Install and Configure WPS-Hadoop
Contents
- 1 Build
- 2 Install
- 3 Getting started
Build
The Project is avaiable here: https://svn.d4science.research-infrastructures.eu/gcube/trunk/data-analysis/wps-hadoop/
We suppose to checkout all in ~/wps-hadoop-source.
Tomcat Embedded Packaging
[{{host}} ~/wps-hadoop-source] mvn clean package -P release,linux-x86_64
After the build, the target/ directory will contain the wps-hadoop-Template:Version-tomcat-embedded.tar.gz. This is a compress folder of the WPS-Hadoop server within a pre-configured tomcat.
Processes Packaging
This is useful for processes update of a pre-installed WPS Hadoop tomcat embedded application.
[{{host}} ~/wps-hadoop-source] mvn clean package -P wps,linux-x86_64
After the build, the target/ directory will contain the wps-hadoop-Template:Version.jar to replace in the WPS Hadoop tomcat embedded lib directory.
Install
To install the Tomcat Embedded package, extract the .tar.gz and run the tomcat.
[{{host}} ~]$ tar xzf wps-hadoop-0.1-SNAPSHOT-tomcat-embedded.tar.gz [{{host}} ~]$ wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/startup.sh Using LD_LIBRARY_PATH: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/lib/natives Using CATALINA_BASE: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded Using CATALINA_HOME: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded Using CATALINA_TMPDIR: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/temp Using JRE_HOME: /usr Using CLASSPATH: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/bootstrap.jar:/home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/tomcat-juli.jar
Getting started
In this section we present a full tutorial to integrate and configure the indicator_i1 process.
Create the application
We suppose to already have created, tested and configured (including install R libs) the R script which performs the indicator_i1 algorithm. Let’s create the application folder inside the hadoopApplications.
Note: in this tutorial we have a application work directory called hadoopApplications. Having an utility folder like this is useful to maintain/develope/test your applications (in R, bash or other); when an application is ready you can compress (jar) it and copy into hadoop hdfs folder.
Create the directory structure
More info about legacy applications structure here.
cd mkdir -p hadoopApplications/ird/indicator_i1 # we choose to put it inside an ird folder group cd hadoopApplications/ird/indicator_i1 mkdir -p application/indicator_i1/bin/ application/indicator_i1/lib
so we have:
tree . |-- application `-- indicator_i1 |-- bin |-- lib
Copy and integrate the .R script
Note: with the header #!/usr/bin/Rscript you allow to run the script as executable (for smart call by the run bash script) Note: in the pre-processing statements some parameters are set, something from args, others by constants (year_attribute_name) Note: in the post-processing statements the results file are copied to the current directory (Sys.getenv("PWD"))
#!/usr/bin/Rscript --vanilla --slave
# Francesco Cerasuolo - Terradue
# pre-processing
args <- commandArgs(TRUE)
wfsUrl <- args[1]
typeName <- args[2]
species <- args[3]
connection_type <- "remote"
data_type <- "WFS"
url <- wfsUrl
layer <- typeName
ogc_filter <- paste('<ogc:Filter xmlns:ogc="http://www.opengis.net/ogc" xmlns:gml="http://www.opengis.net/gml">
<ogc:PropertyIsEqualTo><ogc:PropertyName>species</ogc:PropertyName><ogc:Literal>',
species,
'</ogc:Literal></ogc:PropertyIsEqualTo></ogc:Filter>',
sep="")
year_attribute_name <- "year"
ocean_attribute_name <- "ocean"
species_attribute_name <- "species"
value_attribute_name <- "value"
#Norbert Billet - IRD
#2014/01/27: Norbert - Multi sources edit
#2013/08/30: Norbert - Initial edit
#Atlas_i1_SpeciesByOcean : build a graph of catches by ocean and by year
#52North WPS annotations
# wps.des: id = Atlas_i1_SpeciesByOcean, title = IRD tuna atlas indicator i1, abstract = Graph of species catches by ocean;
# wps.in: id = data_type, type = string, title = Data type (csv or WFS or MDSTServer), value = "WFS";
# wps.in: id = url, type = string, title = Data URL, value = "http://mdst-macroes.ird.fr:8080/constellation/WS/wfs/tuna_atlas";
# wps.in: id = layer, type = string, title = Data layer name, minOccurs = 0, maxOccurs = 1, value = "ns11:i1i2_mv";
# wps.in: id = mdst_query, type = string, title = MDSTServer query. Only used with MDSTServer data type, minOccurs = 0, maxOccurs = 1;
# wps.in: id = ogc_filter, type = string, title = OGC filter to apply on a WFS datasource. Only