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    <p>Mostly the performance hits (usually taken on a collision) are amortized over all calls. So for most realistic use, you wont get <code>O(n)</code> for every call. In fact, the only case where you would incur an <code>O(n)</code> hit on every call is in the pathological case where every key's hash collides with an existing key's hash value (i.e. the worst possible (or most unfortunate) usage of a hashtable).</p> <p>If for example, you know your set of keys beforehand, and you know that they will not have hash collisions (i.e. all their hashes are unique), then you will not suffer collision cases. The other major <code>O(n)</code> operation is hashtable resizing, but the frequency of this depends on the implementation (expanse factor/hash function/collision resolution scheme, etc.) and it will also vary run-to-run depending on the input set.</p> <p>In either case you can avoid sudden runtime slowdowns if you can pre-populate the dict with all keys. the values can just be set to None, and populated with their real values later on. This should cause the only noticeable performance hit when "priming" the dict with keys initially, and future value insertion should be constant time.</p> <p>A completely different question is how you are intending to read/query the structure? do you need to attach separate values and have access to them via a key? should it be ordered? perhaps a <code>set</code> might be more appropriate than a <code>dict</code>, since you do not really require a <code>key:value</code> mapping. </p> <p><strong>Update:</strong></p> <p>Based on your description in the comments, this is starting to sound more like a job for a database to do, even if you are working with a temporary set. You could use an in-memory relational database (e.g. with SQLite). Additionally, you could use an ORM like SQLAlchemy to interact with the database more pythonically and without having to write SQL.</p> <p>It even sounds like you might be reading the data from a database to start with, so maybe you can leverage that further?</p> <p>Storing/querying/updating a massive number of typed records that are keyed uniquely is exactly what RDBMS's have been specialised for with decades of development and research. Using an in-memory version of a pre-existing relational database (such as SQLite's) will probably be a more pragmatic and sustainable choice. </p> <p>Try using python's built in <a href="http://docs.python.org/2/library/sqlite3.html" rel="nofollow"><code>sqlite3</code></a> module and try the in-memory version by providing <code>":memory:"</code> as the db file path on construction:</p> <pre><code>con = sqlite3.connect(":memory:") </code></pre>
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    1. POOptimizing Worst Case Time complexity to O(1) for python dicts
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    1. USPreet Kukreti
    1. USPreet Kukreti
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    1. COHash table resize cost is fully amortizable; it costs n operations, but you need n inserts first before you *need* to resize.
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      1. USMartijn Pieters
    2. CO@PreetKukreti: pre-populate the dict with all keys and None value is possible. But I dont want to hardcode initialization of dict with 500M keys. I was thinking of adding keys using a for loop but then idk if unique hash would be created. How to ensure that?
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      1. UScodersofthedark
    3. COEach key represent a unique seat in a unique flight. The value would be updated to registration_id of the passenger. Update/Delte/Insert call would always be made by the key. IDK if we can use sets here.
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