Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 05:26 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.038 s -0.002748
2021-10-01 05:37 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.935 s 1.592800
2021-10-01 05:38 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.114 s 1.247537
2021-10-01 05:40 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.180 s -0.615011
2021-10-01 05:42 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.408 s -1.279743
2021-10-01 05:45 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.766 s -0.486573
2021-10-01 05:45 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.250 s -0.352374
2021-10-01 05:02 Python csv-read gzip, streaming, nyctaxi_2010-01 10.505 s 0.149722
2021-10-01 05:05 Python dataframe-to-table type_simple_features 0.909 s 0.129213
2021-10-01 05:38 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.859 s -3.265893
2021-10-01 05:39 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.025 s 0.690056
2021-10-01 05:02 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -0.825976
2021-10-01 05:08 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.586 s -0.488037
2021-10-01 05:38 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.789715
2021-10-01 05:39 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.249 s -4.452529
2021-10-01 05:48 Python wide-dataframe use_legacy_dataset=true 0.397 s -0.630469
2021-10-01 06:34 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.609 s -0.746213
2021-10-01 06:44 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.245404
2021-10-01 06:47 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 1.934585
2021-10-01 06:49 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.599 s 0.074532
2021-10-01 06:50 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.351 s 1.403929
2021-10-01 06:58 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -3.109192
2021-10-01 06:58 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.324826
2021-10-01 05:00 Python csv-read gzip, streaming, fanniemae_2016Q4 14.879 s -0.582639
2021-10-01 05:01 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s 0.011223
2021-10-01 05:01 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.513 s 0.166575
2021-10-01 05:04 Python dataframe-to-table type_integers 0.011 s 0.063488
2021-10-01 05:04 Python dataframe-to-table type_floats 0.011 s 0.717631
2021-10-01 05:05 Python dataframe-to-table type_nested 2.860 s 4.140274
2021-10-01 05:36 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.871 s 0.234796
2021-10-01 05:37 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.707 s 0.384177
2021-10-01 05:43 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.751 s -1.169583
2021-10-01 05:44 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.289 s 0.478256
2021-10-01 05:46 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.920 s -0.098209
2021-10-01 06:25 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.203 s 0.495433
2021-10-01 06:26 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.206 s 0.477134
2021-10-01 04:59 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.932 s -0.564538
2021-10-01 05:01 Python csv-read uncompressed, file, nyctaxi_2010-01 0.999 s 0.335874
2021-10-01 05:12 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.570 s 1.956547
2021-10-01 05:26 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.986 s 0.720301
2021-10-01 05:26 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.002 s 0.270242
2021-10-01 05:37 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.990 s 0.161252
2021-10-01 05:38 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.260 s 1.298274
2021-10-01 05:38 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.862 s -4.365623
2021-10-01 05:40 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.309 s -1.166950
2021-10-01 05:40 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.301 s -1.179962
2021-10-01 06:01 R dataframe-to-table type_dict, R 0.062 s -1.269577
2021-10-01 06:38 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.522463
2021-10-01 05:04 Python dataframe-to-table type_strings 0.372 s -0.035701
2021-10-01 05:05 Python dataset-filter nyctaxi_2010-01 4.354 s 0.254650
2021-10-01 05:41 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.452 s -1.396878
2021-10-01 05:44 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.950 s -0.845622
2021-10-01 06:02 R dataframe-to-table type_floats, R 0.114 s -1.744123
2021-10-01 06:28 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.054 s 0.335866
2021-10-01 06:32 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.181 s -1.248658
2021-10-01 06:37 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.493762
2021-10-01 06:47 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.693639
2021-10-01 06:47 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.869 s 1.829125
2021-10-01 06:50 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.662352
2021-10-01 06:58 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.274 s 3.438752
2021-10-01 06:58 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.511527
2021-10-01 06:58 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.819 s 1.594668
2021-10-01 06:58 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.143670
2021-10-01 05:00 Python csv-read uncompressed, file, fanniemae_2016Q4 1.186 s -0.210612
2021-10-01 05:04 Python dataframe-to-table type_dict 0.012 s 1.298701
2021-10-01 05:38 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.762 s 1.549908
2021-10-01 05:45 Python file-write lz4, feather, table, fanniemae_2016Q4 1.153 s 0.746816
2021-10-01 05:45 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.931 s -0.973431
2021-10-01 05:46 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.019 s -1.584584
2021-10-01 05:48 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.246728
2021-10-01 06:01 R dataframe-to-table chi_traffic_2020_Q1, R 5.445 s -0.709300
2021-10-01 06:27 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.914 s 0.159075
2021-10-01 06:30 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.224 s 0.942062
2021-10-01 06:31 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.972 s -0.068217
