log-surgeon-ffi 0.1.0b3

Python FFI bindings for log-surgeon: high-performance parsing of unstructured logs into structured data

log-surgeon-ffi

Python FFI bindings for log-surgeon, a high-performance library for parsing unstructured log messages into structured data.

Overview

log-surgeon-ffi provides a Pythonic interface to the log-surgeon C++ library, enabling efficient extraction of structured information from unstructured log files.

Quick Navigation

Getting Started: Installation • Getting Started • Quick Start • Examples

Core Concepts: Token-Based Parsing • Named Captures • Raw F-Strings

Reference: Parser API • Query API • PATTERN Constants

Why Log Surgeon?

Traditional regex engines are brittle—slow to execute, prone to errors, and they demand complex pattern maintenance. For instance, Meta uses RE2 (a state-of-the-art regex engine) to parse their logs but they face scalability and maintenance challenges; as a result they can only afford to extract limited patterns (timestamp, log level, and component name).

Log Surgeon streamlines the entire process by (1) identifying, extracting, and labeling variable values with semantic context and (2) inferring a log template, all in a single, efficient pass.

Log Surgeon is built to accommodate structural variability: values may shift position, appear multiple times, or change order entirely. You simply define the variable patterns, optionally enriched with surrounding text or sequence of variables. Log Surgeon then JIT-compiles a tagged-DFA state machine to drive the full pipeline.

Key Capabilities

• Extract variables from log messages using regex patterns with named capture groups
• Generate log types (templates) automatically for log analysis
• Parse streams efficiently for large-scale log processing
• Export data to pandas DataFrames and PyArrow Tables

Structured Output and Downstream Capabilities

Unstructured log data is automatically transformed into structured semantic representations:

• Log Types (Templates): Variables are replaced with placeholders to create reusable templates. For example, ~200,000 Spark log messages can be distilled into just 55 distinct log template types, enabling efficient pattern analysis and anomaly detection.

• Semantic Variables: Extracted key-value pairs with semantic context (e.g., app_id, app_name, worker_id) that can be directly used for analysis.

This structured output unlocks powerful downstream capabilities:

• Knowledge Graph Construction: Build relationship graphs between entities extracted from logs (e.g., linking app_id → app_name → worker_id). The structured output from log-surgeon provides an ideal foundation for tools like Stitch, which uses flow reconstruction from logs to perform non-intrusive performance profiling and debugging across distributed systems.

• Template-Based Summarization: Compress massive datasets into compact template sets for human and agent consumption. Log templates serve as natural tokens for LLMs, enabling efficient context windows - instead of feeding millions of raw log lines, provide ~50-100 distinct templates with statistics.

• Hybrid Search: Combine free-text search with structured queries. Log types enable auto-completion and query suggestions on large datasets - instead of searching through millions of raw log lines, search across a compact set of templates first. Then project and filter on structured variables (e.g., status == "ERROR", response_time > 1000), and aggregate for analysis - all in one unified workflow.

• Agentic Automation: Enable AI agents to understand and act on structured log data. Agents can query by template patterns, analyze variable distributions, identify anomalies, and automate debugging workflows using structured rather than raw text.

When to Use log-surgeon

✅ Good fit:

• Large-scale log processing (millions of lines)
• Extracting structured data from semi-structured logs
• Generating log templates for analytics
• Multi-line log events (stack traces, JSON dumps)
• Performance-critical parsing

❌ Not ideal for:

• Simple one-off text extraction (use Python re module)
• Highly irregular text without consistent delimiters
• Patterns requiring full PCRE features (lookahead, backreferences)

Installation

pip install log-surgeon-ffi

This installs the library with pandas and PyArrow support for DataFrame/Arrow table exports.

Verify installation:

python -c "from log_surgeon import Parser; print('✓ Installation successful')"

Optional: If you only need core parsing functionality without DataFrame/Arrow exports, you can install a minimal version (though pandas and PyArrow are included by default for convenience).

Getting Started

After installation, follow these steps:

1. ✅ Read Key Concepts - Critical to understand token-based parsing
2. ✅ Run a Quick Start example - See it working
3. ✅ Use rf"..." for patterns - Avoid escaping issues (see Using Raw F-Strings)
4. ✅ Check examples/ - More complete working examples

---

🚨 STOP: Different from traditional regex - Read this first or your patterns won't work

log-surgeon uses token-based parsing and has different regex behavior than traditional engines.

You MUST read the Key Concepts section and understand it fully before writing patterns, or you will encounter unexpected behavior and pain.

Critical differences:

• .* only matches within a single token (not across delimiters)
• abc|def requires grouping: use (abc)|(def) instead
• Use {0,1} for optional patterns, NOT ?

Tip: Use raw f-strings (rf"...") for regex patterns—see Using Raw F-Strings for details.

