[FIX] core: reduce memory use of BaseModel._flush() #162442
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Pirols
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@cawo-odoo I just pushed a commit with a proposed simplification. |
Motivation: MemoryError exceptions when a large number of records on the same model have dirty fields. Such often happens during upgrades. In the current implementation, the cached data is re-arranged in multiple steps using local data structures. The most problematic is `id_vals[record.id][field.name]`, because it creates a dictionary with a potentially long field name (think studio fields) as key for each dirty record. For thousands of records, this quickly accumulates to 10s or even 100s of MiB in RAM. The idea of this patch is: 1. collect all dirty ids for all dirty fields on the model. This does not cost additional memory, since the OrderedSet of ids per field will be pop()'ed from the cache. 2. Walk over fields and ids collecting all fields and values of each id in the same loop, directly building the "updates", without creating the intermediate data structure. This way, the _flush method only consumes a small amount of memory compared to the memory already consumed by the cache. Using profiling while flushing 50k records in both versions of the code, it has shown that we can save this memory without increasing the runtime. Co-authored-by: Raphael Collet <rco@odoo.com>
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Motivation: MemoryError exceptions when a large number of records on the same model have dirty fields. Such often happens during upgrades.
In the current implementation, the cached data is re-arranged in multiple steps using local data structures. The most problematic is
id_vals[record.id][field.name], because it creates a dictionary with a potentially long field name (think studio fields) as key for each dirty record. For thousands of records, this quickly accumulates to 10s or even 100s of MiB in RAM.The idea of this patch is:
updatesdictionary, without creating the intermediate data structures.This way, the _flush method only consumes a marginal amount of memory compared to the memory already consumed by the cache.
Using profiling while flushing 50k records in both versions of the code, it has shown that the reduction of space complexity does not increase the runtime. As one can see, the vast amount of time is consumed by the SQL operation in both versions of the code:
