GreptimeDB Pipeline Guide
Create GreptimeDB pipeline definition to transform data into specific structured table, including data extraction, processing, type parsing, datetime handling and more.
The workflow
To create GreptimeDB pipeline, we should follow these phases:
Phase 1. Understanding GreptimeDB Pipeline
First, we should read greptimedb pipeline definitions and how it works from GreptimeDB's documentation.
There are pages available, use WebFetch to load and understand them:
- High level information of how to use custom pipeline https://docs.greptime.com/user-guide/logs/use-custom-pipelines/
- Details about pipeline elements and docs for each processor, transform and dispatcher https://docs.greptime.com/reference/pipeline/pipeline-config/
We will always create version 2 pipeline.
Phase 2. Create an initial pipeline that works
Ask user to provide a sample input data. It can be one of:
- text data line
- ndjson data line
- an array of json data
And try to understand what type of information that user want to extract from the sample data.
For text data line, we should try to split it by any potential field separator like space or tab. Find out the datetime part and use date processor to parse it. Try to name each field by its meaning. If it's impossible to understand the text line, we try to use a field called message for all the line.
For ndjson and json, we will find out a datetime field and use date processor on it to generate the time index. And we will use json key for all other fields.
Provide user a sample of how the initial pipeline definition will look like, as well as how the parsed data to be like. We can use a markdown table to show each field name, data type in greptimedb and values:
| Field name 1 (Data type) | Field name 2 (Data type) | ... |
|---|---|---|
| Value 1 | Value 2 | ... |
| Value 1 | Value 2 | ... |
Phase 3. Work on special requirements and verify
The user may have more requirements on particular field, use processor to address them.
If the user want to dispatch data into multiple tables, or using different pipeline to process, there is dispatch available to handle this. User can provide table suffix for dispatched data.
If the user requirements are complex enough for declarative processors, there is also an advanced VRL processor for remapping data. Check reference for more information.
If the greptimedb-mcp-server is available, there is a dryrun-pipeline tool by which we can provide pipeline definition and sample data to test against GreptimeDB's implementation. The output is a table encoded as json.
Phase 4. Check index and table options
The Pipeline system also allow user to specify various index on the result table. We will understand how user will query the table and provide suggestion on index.
Advanced table options can be customized by .greptime_ variables. Use them if user want to customize TTL, append_mode and etc.
Reference
- GreptimeDB Index Options: https://docs.greptime.com/user-guide/manage-data/data-index/
- VRL, the advanced processing language from Vector: https://vector.dev/docs/reference/vrl/
- Using Table Options from Pipeline/VRL: https://docs.greptime.com/reference/pipeline/write-log-api/#set-table-options