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Federico Caselli
fb81f9c8d9
Also replace http://pypi.python.org/pypi with https://pypi.org/project Change-Id: I84b5005c39969a82140706472989f2a30b0c7685
40 lines
1.8 KiB
Python
40 lines
1.8 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 :class:`_engine.Engine` instances, each assigned a "shard id".
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These :class:`_engine.Engine` instances may refer to different databases,
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or different schemas / accounts within the same database, or they can
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even be differentiated only by options that will cause them to access
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different schemas or tables when used.
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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 these examples, different kinds of shards are used against the same basic
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example which accommodates weather data on a per-continent basis. We provide
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example shard_chooser, id_chooser and query_chooser functions. The
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query_chooser illustrates inspection of the SQL expression element in order to
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attempt to determine a 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 <https://www.sqlalchemy.org/trac/wiki/UsageRecipes/EntityName>`_.
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.. autosource::
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"""
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