Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 00:54 Python dataframe-to-table type_strings 0.368 s 0.309887
2021-10-01 00:50 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.718077
2021-10-01 00:55 Python dataframe-to-table type_simple_features 0.928 s -1.511100
2021-10-01 00:54 Python dataframe-to-table type_integers 0.011 s -1.229206
2021-10-01 00:55 Python dataset-filter nyctaxi_2010-01 4.396 s -0.871733
2021-10-01 00:54 Python dataframe-to-table type_dict 0.012 s -0.414052
2021-10-01 00:52 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.591066
2021-10-01 00:54 Python dataframe-to-table chi_traffic_2020_Q1 19.551 s 1.099493
2021-10-01 00:49 Python csv-read uncompressed, file, fanniemae_2016Q4 1.173 s -0.035901
2021-10-01 00:50 Python csv-read gzip, streaming, fanniemae_2016Q4 14.773 s -0.494007
2021-10-01 00:51 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.914 s -1.355697
2021-10-01 00:49 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.837 s -0.486757
2021-10-01 00:54 Python dataframe-to-table type_nested 2.869 s 2.034554
2021-10-01 00:51 Python csv-read uncompressed, file, nyctaxi_2010-01 1.030 s -0.217439
2021-10-01 00:54 Python dataframe-to-table type_floats 0.011 s 0.209508
2021-10-01 00:52 Python csv-read gzip, streaming, nyctaxi_2010-01 10.804 s -0.958874
2021-10-01 00:58 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.018 s -0.882193
2021-10-01 01:02 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.581 s 1.677582
2021-10-01 01:16 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.028 s 0.095837
2021-10-01 01:11 Python dataset-read async=True, nyctaxi_multi_ipc_s3 179.483 s 0.995309
2021-10-01 01:16 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.018 s 0.062245
2021-10-01 01:16 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 0.998 s 0.511125
2021-10-01 01:11 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.229 s 0.392900
2021-10-01 01:26 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.863 s 0.234196
2021-10-01 01:35 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.196674
2021-10-01 01:36 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.817 s 0.528049
2021-10-01 01:37 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.341 s 0.546784
2021-10-01 01:52 R dataframe-to-table chi_traffic_2020_Q1, R 5.399 s 0.045946
2021-10-01 01:52 R dataframe-to-table type_dict, R 0.053 s -0.375596
2021-10-01 01:52 R dataframe-to-table type_integers, R 0.083 s 1.452018
2021-10-01 01:52 R dataframe-to-table type_floats, R 0.110 s -0.580883
2021-10-01 02:17 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.256 s -0.040282
2021-10-01 02:17 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.142765
2021-10-01 02:19 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -1.030814
2021-10-01 02:20 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.246 s -4.777335
2021-10-01 02:22 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.103713
2021-10-01 02:22 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.510 s 0.454297
2021-10-01 02:25 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.293 s 0.928772
2021-10-01 02:25 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.291 s 1.100725
2021-10-01 02:27 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.842 s -2.365209
2021-10-01 01:26 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.965 s 0.332599
2021-10-01 02:17 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.914 s 0.085351
2021-10-01 02:18 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -0.932921
2021-10-01 02:21 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.253 s -0.537604
2021-10-01 02:29 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -1.024848
2021-10-01 02:49 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.645 s 0.657295
2021-10-01 01:27 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.040 s -1.114931
2021-10-01 02:34 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.674 s 1.351849
2021-10-01 02:36 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s 0.054346
2021-10-01 02:38 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 1.617807
2021-10-01 02:40 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.964 s 1.255197
2021-10-01 02:49 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.681 s -0.334342
2021-10-01 02:49 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.831 s 1.288456
2021-10-01 02:49 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.057364
2021-10-01 01:27 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.690 s 0.453695
2021-10-01 01:34 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.695 s 0.195766
2021-10-01 01:52 R dataframe-to-table type_strings, R 0.489 s 0.841731
2021-10-01 02:33 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.469 s 1.463035
2021-10-01 02:38 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.578 s 1.530421
2021-10-01 02:38 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.891 s 1.502706
2021-10-01 02:40 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.529 s 0.985373
2021-10-01 02:41 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.154 s 1.272493
2021-10-01 02:49 JavaScript Parse readBatches, tracks 0.000 s -1.628491
2021-10-01 02:49 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.046785
2021-10-01 02:49 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.157409
2021-10-01 02:49 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.547677
2021-10-01 02:49 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-10-01 02:49 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.198202
2021-10-01 02:49 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.337599
2021-10-01 01:27 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.283 s -1.056245
2021-10-01 01:33 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.234 s 0.652441
2021-10-01 01:37 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.852 s 0.547330
2021-10-01 01:38 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.164923
2021-10-01 02:17 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.935 s -0.122253
2021-10-01 02:35 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.216337