→ Read Key Concepts Now

---

Quick Start

Basic Parsing

from log_surgeon import Parser, PATTERN

# Parse a sample log event
log_line = "16/05/04 04:24:58 INFO Registering worker with 1 core and 4.0 GiB ram\n"

# Create a parser and define extraction patterns
parser = Parser()
parser.add_var("resource", rf"(?<memory_gb>{PATTERN.FLOAT}) GiB ram")
parser.compile()

# Parse a single event
event = parser.parse_event(log_line)

# Access extracted data
print(f"Message: {event.get_log_message().strip()}")
print(f"LogType: {event.get_log_type().strip()}")
print(f"Parsed Logs: {event}")

Output:

Message: 16/05/04 04:24:58 INFO Registering worker with 1 core and 4.0 GiB ram
LogType: 16/05/04 04:24:58 INFO Registering worker with 1 core and <memory_gb> GiB ram
Parsed Logs: {
  "memory_gb": "4.0"
}

The parser extracted structured data from the unstructured log line:

• Message: The original log line
• LogType: Template with variable placeholder <memory_gb> showing the pattern structure
• Parsed variables: Successfully extracted memory_gb value of "4.0" from the pattern match

Try It Yourself

Copy this code and modify the pattern to extract both memory_gb AND cores:

from log_surgeon import Parser, PATTERN

log_line = "16/05/04 04:24:58 INFO Registering worker with 1 core and 4.0 GiB ram\n"
parser = Parser()
# TODO: Add pattern to capture both "1" (cores) and "4.0" (memory_gb)
parser.add_var("resource", rf"...")
parser.compile()

event = parser.parse_event(log_line)
print(f"Cores: {event['cores']}, Memory: {event['memory_gb']}")

Solution

parser.add_var("resource", rf"(?<cores>\d+) core and (?<memory_gb>{PATTERN.FLOAT}) GiB ram")

Multiple Capture Groups

from log_surgeon import Parser, PATTERN

# Parse a sample log event
log_line = """16/05/04 12:22:37 WARN server.TransportChannelHandler: Exception in connection from spark-35/192.168.10.50:55392
java.io.IOException: Connection reset by peer
        at sun.nio.ch.FileDispatcherImpl.read0(Native Method)
        at sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:39)
        at sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:223)
        at sun.nio.ch.IOUtil.read(IOUtil.java:192)
        at sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:380)
        at io.netty.buffer.PooledUnsafeDirectByteBuf.setBytes(PooledUnsafeDirectByteBuf.java:313)
        at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:881)
        at io.netty.channel.socket.nio.NioSocketChannel.doReadBytes(NioSocketChannel.java:242)
        at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:119)
        at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
        at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
        at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
        at java.lang.Thread.run(Thread.java:750)
"""

# Create a parser and define extraction patterns
parser = Parser()

# Add timestamp pattern
parser.add_timestamp("TIMESTAMP_SPARK_1_6", rf"\d{{2}}/\d{{2}}/\d{{2}} \d{{2}}:\d{{2}}:\d{{2}}")

# Add variable patterns
parser.add_var("SYSTEM_LEVEL", rf"(?<level>(INFO)|(WARN)|(ERROR))")
parser.add_var("SPARK_HOST_IP_PORT", rf"(?<spark_host>spark\-{PATTERN.INT})/(?<system_ip>{PATTERN.IPV4}):(?<system_port>{PATTERN.PORT})")
parser.add_var(
  "SYSTEM_EXCEPTION",
  rf"(?<system_exception_type>({PATTERN.JAVA_PACKAGE_SEGMENT})+[{PATTERN.JAVA_IDENTIFIER_CHARSET}]*Exception): "
  rf"(?<system_exception_msg>{PATTERN.LOG_LINE})"
)
parser.add_var(
  rf"SYSTEM_STACK_TRACE",
  rf"(\s{{1,4}}at (?<system_stack>{PATTERN.JAVA_STACK_LOCATION})"
)
parser.compile()

# Parse a single event
event = parser.parse_event(log_line)

# Access extracted data
print(f"Message: {event.get_log_message().strip()}")
print(f"LogType: {event.get_log_type().strip()}")
print(f"Parsed Logs: {event}")

Output:

Message: 16/05/04 12:22:37 WARN server.TransportChannelHandler: Exception in connection from spark-35/192.168.10.50:55392
java.io.IOException: Connection reset by peer
        at sun.nio.ch.FileDispatcherImpl.read0(Native Method)
        at sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:39)
        at sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:223)
        at sun.nio.ch.IOUtil.read(IOUtil.java:192)
        at sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:380)
        at io.netty.buffer.PooledUnsafeDirectByteBuf.setBytes(PooledUnsafeDirectByteBuf.java:313)
        at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:881)
        at io.netty.channel.socket.nio.NioSocketChannel.doReadBytes(NioSocketChannel.java:242)
        at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:119)
        at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
        at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
        at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
        at java.lang.Thread.run(Thread.java:750)
LogType: <timestamp> <level> server.TransportChannelHandler: Exception in connection from <spark_host>/<system_ip>:<system_port>
<system_exception_type>: <system_exception_msg><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>
Parsed Logs: {
  "timestamp": "16/05/04 12:22:37",
  "level": "WARN",
  "spark_host": "spark-35",
  "system_ip": "192.168.10.50",
  "system_port": "55392",
  "system_exception_type": "java.io.IOException",
  "system_exception_msg": "Connection reset by peer",
  "system_stack": [
    "sun.nio.ch.FileDispatcherImpl.read0(Native Method)",
    "sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:39)",
    "sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:223)",
    "sun.nio.ch.IOUtil.read(IOUtil.java:192)",
    "sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:380)",
    "io.netty.buffer.PooledUnsafeDirectByteBuf.setBytes(PooledUnsafeDirectByteBuf.java:313)",
    "io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:881)",
    "io.netty.channel.socket.nio.NioSocketChannel.doReadBytes(NioSocketChannel.java:242)",
    "io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:119)",
    "io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)",
    "io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)",
    "io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)",
    "io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)",
    "io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)",
    "java.lang.Thread.run(Thread.java:750)"
  ]
}

