Comments (3)
I knew Yarn but it still separates real time raw input data generation from map-reduce? In other words, it does my job in two individual steps: 1. run my python module and generate raw inputs and store then in text files 2. use these text files in map-reduce-cluster.
from disco.
In theory you could run the map on empty blobs (as many as your module needs) and just yield
the results of your module to the reduce function instead of anything from the file.
from disco.
thanks. it seems work on me.
from disco.
Related Issues (20)
- goldrush is aquired via git: protocol instead of https - causes errors in some environments HOT 2
- Copyright information HOT 2
- Project status HOT 2
- Disabling colored output on the console HOT 1
- Can't run example from documentation HOT 1
- Each disco node sees itself as the master HOT 1
- Occasional worker.py: Text file busy
- DNS mappings to IP addresses not being cached? HOT 1
- GC failure after removing a dead node HOT 1
- disco.error.DiscoError: Failed to submit jobpack: Job failed to start: invalid_job_inputs HOT 1
- Status of wiki page: "DDFS Evolution" HOT 1
- Compiling folsomite error HOT 7
- ValueError: too many values to unpack
- Fails to compile on Centos 6 HOT 1
- make test fails on python3.5 with error ImportError: No module named 'urlparse' HOT 1
- Erlang/OTP 20 installation issues / unrecommended BIF
- Setting polling interval to 0.1 loops jobtask execution
- Time to give some more love to disco?
- is this project is officially dead? HOT 3
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from disco.