antigravity-lite 0.1.8

Community Version of the B2B Antigravity PySpark Framework. Essential utilities for AWS FinOps and Cloud cost optimization.

🚀 Antigravity Lite (FinOps & AWS Glue Tools)

AWS Financial Auditor and Smart S3 Manager for PySpark Ecosystems

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🛑 The Silent AWS Glue Killer: Spark's Catalyst Optimizer

Have you ever wondered why your massive PySpark cluster just hangs for hours, consuming 100% CPU without writing a single byte of data when processing a Wide Dataframe?

Many Data Engineers blame data skew or bad partitioning, panicking and upscaling AWS Glue Worker instances to expensive G.4X or G.8X tiers. But throwing money at RAM is not the solution. The architectural solution is not buying more RAM; it's isolating the math.

📦 Installation

pip install antigravity-lite

🛠 Included Open-Source Tools

1. Smart S3 Renamer (S3Finalizer - Universal API)

Tired of PySpark polluting your Datalake with part-00000... strings and empty _SUCCESS files? S3Finalizer is a native Boto3 utility that scans raw outputs and renames them sequentially and cleanly without breaking cluster concurrency. It works with Apache Spark, AWS Glue DynamicFrames, and standard S3 files seamlessly.

from antigravity_lite.io.s3_finalizer import S3Finalizer

finalizer = S3Finalizer(bucket_name="my-corporate-datalake")

# Automatically re-sequence and format any outputs natively
finalizer.sequence_files(
    s3_prefix="raw_zone/sales/",
    pattern="ENTERPRISE_REPORT_{seq:04d}.parquet",
    starts_with="",      # Optional: Target specific outputs (e.g. "0000_part")
    ends_with=".parquet",# Optional: Ignore non-parquet files
    contains="part"      # Optional: Filter
)
# Magic Output: ENTERPRISE_REPORT_0001.parquet

2. AWS Glue FinOps Auditor (AgAuditor)

Inject this standalone tool to scan your AWS CloudWatch telemetry and compute exactly how many thousands of dollars you are wasting each month on inflated AWS Worker instances just to keep Spark's Catalyst Optimizer from crashing.

from antigravity_lite.auditor.finops import AgAuditor

# Scan the cluster using CloudWatch and AWS APIs
AgAuditor.run_aws_audit(region="us-east-1", dias_analisis=7)

Console Output: It will accurately map your allocated Workers (G.1X, G.4X) against real compressed JVM Heap usage to reveal your exact financial capital leak.

3. S3 Directory Explorer (AgS3DirectoryLister)

¿Cansado de descubrir que AWS S3 es un espacio plano y no posee "carpetas" reales? Listar jerarquías en Boto3 usando la propiedad CommonPrefixes resulta frustrante. AgS3DirectoryLister abstrae todo el dolor de la paginación nativa y te entrega un árbol de "carpetas" lógicas de forma limpia.

from antigravity_lite.io import AgS3DirectoryLister

explorer = AgS3DirectoryLister()
carpetas_hijas = explorer.list_folders("s3://tu-bucket/datalake/bronze/")

# Imprime un cómodo árbol en tu terminal emulando un `ls`
explorer.print_tree("s3://tu-bucket/datalake/bronze/")

4. Multithreaded S3 Smart Copier (AgS3SmartCopier)

Clonar o agrupar enormes Datalakes en S3 usando scripts tradicionales iterativos asfixia tu red y te toma toda la tarde. Además, prender un clúster de Spark solo para "mover datos" es un desperdicio grosero de facturación de AWS. AgS3SmartCopier levanta un enjambre asíncrono en Python puro ThreadPoolExecutor para transferir miles de archivos aplicando filtros matemáticos, a una fracción del tiempo de un script convencional de Boto3.

from antigravity_lite.io import AgS3SmartCopier

copier = AgS3SmartCopier()

# Copia ultra-rápida y masiva sin arrancar Spark
copier.copy_path(
    origin_path="s3://lago-datos/crudo/",
    dest_path="s3://lago-datos/historico/",
    starts_with="SALES_2026",
    ends_with=".parquet",    # Filtros para ignorar basura oculta
    max_workers=10           # Hilos de la CPU disparados en simultáneo
)

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💎 Commercial Licensing (Antigravity PRO)

The Lite version can tell you you're burning thousands of dollars... Purchasing the Antigravity PRO Enterprise License actually fixes it.

If your AgAuditor report flags an "⚠️ AST/OOM RISK" or your Heap spikes past 85%, you need the DataFrameChunker mathematical engine (Exclusive to the Pro B2B Edition). The enterprise version intercepts Spark's low-level planner and vertically slices the execution plan using Logarithmic Binary Trees (Tree Reduce) to forcibly truncate the AST Lineage. This drops your memory footprint so drastically that you can process half-a-billion operations on tiny G.1X clusters at zero OutOfMemory risk.

💻 Request a Proof-of-Concept or Live Architecture Demo for B2B deployment by connecting via LinkedIn.