I have a coordinator which I'd like to run with different input values.
For example, below are the 'input-events' provided. I would like to run the coordinator job with the following values of param 'benzene_hbase_input_offset':
(-24) - looks at the data of the last 24 hours.
(-96) - looks at the data of the last 96 hours.
Nowadays I run only option '2'. In order to avoid code duplication, I'd like to run the same coordinator with a different input for the 'start-instance' param.
How can I do that?
<input-events>
<data-in name="benzene_dwell_calc-output" dataset="BENZENE_DWELL_CALC_OUTPUT">
<start-instance>${coord:current(benzene_hbase_input_offset)}</start-instance>
<end-instance>${coord:current(-1)}</end-instance>
</data-in>
<data-in name="benzene_dwell_calc-output" dataset="BENZENE_DWELL_CALC_OUTPUT">
<start-instance>${coord:current(benzene_hbase_input_offset)}</start-instance>
<end-instance>${coord:current(-1)}</end-instance>
</data-in>
</input-events>
Set this param in the jog.properties.
In the coordinator place it in the start instance and also place it in the coordinator name so you can distinct between the two
Related
I would like to pass the execution time of the coordinator into the SLA value of nominal time of a workflow. I pass the EL function in a property tag in the workflow and also in coordinator, but I received the message:
EL_ERROR No function is mapped to the name "coord:nominalTime"
I have tried different EL functions but the result was the same.
Which function do I need to use in order to pass the time of coordinator to the SLA argument of nominl time in the workflow?
To clarify, the SLA code snippet exists in workflow action, not in the coordinator.
It's hard to give you an answer without seeing your Oozie XML, but I think this is what you are asking for.
In the coordinator:
<action>
<workflow>
<app-path>wf.xml</app-path>
<configuration>
<property>
<name>nominalTime</name>
<value>${coord:nominalTime()}</value>
</property>
</configuration>
</workflow>
</action>
In the workflow:
<sla:info>
<sla:nominal-time>${nominalTime}</sla:nominal-time>
...
</sla:info>
I'm currently writing a Scala application made of a Producer and a Consumer. The Producers get some data from and external source and writes em inside Kafka. The Consumer reads from Kafka and writes to Elasticsearch.
The consumer is based on Spark Streaming and every 5 seconds fetches new messages from Kafka and writes them to ElasticSearch. The problem is I'm not able to write to ES because I get a lot of errors like the one below :
ERROR] [2015-04-24 11:21:14,734] [org.apache.spark.TaskContextImpl]:
Error in TaskCompletionListener
org.elasticsearch.hadoop.EsHadoopException: Could not write all
entries [3/26560] (maybe ES was overloaded?). Bailing out... at
org.elasticsearch.hadoop.rest.RestRepository.flush(RestRepository.java:225)
~[elasticsearch-spark_2.10-2.1.0.Beta3.jar:2.1.0.Beta3] at
org.elasticsearch.hadoop.rest.RestRepository.close(RestRepository.java:236)
~[elasticsearch-spark_2.10-2.1.0.Beta3.jar:2.1.0.Beta3] at
org.elasticsearch.hadoop.rest.RestService$PartitionWriter.close(RestService.java:125)
~[elasticsearch-spark_2.10-2.1.0.Beta3.jar:2.1.0.Beta3] at
org.elasticsearch.spark.rdd.EsRDDWriter$$anonfun$write$1.apply$mcV$sp(EsRDDWriter.scala:33)
~[elasticsearch-spark_2.10-2.1.0.Beta3.jar:2.1.0.Beta3] at
org.apache.spark.TaskContextImpl$$anon$2.onTaskCompletion(TaskContextImpl.scala:57)
~[spark-core_2.10-1.2.1.jar:1.2.1] at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:68)
[spark-core_2.10-1.2.1.jar:1.2.1] at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:66)
[spark-core_2.10-1.2.1.jar:1.2.1] at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
[na:na] at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
[na:na] at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:66)
[spark-core_2.10-1.2.1.jar:1.2.1] at
org.apache.spark.scheduler.Task.run(Task.scala:58)
[spark-core_2.10-1.2.1.jar:1.2.1] at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200)
[spark-core_2.10-1.2.1.jar:1.2.1] at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
[na:1.7.0_65] at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
[na:1.7.0_65] at java.lang.Thread.run(Thread.java:745) [na:1.7.0_65]
