Create a Scala idea project:Click "Next":Click "Finish" to complete the project creation:To modify an item's properties:First modify the Modules option:Create two folders under SRC and change their properties to source:Then modify the libraries:Because you want to develop the spark program, you need to bring in the jar packages that spark needs to develop:After the import package is complete, create a packa
Create a Scala idea project:Click "Next":Click "Finish" to complete the project creation:To modify an item's properties:First modify the Modules option:Create two folders under SRC and change their properties to source:Then modify the libraries:Because you want to develop the spark program, you need to bring in the jar packages that spark needs to develop:After the import package is complete, create a packa
Open idea under the SRC under main under Scala right click to create a Scala class named Simpleapp, the content is as followsOrg.apache.spark.SparkContext org.apache.spark.sparkcontext._ org.apache.spark.SparkConf"a"). Count () numbs = logdata.filter (line = Line.contains ("B")). Count () println ("Lines with a:%s, Lines with B:%s". Format (Numas, numbs))}}
Packaging files:File-->>projectstructure-click artificats-->> click the Green Plus-click jar-->> Select from module with Depe
/wyfs02/M02/4C/CF/wKiom1RFuiKyoNlfAALlgeb1TgQ404.jpg "style =" float: none; "Title =" 48.png" alt = "wkiom1rfuikyonlfaallgeb1tgq404.jpg"/>
Next, use mr-jobhistory-daemon.sh to start jobhistory Server:
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After startup, you can view the task execution history in jobhistory on the Web Console through http: // spar
Step 2: Use the spark cache mechanism to observe the Efficiency Improvement
Based on the above content, we are executing the following statement:
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Tags: android style http color java using IO strongLiaoliang Spark Open Class Grand forum Phase I: Spark has increased the speed of cloud computing big data by more than 100 times times http://edu.51cto.com/lesson/id-30816.htmlSpark Combat Master Road Series Books http://down.51cto.com/tag-Spark%E6%95%99%E7%A8%8B.htmlLiaoliang Teacher (email [email protected] pho
=Tf.reduce_mean (Tf.abs (A)) L2_a_loss=Tf.reduce_mean (Tf.square (A)) E1_term=tf.multiply (elastic_p1,l1_a_loss) e2_term=tf.multiply (Elastic_p2,l2_a_loss)#here A is an irregular shape that corresponds to the array form of the 3,1 loss also expands the arrays formLoss=tf.expand_dims (Tf.add (Tf.add (Tf.reduce_mean (Tf.square (y_target-model_out)), e1_term), e2_term), 0)#Initialize Variablesinit=Tf.global_variables_initializer () sess.run (init)#Gradient Descentmy_opt=Tf.train.GradientDescentOpti
1. Overview
A feature column is a bridge between the original data and the model. In general, the essence of artificial intelligence is to do weights and offset operations to determine the shape of the model.
Before using the TensorFlow version, the data must be processed in a kind and distributed way before it can be used by the artificial intelligence model. The appearance of feature columns makes the work of data processing much easier. 2, the fun
This article focuses on some of the typical problems I have encountered since using spark and how to solve them, hoping to help the students who meet the same problem.1. Spark environment or configuration relatedQ:in the Spark Client Profile spark-defaults.conf, how should spark.executor.memory and Spark.cores.max be c
In general, there are two functions for printing tensorflow variables:tf.trainable_variables () and Tf.all_variables ()The difference is:Tf.trainable_variables () refers to the variables that need to be trainedTf.all_variables () refers to all variables
In general, we are more concerned with training variables that need to be trained:It is important to note that the entire graph is initialized when the variable name is output
First, print the name of
Step 2: Use the spark cache mechanism to observe the Efficiency Improvement
Based on the above content, we are executing the following statement:
It is found that the same calculation result is 15.
In this case, go to the Web console:
The console clearly shows that we performed the "count" Operation twice.
Now we will execute the "Sparks" variable for the "cache" Operation:
Run the Count operation to view the Web console:
At this tim
Step 2: Use the spark cache mechanism to observe the Efficiency Improvement
Based on the above content, we are executing the following statement:
It is found that the same calculation result is 15.
In this case, go to the Web console:
The console clearly shows that we performed the "count" Operation twice.
Now we will execute the "Sparks" variable for the "cache" Operation:
Run the Count operation to view the Web console:
At this time, we found
Restart idea:
Restart idea:
After restart, enter the following interface:
Step 4: Compile scala code in idea:
First, select "create new project" on the interface that we entered in the previous step ":
Select the "Scala" option in the list on the left:
To facilitate future development, select the "SBT" option on the right:
Click "Next" to go to the next step and set the name and directory of the scala project:
Click "finish" to create the project:
Because we have selec
follows: Step 1: Modify the host name in/etc/hostname and configure the ing between the host name and IP address in/etc/hosts: We use the master machine as the master node of hadoop. First, let's take a look at the IP address of the master machine: The IP address of the current host is "192.168.184.20 ". Modify the host name in/etc/hostname: Enter the configuration file: We can see the default name when installing ubuntu. The name of the machine in the configuration file is
. From the configuration above, we can see that we use the master node as the master node and as the data processing node. This is due to the consideration of three copies of our data and the limited number of machines. Copy the master configured masters and slaves files to the conf folder under the hadoop installation directory of slave1 and slave2 respectively: Go to the slave1 or slave2 node to check the content of the masters and slaves files: It is found that the copy is completel
slave2 machines.
In this case, the id_rsa.pub of slave1 is sent to the master, as shown below:
At the same time, the slave2 id_rsa.pub is sent to the master, as shown below:
Check whether the data has been copied on the master:
Now we can see that the public keys of slave1 and slave2 nodes have been transmitted.
All public keys are integrated on the master node:
Copy the master's public key information authorized_keys to the. SSH directory of slave1 and slave1:
Log on to slave1
The command to end historyserver is as follows:
Step 4: Verify the hadoop distributed Cluster
First, create two directories on the HDFS file system. The creation process is as follows:
/Data/wordcount in HDFS is used to store the data files of the wordcount example provided by hadoop. The program running result is output to the/output/wordcount directory, through web control, we can find that we have successfully created two folders:
Next, upload the data of the local file to the HDFS
Spark Overview
Spark is a general-purpose large-scale data processing engine. Can be simply understood as Spark is a large data distributed processing framework.Spark is a distributed computing framework based on the map reduce algorithm, but the Spark intermediate output and result output can be stored in memory, thu
Label:This article explains the structured data processing of spark, including: Spark SQL, DataFrame, DataSet, and Spark SQL services. This article focuses on the structured data processing of the spark 1.6.x, but because of the rapid development of spark (the writing time o
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