h2o spark

Tags: save overwrite worker ASE body compatible form result printWelcome to the big Data and AI technical articles released by the public number: Qing Research Academy, where you can learn the night white (author's pen name) carefully organized notes, let us make a little progress every day, so that excellent become a habit!One, spark SQL: Similar to Hive, is a data analysis engineWhat is Spark SQL?

Source: http://www.cnblogs.com/shishanyuan/p/4747735.html 1. Introduction to Spark streaming 1.1 Overview Spark Streaming is an extension of the Spark core API that enables the processing of high-throughput, fault-tolerant real-time streaming data. Support for obtaining data from a variety of data sources, including KAFK, Flume, Twitter, ZeroMQ, Kinesis, and

Spark example: Sorting by array and spark example Array sorting is a common operation. The lower performance limit of a comparison-based sorting algorithm is O (nlog (n), but in a distributed environment, we can improve the performance. Here we show the implementation of array sorting in Spark, analyze the performance, and try to find the cause of performance imp

Pre-deployment1.JDK installation, configuring path2. Download the spark-1.6.1-bin-hadoop2.6.tgz and upload to the server to extract3. Create a soft link to the destination folder under/ usr[Email protected] usr]# ln-s spark-1.6. 1-bin-hadoop2. 6 Spark4. Modify the configuration file, target directory /usr/spark/conf/[email protected] conf]# lsdocker.properties.

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

class (according to the CLK. TSV Data Format) Case class click (D: Java. util. Date, UUID: String, landing_page: INT) // Load the file Reg. TSV on HDFS and convert each row of data to a register object; Val Reg = SC. textfile ("HDFS: // chenx: 9000/week2/join/Reg. TSV "). map (_. split ("\ t ")). map (r => (r (1), register (format. parse (R (0), R (1), R (2), R (3 ). tofloat, R (4 ). tofloat ))) // Load the CLK. TSV file on HDFS and convert each row of data to a click object; Val CLK = SC.

3, hands on the abstract class in ScalaThe definition of an abstract class requires the use of the abstract keyword: The above code defines and implements the abstract method, it is important to note that we put the direct running code in the trait subclass of the app, about the inside of the app helps us implement the Main method and manages the code written by the engineer;Here's a look at the use of uninitialized variables in an abstract class: 4, hands-on trait in ScalaTrait

none, and below we look at the use of option: Next, take a look at filter processing: Here's a look at the zip operation for the collection: Here's a look at the partition of the collection: We can use flatten's multi-collection for flattening operations: Flatmap is a combination of map and flatten operations, first map operation and then flatten operation: "Spark Asia-Pacific Research ser

The collection mainly has list, set, Tuple, map, etc., we follow the hands-on practical way to learn. We create a list instance in the Eclipse IDE: Now let's look at the code implementation: In the source code, it is stated that the internal is the method of apply to complete the instantiation; In the same way we can instantiate set: You can also see the implementation of the set instantiation object at this point: Next we'll look at the set in the command-line terminal, first of all set:

5. Apply method and Singleton object in Scala to create a new class: As an additional point, the methods placed in object objects are static methods, as follows: Next look at the use of the Apply method: The above code always when we use "val a = Applytest ()" will cause the call of the Apply method and return the value of the method call, that is, the instantiated object of the applytest. C The lass can also be used by the Apply method, as shown in the following ways: Because the methods

Copy an object The content of the copied "input" folder is as follows: The content of the "conf" file under the hadoop installation directory is the same. Now, run the wordcount program in the pseudo-distributed mode we just built: After the operation is complete, let's check the output result: Some statistical results are as follows: At this time, we will go to the hadoop Web console and find that we have submitted and successfully run the task: After hadoop co

This article, it is necessary to read, write well. But after looking, don't forget to check out the Apache Spark website. Because this article understanding or with the source code, official documents inconsistent. A little mistake! "The Cnblogs Code Editor does not support Scala, so the language keyword is not highlighted"In data analysis, processing Key,value pair data is a very common scenario, for example, we can group, aggregate, or combine two o

[Email protected] ~]$ HDFs dfs-cat People.json{"Name": "Alice", "Pcode": "94304"}{"Name": "Brayden", "age": +, "Pcode": "94304"}{"Name": "Carla", "age": +, "Pcoe": "10036"}{"Name": "Diana", "Age": 46}{"Name": "Etienne", "Pcode": "94104"}[Email protected] ~]$HDFs Dfs-cat Pcodes.json{"Pcode": "10036", "City": "New York", "state": "NY"}{"Pcode:" 87501 "," City ":" Santa Fe "," state ":" NM "}{"Pcode": "94304", "City": "Palo Alto", "state": "CA"}{"Pcode": "94104", "City": "San Francisco", "state": "

Jobs that users submit through different threads can run concurrently, but are subject to resource constraints. Job to the dispatch pool (pool) To request resources, the dispatch pool will be based on the project configuration, decide which scheduling mode to use. FIFO mode by default, the Spark Scheduler Dispatches job execution in FIFO (first-in first Out) mode. Each job is cut into multiple stage. The first job takes all available resources, and

First half Source: http://blog.csdn.net/lsshlsw/article/details/51213610 The latter part is my optimization plan for everyone's reference. +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Sparksql Shuffle the error caused by the operation Org.apache.spark.shuffle.MetadataFetchFailedException: Missing An output location for shuffle 0 Org.apache.spark.shuffle.FetchFailedException: Failed to connect to hostname/192.168.xx.xxx:50268 Error from Rdd's shuf

In the conf file of your spark path, the CP copy Spark-defaults.conf.template is spark-defaults.conf and add the following file spark.eventLog.enabled trueSpark.eventLog.dir hdfs://master:9000/historySpark.eventLog.compress true Distribute configuration to other child nodes I'm using rsync. rsync sparkconf Path/spark