Getting Started with KahlownLite — A Beginner’s GuideKahlownLite is a compact, efficient library designed to simplify high-performance data processing for modern applications. This guide walks you through what KahlownLite is, why you might choose it, how to install and configure it, basic usage patterns, common pitfalls, and where to go next. Examples use clear code snippets and practical tips so you can get up and running quickly.
What is KahlownLite?
KahlownLite is a lightweight data-processing toolkit focused on speed, low memory footprint, and ease of integration. It provides core primitives for streaming, transformation, and aggregation of data with minimal configuration. The project aims to balance simplicity with powerful features by offering:
- Fast, zero-copy streaming operations
- A small runtime footprint suitable for edge or embedded environments
- Composable transformation pipelines
- Interoperability with common data formats (JSON, CSV, binary blobs)
Key takeaway: KahlownLite is intended for developers who need fast, memory-efficient data processing without the overhead of larger frameworks.
Why choose KahlownLite?
- Performance: Built with optimized algorithms to reduce CPU and memory usage.
- Simplicity: Minimal API surface makes it easy to learn and integrate.
- Portability: Small binary size and few dependencies enable use in constrained environments.
- Composable: Pipelines are modular and can be combined to build complex data flows.
Installation
KahlownLite distributes packages for common platforms and languages. Below are example installation commands for two typical environments.
Node.js (npm)
npm install kahlownlite Python (pip)
pip install kahlownlite If building from source, clone the repository and follow the build instructions in the README.
Quick start — Basic usage
The following examples demonstrate basic patterns: creating a pipeline, applying transformations, and consuming results.
Node.js example
const { Pipeline } = require('kahlownlite'); const pipeline = new Pipeline() .source(['{"id":1,"value":10}','{"id":2,"value":20}']) // simple source .parseJson() .map(item => ({ id: item.id, doubled: item.value * 2 })) .collect(); pipeline.run().then(results => { console.log(results); // [{id:1,doubled:20},{id:2,doubled:40}] }); Python example
from kahlownlite import Pipeline pipeline = ( Pipeline() .source(['{"id":1,"value":10}', '{"id":2,"value":20}']) .parse_json() .map(lambda item: {'id': item['id'], 'doubled': item['value'] * 2}) .collect() ) results = pipeline.run() print(results) # [{'id':1,'doubled':20},{'id':2,'doubled':40}] Core concepts
- Source: Where data enters the pipeline (files, streams, arrays).
- Transform: Functions that map, filter, or otherwise mutate items.
- Sink: The pipeline endpoint that collects or forwards results (console, file, network).
- Backpressure: KahlownLite supports flow-control to prevent memory spikes when producers are faster than consumers.
- Zero-copy: Where possible, KahlownLite avoids unnecessary data copying to improve performance.
Common operations
Filtering and aggregation
// Node.js const results = await new Pipeline() .source(['1','2','3','4','5']) .map(Number) .filter(n => n % 2 === 0) .reduce((acc, n) => acc + n, 0) .run(); console.log(results); // 6 (2 + 4) Windowed operations (time or count-based)
# Python pseudocode pipeline = ( Pipeline() .source(stream) .window(count=3) .map(lambda window: sum(item['value'] for item in window)) .sink(write_to_db) ) Streaming JSON parsing (large files)
const pipeline = new Pipeline() .fileSource('large.json') .streamJson() .map(processRecord) .sink(saveRecord); Configuration and tuning
- Buffer size: Increase buffer sizes for high-throughput scenarios, decrease for memory-constrained environments.
- Parallelism: Use parallel map/transform operators to utilize multiple cores; beware of ordering implications.
- Serialization: Choose binary formats for smaller footprint and faster parsing when possible.
Example tuning options (Node.js)
const pipeline = new Pipeline({ bufferSize: 1024 * 1024, parallelism: 4 }); Error handling and retries
KahlownLite supports built-in retry strategies and error handlers. Use the onError operator to catch and respond to failures without terminating the entire pipeline.
pipeline .map(asyncFn) .onError((err, item) => { logError(err, item); return retry(item, 3); // pseudocode }); Debugging tools
- Dry-run mode: Validates pipeline topology without executing heavy IO.
- Verbose logging: Trace item flow through stages to identify bottlenecks.
- Metrics: Expose counters for items processed, latency per stage, and memory usage.
Common pitfalls
- Unbounded sources without sinks can exhaust memory — always apply limits or windowing.
- Parallelism without attention to ordering can produce non-deterministic outputs.
- Forgetting to handle backpressure may lead to dropped data or crashes.
Example project — CSV to JSON transformer
Node.js script that reads a CSV file, filters rows, and writes JSON output.
const { Pipeline } = require('kahlownlite'); const fs = require('fs'); new Pipeline() .fileSource('input.csv') .parseCsv({ header: true }) .filter(row => parseInt(row.age, 10) >= 18) .map(row => ({ id: row.id, name: row.name, age: Number(row.age) })) .toFile('output.json') .run() .then(() => console.log('Finished')); Where to go next
- Read the official API reference for detailed operator semantics.
- Explore community examples and plugins for database connectors, cloud sinks, and format parsers.
- Benchmark KahlownLite against your expected workload to choose optimal settings.
If you want, I can: provide a full API cheat-sheet, write example code for your specific environment, or convert the CSV example into Python. Which would you like?