hyperi-pylib 2.24.3

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

hyperi-pylib

Enterprise infrastructure for all HyperI Python projects — configuration, logging, metrics, database utilities, Kafka, caching, and CLI framework.

Features

Core modules — always installed (uv add hyperi-pylib):

Module	Description	Third-party deps
logging	Structured JSON logging with automatic sensitive data masking	loguru
config	8-layer cascade (CLI → ENV → .env → PostgreSQL → YAML → defaults), container-aware	dynaconf, pyyaml, python-dotenv, mergedeep, tomli-w, dulwich
runtime	Container/K8s/local environment detection with standard path resolution	stdlib only
database	Connection URL builders for PostgreSQL, Redis, and others	stdlib only
cli	DfeApp framework — subclass to get run/version/config-check for free	typer
harness	Timeout monitors and utility helpers	stdlib only
version-check	Startup check for new hyperi-pylib releases (skipped if httpx absent)	httpx (lazy)

Optional modules — enabled by installing the matching extra:

Module	Extra	Third-party deps
http	http	httpx, stamina
metrics	metrics	prometheus-client, psutil
expression	expression	common-expression-language (CEL via Rust/PyO3)
cache	cache	cashews, msgpack, psycopg[binary,pool]
kafka	kafka	confluent-kafka, genson
opentelemetry	opentelemetry	opentelemetry SDK + OTLP + Prometheus exporters

Installation

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

# Core only (logging, config, runtime, database, cli, harness, version-check)
uv add hyperi-pylib

# With common extras
uv add "hyperi-pylib[http,metrics,kafka]"

# Full stack
uv add "hyperi-pylib[http,metrics,expression,cache,kafka,opentelemetry]"

Optional Extras

Extra	Packages	Size
http	httpx + stamina	~1 MB
metrics	prometheus-client + psutil	~1 MB
expression	common-expression-language (CEL)	~6 MB
cache	cashews + msgpack + psycopg[binary,pool]	~14 MB (psycopg C libs)
kafka	confluent-kafka + genson	~11 MB (C libs)
opentelemetry	OpenTelemetry SDK + exporters	~4 MB
presidio	Presidio analyser + anonymiser	~500 MB (spaCy + ML models)
secrets	All secrets backends (Vault + AWS + GCP + Azure)	—
secrets-vault	OpenBao / HashiCorp Vault (uses http extra)	convenience marker
secrets-aws	AWS Secrets Manager via boto3	~100 MB
secrets-gcp	GCP Secret Manager	~80–100 MB
secrets-azure	Azure Key Vault	~50 MB

Quick Start

Logging

from hyperi_pylib.logger import logger

logger.info("Service starting", version="1.0.0")
logger.error("DB connection failed", host="postgres", retry=3)

Auto-detects console vs container — structured JSON in containers, human-readable locally.

Configuration

from hyperi_pylib.config import settings

# Automatic cascade: CLI > ENV > .env > PostgreSQL > settings.yaml > defaults
host = settings.database.host
port = settings.api.port

ENV key mapping: settings.database.host → MYAPP_DATABASE_HOST

Database URLs

from hyperi_pylib import build_database_url

postgres = build_database_url("postgresql")  # reads POSTGRES_HOST, POSTGRES_PORT, etc.
redis = build_database_url("redis")          # reads REDIS_HOST, REDIS_PORT, etc.

Runtime Paths

from hyperi_pylib import get_runtime_paths

runtime = get_runtime_paths()
config = runtime.config_dir / "app.yaml"   # /config in K8s, ~/.config locally
data   = runtime.data_dir  / "state.db"   # /data in K8s, ~/.local/share locally

Metrics

from hyperi_pylib import create_metrics

metrics = create_metrics(namespace="myapp")
metrics.http_requests.inc()
metrics.active_users.set(42)
metrics.request_duration.observe(0.123)

Cache

from hyperi_pylib.cache import PostgresCache, generate_cache_key

cache = PostgresCache(dsn="postgresql://user:pass@host/db")
await cache.init()

key = generate_cache_key("analytics", "events", org_id="acme")
await cache.set(key, {"data": [...]}, ttl_seconds=300, namespace="analytics")
value = await cache.get(key)

await cache.close()

Kafka

from hyperi_pylib.kafka import KafkaClient, KafkaConsumer, KafkaProducer

DfeApp CLI Framework

Subclass DfeApp to get standard CLI lifecycle (run, version, config-check) with no boilerplate:

from hyperi_pylib.cli import DfeApp, VersionInfo

class MyService(DfeApp):
    name = "my-service"
    env_prefix = "MY_SVC"

    def version_info(self) -> VersionInfo:
        return VersionInfo(self.name, "1.0.0")

    async def run_service_async(self, config) -> None:
        ...

if __name__ == "__main__":
    MyService().cli()

Config always uses the Dynaconf cascade — no bespoke loading needed.

Development

make quality   # lint, type-check, security audit
make test      # run test suite
make build     # build wheel

License

FSL-1.1-ALv2 — See LICENSE