used with WFS data type, minOccurs = 0, maxOccurs = 1;
# wps.in: id = year_attribute_name, type = string, title = Year attribute name in the input dataset, value = "year";
# wps.in: id = ocean_attribute_name, type = string, title = Ocean attribute name in the input dataset, value = "ocean";
# wps.in: id = species_attribute_name, type = string, title = Species attribute name in the input dataset, value = "species";
# wps.in: id = value_attribute_name, type = string, title = Value attribute name in the input dataset, value = "value";
# wps.in: id = connection_type, type = string, title = Data connection type (local or remote), value = "remote";
# wps.out: id = result, type = string, title = List of result files path;
if(! require(IRDTunaAtlas)) {
stop("Missing IRDTunaAtlas library")
}
df <- readData(connectionType=connection_type,
dataType=data_type,
url=url,
layer=layer,
MDSTQuery=mdst_query,
ogcFilter=ogc_filter)
result <- Atlas_i1_SpeciesByOcean(df=df,
yearAttributeName=year_attribute_name,
oceanAttributeName=ocean_attribute_name,
speciesAttributeName=species_attribute_name,
valueAttributeName=value_attribute_name)
# Francesco Cerasuolo - Terradue
# post-processing
apply(result, 1, function(x) file.copy(x, paste(Sys.getenv("PWD"), basename(x), sep="/")))
Create the run bash script
[{{host}} indicator_i1]$ vi application/indicator_i1/run #!/bin/bash # INDICATOR I1 SUCCESS=0 ERR_NOINPUT=18 ERR_NOOUTPUT=19 ERR_CURL=30 DEBUG_EXIT=66 function cleanExit () { local retval=$? local msg="" case "$retval" in $SUCCESS) msg="Processing successfully concluded";; $ERR_NOINPUT) msg="Unable to retrieve an input file";; $ERR_NOOUTPUT) msg="No output results";; $ERR_CURL) msg="curl failed to download the GML from $wfsUrl";; $DEBUG_EXIT) msg="Breaking at debug exit";; *) msg="Unknown error";; esac [ "$retval" != 0 ] && echo "Error $retval - $msg, processing aborted" || echo "INFO - $msg" exit "$retval" } # trap an exit signal to exit properly trap cleanExit EXIT # evaluating the applicationPath, to resolve environments variable eval "appPath=\"$applicationPath\"" # evaluating the outputFilesPath (hdfs path) eval "outFilesPath=\"$outputFilesPath\"" # R library path export R_LIBS_USER=/application/share/rlibrary/ export PATH=$appPath/bin:$PATH chmod 755 $appPath/bin/* # data input file info inputDatafileName="inputData.txt" # create and entering work directory mkdir -p ./work cd work #counter (used as key) count=0 type_name=ns11:i1i2_mv # iterate each input (each input is a row and it’s a species identifier) while read species do # for debug echo "INPUT: species=$species" # call the .R script Atlas_i1_SpeciesByOcean.R $wfsUrl $type_name $species # saving produced output files on the hdfs (subfolder: exec<count>) keyDir="exec$count" path="$outFilesPath$keyDir" # hdfs output directory hadoop fs -mkdir $path # create an input info file echo "species=$species" > $inputDatafileName # copy all files to the hdfs path hadoop fs -copyFromLocal ./* $path/ # cleanup rm -f $inputDatafileName let "count += 1" done
Create the application jar and put it into the HDFS
Compress the application folder in a .jar file
Note: you must maintain the jar name with the same name of the application folder name. Simply, from the indicator_i1 folder:
[{{host}} indicator_i1]$ jar cvf indicator_i1.jar application added manifest adding: application/(in = 0) (out= 0)(stored 0%) adding: application/indicator_i1/(in = 0) (out= 0)(stored 0%) adding: application/indicator_i1/run(in = 1871) (out= 918)(deflated 50%) adding: application/indicator_i1/bin/(in = 0) (out= 0)(stored 0%) adding: application/indicator_i1/bin/Atlas_i1_SpeciesByOcean.R(in = 3020) (out= 1095)(deflated 63%) adding: application/indicator_i1/lib/(in = 0) (out= 0)(stored 0%) [{{host}} indicator_i1]$ ll total 16 drwxr-xr-x 3 imarine-wp10 ciop 4096 Apr 29 12:25 application -rw-r--r-- 1 imarine-wp10 ciop 6322 Apr 29 14:06 indicator_i1.jar [{{host}} indicator_i1]$
Copy the jar into the HDFS
From the indicator_i1 folder:
# remove previous jar if present hadoop fs -rm /algorithmRepository/indicator_i1.jar # copy the jar hadoop fs -copyFromLocal ./indicator_i1.jar /algorithmRepository/ # check if the jar is added hadoop fs -ls /algorithmRepository Found 2 items -rw-r--r-- 1 imarine-wp10 supergroup 2577 2014-04-28 17:26 /algorithmRepository/helloWorld.jar -rw-r--r-- 1 imarine-wp10 supergroup 6322 2014-04-29 14:21 /algorithmRepository/indicator_i1.jar
Now the application is stored into the hdfs repository and it can be called from the wps-hadoop web-app.
