Summary: The advent of Apache Spark has made it possible for ordinary people to have big data and real-time data analysis capabilities. In view of this, this article through hands-on Operation demonstration to lead everyone to learn spark quickly. This article is the first part of a four-part tutorial on the Apache
the master machine.
Upload the generated running package to the master (192.168.122.102)
scp spark-1.0-dist.tar.gz [emailprotected]192.168.122.102:~/Run hive on spark Test Cases
After the above-mentioned torture, we finally reached the most tense moment.
Decompress spark-1.0-dist.tar.gz with the hduserid of the master host.
#after login into the master as hduser
when the application provides compute resources for executor startup.It is important to note that if a worker joins cluster, stating that his machine has only 4g of memory, then assigning executor to the above application is that the worker cannot provide any resources because 4gspark-env.shSpark-env.sh the most important is to specify the IP address, if you are running MASTER, you need to specify SPARK_MASTER_IP, if you are ready to run driver or worker will need to specify SPARK_LOCAL_IP, and
The spark kernel is developed by the Scala language, so it is natural to develop spark applications using Scala. If you are unfamiliar with the Scala language, you can read Web tutorials A Scala Tutorial for Java programmers or related Scala books to learn.
This article will introduce 3 Scala spark programming examples
Https://www.iteblog.com/archives/1624.html
Whether we need another new data processing engine. I was very skeptical when I first heard of Flink. In the Big data field, there is no shortage of data processing frameworks, but no framework can fully meet the different processing requirements. Since the advent of Apache Spark, it seems to have become the best framework for solving most of the problems today, s
The previous article "Apache Spark Learning: Deploying Spark to Hadoop 2.2.0" describes how to use MAVEN compilation to build spark jar packages that run directly on the Hadoop 2.2.0, and on this basis, Describes how to build an spark integrated development environment with
Original address: http://blog.jobbole.com/?p=89446I first heard of spark at the end of 2013, when I was interested in Scala, and Spark was written in Scala. After a while, I made an interesting data science project, and it tried to predict surviving on the Titanic . This proves to be a good way to learn more about spark content and programming. I highly recommend
think that since spark also supports hql, can spark be used to access the databases and tables created with hive CLI? The answer may be confusing to you,"It cannot be configured by default ". Why?
Meta data in hive uses the storage engine Derby, which can only be accessed by one user. At the same time, only one person can access the service, even if you log on to the same user. To address this limitation,
Savetocassandra the stored procedure that triggered the data
Another place worth documenting is that if the table created in Cassandra uses the UUID as primary key, use the following function in Scala to generate the UUIDimport java.util.UUIDUUID.randomUUIDVerification stepsUse Cqlsh to see if the data is actually written to the TEST.KV table.SummaryThis experiment combines the following knowledge
Spark SQL
can --py-files add Python .zip , .egg or .py file to search path.Here are some of the options available for a specific cluster manager. For example, Spark standalone cluster with cluster deployment mode, you can also specify --supervise to ensure that driver can be restarted automatically when Non-zero exit code fails. In order to list all spark-submit of the available options, use the --help . To run it.
the source reading, we need to focus on the following two main lines.
static View is RDD, transformation and action
Dynamic View is the life of a job, each job is divided into multiple stages, each stage can contain more than one RDD and its transformation, How these stages are mapped into tasks is distributed into cluster
References (Reference)
Introduction to Spark Internals http://files.meetup.com/3138542/dev-meetup-dec-
http broadcast
spark.broadcast.port
jetty-based, Torrentbroadcast does not use this port, it sends data through the Block manager
executor
driver
random
spark.replclassserver.port
jetty-based, Only for spark shell
Executor/driver
Executor/driver
Random
Block Manager Port
Spark.blockManager.port
Raw socket via Serversocketchannel
documentation.SummaryIn the source reading, we need to focus on the following two main lines.
static View is RDD, transformation and action
Dynamic View is the life of a job, each job is divided into multiple stages, each stage can contain more than one RDD and its transformation, How these stages are mapped into tasks is distributed into cluster
References (Reference)
Introduction to Spark Internals http://files.meetup.com
. Assume that you use git to synchronize the latest source code.
git clone https://github.com/apache/spark.git
Generate an idea Project
sbt/sbt gen-idea
Import Spark Source Code
1. Select File-> Import project and specify the Spark Source Code directory in the pop-up window.
2. Select SBT project as the project type and click Next
3. Click Finish in the new pop
monitoring of computing resources, restarting failed tasks based on monitoring results, or re-distributed task once a new node joins cluster.This part of the content needs to refer to yarn's documentation.SummaryIn the source reading, we need to focus on the following two main lines.
static View is RDD, transformation and action
Dynamic View is the life of a job, each job is divided into multiple stages, each stage can contain more than one RDD and its transformation, How these sta
You are welcome to reprint it. Please indicate the source, huichiro.Summary
There is nothing to say about source code compilation. For Java projects, as long as Maven or ant simple commands are clicked, they will be OK. However, when it comes to spark, it seems that things are not so simple. According to the spark officical document, there will always be compilation errors in one way or another, which is an
, * * w‘ = w - thisIterStepSize * (gradient + regGradient(w)) * Note that regGradient is function of w * * If we set gradient = 0, thisIterStepSize = 1, then * * regGradient(w) = w - w‘ * * TODO: We need to clean it up by separating the logic of regularization out * from updater to regularizer. */ // The following gradientTotal is actually the regularization part of gradient. // Will add the gradientSum computed fr
Run the example one by one to see the results illustrate Hadoop_home environment variablesOrg.apache.spark.examples.sql.hive.JavaSparkHiveExampleModify the run Configuration to add env hadoop_home=${hadoop_home}Run the Java class. After the hive example is exhausted, delete the metastore_db directory.Here's a simple way to run it one by oneEclipse->file->import->run/debug Launch ConfigurationBrowse to the Easy_dev_labs\runconfig directory. Import all.Now from Eclipse->run->run ConfigurationStart
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