The parser extracted multiple named capture groups from a complex multi-line Java stack trace:

• Scalar fields: timestamp, level, spark_host, system_ip, system_port, system_exception_type, system_exception_msg
• Array field: system_stack contains all 15 stack trace locations (demonstrates automatic aggregation of repeated capture groups)
• LogType: Template shows the structure with <newLine> markers indicating line boundaries in the original log

Stream Parsing

When parsing log streams or files, timestamps are required to perform contextual anchoring. Timestamps act as delimiters that separate individual log events, enabling the parser to correctly group multi-line entries (like stack traces) into single events.

from log_surgeon import Parser, PATTERN

# Parse from string (automatically converted to io.StringIO)
SAMPLE_LOGS = """16/05/04 04:31:13 INFO master.Master: Registering app SparkSQL::192.168.10.76
16/05/04 12:32:37 WARN server.TransportChannelHandler: Exception in connection from spark-35/192.168.10.50:55392
java.io.IOException: Connection reset by peer
        at sun.nio.ch.FileDispatcherImpl.read0(Native Method)
        at sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:39)
        at sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:223)
        at sun.nio.ch.IOUtil.read(IOUtil.java:192)
        at sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:380)
        at io.netty.buffer.PooledUnsafeDirectByteBuf.setBytes(PooledUnsafeDirectByteBuf.java:313)
        at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:881)
        at io.netty.channel.socket.nio.NioSocketChannel.doReadBytes(NioSocketChannel.java:242)
        at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:119)
        at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
        at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
        at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
        at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
        at java.lang.Thread.run(Thread.java:750)
16/05/04 04:37:53 INFO master.Master: 192.168.10.76:41747 got disassociated, removing it.
"""

# Define parser with patterns
parser = Parser()
# REQUIRED: Timestamp acts as contextual anchor to separate individual log events in the stream
parser.add_timestamp("TIMESTAMP_SPARK_1_6", rf"\d{{2}}/\d{{2}}/\d{{2}} \d{{2}}:\d{{2}}:\d{{2}}")
parser.add_var("SYSTEM_LEVEL", rf"(?<level>(INFO)|(WARN)|(ERROR))")
parser.add_var("SPARK_APP_NAME", rf"(?<spark_app_name>SparkSQL::{PATTERN.IPV4})")
parser.add_var("SPARK_HOST_IP_PORT", rf"(?<spark_host>spark\-{PATTERN.INT})/(?<system_ip>{PATTERN.IPV4}):(?<system_port>{PATTERN.PORT})")
parser.add_var(
    "SYSTEM_EXCEPTION",
    rf"(?<system_exception_type>({PATTERN.JAVA_PACKAGE_SEGMENT})+[{PATTERN.JAVA_IDENTIFIER_CHARSET}]*Exception): "
    rf"(?<system_exception_msg>{PATTERN.LOG_LINE})"
)
parser.add_var(
    rf"SYSTEM_STACK_TRACE", rf"(\s{{1,4}}at (?<system_stack>{PATTERN.JAVA_STACK_LOCATION})"
)
parser.add_var("IP_PORT", rf"(?<system_ip>{PATTERN.IPV4}):(?<system_port>{PATTERN.PORT})")
parser.compile()

# Stream parsing: iterate over multi-line log events
for idx, event in enumerate(parser.parse(SAMPLE_LOGS)):
    print(f"log-event-{idx} log template type:{event.get_log_type().strip()}")

Output:

log-event-0 log template type:<timestamp> <level> master.Master: Registering app <spark_app_name>
log-event-1 log template type:<timestamp> <level> server.TransportChannelHandler: Exception in connection from <spark_host>/<system_ip>:<system_port>
<system_exception_type>: <system_exception_msg><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack><newLine>        at <system_stack>
log-event-2 log template type:<timestamp> <level> master.Master: <system_ip>:<system_port> got disassociated, removing it.<newLine>

The parser successfully separated the log stream into 3 distinct events using timestamps as contextual anchors:

• Event 0: Single-line app registration log
• Event 1: Multi-line exception with 15 stack trace lines (demonstrates how timestamps bind multi-line events together)
• Event 2: Single-line disassociation log

Each log type shows the template structure with variable placeholders (<level>, <system_ip>, etc.), enabling pattern-based log analysis and grouping.

