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Showing posts from August, 2017

Setting up TICK Stack on Docker

One of the main components of the MALT stack is metrics and monitoring.  The TICK stack is one of the biggest stacks that provide this functionality.  The TICK stack is from the InfluxData company as part of their architectural platform providing metrics.  The T is for Telegraf, which is their agent/collector used to collect metrics data from a variety of sources.  MALT embraces the collector paradigm heavily as a standard way of consuming data and pushing data.  The base of every collector platform are the inputs and the outputs.  Telegraf supports a wide variety of inputs from the OS-level system to databases to general purpose REST endpoints.  Once consumed, Telegraf can push the data to almost anywhere including both Kafka and InfluxData's own data store InfluxDB, the I in the stack.  The key component of MALT is the centralized stream, such as Kafka.  Telegraf makes this extremely easy as metrics may be collected from every service and then pushed to Kafka.  A central Telegra…

Todo Application Demo

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Using Java with Spring and a variety of frameworks, the following is a collection of methods for handling the MALT stack:

https://github.com/nicholashagen/todo-demo-malt



Stay tuned for more information,

Nick

Welcome to the MALT Stack

The MALT stack is a set of principles on applying four key strategies:  Metrics, Alerting, Logging, and Tracing.  Each of these principles is key to monitoring and debugging an application.  By themselves, each of these principles only offer a small portion of insight, but together they open a breadth of runtime knowledge.

The main premise behind the MALT stack is centralizing and aggregating all data points into a single stream.  In most cases, this is applied using Kafka or Kinesis.  The MALT stack otherwise does not enforce any particular technology.  In fact, there are several available technologies that utilize the foundation of MALT.

MALT classifies the following key terms:

Collector : Collectors collect data from running systems such as logs, metrics, traces, etc and push onto streams.  Examples collectors include Logstash, CollectD, FluentD, Telegraf, etc.

Streaming : Streams provide a single, centralized facility for capturing data

Publisher : Publishers consume data from the stre…