writes and high I/O, analyzes excessive SHR memory usage, and proposes configuration, retention policy, NUMA, and query optimizations to improve overall database performance.
This article investigates why an InfluxDB 1.8 instance on a 32‑core, 64 GB server consumes over 58 GB of resident memory and generates heavy disk I/O, examines Go runtime memory accounting, uses system tools such as top, pmap, pprof and iostat for diagnosis, and presents configuration and runtime tweaks that reduce memory pressure and I/O load.
The article investigates why an InfluxDB 1.8 instance on a 32‑core, 64 GB production server consumes over 95% memory and generates heavy disk I/O, analyzes runtime statistics, pprof data, and Go memory‑release behavior, and presents configuration and runtime tweaks that reduce memory usage to ~55% and I/O load to acceptable levels.
This technical guide walks through the fundamentals of Prometheus, compares it with Zabbix, demonstrates high‑availability setups, remote storage with InfluxDB, multi‑instance Redis monitoring, and Grafana integration, providing concrete configuration examples and best‑practice recommendations for reliable ops monitoring.
This article explains the cause of InfluxDB’s max‑value‑per‑tag error when Prometheus remote‑writes high‑cardinality tags, analyzes why the built‑in memory index triggers OOM protection, and presents three practical solutions—including moving indexes to disk, storing high‑cardinality tags as fields, and filtering them before write—to ensure stable monitoring data persistence.
This article explains the fundamentals, application scenarios, key requirements, and open‑source options for time series databases, highlighting how GaussDB (For Influx) addresses high‑performance writes, massive timelines, low storage cost, and elastic scaling for IoT and cloud monitoring workloads.
This article explains how to design and implement a Docker container monitoring system using CAdvisor for metric collection, InfluxDB for time‑series storage, and Grafana for visualization, covering deployment, integration, common issues, and practical configuration details.
This guide walks through installing InfluxDB and Grafana on CentOS, configuring InfluxDB for Flink metrics storage, creating databases and retention policies, integrating the Flink InfluxDB reporter, and building Grafana dashboards to visualize real‑time Flink job metrics.
This article explains how to gather build metrics from Jenkins pipelines, store them in InfluxDB deployed on Kubernetes, and display the data using Grafana dashboards, providing a practical DevOps solution for continuous delivery measurement and feedback.
This article introduces the fundamentals of time‑series databases, explains why InfluxDB was chosen, describes the TSM storage engine and shard concepts, outlines the internal 360 InfluxDB‑HA architecture, compares its performance with a single node, and provides integration and future‑development guidelines.
This article explores InfluxDB’s time‑series strengths, compares TSDB with traditional databases, explains its TSM storage engine and shard concepts, and details the design, architecture, performance benchmarks, integration steps, and future enhancements of a high‑availability InfluxDB‑HA solution used at 360.
To mitigate the inherent single‑point‑failure risk of the open‑source InfluxDB community edition, the article proposes deploying multiple InfluxDB instances with concurrent client writes, tracking failed writes, temporarily storing them, and using custom workers to replay data, while addressing timeout, data consistency, and storage considerations.
This article explains InfluxDB's Continuous Queries feature, covering configuration settings, basic and advanced syntax, execution timing, data range handling, result storage, and the use of RESAMPLE clauses with practical examples and code snippets.
This article explains InfluxDB's built‑in monitoring system, internal measurements, useful commands, HTTP endpoints, and provides detailed instructions for performing full backups and restores, including configuration tweaks, command syntax, and important considerations about formats and data ranges.
This article explains InfluxDB's storage engine workflow—including WAL, Cache, TSM files, compression components, and file management—then provides hardware sizing guidance based on write/query load, series cardinality, and recommends SSD storage with sample configuration settings.
This guide explains InfluxDB’s schemaless data types, best practices for designing tag‑and‑field schemas, and how to choose and configure its in‑memory or TSI indexes, including key parameters such as max‑series‑per‑database and max‑values‑per‑tag for optimal performance.
This article explains InfluxDB's retention policy components—duration, replication, and shard duration—clarifies the concepts of shards and shard groups, describes default configurations, offers recommendations for shard group duration, and outlines practical considerations for performance and data management.
