data-parser 0.0.2

Data parser to parse newline delimited logs into tabular format.

Parse raw logs to tabular format

This package helps to parse new line delimited logs to tabular formats. The user provides the regex, file path and column names, and a dataframe will be returned.
Depending on the supplied mode (local/spark), a pandas dataframe or a spark dataframe will be returned.

Features

Local mode

1. Regex matching is done using multiprocessing.
2. Glob searching for files.
3. Lazy evaluation of files. This allows larger than memory datasets to be parsed, but note that upon parsing, the resultant pandas dataframe must be able to fit in memory.

Installation

Purely for local usage (No pyspark)

pip install data-parser

Both local and pyspark

pip install data-parser[pyspark]

Usage - Local (Pandas)

from data_parser import DataSource

# Bind 9: Feb  5 09:12:11 ns1 named[80090]: client 192.168.10.12#3261: query: www.server.example IN A
dns = DataSource(
    path='/path/to/dnsdir/*.txt',  # Glob patterns supported
    mode='local'
)

# Pandas dataframe is returned
dns_df = dns.parse(
    regex='^([A-Z][a-z]{2})\s+(\d+) (\d{2}\:\d{2}\:\d{2}) (\S+).+client ([^\s#]+)#(\d+)',
    col_names=['month', 'day', 'time', 'nameserver', 'query_ip', 'port'],
    on_error='raise'
)

Usage - Spark (Pyspark)

from data_parser import DataSource

# Bind 9: Feb  5 09:12:11 ns1 named[80090]: client 192.168.10.12#3261: query: www.server.example IN A
dns = DataSource(
    path='/path/to/dns/log',
    mode='spark'
)

# Spark dataframe is returned
dns_df = dns.parse(
    regex='^([A-Z][a-z]{2})\s+(\d+) (\d{2}\:\d{2}\:\d{2}) (\S+).+client ([^\s#]+)#(\d+)',
    col_names=['month', 'day', 'time', 'nameserver', 'query_ip', 'port'],
    on_error='raise'
)