Create the process description xml file
We must create the process xml process description inside the ~/wps-hadoop-source/src/main/resources/com/terradue/wps_hadoop/processes/ directory. We choose to organise the xml into the subpath ird/indicator/. Note: It’s important to have the path aligned to the class package path. Note: The path+processName will be the process identifier.
[{{host}} ~]$ vi wps-hadoop-source/src/main/resources/com/terradue/wps_hadoop/processes/ird/indicator/IndicatorI1.xml <?xml version="1.0" encoding="UTF-8"?> <wps:ProcessDescriptions xmlns:wps="http://www.opengis.net/wps/1.0.0" xmlns:ows="http://www.opengis.net/ows/1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.opengis.net/wps/1.0.0 http://geoserver.itc.nl:8080/wps/schemas/wps/1.0.0/wpsDescribeProcess_response.xsd" xml:lang="en-US" service="WPS" version="1.0.0"> <ProcessDescription wps:processVersion="1.0.0" storeSupported="true" statusSupported="false"> <ows:Identifier>IndicatorI1</ows:Identifier> <ows:Title>IRD Tuna Atlas Indicator i1</ows:Title> <ows:Abstract>Graph of catches of a given species.</ows:Abstract> <ows:Metadata xlink:title="Biodiversity"/> <DataInputs> <Input minOccurs="1" maxOccurs="2147483647"> <ows:Identifier>species</ows:Identifier> <ows:Title>Species Names</ows:Title> <ows:Abstract>Species Names</ows:Abstract> <LiteralData> <ows:DataType ows:reference="xs:string"></ows:DataType> <ows:AllowedValues> <ows:Value>YFT</ows:Value> <ows:Value>SKJ</ows:Value> <ows:Value>BET</ows:Value> <ows:Value>ALB</ows:Value> <ows:Value>BFT</ows:Value> <ows:Value>SBF</ows:Value> <ows:Value>SFA</ows:Value> <ows:Value>BLM</ows:Value> <ows:Value>MLS</ows:Value> <ows:Value>BIL</ows:Value> <ows:Value>SWO</ows:Value> <ows:Value>SSP</ows:Value> </ows:AllowedValues> </LiteralData> </Input> <Input minOccurs="0" maxOccurs="1"> <ows:Identifier>wfsUrl</ows:Identifier> <ows:Title>WFS Url</ows:Title> <ows:Abstract>WFS Url</ows:Abstract> <LiteralData> <ows:DataType ows:reference="xs:string"></ows:DataType> <ows:AnyValue/> </LiteralData> </Input> </DataInputs> <ProcessOutputs> <Output> <ows:Identifier>result</ows:Identifier> <ows:Title>result</ows:Title> <ows:Abstract>result</ows:Abstract> <ComplexOutput> <Default> <Format> <MimeType>application/xml</MimeType> </Format> </Default> <Supported> <Format> <MimeType>application/xml</MimeType> </Format> </Supported> </ComplexOutput> </Output> </ProcessOutputs> </ProcessDescription> </wps:ProcessDescriptions>
Create the wps-hadoop process java class(es)
In this section we create the java class (or the classes, if need) to implement a wps process which can easy act on hadoop. It’s important to define well:
- which parameters are taken from the wps execute request (specified in the process description)
- how these parameters are processed (if we need to transform them)
- which parameters are passed to the hadoop streaming
- which parameters (from parameters taken in 3th) are set as fixed parameters
- which parameter (from parameters taken in 3th) is set as inputResource (determining the parallelism)
For this indicator_i1 example, we have:
- speciesCodes, as inputResource
- wfsUrl, as fixed parameter
The wfsUrl are taken as is from wps execute request, while the speciesCodes are created starting from species names list (from wps execute request too), by searching into a default list of all species and managing case-free characters.
