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Mike Bayer
1e278de4cc
Applied on top of a pure run of black -l 79 in I7eda77fed3d8e73df84b3651fd6cfcfe858d4dc9, this set of changes resolves all remaining flake8 conditions for those codes we have enabled in setup.cfg. Included are resolutions for all remaining flake8 issues including shadowed builtins, long lines, import order, unused imports, duplicate imports, and docstring issues. Change-Id: I4f72d3ba1380dd601610ff80b8fb06a2aff8b0fe
33 lines
1.5 KiB
Python
33 lines
1.5 KiB
Python
"""A basic example of using the SQLAlchemy Sharding API.
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Sharding refers to horizontally scaling data across multiple
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databases.
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The basic components of a "sharded" mapping are:
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* multiple databases, each assigned a 'shard id'
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* a function which can return a single shard id, given an instance
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to be saved; this is called "shard_chooser"
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* a function which can return a list of shard ids which apply to a particular
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instance identifier; this is called "id_chooser".If it returns all shard ids,
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all shards will be searched.
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* a function which can return a list of shard ids to try, given a particular
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Query ("query_chooser"). If it returns all shard ids, all shards will be
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queried and the results joined together.
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In this example, four sqlite databases will store information about weather
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data on a database-per-continent basis. We provide example shard_chooser,
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id_chooser and query_chooser functions. The query_chooser illustrates
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inspection of the SQL expression element in order to attempt to determine a
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single shard being requested.
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The construction of generic sharding routines is an ambitious approach
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to the issue of organizing instances among multiple databases. For a
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more plain-spoken alternative, the "distinct entity" approach
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is a simple method of assigning objects to different tables (and potentially
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database nodes) in an explicit way - described on the wiki at
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`EntityName <http://www.sqlalchemy.org/trac/wiki/UsageRecipes/EntityName>`_.
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.. autosource::
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"""
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