Using Pattern Constants

The PATTERN class provides pre-built regex patterns for common log elements like IP addresses, UUIDs, numbers, and file paths. See the PATTERN reference for the complete list of available patterns.

from log_surgeon import Parser, PATTERN

parser = Parser()
parser.add_var("network", rf"IP: (?<ip>{PATTERN.IPV4}) UUID: (?<id>{PATTERN.UUID})")
parser.add_var("metrics", rf"value=(?<value>{PATTERN.FLOAT})")
parser.compile()

log_line = "IP: 192.168.1.1 UUID: 550e8400-e29b-41d4-a716-446655440000 value=42.5"
event = parser.parse_event(log_line)

print(f"IP: {event['ip']}")
print(f"UUID: {event['id']}")
print(f"Value: {event['value']}")

Output:

IP: 192.168.1.1
UUID: 550e8400-e29b-41d4-a716-446655440000
Value: 42.5

Export to DataFrame

from log_surgeon import Parser, Query

parser = Parser()
parser.add_var(
  "metric",
  rf"metric=(?<metric_name>\w+) value=(?<value>\d+)"
)
parser.compile()

log_data = """
2024-01-01 INFO: metric=cpu value=42
2024-01-01 INFO: metric=memory value=100
2024-01-01 INFO: metric=disk value=7
"""

# Create a query and export to DataFrame
query = (
  Query(parser)
  .select(["metric_name", "value"])
  .from_(log_data)
  .validate_query()
)

df = query.to_dataframe()
print(df)

Filtering Events

from log_surgeon import Parser, Query

parser = Parser()
parser.add_var("metric", rf"metric=(?<metric_name>\w+) value=(?<value>\d+)")
parser.compile()

log_data = """
2024-01-01 INFO: metric=cpu value=42
2024-01-01 INFO: metric=memory value=100
2024-01-01 INFO: metric=disk value=7
2024-01-01 INFO: metric=cpu value=85
"""

# Filter events where value > 50
query = (
  Query(parser)
  .select(["metric_name", "value"])
  .from_(log_data)
  .filter(lambda event: int(event['value']) > 50)
  .validate_query()
)

df = query.to_dataframe()
print(df)
# Output:
#   metric_name  value
# 0      memory    100
# 1         cpu     85

Including Log Template Type and Log Message

Use special fields @log_type and @log_message to include alongside extracted variables:

from log_surgeon import Parser, Query

parser = Parser()
parser.add_var("metric", rf"value=(?<value>\d+)")
parser.compile()

log_data = """
2024-01-01 INFO: Processing value=42
2024-01-01 WARN: Processing value=100
"""

# Select log type, message, and all variables
query = (
  Query(parser)
  .select(["@log_type", "@log_message", "*"])
  .from_(log_data)
  .validate_query()
)

df = query.to_dataframe()
print(df)
# Output:
#                          @log_type                         @log_message value
# 0  <timestamp> INFO: Processing <metric>  2024-01-01 INFO: Processing value=42    42
# 1  <timestamp> WARN: Processing <metric>  2024-01-01 WARN: Processing value=100  100

The "*" wildcard expands to all variables defined in the schema and can be combined with other fields like @log_type and @log_message.

Analyzing Log Types

Discover and analyze log patterns in your data using log type analysis methods:

from log_surgeon import Parser, Query

parser = Parser()
parser.add_var("metric", rf"value=(?<value>\d+)")
parser.add_var("status", rf"status=(?<status>\w+)")
parser.compile()

log_data = """
2024-01-01 INFO: Processing value=42
2024-01-01 INFO: Processing value=100
2024-01-01 WARN: System status=degraded
2024-01-01 INFO: Processing value=7
2024-01-01 ERROR: System status=failed
"""

query = Query(parser).from_(log_data)

# Get all unique log types
print("Unique log types:")
for log_type in query.get_log_types():
  print(f"  {log_type}")

# Reset stream for next analysis
query.from_(log_data)

# Get log type occurrence counts
print("\nLog type counts:")
counts = query.get_log_type_counts()
for log_type, count in sorted(counts.items(), key=lambda x: -x[1]):
  print(f"  {count:3d}  {log_type}")

# Reset stream for next analysis
query.from_(log_data)

# Get sample messages for each log type
print("\nLog type samples:")
samples = query.get_log_type_with_sample(sample_size=2)
for log_type, messages in samples.items():
  print(f"  {log_type}")
  for msg in messages:
    print(f"    - {msg.strip()}")

Output:

Unique log types:
  <timestamp> INFO: Processing <metric>
  <timestamp> WARN: System <status>
  <timestamp> ERROR: System <status>

Log type counts:
    3  <timestamp> INFO: Processing <metric>
    1  <timestamp> WARN: System <status>
    1  <timestamp> ERROR: System <status>

Log type samples:
  <timestamp> INFO: Processing <metric>
    - 2024-01-01 INFO: Processing value=42
    - 2024-01-01 INFO: Processing value=100
  <timestamp> WARN: System <status>
    - 2024-01-01 WARN: System status=degraded
  <timestamp> ERROR: System <status>
    - 2024-01-01 ERROR: System status=failed

Quick Reference

Task	Syntax
Named capture	(?<name>pattern)
Alternation	`(?(opt1)
Optional	{0,1} (NOT ? or *)
Match across tokens	Use [a-z ]* (NOT .*)
Pattern string	rf"..." (raw f-string recommended)
All variables	.select(["*"])
Log type	.select(["@log_type"])
Original message	.select(["@log_message"])

API Reference

Parser

High-level parser for extracting structured data from unstructured log messages.

