Big Data Monitoring Tool Compared: TICK vs ELK Stack

The system is finally stable and in production. Whether it is a complex network of servers or an integration mechanism between multiple databases, you need to make sure that everything runs smoothly. Indeed, it must be possible to intervene promptly when there is a problem. The objectives are to intercept it, decode it, and solve it in the shortest possible time. One word: monitor systems in all directions.

However, which architecture to choose? How do you select the right components for your needs? Questions that are difficult to answer with a simple “google. ” The results are many, and all appear valid: high-performance, highly reliable stacks built specifically to manage large amounts of data.

TICK Stack And ELK Stack: What Are They?

Among the many projects in this area, in the last period, two have been noticed that are slowly becoming standards. The ELK stack, born under the umbrella of the ElasticSearch project, and the TICK, was designed and built around a database-oriented to the storage of time series data. The points of contact are many. Primarily the acronym to indicate the components of the stack. So while ELK stands for Elastic, Logstash, and Kibana, TICK takes up the initials of Telegraf, Influx, Chronograf, and Kapacitor.

Furthermore, both tools can be composited at will for monitoring system metrics such as CPU, RAM, and much more of the system; provide components for log file analysis and application monitoring; provide utilities for the construction of alerting and dashboarding for real-time analysis; they are born in the open source field even if they also offer enterprise and cloud solutions.

To better examine the differences between ELK and TICK, three dimensions of analysis can be contemplated:

  • Acquisition and processing of data from unstructured data sources such as logs
  • Receiving system metrics
  • The configuration of alerting tools

How Does The Management Of The Log Data On The Two Stacks Work

The acquisition and management of log data is the core of the ELK stack. Once the log data has been acquired, they can be sent directly to ElasticSearch or, if the processing is required, to Logstash, which takes care of the parsing. The corresponding tool in the TICK is a Telegraf plugin that collapses in a single moment what Filebeat and Logstash perform separately. Therefore it deals with both log acquisition and parsing.

On this aspect, the ELK stack appears more stable. The Telegraf plugin still looks immature, while Logstash has several plugins to manipulate data at will. In addition, the ELK stack has the Elastic engine on its side, perfect for storing textual data with full-text searches.

The Detection Of System Metrics

Tracking system metrics is one of the most important aspects of monitoring with the TICK stack. Telegraf boasts numerous plugins to collect metrics for different operating systems, “containerized” applications, relational and non-relational databases. The plugins are easy to configure and retrieve a wide range of data, almost plug & play. Also, within the ELK stack, there is an agent for this task.

Specifically, we are talking about MetricBeat with modules for monitoring systems, dockers, and databases conceptually similar to Telegraf plugins. Suppose in the monitoring of the logs the ELK stack is more stable, from the latter point of view. In that case, the TICK is more suitable and flexible since it can redirect the output to other databases and not only InfluxDB. At the same time, Metricbeat has an ElasticSearch privileged channel.

Alerting Functionality

The construction of alerts in the TICK is managed through two components. The first is the Chronograf web interface. The second is Kapacitor, the data processing framework. Thanks to these, it is possible to define thresholds, enable and disable alert rules, build dashboards in real-time, and send alerts via email, Slack, and other channels. Also, in the ELK stack, there is a part dedicated to alerting. The component is called Watcher and has the same functionality as the TICK but is part of those unlocked features with the Elastic Gold license.

Final Evaluation

To get a quantitative understanding of this comparison, three dimensions of analysis can be used. With a number from 1 to 5, the stacks are evaluated based on:

  • Configurability: the difficulty of configuring tools from stacks
  • Resiliency: The ability to implement articulated or custom solutions
  • Extendibility: the ability to cover different use cases.

On the scale, one represents greater complexity, while the five represents a minimum commitment effort to implement even detailed solutions.

Conclusions

Both tools potentially offer the same functionality, but their respective strengths are complementary to each other. The Tick stack lends itself naturally and effectively to monitoring metrics and alerting. The ELK stack is suitable for analyzing textual data, being able to enter detailed analyzes punctually. Nothing prevents the two solutions from being used in a parallel and complementary way to build a 360 ° monitoring architecture.

Also Read: What Is Big Data And Its Importance In The Business World?

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