Consider that the producer is writing 6 messages every 15 seconds so I really don't understand how this "overload" can possibly happen (I even cleaned the topic and flushed all old messages, I thought it was related to an offset issue). The task executed by Spark Streaming every 5 seconds can be summarized by the following code :
val result = KafkaUtils.createStream[String, Array[Byte], StringDecoder, DefaultDecoder](ssc, kafkaParams, Map("wasp.raw" -> 1), StorageLevel.MEMORY_ONLY_SER_2)
val convertedResult = result.map(k => (k._1 ,AvroToJsonUtil.avroToJson(k._2)))
//TO-DO : Remove resource (yahoo/yahoo) hardcoded parameter
log.info(s"*** EXECUTING SPARK STREAMING TASK + ${java.lang.System.currentTimeMillis()}***")
convertedResult.foreachRDD(rdd => {
rdd.map(data => data._2).saveToEs("yahoo/yahoo", Map("es.input.json" -> "true"))
})
If I try to print the messages instead of sending to ES, everything is fine and I actually see only 6 messages. Why can't I write to ES?
For the sake of completeness, I'm using this library to write to ES : elasticsearch-spark_2.10 with the latest beta version.
I found, after many retries, a way to write to ElasticSearch without getting any error. Basically passing the parameter "es.batch.size.entries" -> "1" to the saveToES method solved the problem. I don't understand why using the default or any other batch size leads to the aforementioned error considering that I would expect an error message if I'm trying to write more stuff than the allowed max batch size, not less.
Moreover I've noticed that actually I was writing to ES but not all my messages, I was losing between 1 and 3 messages per batch.
When I pushed dataframe to ES on Spark, I had the same error message. Even with "es.batch.size.entries" -> "1" configuration,I had the same error.
Once I increased thread pool in ES, I could figure out this issue.
for example,
Bulk pool
threadpool.bulk.type: fixed
threadpool.bulk.size: 600
threadpool.bulk.queue_size: 30000
Like it was already mentioned here, this is a document write conflict.
Your convertedResult data stream contains multiple records with the same id. When written to elastic as part of the same batch produces the error above.
Possible solutions:
Generate unique id for each record. Depending on your use case it can be done in a few different ways. As example, one common solution is to create a new field by combining the id and lastModifiedDate fields and use that field as id when writing to elastic.
Perform de-duplication of records based on id - select only one record with particular id and discard other duplicates. Depending on your use case, this could be the most current record (based on time stamp field), most complete (most of the fields contain data), etc.
The #1 solution will store all records that you receive in the stream.
The #2 solution will store only the unique records for a specific id based on your de-duplication logic. This result would be the same as setting "es.batch.size.entries" -> "1", except you will not limit the performance by writing one record at a time.
One of the possibility is the cluster/shard status being RED. Please address this issue which may be due to unassigned replicas. Once status turned GREEN the API call succeeded just fine.
This is a document write conflict.
For example:
Multiple documents specify the same _id for Elasticsearch to use.
These documents are located in different partitions.
Spark writes multiple partitions to ES simultaneously.
Result is Elasticsearch receiving multiple updates for a single Document at once - from multiple sources / through multiple nodes / containing different data
"I was losing between 1 and 3 messages per batch."
Fluctuating number of failures when batch size > 1
Success if batch write size "1"
Just adding another potential reason for this error, hopefully it helps someone.
If your Elasticsearch index has child documents then:
if you are using a custom routing field (not _id), then according to
the documentation the uniqueness of the documents is not guaranteed.
This might cause issues while updating from spark.
If you are using the standard _id, the uniqueness will be preserved, however you need to make sure the following options are provided while writing from Spark to Elasticsearch:
es.mapping.join
es.mapping.routing
My scenario is like,
1. Login by multiple users (100 users)
2. Select 50 items by id's specific to user
I have placed id's for each user in separate csv file say user1.csv, user2.csv, user3.csv and so on.
