Benchmarks with Bodo


Bodo's parallel computing architecture avoids the task overheads and sequential bottlenecks of driver-executor distributed systems, enabling extreme performance and linear scaling.

Data Engineering for ML: TPCxBB Q26

Customer benchmark for data engineering (ETL and feature engineering): Bodo is 10x faster than optimized Spark on a 125-node cluster (AWS c5n.18xlarge) with 4,500 CPU cores, input data is scale 40,000 of TPCxBB with 52 billion rows (2.5TB data in compressed Parquet format).

Data Engineering: TeraSort

Customer benchmark for data engineering: Bodo is 9x faster than optimized Spark on a 125-node cluster (AWS c5n.18xlarge) with 4,500 CPU cores, input data is scale factor 10,000 of TeraSort with 100B rows (4TB in compressed Parquet format).

Retail Product Analytics

Customer benchmark for filtering data using customized user-input filters and and joining the resulting group back with the original dataset. Bodo is 11x faster than optimized PySpark on a 16-node cluster (AWS c5n.18xlarge) with 576 CPU cores (input data is a 120GB data in compressed Parquet).

End-to-End Machine Learning

Customer benchmark for an End-to-End ML pipeline including Data Load, Data Prep, Feature Engineering, ML Training, ML Prediction. Bodo is 120x faster than PySpark on an r5d.16xlarge AWS node (32 CPU cores).

Retail Price Image Management

Customer benchmark for retail price image management using simulations. Bodo on a 4 node cluster (AWS m5.24xlarge) is 85x faster than multi-processing Python on a single node.