clp-ffi-py 0.0.12

Python interface to the CLP Core Features through CLP's FFI

clp-ffi-py

This module provides Python packages to interface with CLP Core Features through CLP's FFI (foreign function interface). At present, this library supplies built-in functions for encoding/decoding log messages using CLP.

[!IMPORTANT] This project is no longer built for Python3.6.

Quick Start

Install with pip:

# Install the latest version
python3 -m pip install --upgrade clp-ffi-py

Note:

• Python 3.7 or higher is required.
• Tested on Linux, macOS and Windows.

To install an older version or download the prebuilt whl package, check the project homepage on PyPI here.

Compatibility

Tested on Python 3.7, 3.8, 3.11 and 3.12, and it should work on any Python version >= 3.7.

API Reference

The API reference for this library can be found on our docs hub.

Building/Packaging

To manually build a package for distribution, follow the steps below.

Requirements

• A C++ compiler that supports C++20 and std::span, e.g:
  • clang++ >= 7
  • g++ >= 10
  • MSVC >= 1930 (included in Visual Studio 2022)
• python3
• python3-dev
• python3-venv
• Task >= 3.35.1

Build commands

• Build a Python wheel incrementally:

  task
  

  The command above will generate both a .tar.gz and .whl package under ./build/dist/.

• Clean up the build:

  task clean
  

CLP IR Readers

CLP IR Readers provide a convenient interface for CLP IR decoding and search methods.

ClpIrStreamReader

• Read/decode any arbitrary CLP IR stream (as an instance of IO[bytes]).
• Can be used as an iterator that returns each log event as a LogEvent object.
• Can search target log events by giving a search query:
  • Searching log events within a certain time range.
  • Searching log messages that match certain wildcard queries.

ClpIrFileReader

• Simple wrapper around CLPIRStreamHandler that calls open with a given local path.

Example Code: Using ClpIrFileReader to iterate and print log events

from pathlib import Path
from clp_ffi_py.ir import ClpIrFileReader

with ClpIrFileReader(Path("example.clp.zst")) as clp_reader:
    for log_event in clp_reader:
        # Print the log message with its timestamp properly formatted.
        print(log_event.get_formatted_message())

Each log event is represented by a LogEvent object, which offers methods to retrieve its underlying details, such as the timestamp and the log message. For more information, use the following code to see all the available methods and the associated docstring.

from clp_ffi_py.ir import LogEvent
help(LogEvent)

Example Code: Using Query to search log events by specifying a certain time range

from typing import List

from clp_ffi_py.ir import ClpIrStreamReader, LogEvent, Query, QueryBuilder

# Create a QueryBuilder object to build the search query.
query_builder: QueryBuilder = QueryBuilder()

# Create a search query that specifies a time range by UNIX epoch timestamp in
# milliseconds. It will search from 2016.Nov.28 21:00 to 2016.Nov.29 3:00.
time_range_query: Query = (
    query_builder
    .set_search_time_lower_bound(1480366800000) # 2016.11.28 21:00
    .set_search_time_upper_bound(1480388400000) # 2016.11.29 03:00
    .build()
)

# A list to store all the log events within the search time range
log_events: List[LogEvent] = []

# Open IRstream compressed log file as a binary file stream, then pass it to
# CLpIrStreamReader.
with open("example.clp.zst", "rb") as compressed_log_file:
    with ClpIrStreamReader(compressed_log_file) as clp_reader:
        for log_event in clp_reader.search(time_range_query):
            log_events.append(log_event)

Example Code: Using Query to search log messages of certain pattern(s) specified by wildcard queries.

from pathlib import Path
from typing import List, Tuple

from clp_ffi_py.ir import ClpIrFileReader, Query, QueryBuilder
from clp_ffi_py.wildcard_query import FullStringWildcardQuery, SubstringWildcardQuery

# Create a QueryBuilder object to build the search query.
query_builder: QueryBuilder = QueryBuilder()

# Add wildcard patterns to filter log messages:
query_builder.add_wildcard_query(SubstringWildcardQuery("uid=*,status=failed"))
query_builder.add_wildcard_query(
    FullStringWildcardQuery("*UID=*,Status=KILLED*", case_sensitive=True)
)

# Initialize a Query object using the builder:
wildcard_search_query: Query = query_builder.build()
# Store the log events that match the criteria in the format:
# [timestamp, message]
matched_log_messages: List[Tuple[int, str]] = []

# A convenience file reader class is also available to interact with a file that
# represents an encoded CLP IR stream directly.
with ClpIrFileReader(Path("example.clp.zst")) as clp_reader:
    for log_event in clp_reader.search(wildcard_search_query):
        matched_log_messages.append((log_event.get_timestamp(), log_event.get_log_message()))