My result should be like Thread 1 should take user1.csv and process all 50 id's in loop controller.
Thread 2 should take user2.csv and so on.
I tried with below example, but still couldn't find the solution.
Eg. I used file path as C:\abc\user${_threadNum}.csv
Or
C:\abc\user${_threadNum}.csv
Variable name in csv file is user_id
Requeat will look like /home/abc/${user_id}
I want thread 1 to use the user1.csv file and substitiue the value of user_id in the request and thread 2 should use user2.csv and so on.
If I execute my above plan, I am getting error as /home/abc/EOF
How is this possible in JMeter? Or any other approach?
Please provide solution with an example, since I am new to JMeter.
Create your files like comman_name_1,comman_name_2,comman_name_3
etc. & select current thread option from dropdown list present in csv data config set.This will allow threads to use different files per thread or put values in a single file and select current thread option.
It's easy, just add CSV Data Set Config to each thread (user1.csv for thread1 and so forth). Configure CSV Data Set config, and select one of this values in Sharing mode field:
Current thread group - each file is opened once for each thread group
in which the element appears
Current thread - each file is opened separately for each thread
UPD: I have done it right now, it works
Script looks like this:
I selected 'Current thread' in each CSV Data Set Config
I was making the mistake of using single underscore in fetching the count of thread.
We need to use double - underscore like this : ${__threadNum}
Note :
I am trying to perform a mapreduce job using the Python MRJob lib and am having some issues getting it to properly distribute across my Hadoop cluster. I believe I am simply missing a basic principle of mapreduce. My cluster is a small, one master one slave test cluster. The basic idea is that I'm just requesting a series of web pages with parameters, doing some analysis on them and returning back some properties on the web page.
The input to my map function is simply a list of URLs with parameters such as the following:
http://guelph.backpage.com/automotive/?layout=bla&keyword=towing
http://guelph.backpage.com/whatever/?p=blah
http://semanticreference.com/search.html?go=Search&q=red
http://copiahcounty.wlbt.com/h/events?ename=drupaleventsxmlapi&s=rrr
http://sweetrococo.livejournal.com/34076.html?mode=ffff
Such that the key-value pairs for the initial input are just key:None, val:URL.
The following is my map function:
def mapper(self, key, url):
'''Yield domain as the key, and (url, query parameter) tuple as the value'''
parsed_url = urlparse(url)
domain = parsed_url.scheme + "://" + parsed_url.netloc + "/"
if self.myclass.check_if_param(parsed_url):
parsed_url_query = parsed_url.query
url_q_dic = parse_qs(parsed_url_query)
for query_param, query_val in url_q_dic.iteritems():
#yielding a tuple in mrjob will yield a list
yield domain, (url, query_param)
Pretty simple, I'm just checking to make sure the URL has a parameter and yielding the URL's domain as key and a tuple giving me the URL and the query parameter as value which MRJob kindly transforms into a list to pass to the reducer, which is the following:
def reducer(self, domain, url_query_params):
final_list = []
for url_query_param in url_query_params:
url_to_list_props = url_query_param[0]
param_to_list_props = url_query_param[1]
#set our target that we will request and do some analysis on
self.myclass.set_target(url_to_list_props, param_to_list_props)
#perform a bunch of requests and do analysis on the URL requested
props_list = self.myclass.get_props()
for prop in props_list:
final_list.append(prop)
#index this stuff to a central db
MapReduceIndexer(domain, final_list).add_prop_info()
yield domain, final_list
My problem is that only one reducer task is run. I would expect the number of reducer tasks to be equal to the number of unique keys emitted by the mapper. The end result with the above code is that I have one reducer which runs on the master, but the slave sits idly and does nothing, which is obviously not ideal. I notice that in my output a few mapper tasks are started, but always only 1 reducer task. Other than that, the task runs smoothly and all works as expected.
My question is... what the heck am I doing wrong? Am I misunderstanding the reduce step or screwing up my key-value pairs somewhere? Why are there not multiple reducers running on this job?
