Logstash vs Ingest Pipeline: Key Differences and When to Use Each
Logstash is a standalone data processing tool that collects, transforms, and ships data to Elasticsearch or other destinations, while an Ingest Pipeline is a lightweight, built-in Elasticsearch feature that processes and transforms data during indexing. Logstash offers more complex processing and plugin support, whereas Ingest Pipelines are simpler and run directly inside Elasticsearch nodes.Quick Comparison
This table summarizes the main differences between Logstash and Ingest Pipeline in Elasticsearch data processing.
| Feature | Logstash | Ingest Pipeline |
|---|---|---|
| Type | Standalone data processing tool | Built-in Elasticsearch feature |
| Processing Location | External server or container | Inside Elasticsearch nodes |
| Complexity | Supports complex pipelines and plugins | Lightweight and simpler processors |
| Performance Impact | Separate resource usage | Runs within Elasticsearch, minimal overhead |
| Use Case | Data collection, enrichment, routing | Simple transformations during indexing |
| Plugin Support | Wide variety of input, filter, output plugins | Limited set of processors |
Key Differences
Logstash is a powerful, standalone tool designed to collect, parse, and transform data from many sources before sending it to Elasticsearch or other systems. It runs independently from Elasticsearch, allowing complex workflows with many plugins for inputs, filters, and outputs. This flexibility makes it ideal for heavy data processing and enrichment tasks.
In contrast, an Ingest Pipeline is a feature built directly into Elasticsearch that processes documents as they are indexed. It uses a set of predefined processors like grok, rename, or date to perform simple transformations. Because it runs inside Elasticsearch nodes, it has less overhead but is less flexible than Logstash.
Choosing between them depends on your needs: use Logstash for complex, multi-source data processing and routing, and use Ingest Pipelines for lightweight, inline document transformations during indexing.
Code Comparison
Here is an example of how to parse a log line with Logstash using the grok filter to extract fields.
input {
stdin {}
}
filter {
grok {
match => { "message" => "%{COMBINEDAPACHELOG}" }
}
}
output {
stdout { codec => rubydebug }
}Ingest Pipeline Equivalent
This is the equivalent Ingest Pipeline in Elasticsearch that uses the grok processor to parse the same log line during indexing.
{
"description": "Parse Apache log",
"processors": [
{
"grok": {
"field": "message",
"patterns": ["%{COMBINEDAPACHELOG}"]
}
}
]
}When to Use Which
Choose Logstash when you need to collect data from multiple sources, perform complex transformations, enrich data with external lookups, or route data to various destinations beyond Elasticsearch.
Choose Ingest Pipeline when you want simple, fast transformations directly inside Elasticsearch during indexing without managing an extra service. It is best for lightweight parsing and field modifications.
In summary, use Logstash for heavy-duty data processing and Ingest Pipelines for efficient inline document processing.
Key Takeaways
Logstash is a standalone, flexible tool for complex data processing before indexing.Ingest Pipelines run inside Elasticsearch for lightweight, inline document transformations.Logstash for multi-source data collection and enrichment.Ingest Pipelines for simple parsing and field changes during indexing.