The BigData100 is an open source project for benchmarking and ranking big data systems. The benchmarks we use are selected from BigDataBench. Currently, the big data systems include Hadoop, Spark, Flink, Hive on Hadoop, Impala, and the workloads cover offline batch processing, iterative machine learning, and interactive query processing from different domains. We will include more systems and workloads in near future.


The benchmarks come from BigDataBench. The batch processing and iterative machine learning part support Hadoop, Spark and Flink, while the interactive query processing benchmarks cover systems like Impala, Spark SQL, Hive on Hadoop, Hive on Tez and Hive on Spark. In the first part, there are 4 data sets and 6 workloads in BigData100. Table 1 summarizes the real-world data sets and scalable data generation tools included into BigData100; Table 2 presents the workloads of BigData100 for batch processing and machine learning.

Table 1: The Summary of Data Sets

Data sets Raw data size Scalable data set
Wikipedia Entries 1.600,000,000 English words (unstructured text) Text Generator of BDGS of BigDataBench
Amazon Movie Reviews 5,700,000 reviews (semi-structured text) Text Generator of BDGS of BigDataBench
Google Web Graph 16777216 nodes, 99184770 edges (unstructured graph) Graph Generator of BDGS of BigDataBench
Facebook Social Network 460,000,000 vectors Graph Generator of BDGS of BigDataBench

Table 2. The summary of the workloads in BigData100

TeraSort IO-Intensive Text From Hadoop
WordCount CPU-Intensive Text From BigDataBench
Grep CPU-Intensive Text From BigDataBench
PageRank Hybrid Graph From BigDataBench
K-means CPU-Intensive Graph From BigDataBench
NaiveBayes CPU-Intensive Text From BigDataBench


  • Jingwei Li,    BAFST, lijingwei@mail.bafst.com
  • Xinhui Tian,    ICT, CAS, tianxinhui@ict.ac.cn
  • Jianfeng Zhan,    ICT, CAS, zhanjianfeng@ict.ac.cn


Now we use a 16-node cluster as the testbed. Table 3 summarizes the configurations of systems.

Table 3: The configurations of cluster

Computation nodes 16 nodes
CPU per node 2*Intel Xeon E5645
Memory per node 32GB
Disk per node 1TB x 2 SATA disks
Network Broadcom NetXtreme II Gigabit Ethernet

Results | 2016.01

We released some preliminary results of BigData100.

Part 1: Batch Processing and iterative Machine Learning

The first BigData100 ranking report covers Spark, Hadoop, and Flink. Table 4 shows the performance numbers of different systems running different benchmarks.

Table 4: the performance metrics of different systems (seconds)

Workload Hadoop v. 2.7.1 Spark v 1.5.1 Flink 0.9.1
WordCount 680 1620 1105
Grep 672 1860 1676
NaiveBayes 490 780 563
PageRank 7753 780 598 (469 in delta PageRank)
Kmeans 6722 672 805











Part 2: Interactive Query Processing

In this part, we have tested five systems with three queries included in BigDataBench. The queries adopt a schema of e-commerce includeing three tables of items, customers, and orders. These three queries cover common operations such as select, aggregation, and join. The data size is described in Table 5, and the results are shown as below. Currently, we only consider the text format for these three tables.

Table 5: The data size used for interactive query processing

Itme 2.19 GB
Customer 14.14 GB
Order 101.99 GB







Runtime System Configurations

Table 6 presents the details of different big data run-time systems configurations

Table 6: The configurations of big data systems


Version 2.7.1
yarn.nodemanager.resource.cpu-vcores 12
yarn.scheduler.maximum-allocation-vcores 12
yarn.nodemanager.resource.memory-mb 22528
mapreduce.map.memory.mb 1024
mapreduce.reduce.memory.mb 1024


Version 1.5.1
Spark.storage.memoryFraction 0.2 (0.5 for iteration)
Spark.shuffle.manager Sort
Spark.default.parallelism 600


Version 0.9.1
Taskmanager.heap.mb 22528
Taskmanager.numberOfTaskSlots 12
Parallelism.default 150
Taskmanager.network.numberOfBuffers 9000