Constructor

• Parser(delimiters: str = r" \t\r\n:,!;%@/\(\)\[\]")
  • Initialize a parser with optional custom delimiters
  • Default delimiters include space, tab, newline, and common punctuation

Methods

• add_var(name: str, regex: str, hide_var_name_if_named_group_present: bool = True) -> Parser

  • Add a variable pattern to the parser's schema
  • Supports named capture groups using (?<name>) syntax
  • Use raw f-strings (rf"...") for regex patterns (see Using Raw F-Strings)
  • Returns self for method chaining

• add_timestamp(name: str, regex: str) -> Parser

  • Add a timestamp pattern to the parser's schema
  • Returns self for method chaining

• compile(enable_debug_logs: bool = False) -> None

  • Build and initialize the parser with the configured schema
  • Must be called after adding variables and before parsing
  • Set enable_debug_logs=True to output debug information to stderr

• load_schema(schema: str, group_name_resolver: GroupNameResolver) -> None

  • Load a pre-built schema string to configure the parser

• parse(input: str | TextIO | BinaryIO | io.StringIO | io.BytesIO) -> Generator[LogEvent, None, None]

  • Parse all log events from a string, file object, or stream
  • Accepts strings, text/binary file objects, StringIO, or BytesIO
  • Yields LogEvent objects for each parsed event

• parse_event(payload: str) -> LogEvent | None

  • Parse a single log event from a string (convenience method)
  • Wraps parse() and returns the first event
  • Returns LogEvent or None if no event found

LogEvent

Represents a parsed log event with extracted variables.

Methods

• get_log_message() -> str

  • Get the original log message

• get_log_type() -> str

  • Get the generated log type (template) with logical group names

• get_capture_group(logical_capture_group_name: str, raw_output: bool = False) -> str | list | None

  • Get the value of a capture group by its logical name
  • If raw_output=False (default), single values are unwrapped from lists
  • Returns None if capture group not found

• get_capture_group_str_representation(field: str, raw_output: bool = False) -> str

  • Get the string representation of a capture group value

• get_resolved_dict() -> dict[str, str | list]

  • Get a dictionary with all capture groups using logical (user-defined) names
  • Physical names (CGPrefix*) are converted to logical names
  • Timestamp fields are consolidated under "timestamp" key
  • Single-value lists are unwrapped to scalar values
  • "@LogType" is excluded from the output

• __getitem__(key: str) -> str | list

  • Access capture group values by name (e.g., event['field_name'])
  • Shorthand for get_capture_group(key, raw_output=False)

• __str__() -> str

  • Get formatted JSON representation of the log event with logical group names
  • Uses get_resolved_dict() internally

Query

Query builder for parsing log events into structured data formats.

Constructor

• Query(parser: Parser)
  • Initialize a query with a configured parser

Methods

• select(fields: list[str]) -> Query

  • Select fields to extract from log events
  • Supports variable names, "*" for all variables, "@log_type" for log type, and "@log_message" for original message
  • The "*" wildcard can be combined with other fields (e.g., ["@log_type", "*"])
  • Returns self for method chaining

• filter(predicate: Callable[[LogEvent], bool]) -> Query

  • Filter log events using a predicate function
  • Predicate receives a LogEvent and returns True to include it, False to exclude
  • Returns self for method chaining
  • Example: query.filter(lambda event: int(event['value']) > 50)

• from_(input: str | TextIO | BinaryIO | io.StringIO | io.BytesIO) -> Query

  • Set the input source to parse
  • Accepts strings, text/binary file objects, StringIO, or BytesIO
  • Strings are automatically wrapped in StringIO
  • Returns self for method chaining

• select_from(input: str | TextIO | BinaryIO | io.StringIO | io.BytesIO) -> Query

  • Alias for from_()
  • Returns self for method chaining

• validate_query() -> Query

  • Validate that the query is properly configured
  • Returns self for method chaining

• to_dataframe() -> pd.DataFrame

  • Convert parsed events to a pandas DataFrame

• to_df() -> pd.DataFrame

  • Alias for to_dataframe()

• to_arrow() -> pa.Table

  • Convert parsed events to a PyArrow Table

• to_pa() -> pa.Table

  • Alias for to_arrow()

• get_rows() -> list[list]

  • Extract rows of field values from parsed events

• get_vars() -> KeysView[str]

  • Get all variable names (logical capture group names) defined in the schema

• get_log_types() -> Generator[str, None, None]

  • Get all unique log types from parsed events
  • Yields log types in the order they are first encountered
  • Useful for discovering log patterns in your data

• get_log_type_counts() -> dict[str, int]

  • Get count of occurrences for each unique log type
  • Returns dictionary mapping log types to their counts
  • Useful for analyzing log type distribution

• get_log_type_with_sample(sample_size: int = 3) -> dict[str, list[str]]

  • Get sample log messages for each unique log type
  • Returns dictionary mapping log types to lists of sample messages
  • Useful for understanding what actual messages match each template

SchemaCompiler

Compiler for constructing log-surgeon schema definitions.