A Query object may have both the search time range and the wildcard queries (WildcardQuery) specified to support more complex search scenarios. QueryBuilder can be used to conveniently construct Query objects. For more details, use the following code to access the related docstring.

from clp_ffi_py.ir import Query, QueryBuilder
from clp_ffi_py import FullStringWildcardQuery, SubstringWildcardQuery, WildcardQuery
help(Query)
help(QueryBuilder)
help(WildcardQuery)
help(FullStringWildcardQuery)
help(SubstringWildcardQuery)

Streaming Decode/Search Directly from S3 Remote Storage

When working with CLP IR files stored on S3-compatible storage systems, smart_open can be used to open and read the IR stream for the following benefits:

• It only performs stream operation and does not download the file to the disk.
• It only invokes a single GET request so that the API access cost is minimized.

Here is an example:

from pathlib import Path
from clp_ffi_py.ir import ClpIrStreamReader

import boto3
import os
import smart_open

# Create a boto3 session by reading AWS credentials from environment variables.
session = boto3.Session(
    aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],
    aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'],
)

url = 's3://clp-example-s3-bucket/example.clp.zst'
# Using `smart_open.open` to stream the encoded CLP IR:
with smart_open.open(
    url, mode="rb", compression="disable", transport_params={"client": session.client("s3")}
) as istream:
    with ClpIrStreamReader(istream, allow_incomplete_stream=True) as clp_reader:
        for log_event in clp_reader:
            # Print the log message with its timestamp properly formatted.
            print(log_event.get_formatted_message())

Note:

• Setting compression="disable" is necessary so that smart_open doesn't undo the IR file's Zstandard compression (based on the file's extension) before streaming it to ClpIrStreamReader; ClpIrStreamReader expects the input stream to be Zstandard-compressed.
• When allow_incomplete_stream is set to False (default), the reader will raise clp_ffi_py.ir.IncompleteStreamError if the stream is incomplete (it doesn't end with the byte sequence indicating the stream's end). In practice, this can occur if you're reading a stream that is still being written or wasn't properly closed.

Parallel Processing

The Query and LogEvent classes can be serialized by pickle. Therefore, decoding and search can be parallelized across streams/files using libraries such as multiprocessing and tqlm.

Testing

# 1. Create and enter a virtual environment
python -m venv venv && . ./venv/bin/activate

# 2. Install development dependencies
pip install -r requirements-dev.txt

# 3. Pull all submodules in preparation for building
git submodule update --init --recursive

# 4. Install
pip install -e .

# 5. Run unit tests
python -m unittest -bv

Note: If the package is installed from a whl file into the site packages, rather than installed locally (pip install -e .), the tester cannot be launched from the project's root directory. If unittest is ran from the root directory, the local clp_ffi_py directory will shadow the clp_ffi_py module installed. To run the tester with the installed package, try the following:

cd tests
python -m unittest -bv

Build and Test with cibuildwheel

This project utilizes cibuildwheel configuration. Whenever modifications are made and committed to GitHub, the cibuildwheel Action will automatically initiate, building this library for several Python environments across diverse OS and architectures. You can access the build outcomes (wheel files) via the GitHub Action page. For instructions on customizing the build targets or running cibuildwheel locally, please refer to the official documentation of cibuildwheel.

Contributing

Before submitting a pull request, run the following error-checking and formatting tools (found in [pyproject.toml]):

• mypy: mypy clp_ffi_py
  • mypy checks for typing errors. You should resolve all typing errors or if an error cannot be resolved (e.g., it's due to a third-party library), you should add a comment # type: ignore to silence the error.
• docformatter: docformatter -i clp_ffi_py tests
  • This formats docstrings. You should review and add any changes to your PR.
• Black: black clp_ffi_py
  • This formats the Python code according to Black's code-style rules. You should review and add any changes to your PR.
• clang-format: clang-format -i src/clp_ffi_py/**
  • This formats the C++ code according to the code-style rules specified in .clang-format. You should review and add any changes to your PR.
• ruff: ruff check --fix clp_ffi_py tests
  • This performs linting according to PEPs. You should review and add any changes to your PR.

Note that docformatter should be run before black to give Black the last.

Additionally, the following tools can be useful during development. However, they cannot be installed using pip. Developers need to install them using other package management tools such as apt-get:

• clang-tidy: clang-tidy --extra-arg=-std=c++17 PATH_TO_THE_FILE
  • This static analysis tool catches improper coding behaviors based on the rules specified in .clang-tidy, and sends suggestions corresponding to each warning. Developers should manually review all the warnings and try with their best effort to fix the reasonable ones.
• bear: bear python setup.py build
  • This tool generates a JSON compilation database on the project's root directory named compile_commands.json. This file will be used by clang-tidy to execute the static analysis. It also helps IDEs to configure code completion (such as VSCode).