xlr8 0.1.9

High-performance database read acceleration layer for MongoDB. Auto-chunks and parallelizes queries across multiple-workers, streams results to parquet with intelligent caching and memory-bounded execution, option to stream data directly to any data lake. Same PyMongo API but dramatically faster results with RUST backend for CPU-heavy tasks.

Accelerate MongoDB analytical queries with parallel execution and Parquet caching

Faster Queries → Less Memory → Real Savings

🦀 Rust-Backed · ⚡ Up to 4x Faster Queries · 📦 10-12x Compression · 📊 Configurable Memory Limits

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Minimal Code Changes

# Before: PyMongo
df = pd.DataFrame(list(collection.find(query)))

# After: XLR8 - just wrap and go!
xlr8_collection = accelerate(collection, schema, mongodb_uri)
df = xlr8_collection.find(query).to_dataframe()

That's it. Same query syntax, same DataFrame output - just faster.

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The Problem

When running analytical queries over large MongoDB collections, you encounter two fundamental bottlenecks:

I/O Bound: PyMongo uses a single cursor, fetching documents one batch at a time. Your CPU sits idle waiting for network round trips.

CPU/GIL Bound: Even with the data in hand, Python's Global Interpreter Lock (GIL) means BSON decoding and DataFrame construction happen on a single core.

These aren't PyMongo limitations - they're inherent to Python's design. XLR8 provides a solution.

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How XLR8 Solves It

XLR8 releases Python's GIL and hands execution to a Rust backend powered by Tokio's async runtime:

1. Query Planning → Splits your query into time-based chunks
2. Parallel Workers → Multiple workers fetch from MongoDB simultaneously
3. BSON → Arrow → Direct conversion without Python overhead
4. Parquet Caching → Results cached for instant reuse
5. DataFrame Assembly → Final merge via DuckDB (GIL-free)

The result? Your analytical queries run significantly faster, especially for large result sets.

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Installation

pip install xlr8

XLR8 requires Python 3.11+ and includes pre-compiled Rust extensions for Linux, macOS, and Windows.

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Quick Start

from pymongo import MongoClient
from xlr8 import accelerate, Schema, Types
from datetime import datetime, timezone, timedelta

# Connect to MongoDB
client = MongoClient("mongodb://localhost:27017")
collection = client["iot"]["sensor_readings"]

# Define your schema
schema = Schema(
    time_field="timestamp",
    fields={
        "timestamp": Types.Timestamp("ms", tz="UTC"),
        "device_id": Types.ObjectId(),
        "reading": Types.Any(),  # Handles int, float, string dynamically
    },
    avg_doc_size_bytes=200,
)

# Wrap collection with XLR8
xlr8_col = accelerate(collection, schema=schema, mongo_uri="mongodb://localhost:27017")

# Query like normal PyMongo
cursor = xlr8_col.find({
    "timestamp": {"$gte": datetime(2024, 1, 1, tzinfo=timezone.utc)}
}).sort("timestamp", 1)

# Get DataFrame - parallel fetch, cached for reuse
df = cursor.to_dataframe(
    chunking_granularity=timedelta(days=7),
    max_workers=8,
)

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Key Features

Feature	Description
🦀 GIL-Free Rust Backend	Python's GIL is released. Rust's Tokio runtime handles async I/O across all cores.
⚡ Parallel MongoDB Fetching	Queries split into time chunks. Each worker has its own MongoDB connection.
💾 Smart Query Cache	Results cached by query hash. Filter cached data by date range.
🔀 DuckDB K-Way Merge	GIL-free sorting across shards - O(N log K) complexity.
🐻‍❄️ Pandas & Polars	to_dataframe() for pandas, to_polars() for Polars.
📊 Memory Control	Set flush_ram_limit_mb to prevent OOM errors on large datasets.
📤 Stream to Data Lakes	stream_to_callback() for S3/GCS ingestion pipelines.

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Performance

Metric	Improvement
Query Speed	Up to 4x faster on large result sets
Compression	10-12x storage reduction with Parquet
Memory	Configurable limits prevent OOM
Repeat Queries	Instant from cache

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Documentation

📖 Full documentation with architecture diagrams: GitHub Repository

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License

Apache 2.0 - See LICENSE for details.