SparkBench: A Comprehensive Spark Benchmarking Suite Characterizing In-memory Data Analytics
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1 SparkBench: A Comprehensive Spark Benchmarking Suite Characterizing In-memory Data Analytics Min LI,, Jian Tan, Yandong Wang, Li Zhang, Valentina Salapura, Alan Bivens IBM TJ Watson Research Center
2 * A paper currently under submission : SPARKBENCH: a Spark Benchmarking Suite Characterizing Large-scale in Memory Data Analysis SparkBench Overview* A Spark benchmarking suite charactering in memory data analysis to provide guidance of Spark system design and performance optimization A data generator automatically generates input data sets with various sizes Diverse and representative workloads ( extensible to new workloads ) Machine learning: Logistic regression, support vector machine, matrix factorization Graph processing: pagerank, svdplusplus, triangle count Streaming: twitter, pageview SQL query applications: hive,rddrelation Explore different parameter configurations easily Reported Metrics: supported: job execution time, input data size, data process rate under development: shuffle data, RDD size, resource consumption, integration with monitoring tool Workload characterization and study of parameter impacts Diverse and representative date sets: Wikipedia, Google web graph, Amazon movie review Charactering workloads in terms of resource consumption, data access patterns and time information, job execution time, shuffle data Studying the impact of Spark configuration parameters
3 What SparkBench is designed for? Provide quantitative comparison for different platforms and hardware cluster setups e.g. the comparison between IBM Power system VS Intel System. IBM cloud VS Amazon cloud Provide quantitative comparison for Spark system optimization Enable in-depth study of performance implication of Spark system in various aspects workload characterization, parameter impact, scalability, fault tolerance Provide insights and guidance for cluster sizing and provisioning If a user aims to provision a spark cluster for usage, what will the performance look like? Help identify resource bottleneck
4 Workloads and Data Sets
5 Agenda Workload Characterization Impact of Parameter Configuration An End-to-end Example of Running SparkBench
6 Application Characterization
7 Machine learning large data sets
8 Machine learning bottlenecked resources: CPU OS cache has been used extensively few disk IO and network IO cpu and memory usage are relatively stable
9 Graph computation large data sets
10 Graph Computation PageRank and triangle count bottlenecked resources: memory CPU and network usage has peaks SVD plusplus and shortest path look alike increased memory usage few network IO bursty disk IO
11 SQL-like queries large data sets
12 SQL-like Queries Less resource intensive suggest to co-run multiple queries to improve system resource utilization
13 Streaming applications large data sets
14 Streaming Applications bimodal pattern of CPU utilization increased use of memory few disk and network IO
15 Workload shuffling VS regular tasks
16 Workload pattern
17 Impact of Spark Configuration
18 Impact of RDD cache size
19 Impact of task parallellism
20 Impact of executor configuration
21 Impact of memory
22 Overall Observation memory is intensively used across all workloads ShuffleMapTasks use OS cache to store intermediate data Machining learning workloads are CPU intensive Demand of graph computation workloads varies from different workloads, generally resource intensive SQL can be resource demanding Streaming workloads demand light resources yet is memory hungry While memory usage is stable, other resource usage can be bursty. Parameter configuration impact the performance signicantly
23 Example
24 Example(1/4): Using SparkBench to Study the Impact of Parameter Configuration Download the package: git clone Setup spark cluster, optionally Ganglia configure bin/config.sh file to point to the Spark master To run each workload individually, cd to the workload directory mvn package/ sbt package bin/gen_data.sh bin/run.sh
25 Example(2/4): Using SparkBench to Study the Impact of Parameter Configuration configuration of SparkBench bin/config.sh, change memoryfraction to zero or different values [workload]/bin/config.sh specify the workload parameters such as number of iterations, the number of points generated in the input dataset. to view result in the bench.report reports the job execution time, the data process rate, the input data set size
26 Example (3/4) : A Screenshot
27 Example(4/4): Visualizing the Impact of RDD Cache Size on Job Execution Time
28 Conclusion SparkBench is a comprehensive Spark-specific benchmarking suite easy to use can be used for various scenarios: performance comparison, cluster provisioning, in-depth study of Spark Available for download:
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