Skip to main content

python interface for interacting with flashbots mempool dumpster

Project description

image

absorb 🧽🫧🫧

the sovereign dataset manager

absorb makes it easy to 1) collect, 2) query, 3) manage, and 4) customize datasets from nearly any data source

Features

  • limitless dataset library: access to millions of datasets across 20+ diverse data sources
  • intuitive cli+python interfaces: collect or query any dataset in a single line of code
  • maximal modularity: built on open standards for frictionless integration with other tools
  • easy extensibility: add new datasets or data sources with just a few lines of code

Contents

  1. Installation
  2. Example Usage
    1. Command Line
    2. Python
  3. Supported Data Sources
  4. Output Format
  5. Configuration

Installation

basic installation

uv tool install paradigm_absorb

install with all extras

uv tool install paradigm_absorb[test,datasources,interactive]

install from source

git clone git@github.com:paradigmxyz/absorb.git
uv tool install --editable .[test,datasources,interactive]

Example Usage

Example Command Line Usage

# collect dataset and save as local files
absorb collect kalshi

# list datasets that are collected or available
absorb ls

# show schemas of dataset
absorb schema kalshi

# create new custom dataset
absorb new custom_dataset

# upload custom dataset
absorb upload custom_dataset

Example Python Usage

import absorb

# collect dataset and save as local files
absorb.collect('kalshi')

# list datasets that are collected or available
datasets = absorb.list()

# get schemas of dataset
schema = absorb.schema('kalshi')

# load dataset as polars DataFrame
df = absorb.load('kalshi')

# scan dataset as polars LazyFrame
lf = absorb.scan('kalshi')

# create new custom dataset
absorb.new('custom_dataset')

# upload custom dataset
absorb.upload('custom_dataset')

Supported Data Sources

absorb collects data from each of these sources:

To list all available datasets and data sources, type absorb ls on the command line.

Output Format

To display information about the schema and other metadata of a dataset, type absorb help <DATASET> on the command line.

absorb stores each dataset as a collection of parquet files.

Datasets can be stored in any location on your disks, and absorb will use symlinks to organize those files in the ABSORB_ROOT tree.

the ABSORB_ROOT filesystem directory is organized as:

{ABSORB_ROOT}/
    datasets/
        <source>/
            tables/
                <datatype>/
                    {filename}.parquet
                table_metadata.json
            repos/
                {repo_name}/
    absorb_config.json

Configuration

absorb uses a config file to specify which datasets to track.

Schema of absorb_config.json:

{
    'tracked_tables': list[TableDict]
}

schema of dataset_config.json:

{
    "name": str,
    "definition": str,
    "parameters": dict[str, Any],
    "repos": [str]
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

paradigm_absorb-0.3.0.tar.gz (66.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

paradigm_absorb-0.3.0-py3-none-any.whl (96.4 kB view details)

Uploaded Python 3

File details

Details for the file paradigm_absorb-0.3.0.tar.gz.

File metadata

  • Download URL: paradigm_absorb-0.3.0.tar.gz
  • Upload date:
  • Size: 66.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.0

File hashes

Hashes for paradigm_absorb-0.3.0.tar.gz
Algorithm Hash digest
SHA256 b8503e13d5330c326359320059bdcdb60ea1e1ecd1cb40c5bc2f4af0fb19bb04
MD5 dfe9e4dfffb3f42db857c6ae19223f63
BLAKE2b-256 17655b122a4acda89ba8805ddd250811cc854ddcca76b8957759834ac4a4f0d4

See more details on using hashes here.

File details

Details for the file paradigm_absorb-0.3.0-py3-none-any.whl.

File metadata

File hashes

Hashes for paradigm_absorb-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 498212d81a155dee65a9889cb6142038ed27e41949a7884da7316bf01b3911fa
MD5 b48f86f3f0af137729b8252ed9971c08
BLAKE2b-256 fb94fe0e1417084aec336b24f7676d8dad83e5f68b947120515770cbd8821126

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page