This article explains the fundamentals of time‑series databases, introduces InfluxDB’s key components such as databases, measurements, retention policies, tags, fields, series, and points, describes the line protocol syntax, compares it with traditional databases, and outlines the design trade‑offs that shape its performance and limitations.
To effectively monitor Dockerized services, this guide walks through selecting a monitoring solution, deploying CAdvisor, integrating it with InfluxDB for persistent storage, visualizing metrics via Grafana, and addressing common issues such as missing utilities, memory stats, and network traffic inaccuracies.
This article walks through the complete deployment of a time‑series monitoring solution using the TICK stack—installing and configuring InfluxDB, Telegraf, and Grafana—to collect, store, and visualize key metrics such as CPU, memory, network, and disk I/O for a big‑data platform.
This tutorial walks through installing Jenkins, InfluxDB, and Grafana, configuring Jenkins plugins, creating the InfluxDB database and user, writing a pipeline script to publish build metrics, and setting up Grafana dashboards with variables to visualize Jenkins build data.
QTSDB is a distributed time‑series database built on InfluxDB 1.7 that adds cluster capabilities such as horizontal scaling, replica fault‑tolerance, automatic data expiration, and high‑throughput writes, while preserving InfluxDB's query syntax and storage engine features.
This article reviews common open‑source monitoring tools, shares the evolution of China Unicom's big‑data platform monitoring, and provides practical guidance on selecting collectors, databases, and visualization components, with detailed configurations for Prometheus, Alertmanager, Grafana, and automation recovery techniques.
This article reviews common open‑source monitoring combinations, compares their strengths and weaknesses, and shares practical guidance on selecting collectors, storage back‑ends, and visualization tools such as Telegraf, InfluxDB, Prometheus, Grafana, and alertmanager for large‑scale data platform operations.
This article presents the design, architecture, and technology choices of a Sonar monitoring system for Spring Boot microservices, covering time‑series database selection, deployment topology, client collection strategies, and future plans for advanced analytics and AI‑driven alerting.
This article explains how to configure Spring Boot Actuator to export metrics to InfluxDB and Prometheus, covering Docker setup, Micrometer dependencies, application properties, sample controller code, test data generation, and visualisation with Grafana.
This article details the architectural evolution of Ctrip's Hickwall monitoring and alerting platform, describing the transition from an Elasticsearch‑based first generation to an InfluxDB‑driven second generation, the design of the Incluster storage layer, data aggregation strategies, and the implementation of high‑performance stream‑based alerting.
This tutorial walks through deploying InfluxDB, cAdvisor, and Grafana containers, configuring them to collect and visualize time‑series metrics such as CPU, memory, network, and disk usage from Docker workloads, and shows how to create dashboards and queries for effective container monitoring.
This article details the challenges of traditional monitoring systems, the design and implementation of a custom high‑availability monitoring platform using a Golang‑based service tree, Raft‑backed storage, InfluxDB for time‑series data, and a modular architecture that supports Windows agents, third‑party reporting, and AI‑driven future enhancements.
This article walks through the fundamentals of InfluxDB as a time‑series database, demonstrates how to write data via its HTTP API, and shows how to configure Grafana for real‑time visualization, high‑availability deployment, and alerting in a big‑data monitoring scenario.
This article translates and expands a comparative analysis of Prometheus against Graphite, InfluxDB, OpenTSDB, Nagios, and Sensu, covering their applicability, data models, storage mechanisms, architecture, and strengths to help readers choose the most suitable time‑series database for monitoring needs.
Learn how to achieve near real‑time, time‑series monitoring of Spring Boot applications without writing code by combining Spring Boot Actuator, Jolokia, InfluxDB, Telegraf, and Grafana, while evaluating alternative tools like Prometheus, Graphite, and JMXTrans and understanding their limitations.
This article presents a comprehensive case study of a business monitoring system, covering its background, architectural analysis, module design, time‑series database selection, visualization with Grafana, alerting strategies, decision‑making logic, and intelligent monitoring experiments, followed by key takeaways and lessons learned.
Octopux is an open‑source bandwidth monitoring platform designed by Baishan Cloud to deliver 99.9% data integrity, cross‑operator and cross‑country coverage, minute‑level granularity, and horizontal scalability for tens of thousands of devices, addressing the limitations of traditional tools like Cacti.
