Logstash Pipelines and Workers for Seamless Data Processing and Transformation

Introduction

At the heart of Logstash lies the concept of pipelines. A Logstash pipeline is a sequence of stages through which data flows. Each pipeline has three essential stages: input, filter, and output.

Input Stage: The input stage fetches data from various sources such as log files, databases, message queues, or network streams. Logstash provides a rich set of input plugins that facilitate data ingestion from various systems, ensuring flexibility and adaptability.

Filter Stage: Once data enters the pipeline, it undergoes transformation and enrichment through the filter stage. Filters are the workhorses of Logstash, enabling users to modify, enhance, or drop data based on predefined rules. Logstash offers an extensive collection of filters, including grok, date, geoip, mutate, and many more. These filters allow data manipulation, parsing, and extraction, ensuring the data is structured and usable.

Output Stage: After the filter stage has processed data, it is sent to the output stage for transmission to desired destinations. Logstash supports various output plugins, such as Elasticsearch, databases, message brokers, or custom endpoints. This flexibility enables seamless integration with various systems and empowers users to route data wherever needed. Below is a sample pipeline shown.

Pipelines Configuration

Multiple pipelines with different configurations can be run on Logstash by configuring the “pipelines.yml” file from the config folder, as shown below.

The pipelines.yml file is written in YAML format and consists of a list of dictionaries. Each dictionary represents a pipeline and contains key-value pairs that define specific settings for that pipeline. The given example describes four pipelines, each identified by its unique ID and associated with a configuration path.

For the first pipeline, pipeline “ABC” the pipeline setting, “pipeline.workers” 3 workers, and its configuration file is in “logstash-8.6.1/config/abc.conf”

If a setting is not explicitly specified in the pipelines.yml file, Logstash will fall back to the default value defined in the logstash.yml configuration file. This ensures that any unspecified settings inherit default values from the main Logstash configuration.

When Logstash starts without arguments, it will use the pipelines.yml file and run instances of all pipelines specified in the file. Similarly, when we use -e or -f to specify a configuration file, Logstash will ignore the pipelines.yml file while starting and logs a warning about it.

Advantages and Tuning of Multiple Pipelines

Multiple pipelines in Logstash offer significant advantages when dealing with event flows that have distinct inputs, filters, and outputs and need to be separated using tags and conditionals.

Having multiple pipelines within a single instance allows for flexibility in defining performance and durability parameters for each event flow. This means that different settings, such as pipeline workers and persistent queues, can be tailored to the specific requirements of each pipeline. By separating pipelines, issues like a blocked output in one pipeline will not cause backpressure in others, ensuring smoother data processing.

However, it is important to consider resource comparison between pipelines, as the default values in Logstash are optimized for a single pipeline setup. To address this, adjusting the number of pipeline workers used by each pipeline is recommended. Logstash assigns one worker per CPU core for each pipeline by default, so reducing the number of workers can help manage resource allocation more effectively.

The “pipeline.batch.size” setting defines the maximum number of events an individual worker thread collects before executing filters and outputs. Large batch size is generally more efficient but may increase memory overhead.

In summary, multiple pipelines in Logstash provide flexibility and customization for event flows with different requirements. They allow for independent performance and durability settings, preventing issues in one pipeline from impacting others. However, it’s important to consider resource competition and adjust settings accordingly. Logstash’s isolation mechanisms ensure separate storage for queues, maintaining data integrity and avoiding pipeline conflicts.

Conclusion

The collaboration between pipelines and workers allows Logstash to handle diverse data sources and destinations while ensuring scalability, fault tolerance, and high throughput. By understanding the concepts of pipelines and workers, users can harness the power of Logstash to streamline their data processing workflows effectively.

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