Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 12:31 Python csv-read gzip, streaming, fanniemae_2016Q4 14.717 s 0.771844
2021-10-06 12:35 Python dataframe-to-table type_integers 0.011 s 0.789040
2021-10-06 12:53 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.171 s 0.811981
2021-10-06 12:35 Python dataframe-to-table chi_traffic_2020_Q1 19.606 s 0.254459
2021-10-06 12:35 Python dataframe-to-table type_dict 0.012 s -0.747081
2021-10-06 12:43 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.985 s 0.722363
2021-10-06 12:30 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.775 s 0.829833
2021-10-06 12:52 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.902 s 0.443606
2021-10-06 12:39 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.292 s 0.765639
2021-10-06 12:32 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s 0.053031
2021-10-06 12:32 Python csv-read uncompressed, file, nyctaxi_2010-01 1.023 s -1.010179
2021-10-06 12:33 Python csv-read gzip, file, nyctaxi_2010-01 9.050 s -1.796623
2021-10-06 12:57 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.005 s 0.426766
2021-10-06 12:30 Python csv-read uncompressed, file, fanniemae_2016Q4 1.185 s -0.636668
2021-10-06 12:36 Python dataframe-to-table type_simple_features 0.912 s 0.044221
2021-10-06 12:33 Python csv-read gzip, streaming, nyctaxi_2010-01 10.710 s -0.660356
2021-10-06 12:36 Python dataset-filter nyctaxi_2010-01 4.359 s 0.352311
2021-10-06 12:32 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.779 s -0.924140
2021-10-06 12:57 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.005 s 0.454728
2021-10-06 12:35 Python dataframe-to-table type_floats 0.011 s 1.370706
2021-10-06 12:57 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.001 s 0.325028
2021-10-06 12:35 Python dataframe-to-table type_strings 0.373 s -0.356986
2021-10-06 12:36 Python dataframe-to-table type_nested 2.870 s 0.934971
2021-10-06 13:08 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.010 s 0.018908
2021-10-06 13:13 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.110 s 0.631972
2021-10-06 13:33 R dataframe-to-table type_strings, R 0.499 s -3.551823
2021-10-06 14:14 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.460 s 1.173435
2021-10-06 14:18 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s 0.619662
2021-10-06 14:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.511 s 0.845383
2021-10-06 14:22 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.504 s 0.074279
2021-10-06 14:30 JavaScript Parse serialize, tracks 0.005 s -0.869336
2021-10-06 14:30 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497214
2021-10-06 14:30 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.026893
2021-10-06 13:11 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.055 s -0.284207
2021-10-06 13:11 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.999 s 2.328025
2021-10-06 13:19 Python file-write lz4, feather, table, nyctaxi_2010-01 1.783 s 1.534295
2021-10-06 13:33 R dataframe-to-table chi_traffic_2020_Q1, R 5.473 s -1.531019
2021-10-06 13:58 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.730218
2021-10-06 14:06 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 0.789264
2021-10-06 14:17 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.247 s 0.526489
2021-10-06 13:10 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.157 s -0.717972
2021-10-06 13:10 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.935 s -0.932041
2021-10-06 14:02 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.985 s -0.161901
2021-10-06 14:05 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.276 s 0.796560
2021-10-06 14:30 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.202706
2021-10-06 13:10 Python file-read lz4, feather, table, fanniemae_2016Q4 0.590 s 2.193322
2021-10-06 13:12 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.159649
2021-10-06 13:18 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.841 s 1.145299
2021-10-06 14:21 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s 0.314103
2021-10-06 13:20 Python wide-dataframe use_legacy_dataset=false 0.620 s 0.293246
2021-10-06 13:33 R dataframe-to-table type_floats, R 0.113 s -1.315575
2021-10-06 14:00 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.119 s 0.625366
2021-10-06 14:08 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.826 s 1.152344
2021-10-06 14:30 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.253677
2021-10-06 14:30 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.314599
2021-10-06 13:09 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.296 s -0.300281
2021-10-06 13:16 Python file-write lz4, feather, table, fanniemae_2016Q4 1.173 s -0.768799
2021-10-06 13:57 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.242 s 0.272699
2021-10-06 13:11 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.328 s -1.260173
2021-10-06 13:20 Python wide-dataframe use_legacy_dataset=true 0.397 s -1.689824
2021-10-06 14:03 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.539 s -0.463701
2021-10-06 14:30 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.562 s -0.224478
2021-10-06 13:10 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.295 s -0.715130
2021-10-06 13:12 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.350046
2021-10-06 13:18 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.822 s 0.630630
2021-10-06 14:09 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.586 s -0.105998
2021-10-06 14:13 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.787 s 1.255633
2021-10-06 14:18 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.178 s 0.396300
2021-10-06 14:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.602 s 0.734364
2021-10-06 14:21 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.634 s -0.623222
2021-10-06 14:30 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.163112
2021-10-06 14:30 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.734060
2021-10-06 14:30 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.889 s -0.227492
2021-10-06 14:30 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.499722
