This was POC only. There is a much better project
called Faust (that is, if you can work with Python's async
).
This python 3 library provides a persistent dict-like data structure. Key-value
pairs are persisted in Kafka, and optionally can be cached locally in RocksDB. For
smaller datasets it is possible to cache in memory by setting use_rocksdb=False
(see below).
Multiple clients can share the same topic, but there are no consistency guarantees - last write wins.
Using the conda
package manager is the quickest way to get going
without building anything:
# bootstrap the conda system
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
/bin/bash Miniconda3-latest-Linux-x86_64.sh
# point to our conda channel
echo "channels:\n - ActivisionGameScience\n - defaults" > ~/.condarc
# create and activate an environment
conda create -n fooenv python=3.6 kafka-backed-dict ipython -y
source activate fooenv
# start ipython and you're cooking!
The API is similar to that of a dict:
from kafka_backed_dict import KafkaBackedDict
# `db_dir` is where the RocksDB database will be stored
# (NOTE: RocksDB doesn't play nice with Windows filesystem)
# if you only want in-mem then pass `use_rocksdb=False` instead
d = KafkaBackedDict('my.kafkabootstrapserver.com:9092', 'my.kafkatopic.1', db_dir='/tmp', read_only=False)
# keys are encoded into byte strings (so 'key1' becomes b'key1', and 5 becomes b'5')
# values go through a round-trip json conversion - if you don't like this then you
# can always store raw bytes yourself, e.g. b'24321'
d['key1'] = 234
print(d['key1']) # prints 234
d['key2'] = b'24321'
print(d['key2']) # prints b'24321'
d['key3'] = {'foo': 'bar', 1: 5}
print(d['key3']) # prints {'foo': 'bar', '1': 5}... notice that 1 was changed to '1' by the json converter
# you can delete keys easily
del(d['key1'])
d['key1'] # raises KeyError
# you can iterate over all keys
for k in d.keys():
print(k)
print(d[k])
You can build and install manually with the following command:
VERSION="0.1.0" python setup.py install
where 0.1.0
should be replaced with whatever tag you checked out.
A conda build recipe is also provided (currently only works in Linux). Assuming you have your environment set up (see e.g. https://github.com/ActivisionGameScience/ags_conda_recipes.git), you can build the package by running
VERSION="0.1.0" conda build conda_recipe
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Copyright (c) 2016, Activision Publishing, Inc.
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