Update: OK so from the answer given I increased mapred.reduce.tasks to higher (it was the default which I now realize is 1). This was indeed why I was getting 1 reducer. I now see 3 reduce tasks being performed simultaneously. I now have an import error on my slave that needs to be resolved but at least I am getting somewhere...
The number of reducers is totally unrelated to the form of your input data. For MRJob it looks like you need bootstrap options
In many real-life situations where you apply MapReduce, the final algorithms end up being several MapReduce steps.
i.e. Map1 , Reduce1 , Map2 , Reduce2 , and so on.
So you have the output from the last reduce that is needed as the input for the next map.
The intermediate data is something you (in general) do not want to keep once the pipeline has been successfully completed. Also because this intermediate data is in general some data structure (like a 'map' or a 'set') you don't want to put too much effort in writing and reading these key-value pairs.
What is the recommended way of doing that in Hadoop?
Is there a (simple) example that shows how to handle this intermediate data in the correct way, including the cleanup afterward?
I think this tutorial on Yahoo's developer network will help you with this: Chaining Jobs
You use the JobClient.runJob(). The output path of the data from the first job becomes the input path to your second job. These need to be passed in as arguments to your jobs with appropriate code to parse them and set up the parameters for the job.
I think that the above method might however be the way the now older mapred API did it, but it should still work. There will be a similar method in the new mapreduce API but i'm not sure what it is.
As far as removing intermediate data after a job has finished you can do this in your code. The way i've done it before is using something like:
FileSystem.delete(Path f, boolean recursive);
Where the path is the location on HDFS of the data. You need to make sure that you only delete this data once no other job requires it.
There are many ways you can do it.
(1) Cascading jobs
Create the JobConf object "job1" for the first job and set all the parameters with "input" as inputdirectory and "temp" as output directory. Execute this job:
JobClient.run(job1).
Immediately below it, create the JobConf object "job2" for the second job and set all the parameters with "temp" as inputdirectory and "output" as output directory. Execute this job:
JobClient.run(job2).
(2) Create two JobConf objects and set all the parameters in them just like (1) except that you don't use JobClient.run.
Then create two Job objects with jobconfs as parameters:
Job job1=new Job(jobconf1);
Job job2=new Job(jobconf2);
Using the jobControl object, you specify the job dependencies and then run the jobs:
JobControl jbcntrl=new JobControl("jbcntrl");
jbcntrl.addJob(job1);
jbcntrl.addJob(job2);
job2.addDependingJob(job1);
jbcntrl.run();
(3) If you need a structure somewhat like Map+ | Reduce | Map*, you can use the ChainMapper and ChainReducer classes that come with Hadoop version 0.19 and onwards.
There are actually a number of ways to do this. I'll focus on two.
One is via Riffle ( http://github.com/cwensel/riffle ) an annotation library for identifying dependent things and 'executing' them in dependency (topological) order.
Or you can use a Cascade (and MapReduceFlow) in Cascading ( http://www.cascading.org/ ). A future version will support Riffle annotations, but it works great now with raw MR JobConf jobs.
A variant on this is to not manage MR jobs by hand at all, but develop your application using the Cascading API. Then the JobConf and job chaining is handled internally via the Cascading planner and Flow classes.
This way you spend your time focusing on your problem, not on the mechanics of managing Hadoop jobs etc. You can even layer different languages on top (like clojure or jruby) to even further simplify your development and applications. http://www.cascading.org/modules.html
I have done job chaining using with JobConf objects one after the other. I took WordCount example for chaining the jobs. One job figures out how many times a word a repeated in the given output. Second job takes first job output as input and figures out total words in the given input. Below is the code that need to be placed in Driver class.
//First Job - Counts, how many times a word encountered in a given file
JobConf job1 = new JobConf(WordCount.class);
job1.setJobName("WordCount");
job1.setOutputKeyClass(Text.class);
job1.setOutputValueClass(IntWritable.class);
job1.setMapperClass(WordCountMapper.class);
job1.setCombinerClass(WordCountReducer.class);
job1.setReducerClass(WordCountReducer.class);
job1.setInputFormat(TextInputFormat.class);
job1.setOutputFormat(TextOutputFormat.class);
//Ensure that a folder with the "input_data" exists on HDFS and contains the input files
FileInputFormat.setInputPaths(job1, new Path("input_data"));
//"first_job_output" contains data that how many times a word occurred in the given file
//This will be the input to the second job. For second job, input data name should be
//"first_job_output".
