Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 18:29 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.487 s -1.627334
2021-10-01 17:48 Python dataframe-to-table type_floats 0.011 s 0.836811
2021-10-01 18:32 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.973 s -0.992570
2021-10-01 17:44 Python csv-read gzip, streaming, fanniemae_2016Q4 14.871 s -0.447519
2021-10-01 18:11 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.023 s 0.174774
2021-10-01 18:28 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.287 s -1.222719
2021-10-01 18:31 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.747 s -1.170259
2021-10-01 18:32 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.528 s -1.586435
2021-10-01 18:33 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.311 s -0.860656
2021-10-01 18:35 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.329 s 0.310951
2021-10-01 18:26 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.117 s 1.058358
2021-10-01 18:28 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.017 s 1.317051
2021-10-01 18:26 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.257 s 1.260084
2021-10-01 18:36 Python file-write lz4, feather, table, nyctaxi_2010-01 1.796 s 0.825510
2021-10-01 17:52 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.815 s -0.772501
2021-10-01 18:25 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.949 s 1.210280
2021-10-01 18:26 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.837 s -1.308488
2021-10-01 18:36 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.229510
2021-10-01 17:46 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.209863
2021-10-01 17:48 Python dataframe-to-table type_dict 0.012 s 0.712900
2021-10-01 17:48 Python dataframe-to-table type_simple_features 0.910 s 0.213910
2021-10-01 18:25 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.756 s 0.127548
2021-10-01 18:28 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.181 s -0.991450
2021-10-01 18:06 Python dataset-read async=True, nyctaxi_multi_ipc_s3 192.280 s -0.435185
2021-10-01 18:27 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.056 s -0.374103
2021-10-01 18:30 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.633 s -1.107792
2021-10-01 18:32 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.850 s -0.936470
2021-10-01 18:34 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.014 s -1.444753
2021-10-01 17:45 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.494 s 0.574083
2021-10-01 17:45 Python csv-read gzip, streaming, nyctaxi_2010-01 10.513 s 0.419467
2021-10-01 18:24 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.988 s 0.155071
2021-10-01 18:26 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.753 s 1.817576
2021-10-01 18:29 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.971 s -1.524995
2021-10-01 18:33 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.265006
2021-10-01 17:48 Python dataframe-to-table type_strings 0.376 s -0.603501
2021-10-01 17:48 Python dataframe-to-table type_nested 2.881 s 1.331448
2021-10-01 18:28 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.355 s -1.521156
2021-10-01 18:30 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.403 s -1.267119
2021-10-01 18:34 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 10.009 s -0.744951
2021-10-01 18:36 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.805 s 0.313860
2021-10-01 18:11 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.012 s 0.144311
2021-10-01 18:26 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.768 s -1.235139
2021-10-01 18:11 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.018 s 0.251411
2021-10-01 18:26 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.849642
2021-10-01 18:35 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.354 s -0.233571
2021-10-01 18:36 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.650721
2021-10-01 17:43 Python csv-read uncompressed, file, fanniemae_2016Q4 1.169 s 0.094535
2021-10-01 18:27 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.260 s -2.403338
2021-10-01 18:29 Python file-read lz4, feather, table, nyctaxi_2010-01 0.673 s -0.868901
2021-10-01 17:43 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.938 s -0.444861
2021-10-01 17:48 Python dataframe-to-table type_integers 0.011 s 1.656532
2021-10-01 18:27 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.385009
2021-10-01 18:35 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.983 s -0.345068
2021-10-01 17:44 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s 0.129345
2021-10-01 18:24 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.825 s 0.425686
2021-10-01 18:06 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.196 s 0.550630
2021-10-01 17:45 Python csv-read uncompressed, file, nyctaxi_2010-01 1.016 s 0.039783
2021-10-01 17:47 Python dataframe-to-table chi_traffic_2020_Q1 19.383 s 1.806402
2021-10-01 17:48 Python dataset-filter nyctaxi_2010-01 4.352 s 0.431184
2021-10-01 17:56 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.339 s 1.301203
2021-10-01 18:25 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.170 s 1.832772
2021-10-01 18:27 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.953 s -2.354979
2021-10-01 18:33 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.974 s -1.788813
2021-10-01 18:50 R dataframe-to-table chi_traffic_2020_Q1, R 5.441 s -0.662612
2021-10-01 18:50 R dataframe-to-table type_floats, R 0.113 s -1.320095
2021-10-01 18:50 R dataframe-to-table type_nested, R 0.538 s -0.587030
2021-10-01 18:50 R dataframe-to-table type_dict, R 0.060 s -0.938632
2021-10-01 18:50 R dataframe-to-table type_strings, R 0.492 s -0.333203
2021-10-01 18:50 R dataframe-to-table type_integers, R 0.085 s -0.592314
2021-10-01 19:16 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.658156
2021-10-01 19:17 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.793217
2021-10-01 19:17 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.149 s -1.420945
2021-10-01 19:21 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.175 s -1.222159
2021-10-01 19:25 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.833 s -0.258021
