bigdata | 易学教程

sqoop import eror - File does not exist:

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I am trying to import data from MySql to HDFS using Sqoop. But I am getting the following error. How to solve this? command : sqoop import --connect jdbc:mysql://localhost/testDB --username root --password password --table student --m 1 error : ERROR tool.ImportTool: Encountered IOException running import job: java.io.FileNotFoundException: File does not exist: hdfs://localhost:54310/usr/lib/sqoop/lib/parquet-format-2.0.0.jar at org.apache.hadoop.hdfs.DistributedFileSystem$18.doCall(DistributedFileSystem.java:1122) at org.apache.hadoop.hdfs.DistributedFileSystem$18.doCall(DistributedFileSystem

Kafka topic per producer

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问题 Lets say I have multiple devices. Each device has different type of sensors. Now I want to send the data from each device for each sensor to kafka. But I am confused about the kafka topics. For processing this real time data Is it good to have kafka topic per device and all the sensors from that device will send the data to particular kafka topic, or I should create one topic and have all the devices send the data to that one topic. If I go with first case where we will create topic per

Modeling a very big data set (1.8 Million rows x 270 Columns) in R

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问题 I am working on a Windows 8 OS with a RAM of 8 GB . I have a data.frame of 1.8 million rows x 270 columns on which I have to perform a glm. (logit/any other classification) I've tried using ff and bigglm packages for handling the data. But I am still facing a problem with the error " Error: cannot allocate vector of size 81.5 Gb ". So, I decreased the number of rows to 10 and tried the steps for bigglm on an object of class ffdf. However the error still is persisting. Can any one suggest me

Why is Spark fast when word count? [duplicate]

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问题 This question already has answers here : Why is Spark faster than Hadoop Map Reduce (2 answers) Closed 2 years ago . Test case: word counting in 6G data in 20+ seconds by Spark. I understand MapReduce , FP and stream programming models, but couldn’t figure out the word counting is so amazing fast. I think it’s an I/O intensive computing in this case, and it’s impossible to scan 6G files in 20+ seconds. I guess there is index is performed before word counting, like Lucene does. The magic

Importing CSV file into Hadoop

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问题 I am new with Hadoop, I have a file to import into hadoop via command line (I access the machine through SSH) How can I import the file in hadoop? How can I check afterward (command)? 回答1: 2 steps to import csv file move csv file to hadoop sanbox (/home/username) using winscp or cyberduck. use -put command to move file from local location to hdfs. hdfs dfs -put /home/username/file.csv /user/data/file.csv 来源： https://stackoverflow.com/questions/34277239/importing-csv-file-into-hadoop

How to convert json array<String> to csv in spark sql

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I have tried this query to get required experience from linkedin data. Dataset<Row> filteredData = spark .sql("select full_name ,experience from (select *, explode(experience['title']) exp from tempTable )" + " a where lower(exp) like '%developer%'"); But I got this error: and finally I tried but I got more rows with the same name . Dataset<Row> filteredData = spark .sql("select full_name ,explode(experience) from (select *, explode(experience['title']) exp from tempTable )" + " a where lower(exp) like '%developer%'"); Please give me hint, how to convert array of string to comma separated

Alternatives to Talend Big data tool [closed]

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问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . i want to know other products like Talend.I would like to know competing products Is there any? please suggest Thanks 回答1: CloverDX and Pentaho are two good options to consider. 来源： https://stackoverflow.com/questions/16980571/alternatives-to-talend-big-data-tool

How to JOIN 3 RDD's using Spark Scala

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问题 I want to join 3 tables using spark rdd . I achieved my objective using spark sql but when I tried to join it using Rdd I am not getting the desired results. Below is my query using spark SQL and the output : scala> actorDF.as("df1").join(movieCastDF.as("df2"),$"df1.act_id"===$"df2.act_id").join(movieDF.as("df3"),$"df2.mov_id"===$"df3.mov_id"). filter(col("df3.mov_title")==="Annie Hall").select($"df1.act_fname",$"df1.act_lname",$"df2.role").show(false) +---------+---------+-----------+ |act

Time differences in Apache Pig?

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问题 In a Big Data context I have a time series S1=(t1, t2, t3 ...) sorted in an ascending order. I would like to produce a series of time differences: S2=(t2-t1, t3-t2 ...) Is there a way to do this in Apache Pig? Short of a very inefficient self-join, I do not see one. If not, what would be an good way to do this suitable for large amounts of data? 回答1: S1 = Generate Id,Timestamp i.e. from t1...tn S2 = Generate Id,Timestamp i.e. from t2...tn S3 = Join S1 by Id,S2 by Id S4 = Extract S1.Timestamp

Finding efficiently all relevant sub ranges for bigdata tables in Hive/ Spark

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问题 Following this question, I would like to ask. I have 2 tables: The first table - MajorRange row | From | To | Group .... -----|--------|---------|--------- 1 | 1200 | 1500 | A 2 | 2200 | 2700 | B 3 | 1700 | 1900 | C 4 | 2100 | 2150 | D ... The second table - SubRange row | From | To | Group .... -----|--------|---------|--------- 1 | 1208 | 1300 | E 2 | 1400 | 1600 | F 3 | 1700 | 2100 | G 4 | 2100 | 2500 | H ... The output table should be the all the SubRange groups who has overlap over the