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Updated on Wed 25 January 2017

Summary of the 1.1 Billion Taxi Rides Benchmarks

This is a listing of successful results of all the various data storage and processing system benchmarks I've conducted using the dataset produced in the Billion Taxi Rides in Redshift blog post. The dataset itself has 1.1 billion records, 51 columns and takes up about 500 GB of disk space uncompressed.

Though these benchmarks used the same dataset, I wouldn't consider any two as comparing apples to apples. The times recorded don't necessarily reflect the top performance these systems are capable of. It's likely each system could have performed faster if more effort was put into setup, configuration and/or better hardware was available. There are some cases where I re-ran a benchmark with more tuning to achieve a better result and in these cases only the fastest benchmark will be listed below.

The table is sorted by the fastest time query 1 finished in (measured in seconds).

Query 1 Query 2 Query 3 Query 4 Setup
0.021 0.053 0.165 0.51 MapD & 8 Nvidia Pascal Titan Xs
0.027 0.083 0.163 0.891 MapD & 8 Nvidia Tesla K80s
0.028 0.2 0.237 0.578 MapD & 4-node g2.8xlarge cluster
0.034 0.061 0.178 0.498 MapD & 2-node p2.8xlarge cluster
0.036 0.131 0.439 0.964 MapD & 4 Nvidia Titan Xs
0.051 0.146 0.047 0.794 kdb+/q & 4 Intel Xeon Phi 7210 CPUs
0.762 2.472 4.131 6.041 BrytlytDB & 2-node p2.16xlarge cluster
1.034 3.058 5.354 12.748 ClickHouse, Intel Core i5 4670K
1.56 1.25 2.25 2.97 Redshift, 6-node ds2.8xlarge cluster
2 2 1 3 BigQuery
4 4 10 21 Presto, 50-node n1-standard-4 cluster
6.41 6.19 6.09 6.63 Amazon Athena
8.1 18.18 n/a n/a Elasticsearch (heavily tuned)
10.19 8.134 19.624 85.942 Spark 2.1, 11 x m3.xlarge cluster w/ HDFS
11 10 21 31 Presto, 10-node n1-standard-4 cluster
14.389 32.148 33.448 67.312 Vertica, Intel Core i5 4670K
34.48 63.3 n/a n/a Elasticsearch (lightly tuned)
35 39 64 81 Presto, 5-node m3.xlarge cluster w/ HDFS
43 45 27 44 Presto, 50-node m3.xlarge cluster w/ S3
152 175 235 368 PostgreSQL 9.5 & cstore_fdw
264 313 620 961 Spark 1.6, 5-node m3.xlarge cluster w/ S3
1103 1198 2278 6446 Spark 2.2, 3-node Raspberry Pi cluster

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