Pipeline Configuration

Pipeline is a mechanism in GreptimeDB for parsing and transforming log data. It consists of a unique name and a set of configuration rules that define how log data is formatted, split, and transformed. Currently, we support JSON (application/json) and plain text (text/plain) formats as input for log data.

These configurations are provided in YAML format, allowing the Pipeline to process data during the log writing process according to the defined rules and store the processed data in the database for subsequent structured queries.

Overall structure

Pipeline consists of two parts: Processors and Transform, both of which are in array format. A Pipeline configuration can contain multiple Processors and multiple Transforms. The data type described by Transform determines the table structure when storing log data in the database.

• Processors are used for preprocessing log data, such as parsing time fields and replacing fields.
• Transform is used for converting data formats, such as converting string types to numeric types.

Here is an example of a simple configuration that includes Processors and Transform:

processors:
  - urlencoding:
      fields:
        - string_field_a
        - string_field_b
      method: decode
      ignore_missing: true
transform:
  - fields:
      - string_field_a
      - string_field_b
    type: string
  # The written data must include the timestamp field
  - fields: 
      - reqTimeSec, req_time_sec
    # epoch is a special field type and must specify precision
    type: epoch, ms
    index: timestamp

Processor

The Processor is used for preprocessing log data, and its configuration is located under the processors field in the YAML file. The Pipeline processes data by applying multiple Processors in sequential order, where each Processor depends on the result of the previous Processor. A Processor consists of a name and multiple configurations, and different types of Processors have different fields in their configuration.

We currently provide the following built-in Processors:

• date: parses formatted time string fields, such as 2024-07-12T16:18:53.048.
• epoch: parses numeric timestamp fields, such as 1720772378893.
• dissect: splits log data fields.
• gsub: replaces log data fields.
• join: merges array-type fields in logs.
• letter: converts log data fields to letters.
• regex: performs regular expression matching on log data fields.
• urlencoding: performs URL encoding/decoding on log data fields.
• csv: parses CSV data fields in logs.

date

The date processor is used to parse time fields. Here's an example configuration:

processors:
  - date:
      fields: 
        - time
      formats:
        - '%Y-%m-%d %H:%M:%S%.3f'
      ignore_missing: true
      timezone: 'Asia/Shanghai'

In the above example, the configuration of the date processor includes the following fields:

• fields: A list of time field names to be parsed.
• formats: Time format strings, supporting multiple format strings. Parsing is attempted in the order provided until successful. You can find reference here for formatting syntax.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.
• timezone: Time zone. Use the time zone identifiers from the tz_database to specify the time zone. Defaults to UTC.

epoch

The epoch processor is used to parse timestamp fields. Here's an example configuration:

processors:
  - epoch:
      fields:
        - reqTimeSec
      resolution: millisecond
      ignore_missing: true

In the above example, the configuration of the epoch processor includes the following fields:

• fields: A list of timestamp field names to be parsed.
• resolution: Timestamp precision, supports s, sec, second, ms, millisecond, milli, us, microsecond, micro, ns, nanosecond, nano. Defaults to ms.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.

dissect

The dissect processor is used to split log data fields. Here's an example configuration:

processors:
  - dissect:
      fields: 
        - message
      patterns:
        - '%{key1} %{key2}'
      ignore_missing: true
      append_separator: '-'

In the above example, the configuration of the dissect processor includes the following fields:

• fields: A list of field names to be split.
• patterns: The dissect pattern for splitting.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.
• append_separator: Specifies the separator for concatenating multiple fields with same field name together. Defaults to an empty string. See + modifier below.

Dissect pattern

Similar to Logstash's dissect pattern, the dissect pattern consists of %{key}, where %{key} is a field name. For example:

"%{key1} %{key2} %{+key3} %{+key4/2} %{key5->} %{?key6} %{*key7} %{&key8}"

Dissect modifiers

The dissect pattern supports the following modifiers:

Modifier	Description	Example
+	Concatenates two or more fields together	%{+key} %{+key}
+ and /n	Concatenates two or more fields in the specified order	%{+key/2} %{+key/1}
->	Ignores any repeating characters on the right side	%{key1->} %{key2->}
?	Ignores matching values	%{?key}
* and &	Sets the output key as * and the output value as &	%{*key} %{&key}

dissect examples

For example, given the following log data:

"key1 key2 key3 key4 key5 key6 key7 key8"

Using the following Dissect pattern:

"%{key1} %{key2} %{+key3} %{+key3/2} %{key5->} %{?key6} %{*key} %{&key}"

The result will be:

{
  "key1": "key1",
  "key2": "key2",
  "key3": "key3 key4",
  "key5": "key5",
  "key7": "key8"
}

gsub

The gsub processor is used to replace values in log data fields. Here's an example configuration:

processors:
  - gsub:
      fields: 
        - message
      pattern: 'old'
      replacement: 'new'
      ignore_missing: true

In the above example, the configuration of the gsub processor includes the following fields:

• fields: A list of field names to be replaced.
• pattern: The string to be replaced. Supports regular expressions.
• replacement: The string to replace with.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.

join

The join processor is used to merge Array-type fields in log data. Here's an example configuration:

processors:
  - join:
      fields:
        - message
      separator: ','
      ignore_missing: true

In the above example, the configuration of the join processor includes the following fields:

• fields: A list of field names to be merged. Note that all the fields mentioned are already of array type. Each field will have its own array merged separately. The content of multiple fields will not be combined.
• separator: The separator for the merged result.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.

join example

For example, given the following log data:

{
  "message": ["a", "b", "c"]
}

Using the following configuration:

processors:
  - join:
      fields:
        - message
      separator: ','