FileOutputFormat.setOutputPath(job1, new Path("first_job_output"));
JobClient.runJob(job1);
//Second Job - Counts total number of words in a given file
JobConf job2 = new JobConf(TotalWords.class);
job2.setJobName("TotalWords");
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(IntWritable.class);
job2.setMapperClass(TotalWordsMapper.class);
job2.setCombinerClass(TotalWordsReducer.class);
job2.setReducerClass(TotalWordsReducer.class);
job2.setInputFormat(TextInputFormat.class);
job2.setOutputFormat(TextOutputFormat.class);
//Path name for this job should match first job's output path name
FileInputFormat.setInputPaths(job2, new Path("first_job_output"));
//This will contain the final output. If you want to send this jobs output
//as input to third job, then third jobs input path name should be "second_job_output"
//In this way, jobs can be chained, sending output one to other as input and get the
//final output
FileOutputFormat.setOutputPath(job2, new Path("second_job_output"));
JobClient.runJob(job2);
Command to run these jobs is:
bin/hadoop jar TotalWords.
We need to give final jobs name for the command. In the above case, it is TotalWords.
You may run MR chain in the manner as given in the code.
PLEASE NOTE: Only the driver code has been provided
public class WordCountSorting {
// here the word keys shall be sorted
//let us write the wordcount logic first
public static void main(String[] args)throws IOException,InterruptedException,ClassNotFoundException {
//THE DRIVER CODE FOR MR CHAIN
Configuration conf1=new Configuration();
Job j1=Job.getInstance(conf1);
j1.setJarByClass(WordCountSorting.class);
j1.setMapperClass(MyMapper.class);
j1.setReducerClass(MyReducer.class);
j1.setMapOutputKeyClass(Text.class);
j1.setMapOutputValueClass(IntWritable.class);
j1.setOutputKeyClass(LongWritable.class);
j1.setOutputValueClass(Text.class);
Path outputPath=new Path("FirstMapper");
FileInputFormat.addInputPath(j1,new Path(args[0]));
FileOutputFormat.setOutputPath(j1,outputPath);
outputPath.getFileSystem(conf1).delete(outputPath);
j1.waitForCompletion(true);
Configuration conf2=new Configuration();
Job j2=Job.getInstance(conf2);
j2.setJarByClass(WordCountSorting.class);
j2.setMapperClass(MyMapper2.class);
j2.setNumReduceTasks(0);
j2.setOutputKeyClass(Text.class);
j2.setOutputValueClass(IntWritable.class);
Path outputPath1=new Path(args[1]);
FileInputFormat.addInputPath(j2, outputPath);
FileOutputFormat.setOutputPath(j2, outputPath1);
outputPath1.getFileSystem(conf2).delete(outputPath1, true);
System.exit(j2.waitForCompletion(true)?0:1);
}
}
THE SEQUENCE IS
(JOB1)MAP->REDUCE-> (JOB2)MAP
This was done to get the keys sorted yet there are more ways such as using a treemap
Yet I want to focus your attention onto the way the Jobs have been chained!!
Thank you
You can use oozie for barch processing your MapReduce jobs. http://issues.apache.org/jira/browse/HADOOP-5303
There are examples in Apache Mahout project that chains together multiple MapReduce jobs. One of the examples can be found at:
RecommenderJob.java
http://search-lucene.com/c/Mahout:/core/src/main/java/org/apache/mahout/cf/taste/hadoop/item/RecommenderJob.java%7C%7CRecommenderJob
We can make use of waitForCompletion(true) method of the Job to define the dependency among the job.
