high performance spark pdf

ObjectiveIn the field of big data computing, Spark has become one of the increasingly popular and increasingly popular computing platforms. Spark's capabilities include offline batch processing in big data, SQL class processing, streaming/real-time computing, machine learning, graph computing, and many different types of computing operations, with a wide range of applications and prospects. In the mass reviews, many students have tried to use

Spark Applications-peilong Li 8. Avoid Cartesian operation The Rdd.cartesian operation is time-consuming, especially when the dataset is large, the order of magnitude of the Cartesian is square-level, both time-consuming and space consuming. >>> Rdd = Sc.parallelize ([1, 2]) >>> sorted (Rdd.cartesian (RDD). Collect ()) [(1, 1), (1, 2), (2 , 1), (2, 2)] 9. Avoid shuffle when possible The shuffle in spark

Content:1, the traditional spark memory management problem;2, Spark unified memory management;3, Outlook;========== the traditional Spark memory management problem ============Spark memory is divided into three parts:Execution:shuffles, Joins, Sort, aggregations, etc., by default, spark.shuffle.memoryfraction default i

, there are a large number of 0 and 1. The gzip algorithm is used for compression, and the size after compression is 1.9 GB, in this step, the query is reduced from 40.232 to 20.12 S. Step 2: A large Wide Table has more than 1800 columns, but only 20 columns are used effectively. Therefore, rcfile only loads valid columns. In this step, the query speed is reduced from 20 s to 12 s. Step 3: Jprofile is used to analyze why the CPU load is too high and f

Content:1, Spark performance optimization needs to think about the basic issues;2, CPU and memory;3. Degree of parallelism and task;4, the network;========== Liaoliang daily Big Data quotes ============Liaoliang daily Big Data quotes Spark 0080 (2016.1.26 in Shenzhen): If the CPU usage in spark is not

the ideal situation, some tasks will run faster, such as 50s is over, some tasks, may be slower, to 1 minutes and a half to run, so if your task number, just set the same number of CPU core, may still lead to waste of resources, because, For example, 150 task,10 first run out, the remaining 140 are still running, but this time, there are 10 CPU core is free, resulting in waste. that if the number of tasks is set to a total of one or more CPU cores, then once a task has run out, another task can

When you start writing Apache Spark code or browsing public APIs, you will encounter a variety of terminology, such as Transformation,action,rdd and so on. Understanding these is the basis for writing Spark code. Similarly, when your task starts to fail or you need to understand why your application is so time-consuming through the Web interface, you need to know some new nouns: job, stage, task. Understand

Download the High-imitation QQ source code (android front-end + JAVA background + spark A openfire (XMPP + open source code ); B. android front-end source code (similar to QQ's high UI ); C java background source code (HTML5 on the UI ); Dspark (for windows ); Get: do not enjoy the income of labor The system is mainly implemented as follows: JAVA background (

;/dev/null 2>1echo "Realserver Start OK";;StopIfconfig lo:0 DownRoute del $SNS _VIP >/dev/null 2>1echo "0" >/proc/sys/net/ipv4/conf/lo/arp_ignoreecho "0" >/proc/sys/net/ipv4/conf/lo/arp_announceecho "0" >/proc/sys/net/ipv4/conf/all/arp_ignoreecho "0" >/proc/sys/net/ipv4/conf/all/arp_announceecho "Realserver stoped";;*)echo "Usage: $ {start|stop}"Exit 1EsacExit 0chmod 755/etc/init.d/realserverService Realserver StartLo: Link/loopback 00:00:00:00:00:00 BRD 00:00:00:00:00:00inet 127.0.0.1/8 Scope h

Spark Source Analysis Reproduced: Http://blog.sina.com.cn/s/articlelist_2628346427_2_1.html Http://blog.sina.com.cn/s/blog_9ca9623b0102webd.html Spark Performance Optimization-------Development tuning reprint 2016-05-15 12:58:17 Development tuning, know spark basic development principles, including: RDD lineage design

3.3.3virtualmetadata13.3.4diskrecovery13.4 Stripe (asmstriping) 13.4.1ASM file Template (asmfiletemplate) 13.4.2ASM alias ( Asmfilealias) Interaction between 13.5RDBMS and ASM 13.6ASM Instance restores the interaction between 13.7ASM and Osfilesystem 13.7.1dbms_file_ Transfer package 13.7.2RMAN The Convert method 13.7.3ASM and Tts13.8asm Limitations 13.9 Summary 14th Chapter performance and RAC14.1RSeveral features of AC 14.2awr14.2.1 enable AWR14.2.

What is Tachyon?Tachyon is a high-performance, fault-tolerant, memory-based, open-source distributed storage System with Java-like file APIs, a plug-in underlying filesystem, compatibility with Hadoop MapReduce, and Apache Spark. Tachyon provides cross-cluster file sharing services that provide memory-level speed for cluster frameworks such as

In Spark, the most basic principle is that each task processes a partition of an RDD. 1, the advantages of mappartitions operation:If it is a normal map, such as 10,000 data in a partition, OK, then your function will be executed and calculated 10,000 times.However, after using the mappartitions operation, a task will only execute once function,function receive all partition data at once. As long as it executes once, the

Recommendation Model Evaluation In this article, we evaluate the performance of the Spark Machine Learning 1.0: Recommendation engine-Movie recommendation model. Mse/rmse Mean Variance (MSE) is the sum of the values of the POW (forecast score-actual score, 2), divided by the number of items, for each actual existing rating. and the RMS Difference (RMSE) is the MSE open radical. We first use ratings to gene