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    <p>"Do child processes spawned via multiprocessing share objects created earlier in the program?"</p> <p>No. </p> <p>Processes have independent memory space.</p> <p><strong>Solution 1</strong></p> <p>To make best use of a large structure with lots of workers, do this.</p> <ol> <li><p>Write each worker as a "filter" -- reads intermediate results from stdin, does work, writes intermediate results on stdout.</p></li> <li><p>Connect all the workers as a pipeline:</p> <pre><code>process1 &lt;source | process2 | process3 | ... | processn &gt;result </code></pre></li> </ol> <p>Each process reads, does work and writes.</p> <p>This is remarkably efficient since all processes are running concurrently. The writes and reads pass directly through shared buffers between the processes.</p> <hr> <p><strong>Solution 2</strong></p> <p>In some cases, you have a more complex structure -- often a "fan-out" structure. In this case you have a parent with multiple children.</p> <ol> <li><p>Parent opens source data. Parent forks a number of children.</p></li> <li><p>Parent reads source, farms parts of the source out to each concurrently running child.</p></li> <li><p>When parent reaches the end, close the pipe. Child gets end of file and finishes normally.</p></li> </ol> <p>The child parts are pleasant to write because each child simply reads <code>sys.stdin</code>. </p> <p>The parent has a little bit of fancy footwork in spawning all the children and retaining the pipes properly, but it's not too bad.</p> <p>Fan-in is the opposite structure. A number of independently running processes need to interleave their inputs into a common process. The collector is not as easy to write, since it has to read from many sources. </p> <p>Reading from many named pipes is often done using the <code>select</code> module to see which pipes have pending input.</p> <hr> <p><strong>Solution 3</strong></p> <p>Shared lookup is the definition of a database. </p> <p>Solution 3A -- load a database. Let the workers process the data in the database.</p> <p>Solution 3B -- create a very simple server using <a href="http://werkzeug.pocoo.org/" rel="noreferrer">werkzeug</a> (or similar) to provide WSGI applications that respond to HTTP GET so the workers can query the server.</p> <hr> <p><strong>Solution 4</strong></p> <p>Shared filesystem object. Unix OS offers shared memory objects. These are just files that are mapped to memory so that swapping I/O is done instead of more convention buffered reads.</p> <p>You can do this from a Python context in several ways</p> <ol> <li><p>Write a startup program that (1) breaks your original gigantic object into smaller objects, and (2) starts workers, each with a smaller object. The smaller objects could be pickled Python objects to save a tiny bit of file reading time.</p></li> <li><p>Write a startup program that (1) reads your original gigantic object and writes a page-structured, byte-coded file using <code>seek</code> operations to assure that individual sections are easy to find with simple seeks. This is what a database engine does -- break the data into pages, make each page easy to locate via a <code>seek</code>.</p> <p>Spawn workers with access this this large page-structured file. Each worker can seek to the relevant parts and do their work there.</p></li> </ol>
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    1. POmultiprocessing: sharing a large read-only object between processes?
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    1. USS.Lott
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    1. COMy processes aren't really fitlers; they're all the same, just processing different pieces of data.
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      1. USParand
    2. COThey can often be structured as filters. They read their piece of data, do their work, and write their result for later processing.
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    3. COI like your solution, but what happens with the blocking I/O? What if the parent blocks reading/writing from/to one of its children? Select does notify you that you can write, but it doesn't say how much. Same for reading.
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      1. USCristian Ciupitu
 

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