Apache Spark This is a benchmark of Apache Spark with its PySpark interface. Apache Spark is an open-source unified analytics engine for large-scale data processing and dealing with big data. This test profile benchmars the Apache Spark in a single-system configuration using spark-submit. The test makes use of DIYBigData's pyspark-benchmark (https://github.com/DIYBigData/pyspark-benchmark/) for generating of test data and various Apache Spark operations.
To run this test with the Phoronix Test Suite , the basic command is: phoronix-test-suite benchmark spark .
Test Created 4 August 2022
Last Updated 20 January 2023
Test Type System
Average Install Time 4 Seconds
Average Run Time 47 Minutes, 28 Seconds
Test Dependencies Java + Python
Accolades 30k+ Downloads Public Result Uploads * Reported Installs ** Reported Test Completions ** Test Profile Page Views OpenBenchmarking.org Events Apache Spark Popularity Statistics pts/spark 2022.08 2022.09 2022.10 2022.11 2022.12 2023.01 2023.02 2023.03 2023.04 2023.05 2023.06 2023.07 2023.08 2023.09 2023.10 2023.11 2023.12 2024.01 2024.02 2024.03 2024.04 2024.05 2024.06 2024.07 2024.08 2024.09 2024.10 5K 10K 15K 20K 25K
* Uploading of benchmark result data to OpenBenchmarking.org is always optional (opt-in) via the Phoronix Test Suite for users wishing to share their results publicly. ** Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting while running benchmarks from an Internet-connected platform. Data updated weekly as of 14 October 2024.
20000000 21.5% 10000000 22.5% 1000000 33.6% 40000000 22.4% Row Count Option Popularity OpenBenchmarking.org
100 31.1% 2000 24.8% 500 22.6% 1000 21.5% Partitions Option Popularity OpenBenchmarking.org
Performance MetricsAnalyze Test Configuration: pts/spark-1.0.x - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 100 - Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 100 - Repartition Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 1000000 - Partitions: 2000 - Group By Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 2000 - Repartition Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 1000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 1000000 - Partitions: 500 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 100 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 100 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 40000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 40000000 - Partitions: 2000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 40000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 500 - Repartition Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 1000000 - Partitions: 500 - Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 500 - Group By Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 500 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 500 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 40000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 100 - Repartition Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 100 - Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 100 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 10000000 - Partitions: 100 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 500 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 20000000 - Partitions: 1000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 1000000 - Partitions: 1000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 10000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 2000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 10000000 - Partitions: 100 - Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 1000 - Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 1000 - Group By Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 10000000 - Partitions: 100 - Repartition Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 1000 - Repartition Test Time pts/spark-1.0.x - Row Count: 1000000 - Partitions: 1000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 10000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 40000000 - Partitions: 500 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 40000000 - Partitions: 2000 - Repartition Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 2000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 2000 - Group By Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 2000 - Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 1000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 500 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 20000000 - Partitions: 100 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 500 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 20000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 20000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 20000000 - Partitions: 500 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 20000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 10000000 - Partitions: 1000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 20000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 1000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 10000000 - Partitions: 2000 - Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 2000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 2000 - Repartition Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 2000 - Group By Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 100 - Repartition Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 100 - Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 500 - Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 500 - Repartition Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 500 - Group By Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 500 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 1000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 1000 - Inner Join Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 1000 - Repartition Test Time pts/spark-1.0.x - Row Count: 10000000 - Partitions: 1000 - Group By Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 1000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.x - Row Count: 40000000 - Partitions: 1000 - Calculate Pi Benchmark pts/spark-1.0.x - Row Count: 40000000 - Partitions: 1000 - SHA-512 Benchmark Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 500 - Repartition Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 500 - Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 1000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 1000 - Group By Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 1000 - Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 1000 - Repartition Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 500 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 500 - Group By Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 2000 - Group By Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 2000 - Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 2000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 20000000 - Partitions: 2000 - Repartition Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 500 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 500 - Group By Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 500 - Inner Join Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 500 - Repartition Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 1000 - Broadcast Inner Join Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 1000 - Repartition Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 1000 - Group By Test Time pts/spark-1.0.x - Row Count: 40000000 - Partitions: 1000 - Inner Join Test Time Apache Spark 3.3 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark OpenBenchmarking.org metrics for this test profile configuration based on 434 public results since 4 August 2022 with the latest data as of 14 April 2024 .
Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.
Component
Percentile Rank
# Compatible Public Results
Seconds (Average)
OpenBenchmarking.org Distribution Of Public Results - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark 434 Results Range From 9 To 2802 Seconds 9 65 121 177 233 289 345 401 457 513 569 625 681 737 793 849 905 961 1017 1073 1129 1185 1241 1297 1353 1409 1465 1521 1577 1633 1689 1745 1801 1857 1913 1969 2025 2081 2137 2193 2249 2305 2361 2417 2473 2529 2585 2641 2697 2753 2809 30 60 90 120 150
Based on OpenBenchmarking.org data, the selected test / test configuration (Apache Spark 3.3 - Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark ) has an average run-time of 16 minutes . By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.
OpenBenchmarking.org Minutes Time Required To Complete Benchmark Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Run-Time 20 40 60 80 100 Min: 2 / Avg: 15.24 / Max: 104
Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.1% .
OpenBenchmarking.org Percent, Fewer Is Better Average Deviation Between Runs Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Deviation 2 4 6 8 10 Min: 0 / Avg: 0.14 / Max: 2
Does It Scale Well With Increasing Cores? Yes , based on the automated analysis of the collected public benchmark data, this test / test settings does generally scale well with increasing CPU core counts. Data based on publicly available results for this test / test settings, separated by vendor, result divided by the reference CPU clock speed, grouped by matching physical CPU core count, and normalized against the smallest core count tested from each vendor for each CPU having a sufficient number of test samples and statistically significant data.
Intel AMD OpenBenchmarking.org Relative Core Scaling To Base Apache Spark CPU Core Scaling Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark 4 8 12 16 24 32 64 128 3 6 9 12 15
Tested CPU Architectures This benchmark has been successfully tested on the below mentioned architectures. The CPU architectures listed is where successful OpenBenchmarking.org result uploads occurred, namely for helping to determine if a given test is compatible with various alternative CPU architectures.
CPU Architecture
Kernel Identifier
Verified On
Intel / AMD x86 64-bit
x86_64
(Many Processors)
ARMv8 64-bit
aarch64
ARMv8 Cortex-A72, ARMv8 Cortex-A76 4-Core, ARMv8 Neoverse-N1, ARMv8 Neoverse-N1 128-Core, ARMv8 Neoverse-N1 64-Core, ARMv8 Neoverse-N2, ARMv8 Neoverse-V1, ARMv8 Neoverse-V1 64-Core, Apple M1, Apple M2, Rockchip ARMv8 Cortex-A76 4-Core, rk1-mainline-emmc, rk1-mainline-nvme, rk1-rok-emmc
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