hyperi-pylib 2.24.2

Enterprise infrastructure for containerized Python applications - configuration, logging, timeouts, database, metrics, and application framework

hyperi-pylib

Enterprise Python infrastructure for HyperI projects.

hyperi-pylib provides metrics, logging, and infrastructure utilities for all HyperI Python projects.

Note: The previous Application.* framework (API/Daemon/CLI/Oneshot/MCP) was removed in 2.13.x. Use the individual modules (logger, metrics, config, kafka, http, cache) directly or restore the legacy package from history if you need it.

Features

• Logging: Structured JSON logging with sensitive data masking
• PII Anonymization: ML-based anonymization with Presidio integration
• Database Utilities: ClickHouse, PostgreSQL, MySQL, Redis connection helpers
• Configuration: Multi-layer cascade with environment variable support
• Runtime: Application metadata and environment detection
• Metrics: Prometheus and OpenTelemetry backends
• Kafka: Complete Kafka client library with admin, consumer, producer, and schema analysis
• Harness: Smart timeout monitors and container registry utilities
• CLI: Typer-based CLI utilities

Installation

Package naming: hyperi-pylib on PyPI, hyperi_pylib for Python imports.

pyproject.toml Configuration (Recommended)

For projects using uv with both JFrog (hyperi-pylib) and PyPI packages, add this to your pyproject.toml:

[tool.uv]
index-strategy = "unsafe-best-match"

[[tool.uv.index]]
name = "hypersec-jfrog"
url = "https://hypersec.jfrog.io/artifactory/api/pypi/hypersec-pypi/simple"
explicit = true

[[tool.uv.index]]
name = "pypi"
url = "https://pypi.org/simple"
default = true

[tool.uv.sources]
hyperi-pylib = { index = "hypersec-jfrog" }

Key settings:

• index-strategy = "unsafe-best-match" - Allows mixing packages from JFrog and PyPI
• explicit = true on JFrog - Only use JFrog for explicitly mapped packages
• default = true on PyPI - Use PyPI for everything else
• [tool.uv.sources] - Explicitly route hyperi-pylib to JFrog

Then set credentials via environment variables and install:

# uv looks for UV_INDEX_{NAME}_USERNAME and UV_INDEX_{NAME}_PASSWORD
# where {NAME} is the uppercase index name with non-alphanumeric chars replaced by underscores
export UV_INDEX_HYPERSEC_JFROG_USERNAME="[e-mail]"
export UV_INDEX_HYPERSEC_JFROG_PASSWORD="your-jfrog-api-key"

uv sync

Command-Line Installation

# Set credentials (same env vars work for CLI)
export UV_INDEX_HYPERSEC_JFROG_USERNAME="[e-mail]"
export UV_INDEX_HYPERSEC_JFROG_PASSWORD="your-jfrog-api-key"

# Using uv with extra-index-url
uv pip install hyperi-pylib \
  --extra-index-url https://hypersec.jfrog.io/artifactory/api/pypi/hypersec-pypi/simple

# With optional dependencies
uv pip install hyperi-pylib[presidio,opentelemetry] \
  --extra-index-url https://hypersec.jfrog.io/artifactory/api/pypi/hypersec-pypi/simple

Optional Dependencies

• hyperi-pylib[presidio] - PII anonymization
• hyperi-pylib[opentelemetry] - OpenTelemetry metrics
• hyperi-pylib[api] - FastAPI support
• hyperi-pylib[metrics] - Prometheus metrics

Quick Start

Logging

from hyperi_pylib import logger

logger.setup(app_name="my-app", json_output=True)
logger.info("Application started")
logger.error("Something went wrong", exc_info=True)

Configuration

from hyperi_pylib import config

# Initialize configuration
config.setup(app_name="my-app")

# Access settings
settings = config.get_settings()
db_config = config.get_database_config()

Database Connections

from hyperi_pylib import database

# Get database URLs from environment
postgres_url = database.get_postgresql_url()
redis_url = database.get_redis_url()

# Build custom connection URL
url = database.build_database_url(
    driver="postgresql",
    host="localhost",
    port=5432,
    database="mydb",
    username="user",
    password="pass"
)

Kafka

from hyperi_pylib.kafka import KafkaClient

# Create client with configuration
client = KafkaClient.from_config(config_file="kafka.properties")

# Produce messages
await client.produce("my-topic", {"key": "value"})

# Consume messages
async for message in client.consume("my-topic", group_id="my-group"):
    process(message)

Metrics

from hyperi_pylib.metrics import create_metrics_backend

# Prometheus backend
metrics = create_metrics_backend("prometheus")
counter = metrics.counter("requests_total", "Total requests")
counter.inc()

Documentation

• Metrics: docs/METRICS.md
• Logging: docs/LOGGING.md
• Anonymizer: docs/ANONYMIZER.md

Development

# Quality checks
ruff check src/ tests/
ruff format src/ tests/

# Tests
pytest tests/

# Build
uv build

License

FSL-1.1-ALv2 - See LICENSE