Optimize noSQLdask: connection pooling, batched queries, dask.delayed#80
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Optimize noSQLdask: connection pooling, batched queries, dask.delayed#80
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CassandraBag: - Single shared Cassandra session instead of new connection per partition (saves ~100-500ms × npartitions in connection overhead) - Query all keys in one CQL query with IN (...) instead of looping per key (reduces queries from keys × partitions to just partitions) - dask.delayed → pd.DataFrame instead of dask.bag (avoids double materialization: bag → DataFrame → pivot) - extend() instead of list concatenation (O(n) vs O(n^2)) - Configurable fetch_size (default 50000) for large result sets - Context manager support (with ... as bag:) - close() method for explicit resource cleanup MongoBag: - Single shared MongoClient instead of new connection per partition - datetime objects in queries instead of string comparison (allows MongoDB to use time-based indexes efficiently) - dask.delayed → pd.DataFrame instead of dask.bag - Projection support to limit returned fields - getDataFrame() convenience method - Context manager support - Backwards-compatible read_datetime_interval_from_collection() Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
Rewrote both
CassandraBagandMongoBagfor significantly better performance with large datasets.CassandraBag fixes
key IN (...)in one querydask.bag→.to_dataframe().compute().pivot()dask.delayed→pd.DataFrameitems = items + list(rows)items.extend(rows)MongoBag fixes
datetimeobjects in querydask.bagdask.delayed→pd.DataFrameprojectionparamBoth
with CassandraBag(...) as bag:)close()method for explicit cleanupgetDataFrame()convenience method on MongoBagTest plan
pytest— 99 passed, 10 xfailedmkdocs build— clean🤖 Generated with Claude Code