In my scenario I had 3 jobs which were dependent on each other. In the driver class I used the below code and it works as expected.
public static void main(String[] args) throws Exception {
// TODO Auto-generated method stub
CCJobExecution ccJobExecution = new CCJobExecution();
Job distanceTimeFraudJob = ccJobExecution.configureDistanceTimeFraud(new Configuration(),args[0], args[1]);
Job spendingFraudJob = ccJobExecution.configureSpendingFraud(new Configuration(),args[0], args[1]);
Job locationFraudJob = ccJobExecution.configureLocationFraud(new Configuration(),args[0], args[1]);
System.out.println("****************Started Executing distanceTimeFraudJob ================");
distanceTimeFraudJob.submit();
if(distanceTimeFraudJob.waitForCompletion(true))
{
System.out.println("=================Completed DistanceTimeFraudJob================= ");
System.out.println("=================Started Executing spendingFraudJob ================");
spendingFraudJob.submit();
if(spendingFraudJob.waitForCompletion(true))
{
System.out.println("=================Completed spendingFraudJob================= ");
System.out.println("=================Started locationFraudJob================= ");
locationFraudJob.submit();
if(locationFraudJob.waitForCompletion(true))
{
System.out.println("=================Completed locationFraudJob=================");
}
}
}
}
The new Class org.apache.hadoop.mapreduce.lib.chain.ChainMapper help this scenario
Although there are complex server based Hadoop workflow engines e.g., oozie, I have a simple java library that enables execution of multiple Hadoop jobs as a workflow. The job configuration and workflow defining inter job dependency is configured in a JSON file. Everything is externally configurable and does not require any change in existing map reduce implementation to be part of a workflow.
Details can be found here. Source code and jar is available in github.
http://pkghosh.wordpress.com/2011/05/22/hadoop-orchestration/
Pranab
I think oozie helps the consequent jobs to receive the inputs directly from the previous job. This avoids the I/o operation performed with jobcontrol.
If you want to programmatically chain your jobs, you will wnat to use JobControl. The usage is quite simple:
JobControl jobControl = new JobControl(name);
After that you add ControlledJob instances. ControlledJob defines a job with it's dependencies, thus automatically pluging inputs and outputs to fit a "chain" of jobs.
jobControl.add(new ControlledJob(job, Arrays.asList(controlledjob1, controlledjob2));
jobControl.run();
starts the chain. You will want to put that in a speerate thread. This allows to check the status of your chain whil it runs:
while (!jobControl.allFinished()) {
System.out.println("Jobs in waiting state: " + jobControl.getWaitingJobList().size());
System.out.println("Jobs in ready state: " + jobControl.getReadyJobsList().size());
System.out.println("Jobs in running state: " + jobControl.getRunningJobList().size());
List<ControlledJob> successfulJobList = jobControl.getSuccessfulJobList();
System.out.println("Jobs in success state: " + successfulJobList.size());
List<ControlledJob> failedJobList = jobControl.getFailedJobList();
System.out.println("Jobs in failed state: " + failedJobList.size());
}
As you have mentioned in your requirement that you want o/p of MRJob1 to be the i/p of MRJob2 and so on, you can consider using oozie workflow for this usecase. Also you might consider writing your intermediate data to HDFS since it will used by the next MRJob. And after the job completes you can clean-up your intermediate data.
<start to="mr-action1"/>
<action name="mr-action1">
<!-- action for MRJob1-->
<!-- set output path = /tmp/intermediate/mr1-->
<ok to="end"/>
<error to="end"/>
</action>
<action name="mr-action2">
<!-- action for MRJob2-->
<!-- set input path = /tmp/intermediate/mr1-->
<ok to="end"/>
<error to="end"/>
</action>
<action name="success">
<!-- action for success-->
<ok to="end"/>
<error to="end"/>
</action>
<action name="fail">
<!-- action for fail-->
<ok to="end"/>
<error to="end"/>
</action>
<end name="end"/>
New answer since the confirmed answer with the JobClient.run() is not working in the new API:
If you have two jobs like this:
Configuration conf1 = new Configuration();
Job job1 = Job.getInstance(conf1, "a");
Configuration conf2 = new Configuration();
Job job2 = Job.getInstance(conf2, "b");
Then the only thing you should do is adding the following line before creating 'job2':
job1.waitForCompletion(true);