轻量级大数据运算系统Helius

针对Spark数据集不可变，以及Java虚拟机（JVM）依赖环境引起的代码执行、内存管理、数据序列化/反序列化等开销过多的不足，采用C/C++语言，设计并实现了一种轻量级的大数据运算系统--Helius。Helius支持Spark的基本操作，同时允许数据集整体修改；同时，Helius利用C/C++优化内存管理和网络传输，并采用stateless worker机制简化分布式计算平台的容错恢复过程。实验结果显示：5次迭代中，Helius运行PageRank算法的时间仅为Spark的25.12%~53.14%，运行TPCH Q6的时间仅为Spark的57.37%；在PageRank迭代1次的基础上，运行在Helius系统下时，master节点IP接收和发送数据量约为运行于Spark系统的40%和15%，而且200 s的运行过程中，Helius占用的总内存约为Spark的25%。实验结果与分析表明，与Spark相比，Helius具有节约内存、不需要序列化和反序列化、减少网络交互以及容错简单等优点。

Helius: a lightweight big data processing system

Concerning the limitations of Spark, including immutable datasets and significant costs of code execution, memory management and data serialization/deserialization caused by running environment of Java Virtual Machine (JVM), a light-weight big data processing system, named Helius, was implemented in C/C++. Helius supports the basic operations of Spark, while allowing the data set to be modified as a whole. In Helius, the C/C++ is utilized to optimize the memory management and network communication, and a stateless worker mechanism is utilized to simplify the fault tolerance and recovery process of the distributed computing platform. The experimental results showed that in 5 iterations, the running time in Helius was only 25.12% to 53.14% of that in Spark when running PageRank iterative jobs, and the running time in Helius was only 57.37% of that in Spark when processing TPCH Q6. On the basis of one iteration of PageRank, the IP incoming and outcoming data sizes of master node in Helius were about 40% and 15% of those in Sparks, and the total memory consumed in the worker node in Helius was only 25% of that in Spark.Compared with Spark, Helius has the advantages of saving memory, eliminating the need for serialization and deserialization, reducing network interaction and simplifying fault tolerance.