Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 03:50 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.152107
2021-10-06 03:54 Python dataframe-to-table type_nested 2.865 s 1.112416
2021-10-06 03:54 Python dataframe-to-table type_dict 0.012 s 1.071136
2021-10-06 03:57 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.955 s -0.278989
2021-10-06 04:02 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.227 s 0.763778
2021-10-06 03:51 Python csv-read uncompressed, file, nyctaxi_2010-01 0.999 s 1.380230
2021-10-06 03:54 Python dataframe-to-table type_simple_features 0.919 s -0.655613
2021-10-06 03:49 Python csv-read uncompressed, file, fanniemae_2016Q4 1.173 s 0.017735
2021-10-06 03:54 Python dataframe-to-table type_integers 0.011 s 1.076792
2021-10-06 03:54 Python dataframe-to-table type_strings 0.371 s 0.057337
2021-10-06 03:51 Python csv-read gzip, streaming, nyctaxi_2010-01 10.507 s 1.120269
2021-10-06 03:51 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.638 s 0.191075
2021-10-06 03:53 Python dataframe-to-table chi_traffic_2020_Q1 19.412 s 1.193158
2021-10-06 03:54 Python dataset-filter nyctaxi_2010-01 4.360 s 0.302313
2021-10-06 03:54 Python dataframe-to-table type_floats 0.011 s 0.425537
2021-10-06 03:49 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.958 s -0.351419
2021-10-06 03:50 Python csv-read gzip, streaming, fanniemae_2016Q4 14.891 s -0.345368
2021-10-06 03:52 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.472677
2021-10-06 04:15 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.031 s 0.022323
2021-10-06 04:36 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.776 s 1.267168
2021-10-06 04:38 Python file-write lz4, feather, table, nyctaxi_2010-01 1.811 s 0.005356
2021-10-06 05:17 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.909 s 0.151898
2021-10-06 05:18 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.929 s -0.561554
2021-10-06 05:33 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.453 s 1.402031
2021-10-06 05:38 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -2.049913
2021-10-06 05:49 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.145235
2021-10-06 05:49 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.906 s -0.623086
2021-10-06 05:49 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.001601
2021-10-06 05:20 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.205 s 2.077019
2021-10-06 05:49 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.151666
2021-10-06 04:15 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.008 s 0.420829
2021-10-06 04:28 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.839 s -0.358352
2021-10-06 04:29 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.299 s -1.368913
2021-10-06 04:29 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s 0.005755
2021-10-06 04:36 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.771 s 0.770877
2021-10-06 05:21 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.004 s -1.350238
2021-10-06 05:22 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.525 s 0.181785
2021-10-06 05:25 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.296 s 0.876583
2021-10-06 05:29 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.054288
2021-10-06 05:38 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.100029
2021-10-06 05:38 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.609 s 0.737608
2021-10-06 04:27 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.817 s -0.705241
2021-10-06 04:29 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.943 s -1.114830
2021-10-06 04:30 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.056 s -1.142224
2021-10-06 05:23 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.842 s 0.883939
2021-10-06 04:12 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.432 s -0.955818
2021-10-06 04:27 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.832 s 0.346282
2021-10-06 04:29 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.820 s -1.413321
2021-10-06 04:52 R dataframe-to-table type_nested, R 0.541 s -1.581109
2021-10-06 05:15 R dataframe-to-table type_simple_features, R 275.805 s -1.507607
2021-10-06 05:24 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.262 s 0.932366
2021-10-06 05:28 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.543 s 1.046644
2021-10-06 05:35 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.518174
2021-10-06 04:16 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.004 s 0.271388
2021-10-06 04:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.450800
2021-10-06 04:38 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.329228
2021-10-06 05:19 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.152 s 1.442858
2021-10-06 05:39 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.885 s 0.807777
2021-10-06 05:40 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.481 s -1.364871
2021-10-06 05:49 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.090796
2021-10-06 04:28 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.301 s -0.516282
2021-10-06 04:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.455 s -1.195123
2021-10-06 04:28 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.149 s -0.380921
2021-10-06 04:38 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.335 s 0.298024
2021-10-06 05:30 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.191 s 0.933058
2021-10-06 05:32 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.784 s 1.413454
2021-10-06 05:34 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.630 s 1.797190
2021-10-06 05:37 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.872 s 0.833148
2021-10-06 05:39 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -1.393335
2021-10-06 05:40 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s 0.326487
2021-10-06 04:29 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.050 s -0.086485
2021-10-06 05:18 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s 0.168366
2021-10-06 05:49 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.908 s -0.058311