2021-10-01 02:49 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.484 s 0.332188
2021-10-01 01:28 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.867 s -1.138977
2021-10-01 01:28 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.206 s -3.244819
2021-10-01 02:38 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.213 s -1.529585
2021-10-01 02:49 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s -0.028056
2021-10-01 02:49 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.779468
2021-10-01 02:49 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.562212
2021-10-01 02:49 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.212917
2021-10-01 02:49 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.089252
2021-10-01 01:28 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.315 s -1.138144
2021-10-01 02:38 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.220903
2021-10-01 02:41 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.687254
2021-10-01 02:49 JavaScript Parse Table.from, tracks 0.000 s -1.128381
2021-10-01 02:49 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.685 s -0.438135
2021-10-01 02:49 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.717 s 0.127654
2021-10-01 02:49 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.879 s 0.457889
2021-10-01 02:49 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.989446
2021-10-01 02:49 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.009746
2021-10-01 01:28 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.875 s -2.444790
2021-10-01 01:29 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.930756
2021-10-01 01:29 Python file-read lz4, feather, table, fanniemae_2016Q4 0.593 s 1.714057
2021-10-01 01:30 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 1.456784
2021-10-01 01:31 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.665 s -0.019147
2021-10-01 01:38 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.440 s -0.651535
2021-10-01 01:38 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.839 s 0.000598
2021-10-01 02:20 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.125 s 0.324961
2021-10-01 02:21 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.972 s -0.107004
2021-10-01 02:23 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.847 s 1.036814
2021-10-01 02:27 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.753 s 0.920880
2021-10-01 02:29 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.596 s 0.371604
2021-10-01 02:31 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.829 s 1.476621
2021-10-01 02:39 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.607 s -0.054632
2021-10-01 01:28 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.784 s -1.922700
2021-10-01 01:29 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.900 s -2.215947
2021-10-01 01:35 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.757 s -0.362573
2021-10-01 01:29 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.200 s -1.940309
2021-10-01 01:29 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.051 s -0.276891
2021-10-01 01:35 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.216 s -0.026770
2021-10-01 02:19 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.398 s -0.869397
2021-10-01 02:32 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.800 s 1.597357
2021-10-01 02:37 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.494 s -0.816214
2021-10-01 02:38 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.243964
2021-10-01 02:49 JavaScript Parse serialize, tracks 0.005 s 0.533164
2021-10-01 01:30 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.986 s 0.205788
2021-10-01 01:35 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.798 s 1.309825
2021-10-01 01:38 Python file-write lz4, feather, table, nyctaxi_2010-01 1.843 s -1.623460
2021-10-01 01:53 R dataframe-to-table type_nested, R 0.539 s -0.615481
2021-10-01 02:18 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.920 s -0.158708
2021-10-01 02:39 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.513 s 0.462966
2021-10-01 01:30 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.049 s -0.722250
2021-10-01 01:34 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.373 s -0.254659
2021-10-01 01:36 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.849 s 1.538733
2021-10-01 02:30 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.231 s 0.399847
2021-10-01 02:49 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.126838
2021-10-01 02:49 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.113860
2021-10-01 01:30 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.980 s 0.201134
2021-10-01 01:38 Python wide-dataframe use_legacy_dataset=false 0.620 s -0.478871
2021-10-01 02:21 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.252611
2021-10-01 02:49 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.042128
2021-10-01 01:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.130 s 0.127052
2021-10-01 02:49 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.508638
2021-10-01 01:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.662 s 1.523926
2021-10-01 02:16 R dataframe-to-table type_simple_features, R 275.253 s -0.737272
2021-10-01 02:39 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.605 s 1.230301
2021-10-01 02:41 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.350 s 1.171557
2021-10-01 02:42 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.498 s 0.113470
2021-10-01 02:49 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.050890
2021-10-01 01:32 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.080 s 1.096310
2021-10-01 01:33 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.445 s 1.007219
2021-10-01 02:16 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.245 s 0.085233
2021-10-01 02:40 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.445651
2021-10-01 02:41 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s -0.075645
2021-10-01 02:49 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.891424
2021-10-01 02:49 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.289095