2021-10-01 06:38 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.562 s 1.063342
2021-10-01 05:21 Python dataset-read async=True, nyctaxi_multi_ipc_s3 177.727 s 1.180404
2021-10-01 05:41 Python file-read lz4, feather, table, nyctaxi_2010-01 0.663 s 1.469914
2021-10-01 05:47 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.963 s -0.148310
2021-10-01 06:27 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.402124
2021-10-01 06:27 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.419738
2021-10-01 06:31 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.485 s 1.547727
2021-10-01 06:40 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.944 s -0.809114
2021-10-01 06:48 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.509 s 0.916151
2021-10-01 06:49 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.417576
2021-10-01 06:51 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.175 s 1.505245
2021-10-01 06:58 JavaScript Parse Table.from, tracks 0.000 s -0.809117
2021-10-01 06:58 JavaScript Parse readBatches, tracks 0.000 s -0.675218
2021-10-01 06:58 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -3.248567
2021-10-01 06:58 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.208248
2021-10-01 06:58 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.002729
2021-10-01 06:58 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.489355
2021-10-01 05:22 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.181 s 0.584559
2021-10-01 05:37 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.173 s 1.840074
2021-10-01 05:42 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.656 s -1.156705
2021-10-01 05:47 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s 0.010554
2021-10-01 06:01 R dataframe-to-table type_strings, R 0.491 s -0.176245
2021-10-01 06:02 R dataframe-to-table type_nested, R 0.536 s 0.476693
2021-10-01 06:29 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.139 s -0.610595
2021-10-01 06:31 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.672 s 0.874663
2021-10-01 06:41 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.899 s -0.359210
2021-10-01 06:46 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.228417
2021-10-01 06:50 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.470 s 0.774716
2021-10-01 06:58 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.222 s 3.507349
2021-10-01 06:58 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.700 s -0.439847
2021-10-01 05:39 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.942 s -4.116307
2021-10-01 05:41 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.956 s -1.372119
2021-10-01 05:47 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.334 s 0.220765
2021-10-01 05:48 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.837 s 0.027603
2021-10-01 06:01 R dataframe-to-table type_integers, R 0.085 s -0.015521
2021-10-01 06:25 R dataframe-to-table type_simple_features, R 274.679 s 0.285537
2021-10-01 06:26 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.859 s 0.589458
2021-10-01 06:26 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.868 s 0.538191
2021-10-01 06:30 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.141608
2021-10-01 06:39 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.217 s 0.726936
2021-10-01 06:48 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 1.570001
2021-10-01 06:58 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.726 s 0.007267
2021-10-01 06:58 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.852 s 1.115610
2021-10-01 06:58 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.416812
2021-10-01 06:58 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.511017
2021-10-01 05:04 Python dataframe-to-table chi_traffic_2020_Q1 19.507 s 1.510230
2021-10-01 05:39 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.514640
2021-10-01 05:40 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.057 s -1.191234
2021-10-01 05:48 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.763976
2021-10-01 06:28 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.407 s -1.323124
2021-10-01 06:29 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.165 s 0.504543
2021-10-01 06:36 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.067 s -0.854603
2021-10-01 06:48 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.175 s 0.144759
2021-10-01 06:58 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.470266
2021-10-01 06:35 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.627 s -1.200597
2021-10-01 06:45 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s 1.606275
2021-10-01 06:47 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.588177
2021-10-01 06:58 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.601826
2021-10-01 06:58 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.938960
2021-10-01 06:42 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.678 s -3.094179
2021-10-01 06:47 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.207 s -1.103751
2021-10-01 06:58 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.809657
2021-10-01 06:58 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.864578
2021-10-01 06:58 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.218245
2021-10-01 06:49 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.432151
2021-10-01 06:58 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.506132
2021-10-01 06:58 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.942492
2021-10-01 06:51 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.492 s 0.134825
2021-10-01 06:58 JavaScript Parse serialize, tracks 0.003 s 2.674123
2021-10-01 06:58 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.158495
2021-10-01 06:58 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.601826
2021-10-01 06:58 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.364521
2021-10-01 06:58 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.497 s 0.169388
2021-10-01 06:44 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.757 s -0.328185
2021-10-01 06:49 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.928 s 1.564656