2021-10-01 19:14 R dataframe-to-table type_simple_features, R 275.652 s -1.498261
2021-10-01 19:18 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.710089
2021-10-01 19:30 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.905 s -0.680041
2021-10-01 19:14 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.656 s -3.064552
2021-10-01 19:23 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.621 s -1.172380
2021-10-01 19:19 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 0.186194
2021-10-01 19:27 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.411 s -1.680788
2021-10-01 19:32 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.766 s -0.725457
2021-10-01 19:26 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.562 s 0.981971
2021-10-01 19:31 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.583 s -0.927956
2021-10-01 19:16 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.384 s -0.019479
2021-10-01 19:15 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.344894
2021-10-01 19:14 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.305 s -2.726070
2021-10-01 19:15 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.888 s 0.362304
2021-10-01 19:18 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.242 s 0.047616
2021-10-01 19:25 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.053 s -0.858893
2021-10-01 19:29 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.942 s -0.867292
2021-10-01 19:22 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.592 s -0.754645
2021-10-01 19:19 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.946 s 1.423025
2021-10-01 19:28 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.206 s 0.865223
2021-10-01 19:20 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.504 s 0.784353
2021-10-01 19:16 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.913 s 0.359841
2021-10-01 19:14 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.201 s 0.544528
2021-10-01 19:33 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -0.906772
2021-10-01 19:34 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.243 s 1.481510
2021-10-01 19:34 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.484 s 1.058032
2021-10-01 19:37 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.304816
2021-10-01 19:35 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.174 s 1.287464
2021-10-01 19:36 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 1.178947
2021-10-01 19:36 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.871 s 1.369684
2021-10-01 19:36 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.582 s 1.211835
2021-10-01 19:36 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.365189
2021-10-01 19:36 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.224467
2021-10-01 19:36 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 1.086220
2021-10-01 19:37 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.504 s 1.712528
2021-10-01 19:47 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.844697
2021-10-01 19:17 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.164 s 0.618802
2021-10-01 19:37 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.928 s 1.075923
2021-10-01 19:40 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.486 s 0.132098
2021-10-01 19:47 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.610 s -0.284383
2021-10-01 19:38 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.658 s -0.970896
2021-10-01 19:38 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -2.438445
2021-10-01 19:47 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.841 s 0.962696
2021-10-01 19:47 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.190990
2021-10-01 19:47 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.879311
2021-10-01 19:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 1.070823
2021-10-01 19:47 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.732 s 0.082252
2021-10-01 19:47 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.041916
2021-10-01 19:39 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s 0.054973
2021-10-01 19:47 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.656 s 0.482199
2021-10-01 19:47 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.122790
2021-10-01 19:39 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.082187
2021-10-01 19:47 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.380103
2021-10-01 19:47 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.947 s -0.919039
2021-10-01 19:47 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.180873
2021-10-01 19:39 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.163 s 1.057459
2021-10-01 19:47 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.641 s -0.283273
2021-10-01 19:47 JavaScript Parse Table.from, tracks 0.000 s -0.773144
2021-10-01 19:47 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.030237
2021-10-01 19:47 JavaScript Parse readBatches, tracks 0.000 s -0.592662
2021-10-01 19:47 JavaScript Parse serialize, tracks 0.005 s -0.120043
2021-10-01 19:47 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.197725
2021-10-01 19:47 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.224026
2021-10-01 19:47 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.476 s 0.440620
2021-10-01 19:47 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.841986
2021-10-01 19:47 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.603070
2021-10-01 19:47 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.423597
2021-10-01 19:47 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.167225
2021-10-01 19:47 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.044445
2021-10-01 19:47 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.586588
2021-10-01 19:47 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.410548
2021-10-01 19:47 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.030237
2021-10-01 19:47 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.421922
2021-10-01 19:47 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.786336
2021-10-01 19:47 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.436266