Text logs in CSV format

The process of parsing files in CSV format is based on converting each row into a simple JSON object mapping keys to values. To do that, each column must be given a name.

CSV logs without header

To parse CSV logs without a header row, Panther needs to know which names to assign to each column.
Let's assume our logs are CSV with 7 columns: year, month, day, time, action, ip_address, message. Some example rows of this file could be:
2020,09,01,10:35:25,RESTART,-,"System restarts"
We would use the following LogSchema to define log type:
# CSV files come in many flavors and you can choose the delimiter character to split each row
delimiter: ","
# Names in the 'columns' array will be mapped to columns in each row.
# If you want to skip a column, you can set the name at the same index to an empty string ("")
- year
- month
- day
- time
- action
- ip_address
- message
# CSV files sometimes use placeholder values for missing or N/A data.
# You can define such values with 'emptyValues' and they will be ignored.
emptyValues: ["-"]
# The 'expandFields' directive will render a template string injecting generated fields into the key/value pairs
# Since the timestamp is split across multiple columns, we need to re-assemble it into RFC3339 format
# The following will add a 'timestamp' field by replacing the fields from CSV values
timestamp: '%{year}-%{month}-%{day}T%{time}Z'
- name: timestamp
type: timestamp
- rfc3339
isEventTime: true
required: true
- name: action
type: string
required: true
- name: ip_address
type: string
indicators: [ip]
- name: message
type: string

CSV logs with header

Avoid using such schemas in combination with others. Use a separate source or S3 prefix.
To parse CSV logs that starts with a header row, Panther has two options:
  • Use the names defined in the header as the names for the JSON fields or,
  • Skip the header and define the names the same way we did for headerless CSV files
To use the names in the header the configuration for the parser should be:
delimiter: ","
# Setting 'hasHeader' to true without specifying a 'columns' field,
# tells Panther to set the column names from values in the header.
hasHeader: true
# In case you want to rename a column you can use the 'expandFields' directive
# Let's assume that the header contains '$cost' as column name and you want to 'normalize' it as 'cost_us_dollars'
"cost_us_dollars": '%{$cost}'
To ignore the header and define your set of names for the columns use:
delimiter: ","
# Setting 'hasHeader' to true while also specifying a 'columns' field,
# tells Panther to ignore the header and use the names in the 'columns' array
hasHeader: true
- foo
- bar
- baz