Constructor

• SchemaCompiler(delimiters: str = DEFAULT_DELIMITERS)
  • Initialize a schema compiler with optional custom delimiters

Methods

• add_var(name: str, regex: str, hide_var_name_if_named_group_present: bool = True) -> SchemaCompiler

  • Add a variable pattern to the schema
  • Returns self for method chaining

• add_timestamp(name: str, regex: str) -> SchemaCompiler

  • Add a timestamp pattern to the schema
  • Returns self for method chaining

• remove_var(var_name: str) -> SchemaCompiler

  • Remove a variable from the schema
  • Returns self for method chaining

• get_var(var_name: str) -> Variable

  • Get a variable by name

• compile() -> str

  • Compile the final schema string

• get_capture_group_name_resolver() -> GroupNameResolver

  • Get the resolver for mapping logical to physical capture group names

GroupNameResolver

Bidirectional mapping between logical (user-defined) and physical (auto-generated) group names.

Constructor

• GroupNameResolver(physical_name_prefix: str)
  • Initialize with a prefix for auto-generated physical names

Methods

• create_new_physical_name(logical_name: str) -> str

  • Create a new unique physical name for a logical name
  • Each call generates a new physical name

• get_physical_names(logical_name: str) -> set[str]

  • Get all physical names associated with a logical name

• get_logical_name(physical_name: str) -> str

  • Get the logical name for a physical name

• get_all_logical_names() -> KeysView[str]

  • Get all logical names that have been registered

PATTERN

Collection of pre-built regex patterns optimized for log parsing. These patterns follow log-surgeon's syntax requirements and are ready to use with named capture groups.

Available Patterns

Network Patterns

Pattern	Description	Example Match
PATTERN.UUID	UUID (Universally Unique Identifier)	550e8400-e29b-41d4-a716-446655440000
PATTERN.IP_OCTET	Single IPv4 octet (0-255)	192, 10, 255
PATTERN.IPV4	IPv4 address	192.168.1.1, 10.0.0.1
PATTERN.PORT	Network port number (1-5 digits)	80, 8080, 65535

Numeric Patterns

Pattern	Description	Example Match
PATTERN.INT	Integer with optional negative sign	42, -123, 0
PATTERN.FLOAT	Float with optional negative sign	3.14, -123.456, 0.5

File System Patterns

Pattern	Description	Example Match
PATTERN.LINUX_FILE_NAME_CHARSET	Character set for Linux file names	a-zA-Z0-9 ._-
PATTERN.LINUX_FILE_NAME	Linux file name	app.log, config-2024.yaml
PATTERN.LINUX_FILE_PATH	Linux file path (relative)	logs/app.log, var/log/system.log

Character Sets and Word Patterns

Pattern	Description	Example Match
PATTERN.JAVA_IDENTIFIER_CHARSET	Java identifier character set	a-zA-Z0-9_
PATTERN.JAVA_IDENTIFIER	Java identifier	myVariable, $value, Test123
PATTERN.LOG_LINE_CHARSET	Common log line characters	Alphanumeric + symbols + whitespace
PATTERN.LOG_LINE	General log line content	Error: connection timeout
PATTERN.LOG_LINE_NO_WHITE_SPACE_CHARSET	Log line chars without whitespace	Alphanumeric + symbols only
PATTERN.LOG_LINE_NO_WHITE_SPACE	Log content without spaces	ERROR, /var/log/app.log

Java-Specific Patterns

Pattern	Description	Example Match
PATTERN.JAVA_LITERAL_CHARSET	Java literal character set	a-zA-Z0-9_$
PATTERN.JAVA_PACKAGE_SEGMENT	Single Java package segment	com., example.
PATTERN.JAVA_CLASS_NAME	Java class name	MyClass, ArrayList
PATTERN.JAVA_FULLY_QUALIFIED_CLASS_NAME	Fully qualified class name	java.util.ArrayList
PATTERN.JAVA_LOGGING_CODE_LOCATION_HINT	Java logging location hint	~[MyClass.java:42?]
PATTERN.JAVA_STACK_LOCATION	Java stack trace location	java.util.ArrayList.add(ArrayList.java:123)

Example Usage

from log_surgeon import Parser, PATTERN

parser = Parser()

# Network patterns
parser.add_var("network", rf"IP: (?<ip>{PATTERN.IPV4}) Port: (?<port>{PATTERN.PORT})")

# Numeric patterns
parser.add_var("metrics", rf"value=(?<value>{PATTERN.FLOAT}) count=(?<count>{PATTERN.INT})")

# File system patterns
parser.add_var("file", rf"Opening (?<filepath>{PATTERN.LINUX_FILE_PATH})")

# Java patterns
parser.add_var("exception", rf"at (?<stack>{PATTERN.JAVA_STACK_LOCATION})")

parser.compile()

Composing Patterns

PATTERN constants can be composed to build more complex patterns:

from log_surgeon import Parser, PATTERN

parser = Parser()

# Combine multiple patterns
parser.add_var(
    "server_info",
    rf"Server (?<name>{PATTERN.JAVA_IDENTIFIER}) at (?<ip>{PATTERN.IPV4}):(?<port>{PATTERN.PORT})"
)

# Use character sets to build custom patterns
parser.add_var(
    "custom_id",
    rf"ID-(?<id>[{PATTERN.JAVA_IDENTIFIER_CHARSET}]+)"
)

parser.compile()

Key Concepts

⚠️ CRITICAL: You must understand these concepts to use log-surgeon correctly.

log-surgeon works fundamentally differently from traditional regex engines like Python's re module, PCRE, or JavaScript regex. Skipping this section will lead to patterns that don't work as expected.

