Parsing CSV logs with Lua

Parse structured application logs in CSV format using Lua transform
type: guidedomain: transformstransform: lua

Vector has many built-in parsers for structured logs formats. However, when you need to ship logs in a custom or application-specific format, programmable transforms have got you covered.

This guide walks through reading CSV logs using file source and parsing them using lua transform with a loadable Lua module.

Getting Started

For certainty, it is assumed in the following that the logs to be read are produced by csvlog in PostgreSQL. For example, there might be the following log file:

log.csv
2020-04-09 12:48:49.661 UTC,,,1,,localhost.1,1,,2020-04-09 12:48:49 UTC,,0,LOG,00000,"ending log output to stderr",,"Future log output will go to log destination ""csvlog"".",,,,,,,""
2020-04-09 12:48:49.669 UTC,,,27,,localhost.1b,1,,2020-04-09 12:48:49 UTC,,0,LOG,00000,"database system was shut down at 2020-04-09 12:48:25 UTC",,,,,,,,,""
2020-04-09 12:48:49.683 UTC,,,1,,localhost.1,2,,2020-04-09 12:48:49 UTC,,0,LOG,00000,"database system is ready to accept connections",,,,,,,,,""

Let us draft an initial version of the Vector's configuration file:

vector.toml
data_dir = "."
[sources.file]
type = "file"
include = ["*.csv"]
start_at_beginning = true
[transforms.lua]
inputs = ["file"]
type = "lua"
version = "2"
hooks.process = """
function (event, emit)
-- to be expanded
emit(event)
end
"""
[sinks.console]
inputs = ["lua"]
type = "console"
encoding = "json"

This config sets up a pipeline that reads log files, pipes them through the parsing transform (which currently is configured to just pass the events through), and displays the produced log events using console sink.

At this point, running vector --config vector.toml results in the following output:

{"file":"log.csv","host":"localhost","message":"2020-04-09 12:48:49.661 UTC,,,1,,localhost.1,1,,2020-04-09 12:48:49 UTC,,0,LOG,00000,\"ending log output to stderr\",,\"Future log output will go to log destination \"\"csvlog\"\".\",,,,,,,\"\"","timestamp":"2020-04-09T14:33:28Z"}
{"file":"log.csv","host":"localhost","message":"2020-04-09 12:48:49.669 UTC,,,27,,localhost.1b,1,,2020-04-09 12:48:49 UTC,,0,LOG,00000,\"database system was shut down at 2020-04-09 12:48:25 UTC\",,,,,,,,,\"\"","timestamp":"2020-04-09T14:33:28Z"}
{"file":"log.csv","host":"localhost","message":"2020-04-09 12:48:49.683 UTC,,,1,,localhost.1,2,,2020-04-09 12:48:49 UTC,,0,LOG,00000,\"database system is ready to accept connections\",,,,,,,,,\"\"","timestamp":"2020-04-09T14:33:28Z"}

Adding the CSV Module

In order to perform actual parsing, it is possible to leverage lua-csv. Because it consists of a single file, it is possible to just download it to the same directory where vector.toml is stored:

curl -o csv.lua https://raw.githubusercontent.com/geoffleyland/lua-csv/d20cd42d61dc52e7f6bcb13b596ac7a7d4282fbf/lua/csv.lua

Then it would be possible to load it by calling require Lua function in the source configuration section:

source = """
csv = require("csv")
"""

With this source the csv module is loaded when Vector is started up (or if the lua transform is added later and the config is automatically reloaded) and can be used through the global variable csv.

Implementing Custom Parsing

With the csv module, the hooks.process can be changed to the following:

hooks.process = """
function (event, emit)
fields = csv.openstring(event.log.message):lines()() -- parse the `message` field
event.log.message = nil -- drop the `message` field
column_names = { -- a sequence containing CSV column names
-- ...
}
for column, value in ipairs(fields) do -- iterate over CSV columns
column_name = column_names[column] -- get column name
event.log[column_name] = value -- set the corresponding field in the event
end
emit(event) -- emit the transformed event
end
"""

