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pull based pandas dataframe syncing

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pull based pandas dataframe syncing

To reduce network consumption, it syncs dataframe from the other nodes only on demand. When your task is divide and conquer style, you should consider dask instead.


  • Once the cache has been synced, it will not call remotes. So cache's locality is 1.
  • Ideal situations is that you need to read some dataframe multiple times on serveral nodes and the data frame should be updated frequently.
  • Only unique str name is required configuration when you add a new dataframe on the system.
  • No configuration, no operation is needed when a new node is added and a node is crashed and restored.
  • No configuration, no operation makes it be easy to scale up in the cloud.


  • Coordination via zookeeper
  • Synchronize files via http POST

Start Service

$ uvicorn pullframe.sender:app


Load / Save

from pullframe import pullframe

with pullframe(hosts, directory, sync_timeo 60.0) as pf:
    # set start as None if you want to load from the very beginning
    # set end as None if you want to load from the very ending
    df = pf.load(name, start: Optional[datetime], end: Optional[datetime]), df)


  • Check cache discrepency/corruption between nodes.
  • Stable backup using Amazon S3 / Google cloud storage.
  • Replace zookeeper client to zake (fake kazoo client) during tests.


  • zookeeper
  • the dataframe's index should be datetime
  • linux
  • python>=3.7
  • python = "^3.7"
  • pandas = "^1.0.0"
  • tables = "^3.6.1"
  • fastapi = "^0.58.0"
  • aiofiles = "^0.5.0"
  • kazoo = "^2.7.0"

Free software: MIT License


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