Big Data Benchmark from AMPLab of UC Berkeley

Benchmarks are important to understand the performance and quantitative and qualitative comparison of different systems. Many analytic frameworks, such as Hive, Impala and Shark, are designed and implemented these years and become fundamental software for processing big data. How to benchmark these big data analytic systems is an interesting problem. The Big Data Benchmark ∞ The Big Data Benchmark from AMPLab, UC Berkeley provides quantitative and qualitative comparisons of five systems by the time this post is written: Redshift – a hosted MPP database offered by based on the ParAccel data warehouse, Hive – a Hadoop-based data warehousing system, Shark – a Hive-compatible SQL engine which runs on top of the Spark computing framework, Impala – a Hive-compatible* SQL engine with its own MPP-like execution engine and Stinger/Tez – Tez is a next generation Hadoop execution engine currently in development. » Read more

PUMA: A MapReduce Benchmark Suite

MapReduce is a well-known programming model designed for generating and processing large data. There are various MapReduce implementations. One widely known and used one may be Hadoop. Benchmarking MapReduce frameworks gets to be important. Faraz Ahmad et al. developed a benchmark suite: PUMA MapReduce Benchmark. During our work on MapReduce, we developed a benchmark suite which represents a broad range of MapReduce applications exhibiting application characteristics with high/low computation and high/low shuffle volumes. » Read more

Hadoop TeraSort Benchmark

TeraSort is one of Hadoop’s widely used benchmarks. Hadoop’s distribution contains both the input generator and sorting implementations: the TeraGen generates the input and TeraSort conducts the sorting. Here, we provide a short tutorial for using the Hadoop TeraSort benchmark. TeraGen generates random data that can be used as input data for a subsequent running of TeraSort. Generate input by TeraGen The syntax for TeraGen: $ hadoop jar hadoop-*examples*.jar teragen <number of 100-byte rows> <output dir> To make the TeraGen run on multiple nodes with multiple tasks, you may need to specify the number of map tasks (30 here as an example; for Hadoop 2): $ hadoop -D mapreduce.job.maps 30 jar hadoop-*examples*.jar teragen <number of 100-byte rows> <output dir> The number of mappers depends on the number of rows you will generate and the number of nodes you have. » Read more

Reading List for Distributed Systems and Cloud Computing

Understanding the literature is usually the first step to do research, which is the same for systems research on cloud computing. A reading list may help a lot to those that just start in cloud computing research. Prof. Lin Gu, my PhD supervisor, compiled a reading list for system research on cloud computing. The reading list includes a list of papers related to Internet-scale systems and datacenters, techniques in distributed computing like Paxos, execution frameworks like MapReduce, distributed file systems like GFS, and storage systems like Dynamo. » Read more

Hadoop Default Ports

Hadoop’s namenode and datanodes expose a bunch of TCP ports used by Hadoop’s daemons to communicate to each other or listen directly to users’ requests. These ports information are needed by both the Hadoop users and cluster administrators to write programs or configure firewalls/gateways accordingly. A post written by Philip Zeyliger from Cloudera’s blog summarizes the default ports used by Hadoop: Hadoop Default Ports Quick Reference. » Read more

A Simple Sort Benchmark on Hadoop

After installing Hadoop, we usually run some benchmark programs to test whether the system works well. In the post of the Hadoop install tutorial, we show a very simple to grep strings from a simple sets of files. In this post, we introduce the Sort for testing and benchmarking Hadoop. The Sort program is also included in the Hadoop distribution package, and the package also includes a input data generator which generate 10 GB * (number of slave nodes) input data to sort. » Read more

Pitfalls and Lessons on Configuing and Tuning Hadoop

This post lists pitfalls and lessons learning when configuring and tuning Hadoop. Hadoop with IPv6 Hadoo doesn’t support IPv6 currently (up to 0.20.2 and 0.21.0): Hadoop and IPv6. The performance of the cluster may suffer from turning IPv6 on in clusters: mail archive. One good practice is to disable IPv6 on servers in the Hadoop cluster. Hostname vs. IP Hostname is preferred. » Read more

Setting Up Standalone (Local) Hadoop

Hadoop is designed to run on hundreds to thousands of computers inside cluster. However, Hadoop is configured to run things in a non-distributed mode as a single Java process by default. This is specially useful for debugging since distributed debugging is really a nightmare. This post introduces how to set up a standalone Hadoop environment. 1. Hadoop package and software installation Follow the instruction of “1. » Read more

mrcc – A Distributed C Compiler System on MapReduce

The mrcc project’s homepage is here: mrcc project. Abstract mrcc is an open source compilation system that uses MapReduce to distribute C code compilation across the servers of the cloud computing platform. mrcc is built to use Hadoop by default, but it is easy to port it to other could computing platforms, such as MRlite, by only changing the interface to the platform. » Read more