Redis, the popular open source in-memory data store, has been used as a persistent on-disk database that supports a variety of data structures such as lists, sets, sorted sets (with range queries), strings, geospatial indexes (with radius queries), bitmaps, hashes, and Hyper Logs. The in-memory store is used to solve various problems in areas such as real-time messaging, caching, and statistic calculation.

Provisioning an Elasticsearch cluster in Qbox is easy. In this article, we walk you through the initial steps to start and configure your cluster. We then setup and configure logstash to ship the logs to elasticsearch in order to monitor Redis performance. Redis performance logs shipped to elasticsearch can then be visualized and analyzed via Kibana dashboards.

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A comprehensive log management and analysis strategy is mission critical, enabling organisations to understand the relationship between operational, security, and change management events and maintain a comprehensive understanding of their infrastructure. Log files from web servers, applications, and operating systems also provide valuable data, though in different formats, and in a random and distributed fashion.

Why is Apache Web Server so popular? It’s free and open source, and open source is becoming vastly more popular than proprietary software. It’s maintained by dedicated developers, it provides security, is well suited for small and large websites alike, can be easily set up on all major operating systems, as well as being extremely powerful and flexible. Does that sound about right?

Provisioning an Elasticsearch cluster in Qbox is easy. In this article, we walk you through the initial steps and show you how simple it is to start and configure your cluster. We then install and configure logstash to ship our apache logs to elasticsearch. Apache logs shipped to elasticsearch can then be visualized and analyzed via Kibana dashboards.

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A comprehensive log management and analysis strategy is mission critical, enabling organizations to understand the relationship between operational, security, and change management events and to maintain a comprehensive understanding of their infrastructure. Log files from web servers, applications, and operating systems also provide valuable data, although in different formats, and in a random and distributed fashion.

As with any web server, the task of logging NGINX is somewhat of a challenge. NGINX access and error logs can produce thousands of log lines every second, and this data, if monitored properly, can provide you with valuable information not only on what has already transpired but also on what is about to happen. But how do you extract actionable insights from this information? How do you effectively monitor such a large amount of data?

NGINX access logs contain a wealth of information including client requests and currently active client connections that, if monitored efficiently, can provide a clear picture of how the web server and the application that it serves is behaving. This tutorial describes how Qbox can be used to overcome this challenge by monitoring NGINX access logs with Qbox provisioned Elasticsearch Stack.

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A comprehensive log management and analysis strategy is mission critical, enabling organizations to understand the relationship between operational, security, and change management events and to maintain a comprehensive understanding of their infrastructure. Log files from web servers, applications, and operating systems also provide valuable data, although in different formats, and in a random and distributed fashion.

Logs are a crucial part of any system because they give you insight into what a system is doing as well what happened. Virtually every process running on a system generates logs in some form or another. These logs are usually written to files on local disks. When your system grows to multiple hosts, managing the logs and accessing them can get complicated. 

Searching for a particular error across hundreds of log files on hundreds of servers is difficult without good tools. A common approach to this problem is to set up a centralized logging solution so that multiple logs can be aggregated in a central location. To effectively consolidate, manage, and analyze these different logs, many customers choose to implement centralized logging solutions using Elasticsearch, Logstash, and Kibana, popularly known as ELK Stack.

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A common use case that comes up when we use any product is how can we get metrics from it? How can we monitor it? Elasticsearch, since its early release, has always provided a way to monitor it using the _cat/stats API. However, for Logstash there wasn’t a way to gather metrics and monitor it until recently. With the release of Logstash 5.0+, Logstash has introduced a set of APIs to monitor Logstash.  In this article we explore the monitoring APIs exposed by Logstash, which includes the Node Info API, the Plugins API, the Node Stats API, and the Hot Threads API. 

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“ChatOps” has recently become a buzzword in places that are aiming for continuous delivery. It is based on chat clients like Slack and Hipchat and is plugged in with chatbots for real-time communication and task execution among members of development and IT operations teams.

Chat has become an integral part of the “better” delivery models. With huge amounts of data flowing within the system, wouldn’t it be nice if we could put it into an analysis tool and churn out some results that might improve the business?

In this article, we explore how to integrate Slack with Elasticsearch and perform basic data analyses for examples.

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