Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 14:11 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.393 s -1.224582
2021-10-01 14:17 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.104118
2021-10-01 13:33 Python dataframe-to-table type_simple_features 0.910 s 0.303398
2021-10-01 14:17 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.452131
2021-10-01 13:29 Python csv-read uncompressed, file, nyctaxi_2010-01 0.991 s 0.491847
2021-10-01 13:32 Python dataframe-to-table type_dict 0.012 s -0.710569
2021-10-01 13:40 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.587 s 1.396370
2021-10-01 14:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.946 s 1.303939
2021-10-01 14:13 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.749 s -1.212288
2021-10-01 14:09 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.233 s -2.012805
2021-10-01 13:28 Python csv-read gzip, streaming, fanniemae_2016Q4 14.849 s -0.449777
2021-10-01 13:36 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.901 s 0.251415
2021-10-01 14:08 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.822 s -1.108201
2021-10-01 13:30 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.270402
2021-10-01 13:50 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.188 s 0.601807
2021-10-01 14:09 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.046 s -0.543897
2021-10-01 14:10 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.179 s -0.581551
2021-10-01 14:18 Python wide-dataframe use_legacy_dataset=false 0.616 s 0.565707
2021-10-01 14:31 R dataframe-to-table chi_traffic_2020_Q1, R 5.433 s -0.529162
2021-10-01 13:50 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.806 s -0.405636
2021-10-01 14:09 Python file-read lz4, feather, table, fanniemae_2016Q4 0.598 s 0.726185
2021-10-01 14:32 R dataframe-to-table type_floats, R 0.113 s -1.304462
2021-10-01 13:55 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.031 s 0.061533
2021-10-01 14:10 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.323 s -1.389613
2021-10-01 14:13 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.945 s -0.890911
2021-10-01 13:32 Python dataframe-to-table type_floats 0.011 s 0.946871
2021-10-01 14:16 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.951 s -0.364884
2021-10-01 14:07 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.776 s 1.236490
2021-10-01 14:08 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.276246
2021-10-01 14:14 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.908 s -1.383595
2021-10-01 14:18 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.305214
2021-10-01 14:32 R dataframe-to-table type_strings, R 0.493 s -0.785303
2021-10-01 13:30 Python csv-read gzip, streaming, nyctaxi_2010-01 10.466 s 0.557698
2021-10-01 14:11 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.207682
2021-10-01 13:55 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.043 s -0.093775
2021-10-01 14:08 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.112 s 1.329706
2021-10-01 14:15 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.958 s -1.551350
2021-10-01 14:32 R dataframe-to-table type_integers, R 0.084 s -0.068418
2021-10-01 14:07 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.251 s 1.542130
2021-10-01 14:10 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.462 s -1.517893
2021-10-01 13:29 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.560103
2021-10-01 13:27 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.930 s -0.465410
2021-10-01 13:33 Python dataframe-to-table type_nested 2.871 s 1.658521
2021-10-01 14:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.946 s 0.454992
2021-10-01 14:17 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.991 s -0.407575
2021-10-01 13:33 Python dataset-filter nyctaxi_2010-01 4.348 s 0.551911
2021-10-01 14:06 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.742 s 0.195540
2021-10-01 13:29 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.491 s 0.508012
2021-10-01 13:32 Python dataframe-to-table chi_traffic_2020_Q1 19.345 s 2.047481
2021-10-01 13:55 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.996 s 0.374940
2021-10-01 14:12 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.599 s -0.977720
2021-10-01 14:14 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.394 s -0.412122
2021-10-01 14:32 R dataframe-to-table type_nested, R 0.539 s -0.658380
2021-10-01 14:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.790 s 0.591856
2021-10-01 14:08 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.750 s -0.991391
2021-10-01 14:11 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.987 s -1.622051
2021-10-01 14:16 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.996 s -1.156213
2021-10-01 14:07 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.183 s 1.534827
2021-10-01 14:14 Python file-write lz4, feather, table, fanniemae_2016Q4 1.154 s 0.551996
2021-10-01 14:15 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.249 s -0.309256
2021-10-01 14:17 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.344 s 0.177053
2021-10-01 14:56 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.200 s 0.560906
2021-10-01 15:00 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.212 s 1.670804
2021-10-01 15:12 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.907 s -0.707615
2021-10-01 14:58 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.384 s -0.087262
2021-10-01 13:28 Python csv-read uncompressed, file, fanniemae_2016Q4 1.181 s -0.122456
2021-10-01 13:32 Python dataframe-to-table type_strings 0.372 s -0.078585
2021-10-01 13:32 Python dataframe-to-table type_integers 0.011 s 1.341870
2021-10-01 14:09 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.963 s -2.789625
2021-10-01 14:09 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.069 s -0.891917
2021-10-01 14:09 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.284 s -1.189084
2021-10-01 14:18 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.798 s 0.371570
2021-10-01 14:32 R dataframe-to-table type_dict, R 0.063 s -1.282842
2021-10-01 14:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.924 s -0.261084
