CSDN大数据技术：
部分摘录：
加州大学伯克利分校AMP实验室博士Matei Zaharia：Spark的现状和未来 ----（Matei Zaharia是加州大学伯克利分校AMP实验室博士研究生，Databricks公司的联合创始人兼现任CTO。Zaharia致力于于大规模数据密集型计算的系统和算法。研究项目包括：Spark、Shark、Multi-Resource Fairness、MapReduce Scheduling、SNAP Sequence Aligner）
Spark是发源于美国加州大学伯克利分校AMPLab的集群计算平台，立足于内存计算，从多迭代批量处理出发，兼收并蓄数据仓库、流处理和图计算等多种计算范式，是罕见的全能选手。
Project History：
Spark started as research project in 2009
Open sourced in 2010
Growing community since
Entered Apache lncubator in June 2013
Release Growth：
Spark 0.6 ---- Java API、Maven、standalone mode ，17 contributors
Spark 0.7 ---- Python API、Spark Streaming ，31 contributors
Spark 0.8 ---- YARN、MLlib、monitoring UI ，67 contributors ---- High availability for standalone mode (0.8.1)
Spark 0.9 ---- Scala 2.10 support、Configuration system、Spark Streaming improvement
Projects Bulit on Spark：
Shark(SQL)、Spark Streaming(real-time)、GraphX(graph)、MLbase(machine learning)
Databricks公司CEO Ion Stoica：将数据转化为价值 ----（Ion Stoica是UC Berkeley计算机教授，AMPLab共同创始人，弹性P2P协议Chord、集群内存计算框架Spark、集群资源管理平台Mesos都出自他）
Turning Data into Value
What do We Need？
interactive queries(交互式查询) ---- enable faster decision
Queries on streaming data(基于数据流的查询) ---- enable decisions on real-time data ---- Eg：fraud detection(欺诈检测)、detect DDoS attacks(检测DDoS攻击)
Sophisticated data processing(复杂的数据处理) ---- enable "better" decision
Our Goal：
Support batch、Streaming、and interactive computation（批处理、流处理、交互计算）...... in a unified framework
Easy to develop sophisticated algorithms(e.g..，graph，ML algos)
Big Data Challenge：Time 、Money 、Answer Quality
处理速度与精确性的权衡：反比
Tim Tully ：集成Spark/Shark到雅虎数据分析平台
Sharethrough数据专家Ryan Weald：产品化Spark流媒体
Keys to Fault Tolerance：
Receive fault tolerance ---- Use Actors with supervisor、Use self healing connection pools
Monitoring job progress
RDDs：弹性分布式数据集
Low latency & Scale (低延时&大规模)
iterative and Interactive computation (迭代式和交互式计算)
Databricks创始人Patrick Wendell：理解Spark应用程序的性能 ---- (专注于大规模数据密集型计算。致力于Spark的性能基准测试，同时是spark-perf的合著者。此次峰会他就Spark 深度挖掘、UI概述和测试设备、普通性能和错误)
Summary of Components：
Tasks：Fundamental unit of work
Stage：Set of tasks that run in parallel
DAG：Logical graph of RDD operations
RDD：Parallel dataset with partitions
Demo of perf UI ---- Problems：
Scheduling and launching tasks
Execution of tasks
Writing data between stages
Collecting results
Databricks客户端解决方案主管Pat McDonough：用Spark并行程序设计 ---- (从Spark的性能、组件等方面全面介绍Spark的各种优异性能)
UC Berkeley博士Tathagata Das：用Spark流实时大数据处理 ---- (什么是Spark流，为什么选择Spark流，其性能和容错机制)
DStreams+RDDs=Power
Fault-tolerance：
Batches of input data are replicated in memory for fault-tolerance
Data lost due to worker failure，can be recomputed from replicated input data
All transformations are fault-tolerant，and exactly-once transformations
Higher throughput than Storm：
Spark Streaming：670K records/sec/node
Storm：115K records/sec/node
Fast Fault Recovery：
Recovers from faults/stragglers within 1 sec
Spark 0.9 in Jan 2014 ---- out of alpha
Automated master fault recovery
Performance optimizations
Web UI，and better monitoring capabilities
Cluster Manager UI ---- Standalone mode：<master>：8080
Executor Logs ---- Stored by cluster manager on each worker
Spark Driver Logs ---- Spark initializes a log4j when created ，Include log4j.properties file on the classpath
Application Web UI ---- http://spark-application-host:4040 ---- For executor / task / stage / memory status，etc