Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-03 22:36 Python csv-read gzip, streaming, nyctaxi_2010-01 10.872 s -1.868052
2021-10-03 22:37 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.413097
2021-10-04 00:04 R dataframe-to-table type_simple_features, R 275.767 s -1.588601
2021-10-03 23:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.495317
2021-10-03 23:14 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.804494
2021-10-03 23:16 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.308 s -1.355280
2021-10-03 22:39 Python dataframe-to-table type_strings 0.373 s -0.239068
2021-10-03 22:39 Python dataframe-to-table type_floats 0.012 s -0.276024
2021-10-03 22:39 Python dataframe-to-table type_simple_features 0.909 s 0.353856
2021-10-03 22:39 Python dataset-filter nyctaxi_2010-01 4.347 s 0.705379
2021-10-03 23:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.260 s 1.106672
2021-10-03 22:42 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.912 s 0.281877
2021-10-03 22:47 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.173 s 1.034311
2021-10-03 22:56 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.890 s 0.638739
2021-10-03 22:56 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.179 s 0.745134
2021-10-03 23:00 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.020 s 0.236117
2021-10-03 23:11 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.851 s 0.285017
2021-10-03 23:00 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.038 s -0.085467
2021-10-03 23:11 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.994 s 0.091047
2021-10-03 23:12 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.936 s 1.412150
2021-10-03 23:15 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.058 s -1.242415
2021-10-03 23:11 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.726 s 0.253965
2021-10-03 23:14 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.925 s -1.259544
2021-10-03 23:14 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.238 s -1.394462
2021-10-03 23:12 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.766 s 1.322305
2021-10-03 23:13 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.821 s -0.748972
2021-10-03 23:13 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.780 s -1.210263
2021-10-03 23:16 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.120003
2021-10-03 23:15 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.322 s -1.401494
2021-10-03 23:17 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.981 s -1.481946
2021-10-03 23:17 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.389 s -1.013712
2021-10-03 23:20 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.333 s 0.295189
2021-10-03 23:21 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.769 s -0.258007
2021-10-03 23:18 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.652 s -1.082217
2021-10-03 23:19 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.741 s -0.990132
2021-10-03 23:20 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.943 s -0.772082
2021-10-03 23:21 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.323 s -0.884914
2021-10-03 23:26 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.284650
2021-10-03 23:21 Python file-write lz4, feather, table, fanniemae_2016Q4 1.191 s -1.930873
2021-10-03 23:22 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.984 s -1.732657
2021-10-03 23:25 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.507 s -1.116065
2021-10-03 23:39 R dataframe-to-table chi_traffic_2020_Q1, R 5.381 s 0.314843
2021-10-03 23:24 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.351 s 0.019289
2021-10-03 23:23 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.968 s -0.428559
2021-10-03 23:23 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.992 s -0.913464
2021-10-03 23:24 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 12.031 s -0.637999
2021-10-03 23:25 Python file-write lz4, feather, table, nyctaxi_2010-01 1.844 s -1.799784
2021-10-03 23:25 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.843 s 0.012431
2021-10-03 23:25 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.315821
2021-10-03 23:40 R dataframe-to-table type_integers, R 0.086 s -1.279311
2021-10-03 23:41 R dataframe-to-table type_floats, R 0.107 s 0.780801
2021-10-03 23:41 R dataframe-to-table type_nested, R 0.540 s -1.003877
2021-10-03 23:40 R dataframe-to-table type_dict, R 0.051 s -0.162840
2021-10-03 23:40 R dataframe-to-table type_strings, R 0.492 s -0.098296
2021-10-03 22:39 Python dataframe-to-table type_nested 2.868 s 1.354986
2021-10-03 23:00 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.008 s 0.202329
2021-10-03 23:16 Python file-read lz4, feather, table, nyctaxi_2010-01 0.677 s -1.648068
2021-10-03 22:34 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.104 s -1.377305
2021-10-03 22:39 Python dataframe-to-table type_integers 0.011 s 1.266643
2021-10-03 22:34 Python csv-read uncompressed, file, fanniemae_2016Q4 1.147 s 1.456747
2021-10-03 22:36 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s -0.127395
2021-10-03 22:38 Python dataframe-to-table chi_traffic_2020_Q1 19.533 s 0.956475
2021-10-03 22:39 Python dataframe-to-table type_dict 0.012 s 0.810620
2021-10-03 23:13 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.111 s 1.324687
2021-10-03 23:15 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.078 s -1.179963
2021-10-03 23:16 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.474 s -1.466254
2021-10-03 22:35 Python csv-read gzip, streaming, fanniemae_2016Q4 15.065 s -1.500784
2021-10-03 22:35 Python csv-read gzip, file, fanniemae_2016Q4 6.023 s 1.663101
2021-10-04 00:25 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.935 s -0.796604
2021-10-04 00:28 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.582 s -0.955705
2021-10-04 00:29 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.755 s -0.611135
2021-10-04 00:31 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.236 s 1.740947
2021-10-04 00:34 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.275846
2021-10-04 00:45 JavaScript Parse Table.from, tracks 0.000 s -0.497835
2021-10-03 22:36 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.920 s -1.890284