2021-10-01 15:16 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.481 s 1.763207
2021-10-01 15:21 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -2.402241
2021-10-01 15:06 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.073 s -0.997330
2021-10-01 14:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.658 s -3.599085
2021-10-01 14:57 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.456930
2021-10-01 15:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.513 s 0.394346
2021-10-01 15:29 JavaScript Parse Table.from, tracks 0.000 s -1.424478
2021-10-01 15:29 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.134919
2021-10-01 15:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.175 s 0.140638
2021-10-01 15:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.607 s 0.004304
2021-10-01 15:29 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.645 s -0.388865
2021-10-01 15:29 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.932 s -0.622815
2021-10-01 14:56 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.389 s -3.844389
2021-10-01 15:18 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.876 s 1.384189
2021-10-01 15:29 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.138003
2021-10-01 15:29 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.045 s 1.925772
2021-10-01 14:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.586900
2021-10-01 15:01 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.959 s 0.647659
2021-10-01 15:21 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.168 s 1.076687
2021-10-01 15:29 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.426018
2021-10-01 15:04 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.597 s -0.796466
2021-10-01 15:18 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.511 s 0.713163
2021-10-01 15:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.927 s 1.111592
2021-10-01 14:58 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.059 s -0.509833
2021-10-01 15:18 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 1.129019
2021-10-01 15:20 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.471 s 0.528539
2021-10-01 15:29 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.715356
2021-10-01 15:29 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.000798
2021-10-01 15:05 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.626 s -1.241517
2021-10-01 15:18 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.092 s -0.120651
2021-10-01 15:29 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.675976
2021-10-01 15:16 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.235 s 2.120109
2021-10-01 15:29 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.089866
2021-10-01 15:29 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.747 s -0.006258
2021-10-01 15:29 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.487257
2021-10-01 15:29 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.639139
2021-10-01 15:09 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.153255
2021-10-01 15:11 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.939 s -0.801968
2021-10-01 15:14 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.764 s -0.666027
2021-10-01 15:17 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.302080
2021-10-01 14:59 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.136 s -0.442187
2021-10-01 15:29 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.640351
2021-10-01 15:29 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.045 s 2.178113
2021-10-01 15:29 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.520 s -0.204751
2021-10-01 14:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.867 s 0.587416
2021-10-01 14:59 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.185 s -0.652165
2021-10-01 15:15 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.444368
2021-10-01 15:29 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.391279
2021-10-01 15:29 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.112839
2021-10-01 15:29 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.603070
2021-10-01 15:29 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.036245
2021-10-01 15:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.158 s -1.134568
2021-10-01 15:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.559 s 0.556517
2021-10-01 14:55 R dataframe-to-table type_simple_features, R 275.458 s -1.131070
2021-10-01 15:29 JavaScript Parse readBatches, tracks 0.000 s -1.536637
2021-10-01 15:29 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.993138
2021-10-01 15:07 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.197984
2021-10-01 15:08 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.552 s 1.239075
2021-10-01 15:13 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.578 s -0.813371
2021-10-01 15:29 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.613 s -0.241976
2021-10-01 15:00 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.585220
2021-10-01 15:01 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.674 s 0.144582
2021-10-01 15:20 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.126067
2021-10-01 15:29 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.716990
2021-10-01 15:10 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.202 s 1.005916
2021-10-01 15:20 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.977570
2021-10-01 15:29 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.641 s 0.755130
2021-10-01 15:29 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.849 s 0.792587
2021-10-01 15:29 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.487257
2021-10-01 15:17 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.171 s 1.647840
2021-10-01 15:18 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 1.399930
2021-10-01 15:29 JavaScript Parse serialize, tracks 0.005 s -0.568253
2021-10-01 15:29 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.164086
2021-10-01 15:21 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.507 s 0.083783
2021-10-01 15:29 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.058461