2021-10-06 14:30 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.282433
2021-10-06 13:19 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.304 s 0.536614
2021-10-06 14:08 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.751 s 0.698587
2021-10-06 14:17 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.482 s 1.669024
2021-10-06 14:30 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.771304
2021-10-06 14:30 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.603468
2021-10-06 14:30 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.583710
2021-10-06 14:30 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.490 s 0.389130
2021-10-06 13:16 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.377 s -0.307749
2021-10-06 14:00 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.169 s 0.340032
2021-10-06 14:20 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.708409
2021-10-06 13:11 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.238 s -0.901088
2021-10-06 13:12 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.333 s -1.237232
2021-10-06 13:33 R dataframe-to-table type_integers, R 0.086 s -1.395171
2021-10-06 13:57 R dataframe-to-table type_simple_features, R 276.536 s -2.816548
2021-10-06 14:04 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.850 s 0.778550
2021-10-06 14:16 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.058777
2021-10-06 14:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.186 s -1.152509
2021-10-06 14:30 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.198442
2021-10-06 14:30 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.269524
2021-10-06 13:09 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.833 s -0.216259
2021-10-06 13:10 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.888 s -1.252551
2021-10-06 13:19 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.334 s 0.938436
2021-10-06 13:59 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.868727
2021-10-06 13:59 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.370 s 0.884385
2021-10-06 14:01 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.260 s -1.059480
2021-10-06 14:22 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -2.585664
2021-10-06 14:30 JavaScript Parse Table.from, tracks 0.000 s 0.206333
2021-10-06 13:10 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.821 s -1.346361
2021-10-06 13:17 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.779 s 0.678773
2021-10-06 13:19 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.803 s 0.277300
2021-10-06 13:33 R dataframe-to-table type_nested, R 0.540 s -0.746145
2021-10-06 13:57 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.907 s 0.309617
2021-10-06 13:58 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.902 s 0.225191
2021-10-06 14:21 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.111 s -2.486537
2021-10-06 14:30 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.136462
2021-10-06 14:30 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.151665
2021-10-06 13:08 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.523 s -3.940733
2021-10-06 13:14 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.293 s 0.314546
2021-10-06 13:17 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.767 s 1.310604
2021-10-06 13:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.087107
2021-10-06 14:00 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.592962
2021-10-06 14:01 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.406074
2021-10-06 14:19 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.273255
2021-10-06 14:30 JavaScript Parse readBatches, tracks 0.000 s 0.412728
2021-10-06 13:08 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.707 s 0.384628
2021-10-06 13:09 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.259 s -0.407709
2021-10-06 14:15 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.648 s 1.287463
2021-10-06 13:13 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.945 s -1.061331
2021-10-06 13:15 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.447 s 0.753831
2021-10-06 14:02 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s 0.030628
2021-10-06 14:10 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.148074
2021-10-06 13:16 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.259 s -0.305150
2021-10-06 14:21 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.484 s -1.801340
2021-10-06 14:30 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.708 s 0.217514
2021-10-06 14:30 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.639802
2021-10-06 13:33 R dataframe-to-table type_dict, R 0.050 s 0.037772
2021-10-06 13:59 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.903 s 0.973592
2021-10-06 14:22 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.182 s 0.754202
2021-10-06 14:30 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.608 s -0.278808
2021-10-06 14:30 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.641 s 0.827332
2021-10-06 14:30 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497214
2021-10-06 14:30 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.389365
2021-10-06 14:30 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.023918
2021-10-06 13:09 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.991 s 0.157824
2021-10-06 13:12 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.457 s -1.110066
2021-10-06 13:15 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.721 s 0.028597
2021-10-06 13:16 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.705 s 0.183513
2021-10-06 14:20 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.928 s 0.742968
2021-10-06 14:11 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.227 s -0.241297
2021-10-06 14:19 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.865 s 0.796537
2021-10-06 14:30 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.748304
2021-10-06 14:12 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.823 s 1.136117
2021-10-06 14:19 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.564 s 0.862700
2021-10-06 14:30 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.901 s 0.064370