The result will be:

{
  "message": "a,b,c"
}

letter

The letter processor is used to convert the case of characters in log data fields. Here's an example configuration:

processors:
  - letter:
      fields:
        - message
      method: upper
      ignore_missing: true

In the above example, the configuration of the letter processor includes the following fields:

• fields: A list of field names to be transformed.
• method: The transformation method, supports upper, lower, capital. Defaults to lower. Note the capital only changes the first letter to uppercase.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.

regex

The regex processor is used to perform regular expression matching on log data fields. Here's an example configuration:

processors:
  - regex:
      fields:
        - message
      patterns:
        - ':(?<id>[0-9])'
      ignore_missing: true

In the above example, the configuration of the regex processor includes the following fields:

• fields: A list of field names to be matched.
• pattern: The regular expression pattern to match. Named capture groups are required to extract corresponding data from the respective field.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.

Rules for named capture groups in regex

The regex processor supports the syntax (?<group-name>...) to define named capture groups. The data will be processed into the following format:

{
  "<field-name>_<group-name>": "<value>"
}

For example, if the field name specified in the regex processor is message, and the corresponding content is "[ERROR] error message", you can set the pattern as \[(?<level>[A-Z]+)\] (?<content>.+), and the data will be processed as:

{
  "message_level": "ERROR",
  "message_content": "error message"
}

urlencoding

The urlencoding processor is used to perform URL encoding on log data fields. Here's an example configuration:

processors:
  - urlencoding:
      fields:
        - string_field_a
        - string_field_b
      method: decode
      ignore_missing: true

In the above example, the configuration of the urlencoding processor includes the following fields:

• fields: A list of field names to be encoded.
• method: The encoding method, supports encode, decode. Defaults to encode.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.

csv

The csv processor is used to parse CSV-type fields in log data that do not have a header. Here's an example configuration:

processors:
  - csv:
      fields:
        - message
      separator: ','
      quote: '"'
      trim: true
      ignore_missing: true

In the above example, the configuration of the csv processor includes the following fields:

• fields: A list of field names to be parsed.
• separator: The separator.
• quote: The quotation mark.
• trim: Whether to trim whitespace. Defaults to false.
• ignore_missing: Ignores the case when the field is missing. Defaults to false. If the field is missing and this configuration is set to false, an exception will be thrown.

Transform

Transform is used to convert log data, and its configuration is located under the transform field in the YAML file.

A Transform consists of one or more configurations, and each configuration contains the following fields:

• fields: A list of field names to be transformed.
• type: The transformation type.
• index: Index type (optional).
• on_failure: Handling method for transformation failures (optional).
• default: Default value (optional).

The fields field

Each field name is a string. When a field name contains ,, the field will be renamed. For example, reqTimeSec, req_time_sec means renaming the reqTimeSec field to req_time_sec, and the final data will be written to the req_time_sec column in GreptimeDB.

The type field

GreptimeDB currently provides the following built-in transformation types:

• int8, int16, int32, int64: Integer types.
• uint8, uint16, uint32, uint64: Unsigned integer types.
• float32, float64: Floating-point types.
• string: String type.
• time: Time type, which will be converted to GreptimeDB timestamp(9) type.
• epoch: Timestamp type, which will be converted to GreptimeDB timestamp(n) type. The value of n depends on the precision of the epoch. When the precision is s, n is 0; when the precision is ms, n is 3; when the precision is us, n is 6; when the precision is ns, n is 9.

If a field obtains an illegal value during the transformation process, the Pipeline will throw an exception. For example, when converting a string abc to an integer, an exception will be thrown because the string is not a valid integer.

The index field

The Pipeline will write the processed data to the automatically created table in GreptimeDB. To improve query efficiency, GreptimeDB creates indexes for certain columns in the table. The index field is used to specify which fields need to be indexed. For information about GreptimeDB column types, please refer to the Data Model documentation.

GreptimeDB supports the following three types of index for fields:

• tag: Specifies a column as a Tag column.
• fulltext: Specifies a column to use the fulltext index type. The column must be of string type.
• timestamp: Specifies a column as a timestamp index column.

When index field is not provided, GreptimeDB treats the field as a Field column.

In GreptimeDB, a table must include one column of type timestamp as the time index column. Therefore, a Pipeline can have only one time index column.

The Timestamp column

Specify which field is the timestamp index column using index: timestamp. Refer to the Transform Example below for syntax.

The Tag column

Specify which field is the Tag column using index: tag. Refer to the Transform Example below for syntax.

The Fulltext column

Specify which field will be used for full-text search using index: fulltext. This index greatly improves the performance of log search. Refer to the Transform Example below for syntax.

The on_failure field

The on_failure field is used to specify the handling method when a transformation fails. It supports the following methods:

• ignore: Ignore the failed field and do not write it to the database.
• default: Write the default value. The default value is specified by the default field.

The default field

The default field is used to specify the default value when a transformation fails.

Transform Example

For example, with the following log data:

{
  "num_field_a": "3",
  "string_field_a": "john",
  "string_field_b": "It was snowing when he was born.",
  "time_field_a": 1625760000
}

Using the following configuration:

transform:
  - fields:
      - string_field_a, name
    type: string
    index: tag
  - fields:
      - num_field_a, age
    type: int32
  - fields:
      - string_field_b, description
    type: string
    index: fulltext
  - fields:
      - time_field_a, bron_time
    type: epoch, s
    index: timestamp

The result will be:

{
  "name": "john",
  "age": 3,
  "description": "It was snowing when he was born.",
  "bron_time": 2021-07-08 16:00:00
}