Token-Based Parsing and Delimiters

CRITICAL: log-surgeon uses token-based parsing, not character-based regex matching like traditional regex engines. This is the most important difference that affects how patterns work.

How Tokenization Works

Delimiters are characters used to split log messages into tokens. The default delimiters include:

• Whitespace: space, tab (\t), newline (\n), carriage return (\r)
• Punctuation: :, ,, !, ;, %, @, /, (, ), [, ]

For example, with default delimiters, the log message:

"abc def ghi"

is tokenized into three tokens: ["abc", "def", "ghi"]

You can customize delimiters when creating a Parser:

parser = Parser(delimiters=r" \t\n,:")  # Custom delimiters

Token-Based Pattern Matching

Critical: Patterns like .* only match within a single token, not across multiple tokens or delimiters.

from log_surgeon import Parser

parser = Parser()  # Default delimiters include space
parser.add_var("token", rf"(?<match>d.*)")
parser.compile()

# With "abc def ghi" tokenized as ["abc", "def", "ghi"]
event = parser.parse_event("abc def ghi")

# ✅ Matches only "def" (single token starting with 'd')
# ❌ Does NOT match "def ghi" (would cross token boundary)
print(event['match'])  # Output: "def"

In a traditional regex engine, d.* would match "def ghi" (everything from 'd' to end). In log-surgeon, d.* matches only "def" because patterns cannot cross delimiter boundaries.

Why Token-Based?

Token-based parsing enables:

• Faster parsing by reducing search space
• Predictable behavior aligned with log structure
• Efficient log type generation for analytics

Working with Token Boundaries

To match across multiple tokens, you must use character classes like [a-zA-Z]* instead of .:

from log_surgeon import Parser

parser = Parser()  # Default delimiters include space

# ❌ Using .* - only matches within a single token
parser.add_var("wrong", rf"(?<match>d.*)")  # Matches only "def"

# ✅ Using character classes - matches across tokens
parser.add_var("correct", rf"(?<match>d[a-z ]*i)")  # Matches "def ghi"
parser.compile()

event = parser.parse_event("abc def ghi")
print(event['match'])  # Output: "def ghi"

Key Rule: Character classes like [a-zA-Z]*, [a-z ]*, or [\w\s]* can match across token boundaries, but .* cannot.

Alternation Requires Grouping

CRITICAL: Alternation (|) works differently in log-surgeon compared to traditional regex engines. You must use parentheses to group alternatives.

from log_surgeon import Parser

parser = Parser()

# ❌ WRONG: Without grouping - matches "ab" AND ("c" OR "d") AND "ef"
parser.add_var("wrong", rf"(?<word>abc|def)")
# In log-surgeon, this is interpreted as: "ab" + "c|d" + "ef"
# Matches: "abcef" or "abdef" (NOT "abc" or "def")

# ✅ CORRECT: With grouping - matches "abc" OR "def"
parser.add_var("correct", rf"(?<word>(abc)|(def))")
# Matches: "abc" or "def"
parser.compile()

In traditional regex engines, abc|def means "abc" OR "def". In log-surgeon, abc|def means "ab" + ("c" OR "d") + "ef".

Key Rule: Always use (abc)|(def) syntax for alternation to match complete alternatives.

# More examples:
parser.add_var("level", rf"(?<level>(ERROR)|(WARN)|(INFO))")  # ✅ Correct
parser.add_var("status", rf"(?<status>(success)|(failure))")  # ✅ Correct
parser.add_var("bad", rf"(?<status>success|failure)")         # ❌ Wrong - unexpected behavior

Optional Patterns

For optional patterns, use {0,1} instead of *:

from log_surgeon import Parser

parser = Parser()

# ❌ Avoid using * for optional patterns (matches 0 or more)
parser.add_var("avoid", rf"(?<level>(ERROR)|(WARN))*")  # Can match empty string or multiple repetitions

# ❌ Do not use ? for optional patterns
parser.add_var("avoid2", rf"(?<level>(ERROR)|(WARN))?")  # May not work as expected

# ✅ Use {0,1} for optional patterns (matches 0 or 1)
parser.add_var("optional", rf"(?<level>(ERROR)|(WARN)){0,1}")  # Matches 0 or 1 occurrence
parser.compile()

Best Practice: Use {0,1} for optional elements. Avoid * (0 or more) and ? for optional matching.

You can also explicitly include delimiters in your pattern:

# To match "def ghi", explicitly include the space delimiter
parser.add_var("multi", rf"(?<match>d\w+\s+\w+)")
# This matches "def " as one token segment, followed by "ghi"

Or adjust your delimiters to change tokenization behavior:

# Use only newline as delimiter to treat entire lines as tokens
parser = Parser(delimiters=r"\n")

Named Capture Groups

Use named capture groups in regex patterns to extract specific fields:

parser.add_var("metric", rf"metric=(?<metric_name>\w+) value=(?<value>\d+)")

The syntax (?<name>pattern) creates a capture group that can be accessed as event['name'].