Note that the column_names can be created just once, in the source section instead to speed up processing. Putting it there and using the column names from the PostgreSQL documentation results in the following definition of the whole transform:

vector.toml
# ...
[transforms.lua]
inputs = ["file"]
type = "lua"
version = "2"
source = """
csv = require("csv") -- load external module for parsing CSV
column_names = { -- a sequence containing CSV column names
"log_time",
"user_name",
"database_name",
"process_id",
"connection_from",
"session_id",
"session_line_num",
"command_tag",
"session_start_time",
"virtual_transaction_id",
"transaction_id",
"error_severity",
"sql_state_code",
"message",
"detail",
"hint",
"internal_query",
"internal_query_pos",
"context",
"query",
"query_pos",
"location",
"application_name",
}
"""
hooks.process = """
function (event, emit)
fields = csv.openstring(event.log.message):lines()() -- parse the `message` field
event.log.message = nil -- drop the `message` field
for column, value in ipairs(fields) do -- iterate over CSV columns
column_name = column_names[column] -- get column name
event.log[column_name] = value -- set the corresponding field in the event
end
emit(event) -- emit the transformed event
end
"""
#...

Trying to run vector --config vector.toml with the same input file results in structured events being output:

{"application_name":"","command_tag":"","connection_from":"","context":"","database_name":"","detail":"","error_severity":"LOG","file":"log.csv","hint":"Future log output will go to log destination \"csvlog\".","host":"localhost","internal_query":"","internal_query_pos":"","location":"","log_time":"2020-04-09 12:48:49.661 UTC","message":"ending log output to stderr","process_id":"1","query":"","query_pos":"","session_id":"localhost.1","session_line_num":"1","session_start_time":"2020-04-09 12:48:49 UTC","sql_state_code":"00000","timestamp":"2020-04-09T19:49:07Z","transaction_id":"0","user_name":"","virtual_transaction_id":""}
{"application_name":"","command_tag":"","connection_from":"","context":"","database_name":"","detail":"","error_severity":"LOG","file":"log.csv","hint":"","host":"localhost","internal_query":"","internal_query_pos":"","location":"","log_time":"2020-04-09 12:48:49.669 UTC","message":"database system was shut down at 2020-04-09 12:48:25 UTC","process_id":"27","query":"","query_pos":"","session_id":"localhost.1b","session_line_num":"1","session_start_time":"2020-04-09 12:48:49 UTC","sql_state_code":"00000","timestamp":"2020-04-09T19:49:07Z","transaction_id":"0","user_name":"","virtual_transaction_id":""}
{"application_name":"","command_tag":"","connection_from":"","context":"","database_name":"","detail":"","error_severity":"LOG","file":"log.csv","hint":"","host":"localhost","internal_query":"","internal_query_pos":"","location":"","log_time":"2020-04-09 12:48:49.683 UTC","message":"database system is ready to accept connections","process_id":"1","query":"","query_pos":"","session_id":"localhost.1","session_line_num":"2","session_start_time":"2020-04-09 12:48:49 UTC","sql_state_code":"00000","timestamp":"2020-04-09T19:49:07Z","transaction_id":"0","user_name":"","virtual_transaction_id":""}

Or, applying pretty formatting to one of the output events:

{
"application_name": "",
"command_tag": "",
"connection_from": "",
"context": "",
"database_name": "",
"detail": "",
"error_severity": "LOG",
"file": "log.csv",
"hint": "Future log output will go to log destination \"csvlog\".",
"host": "localhost",
"internal_query": "",
"internal_query_pos": "",
"location": "",
"log_time": "2020-04-09 12:48:49.661 UTC",
"message": "ending log output to stderr",
"process_id": "1",
"query": "",
"query_pos": "",
"session_id": "localhost.1",
"session_line_num": "1",
"session_start_time": "2020-04-09 12:48:49 UTC",
"sql_state_code": "00000",
"timestamp": "2020-04-09T19:49:07Z",
"transaction_id": "0",
"user_name": "",
"virtual_transaction_id": ""
}

Further Improvements

After the task of parsing the CSV logs is accomplished, the following improvements can take place.

Support for Multi-line Strings

CSV supports line breaks in strings. However, by default file source creates a separate event from each line.

There are two options to deal with this:

  1. For simple cases it might be possible to use the multiline configuration option in the file source.
  2. For more complex cases the messages from multiple events can be conditionally concatenated in the Lua code. See the aggregations guide for more details on this.

Change Fields Types

By default, all columns are parsed as strings. It is possible to convert them to other data types right in the Lua code using built-in functions, such as tonumber. Alternatively, it is possible to add the coercer transform after the lua transform, for example, to parse timestamps.