2021-10-06 04:35 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.766 s -0.205012
2021-10-06 04:35 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.361 s -1.175106
2021-10-06 04:51 R dataframe-to-table chi_traffic_2020_Q1, R 5.355 s 0.788613
2021-10-06 05:36 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.501164
2021-10-06 04:38 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.783 s 0.513971
2021-10-06 05:49 JavaScript Parse serialize, tracks 0.005 s -0.841457
2021-10-06 05:49 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.684435
2021-10-06 04:29 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.245 s -1.075726
2021-10-06 04:37 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.833 s 1.347627
2021-10-06 05:16 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.920 s 0.207588
2021-10-06 05:49 JavaScript Parse readBatches, tracks 0.000 s 0.419456
2021-10-06 05:49 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.552588
2021-10-06 05:49 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.057004
2021-10-06 04:33 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.320 s 0.239463
2021-10-06 05:38 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.746406
2021-10-06 05:40 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -3.268344
2021-10-06 05:49 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.720 s -0.673092
2021-10-06 05:16 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.244 s 0.258397
2021-10-06 05:18 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.380 s 0.284994
2021-10-06 05:49 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.420891
2021-10-06 04:27 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.648 s -4.741530
2021-10-06 04:27 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.987 s 0.251695
2021-10-06 04:30 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.286 s -1.154206
2021-10-06 04:37 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.792 s 0.885616
2021-10-06 05:19 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.117 s 0.841008
2021-10-06 05:27 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.728101
2021-10-06 04:30 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.305 s -1.210290
2021-10-06 04:38 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.005136
2021-10-06 04:52 R dataframe-to-table type_floats, R 0.107 s 0.667736
2021-10-06 05:41 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.178 s 0.816519
2021-10-06 05:41 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.491 s 0.112789
2021-10-06 05:49 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.545 s -0.163833
2021-10-06 05:49 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.591 s -1.548116
2021-10-06 04:31 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.179 s -0.585044
2021-10-06 04:34 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.371 s -0.259512
2021-10-06 04:35 Python file-write lz4, feather, table, fanniemae_2016Q4 1.156 s 0.428945
2021-10-06 04:37 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.346 s 0.242307
2021-10-06 04:52 R dataframe-to-table type_strings, R 0.490 s 0.634080
2021-10-06 05:31 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.823 s 1.230769
2021-10-06 05:49 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 04:12 Python dataset-read async=True, nyctaxi_multi_ipc_s3 198.781 s -1.200376
2021-10-06 04:28 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.906 s -1.627126
2021-10-06 04:52 R dataframe-to-table type_integers, R 0.084 s 0.803679
2021-10-06 05:37 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.236458
2021-10-06 05:49 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.665 s -0.387572
2021-10-06 04:28 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.250 s -0.207245
2021-10-06 04:34 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.680 s 0.261088
2021-10-06 05:20 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.617167
2021-10-06 05:21 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.113088
2021-10-06 05:37 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.685334
2021-10-06 05:49 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.710069
2021-10-06 04:32 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.948 s -1.175611
2021-10-06 04:32 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.089 s 0.830487
2021-10-06 04:33 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.450 s 0.796397
2021-10-06 05:17 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.976622
2021-10-06 05:18 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.040 s 2.849944
2021-10-06 05:26 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.706 s 1.009204
2021-10-06 05:49 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.434086
2021-10-06 04:52 R dataframe-to-table type_dict, R 0.050 s 0.031409
2021-10-06 05:16 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.251 s 0.001525
2021-10-06 05:49 JavaScript Parse Table.from, tracks 0.000 s 0.931744
2021-10-06 05:35 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 1.118698
2021-10-06 05:38 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.594 s 0.571515
2021-10-06 05:49 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.379985
2021-10-06 05:40 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.618 s -0.347838
2021-10-06 05:49 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.431676
2021-10-06 05:49 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.001125
2021-10-06 05:41 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -2.814513
2021-10-06 05:49 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.419921
2021-10-06 05:49 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.685 s 0.355146
2021-10-06 05:49 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.109913
2021-10-06 05:49 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.307200
2021-10-06 05:49 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.284146
2021-10-06 05:49 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 05:49 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.697860
2021-10-06 05:49 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.533839