Note: See Using Raw F-Strings for best practices on writing regex patterns.

Using Raw F-Strings for Regex Patterns

⚠️ STRONGLY RECOMMENDED: Use raw f-strings (rf"...") for all regex patterns.

While not absolutely required, using regular strings will likely cause escaping issues and pattern failures. Raw f-strings prevent these problems.

Raw f-strings combine the benefits of:

• Raw strings (r"..."): No need to double-escape regex special characters like \d, \w, \n
• F-strings (f"..."): Easy interpolation of variables and pattern constants

Why Use Raw F-Strings?

# ❌ Without raw strings - requires double-escaping
parser.add_var("metric", "value=(\\d+)")  # Hard to read, error-prone

# ✅ With raw f-strings - single escaping, clean and readable
parser.add_var("metric", rf"value=(?<value>\d+)")

Watch Out for Braces in F-Strings

When using f-strings, literal { and } characters must be escaped by doubling them:

from log_surgeon import Parser, Pattern

parser = Parser()

# ✅ Correct: Escape literal braces in regex
parser.add_var("json", rf"data={{(?<content>[^}}]+)}}")  # Matches: data={...}
parser.add_var("range", rf"range={{(?<min>\d+),(?<max>\d+)}}")  # Matches: range={10,20}

# ✅ Using Pattern constants with interpolation
parser.add_var("ip", rf"IP: (?<ip>{Pattern.IPV4})")
parser.add_var("float", rf"value=(?<val>{Pattern.FLOAT})")

# ✅ Common regex patterns
parser.add_var("digits", rf"\d+ items")  # No double-escaping needed
parser.add_var("word", rf"name=(?<name>\w+)")
parser.add_var("whitespace", rf"split\s+by\s+spaces")

parser.compile()

Examples: Raw F-Strings vs Regular Strings

# Regular string - requires double-escaping
parser.add_var("path", "path=(?<path>\\w+/\\w+)")  # Hard to read

# Raw f-string - natural regex syntax
parser.add_var("path", rf"path=(?<path>\w+/\w+)")  # Clean and readable

# With interpolation
log_level = "INFO|WARN|ERROR"
parser.add_var("level", rf"(?<level>{log_level})")  # Easy to compose

Recommendation: Consistently use rf"..." for all regex patterns. This approach:

• Avoids double-escaping mistakes that break patterns
• Makes patterns more readable
• Allows easy use of Pattern constants and variables
• Only requires watching for literal braces { and } in f-strings (escape as {{ and }})

Using regular strings ("...") will require double-escaping (e.g., "\\d+") which is error-prone and hard to read.

Logical vs Physical Names

Internally, log-surgeon uses "physical" names (e.g., CGPrefix0, CGPrefix1) for capture groups, while you work with "logical" names (e.g., user_id, thread). The GroupNameResolver handles this mapping automatically.

Schema Format

The schema defines delimiters, timestamps, and variables for parsing:

// schema delimiters
delimiters: \t\r\n:,!;%@/\(\)\[\]

// schema timestamps
timestamp:<timestamp_regex>

// schema variables
variable_name:<variable_regex>

When using the fluent API (Parser.add_var() and Parser.compile()), the schema is built automatically.

Common Pitfalls

❌ Pattern doesn't match anything

• Check: Are you using .* to match across tokens? Use [a-zA-Z ]* instead
• Check: Did you forget to call parser.compile()?
• Check: Are your delimiters splitting tokens unexpectedly?

❌ Alternation not working (abc|def)

• Problem: (?<name>abc|def) doesn't match "abc" or "def" as expected
• Solution: Use (?<name>(abc)|(def)) with explicit grouping

❌ Pattern works in regex tester but not here

• Remember: log-surgeon is token-based, not character-based
• Traditional regex engines match across entire strings
• log-surgeon matches within token boundaries (delimited by spaces, colons, etc.)
• Read: Token-Based Parsing

❌ Escape sequence errors in Python

• Problem: parser.add_var("digits", "(?<num>\d+)") raises SyntaxError
• Solution: Use rf"..." (raw f-string) instead of "..." or f"..."
• Example: parser.add_var("digits", rf"(?<num>\d+)")

❌ Optional pattern matching incorrectly

• Problem: Using ? or * for optional patterns
• Solution: Use {0,1} for optional elements
• Example: (?<level>(ERROR)|(WARN)){0,1} for optional log level

Development

Building from Source

# Clone the repository
git clone https://github.com/y-scope/log-surgeon-ffi-py.git
cd log-surgeon-ffi-py

# Install the project in editable mode
pip install -e .

# Build the extension
cmake -S . -B build
cmake --build build

Running Tests

# Install test dependencies
pip install pytest

# Run tests
python -m pytest tests/

Requirements

• Python >= 3.9
• pandas
• pyarrow

Build Requirements

• C++20 compatible compiler
• CMake >= 3.15

License

Apache License 2.0 - See LICENSE for details.

Links

• Homepage
• Bug Tracker
• log-surgeon C++ library

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.