Computers, fundamentally, just deal with numbers. They store letters and other characters by assigning a number for each one. 

Before Unicode was invented, there were hundreds of different encoding systems for assigning these numbers. No single encoding could contain enough characters. For example, the European Union alone requires several different encodings to cover all its languages. Even for a single language like English no single encoding is adequate for all the letters, punctuation, and technical symbols in common use.

Unicode provides a unique number for every character, no matter what the platform, no matter what the program, no matter what the language. Unicode is the single universal character set for text that enables the interchange, processing, storage and display of text in many languages. The Unicode standard serves as a foundation for the globalization of modern software. Making use of software that supports Unicode to develop or run business applications will enable us to reduce our development and deployment time and costs, enabling us to expand into new markets more quickly.

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Alerting and analytics go together like cookies and milk. We've known this, but we didn't build alerting into our Elasticsearch service because there are so many good solutions out there that we thought we would be reinventing the wheel.

However, as uses for Elasticsearch have diversified, we changed our opinion. We’ve implemented enough alerting solutions for our customers that we decided to make the best turnkey alerting solution available for all new clusters. 

Today we announce hosted ElastAlert -- the superb open-source alerting tool built by the team at Yelp Engineering -- now available on all new Elasticsearch clusters on AWS.

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Not yet enjoying the benefits of a hosted ELK-stack enterprise search on Qbox? Discover how easy it is to manage and scale your Elasticsearch environment.

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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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Analyzers are made up of two main components: a Tokenizer and a set of Token Filters. The tokenizer splits text into tokens according to some set of rules, and the token filters each perform operations on those tokens. The result is a stream of processed tokens, which are either stored in the index or used to query results.

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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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