Optimize memory usage in ShardBulkInferenceActionFilter #124313
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This refactor improves memory efficiency by processing inference requests in batches, capped by a max input length. Changes include: - A new dynamic operator setting to control the maximum batch size in bytes. - Dropping input data from inference responses when the legacy semantic text format isn’t used, saving memory. - Clearing inference results dynamically after each bulk item to free up memory sooner. This is a step toward enabling circuit breakers to better handle memory usage when dealing with large inputs.
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Pinging @elastic/search-inference-team (Team:Search - Inference) |
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kderusso
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Mar 7, 2025
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jan-elastic
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| // ignore delete request | ||
| continue; | ||
| if (useLegacyFormat) { | ||
| var newDocMap = indexRequest.sourceAsMap(); |
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I'll close my PR; it conflicts badly with this.
I'll check whether this resolves the inefficiency I spotted and let you know.
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Mikep86
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…24863) This refactor improves memory efficiency by processing inference requests in batches, capped by a max input length. Changes include: - A new dynamic operator setting to control the maximum batch size in bytes. - Dropping input data from inference responses when the legacy semantic text format isn’t used, saving memory. - Clearing inference results dynamically after each bulk item to free up memory sooner. This is a step toward enabling circuit breakers to better handle memory usage when dealing with large inputs.
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This refactor improves memory efficiency by processing inference requests in batches, capped by a max input length.
Changes include:
This is a step toward enabling circuit breakers to better handle memory usage when dealing with large inputs.