2021-10-03 23:12 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.180 s 1.403482
2021-10-04 00:32 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.487 s 0.626968
2021-10-04 00:33 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.163 s 1.849429
2021-10-04 00:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.604 s 0.901934
2021-10-04 00:45 JavaScript Parse Table.from, tracks 0.000 s -0.497835
2021-10-04 00:35 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.534 s -2.560754
2021-10-04 00:46 JavaScript Parse readBatches, tracks 0.000 s -0.304048
2021-10-04 00:35 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.858264
2021-10-04 00:46 JavaScript Parse readBatches, tracks 0.000 s -0.304048
2021-10-04 00:48 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.715 s -0.575058
2021-10-04 00:49 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.658485
2021-10-04 00:36 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.935 s 0.925953
2021-10-04 00:37 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.170 s 0.899159
2021-10-04 00:47 JavaScript Parse serialize, tracks 0.003 s 2.714227
2021-10-04 00:50 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.405482
2021-10-04 00:36 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.631 s -0.534075
2021-10-04 00:47 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.865172
2021-10-04 00:49 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823
2021-10-04 00:49 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.171733
2021-10-04 00:36 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.354 s 0.738716
2021-10-04 00:47 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.884047
2021-10-04 00:37 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.480 s -1.373634
2021-10-04 00:47 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.630 s -0.278486
2021-10-04 00:38 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.511 s 0.063903
2021-10-04 00:48 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.636 s -0.363780
2021-10-04 00:49 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.197917
2021-10-04 00:50 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.368839
2021-10-04 00:05 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.202 s 0.575685
2021-10-04 00:48 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.680363
2021-10-04 00:49 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823
2021-10-04 00:07 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.867 s 0.584139
2021-10-04 00:48 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.721 s 0.147055
2021-10-04 00:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.689408
2021-10-04 00:48 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.168739
2021-10-04 00:48 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.842 s 0.901646
2021-10-04 00:50 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.926351
2021-10-04 00:48 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.279778
2021-10-04 00:48 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.912 s -0.194042
2021-10-04 00:06 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.886 s 0.459508
2021-10-04 00:48 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.549823
2021-10-04 00:49 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.582417
2021-10-04 00:49 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.615414
2021-10-04 00:49 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.712156
2021-10-04 00:50 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.091573
2021-10-04 00:50 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.936745
2021-10-04 00:07 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.916220
2021-10-04 00:50 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.497 s 0.106917
2021-10-04 00:08 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.892 s 1.648951
2021-10-04 00:08 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.570 s -1.581193
2021-10-04 00:06 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.209 s 0.462461
2021-10-04 00:09 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.373 s 0.626824
2021-10-04 00:11 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.188 s -0.816168
2021-10-04 00:11 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.115 s 1.135522
2021-10-04 00:12 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.233 s 0.592152
2021-10-04 00:13 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.544840
2021-10-04 00:14 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.992 s -0.904402
2021-10-04 00:16 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.159 s -0.968306
2021-10-04 00:14 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.685 s -0.007804
2021-10-04 00:15 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.521 s 0.213103
2021-10-04 00:23 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.405 s -0.656992
2021-10-04 00:33 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.567 s 1.113556
2021-10-04 00:23 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.606 s -0.253732
2021-10-04 00:27 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.904 s -0.792926
2021-10-04 00:34 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.191 s -1.965006
2021-10-04 00:37 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -1.605991
2021-10-04 00:18 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.567 s -0.572117
2021-10-04 00:30 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.717040
2021-10-04 00:33 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.936093
2021-10-04 00:36 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.107 s -2.884953
2021-10-04 00:19 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.623 s -1.039116
2021-10-04 00:33 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.871 s 1.115880
2021-10-04 00:21 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.750467
2021-10-04 00:24 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.208 s 0.615068
2021-10-04 00:21 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.031 s -0.645867
2021-10-04 00:48 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.622493
2021-10-04 00:49 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.847090