Parallel Query Execution

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==Purpose of Parallelism==
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==Purpose==
 
Postgres currently supports full parellism in client-side code.  Applications can open multiple database connections and manage them asyncronously, or via threads.
 
Postgres currently supports full parellism in client-side code.  Applications can open multiple database connections and manage them asyncronously, or via threads.
  
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*  server-side languages can potentially do parallel operations
 
*  server-side languages can potentially do parallel operations
  
==Challenges of Parallelism==
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==Challenges==
 
For parallelism to be added to a single-threaded task, the task must be able to be broken into sufficiently-large parts and executed independently.  (If the sub-parts are too small, the overhead of doing parallelism overwhelms the benefits of parallelism.)  Unfortunately, unlike a GUI application, the Postgres backend executes a query by performing many small tasks that must be executed in sequence, e.g. parser, planner, executor.
 
For parallelism to be added to a single-threaded task, the task must be able to be broken into sufficiently-large parts and executed independently.  (If the sub-parts are too small, the overhead of doing parallelism overwhelms the benefits of parallelism.)  Unfortunately, unlike a GUI application, the Postgres backend executes a query by performing many small tasks that must be executed in sequence, e.g. parser, planner, executor.
  
 
This means that databases allow parallelism only in limited situations, mostly for large queries that can become CPU or I/O bound.  For example, it is unlikely that selecting a row based on a primary key would benefit from parallelism.  In contrast, large queries can often benefit from parallelism.
 
This means that databases allow parallelism only in limited situations, mostly for large queries that can become CPU or I/O bound.  For example, it is unlikely that selecting a row based on a primary key would benefit from parallelism.  In contrast, large queries can often benefit from parallelism.
  
==Benefits of Parallelism==
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==Benefits==
 
There are three possible benefits of parallelism:
 
There are three possible benefits of parallelism:
  
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* using multiple CPUs and I/O channels
 
* using multiple CPUs and I/O channels
  
==Parallelism Approaches==
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==Approaches==
 
There are several methods to add parallelism:
 
There are several methods to add parallelism:
  
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*  create full backends that can execute parts of a query in parallel and return results
 
*  create full backends that can execute parts of a query in parallel and return results
  
==Parallelism Opportunties==
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==Opportunties==
 
Parallel opportunities include:
 
Parallel opportunities include:
  

Revision as of 04:29, 15 January 2013

This is currently under development. See the ToDo list.

Contents

Purpose

Postgres currently supports full parellism in client-side code. Applications can open multiple database connections and manage them asyncronously, or via threads.

On the server-side, there is already some parallelism:

  • server-side languages can potentially do parallel operations

Challenges

For parallelism to be added to a single-threaded task, the task must be able to be broken into sufficiently-large parts and executed independently. (If the sub-parts are too small, the overhead of doing parallelism overwhelms the benefits of parallelism.) Unfortunately, unlike a GUI application, the Postgres backend executes a query by performing many small tasks that must be executed in sequence, e.g. parser, planner, executor.

This means that databases allow parallelism only in limited situations, mostly for large queries that can become CPU or I/O bound. For example, it is unlikely that selecting a row based on a primary key would benefit from parallelism. In contrast, large queries can often benefit from parallelism.

Benefits

There are three possible benefits of parallelism:

  • using multiple CPUs
  • using multiple I/O channels
  • using multiple CPUs and I/O channels

Approaches

There are several methods to add parallelism:

  • user fork (or a thread on Windows) and only call libc and parallel-specific functions to do parallel computation or I/O. This avoids the problem of trying to make the existing backend code thread-safe.
  • same as above, but modify some existing backend modules to be fork/thread-safe, with or without shared memory access; this might allow entire executor node trees to be run in parallel
  • create full backends that can execute parts of a query in parallel and return results

Opportunties

Parallel opportunities include:

  • sorting
  • tablespaces
  • partitions
  • multi-table queries
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