The primary class created is, in this case, IndicatorI1.java, and it must extends StreamingAbstractAlgorithm. At least one class like this must be created. However, in this case two simple classes are created: Constants.java and SpeciesCodes.java (species codes simple db with search engine). Note: We choose to create the classes inside the package com.terradue.wps_hadoop.processes.ird.indicator, the same path structure of the process description created.
Here’s the list of java classes:
[{{host}} ~]$ ll wps-hadoop-source/src/main/java/com/terradue/wps_hadoop/processes/ird/indicator/ total 24 -rw-r--r-- 1 imarine-wp10 ciop 377 Apr 28 17:35 Constants.java -rw-r--r-- 1 imarine-wp10 ciop 3026 Apr 28 17:35 IndicatorI1.java -rw-r--r-- 1 imarine-wp10 ciop 1545 Apr 28 17:35 SpeciesCodes.java
IndicatorI1.java
[{{host}} ~]$ vi wps-hadoop-source/src/main/java/com/terradue/wps_hadoop/processes/ird/indicator/IndicatorI1.java /** * */ package com.terradue.wps_hadoop.processes.ird.indicator; import java.util.ArrayList; import java.util.HashMap; import java.util.List; import java.util.Map; import org.apache.log4j.Logger; import org.n52.wps.io.data.IData; import org.n52.wps.io.data.binding.complex.GenericFileDataBinding; import org.n52.wps.io.data.binding.literal.LiteralStringBinding; import com.terradue.wps_hadoop.common.input.InputUtils; import com.terradue.wps_hadoop.common.input.ListInputResource; import com.terradue.wps_hadoop.streaming.ResultsInfo; import com.terradue.wps_hadoop.streaming.StreamingAbstractAlgorithm; import com.terradue.wps_hadoop.streaming.StreamingPackagedAlgorithm; import com.terradue.wps_hadoop.streaming.WpsHadoopConfiguration; /** * @author fcerasuolo * */ public class IndicatorI1 extends StreamingAbstractAlgorithm { protected final Logger logger = Logger.getLogger(getClass()); private List<String> errors = new ArrayList<String>(); @Override public Map<String, IData> run(Map<String, List<IData>> inputData) { List<String> names = InputUtils.getListStringInputParameter(inputData, "species", true); List<String> speciesCodes = getSpeciesCodesFromSpeciesNames(names); String wfsUrl = InputUtils.getStringInputParameter(inputData, "wfsUrl"); logger.info("Running Job TUNA ATLAS INDICATOR I1..."); // get the configuration with default values WpsHadoopConfiguration conf = new WpsHadoopConfiguration(); // create a new hadoop streaming algorithm StreamingPackagedAlgorithm streaming = new StreamingPackagedAlgorithm(conf); // set algorithm name streaming.setAlgorithmName("indicator_i1"); // used if you want to copy the jar at runtime into the hdfs, for quick tests // streaming.setAlgorithmPackage(new File("/home/imarine-wp10/hadoopApplications/ird/indicator_i1/indicator_i1.jar"), true); // set the input resource streaming.setInputResource(new ListInputResource(speciesCodes)); // adding parameters streaming.addFixedParameter("wfsUrl", wfsUrl==null ? Constants.defaultWfsUrl : wfsUrl); // set verbose debug mode (default false) streaming.setDebugMode(true); try { // let's run! ResultsInfo result = streaming.runAsync(this); Map<String, IData> wpsResultMap = new HashMap<String, IData>(); wpsResultMap.put("result", result.getXmlFileDataBinding()); return wpsResultMap; } catch (Exception e) { e.printStackTrace(); throw new RuntimeException("Execution job failed! " + e.getMessage()); } } @Override public List<String> getErrors() { return errors; } @Override public Class<?> getInputDataType(String id) { return LiteralStringBinding.class; } @Override public Class<?> getOutputDataType(String id) { if (id.contentEquals("result")) return GenericFileDataBinding.class; else return null; } /** * @param names * @return */ private List<String> getSpeciesCodesFromSpeciesNames(List<String> names) { List<String> speciesCodes = new ArrayList<String>(); for (String name: names) speciesCodes.add(SpeciesCodes.getSpeciesCode(name)); return speciesCodes; } }
SpeciesCodes.java
/** * */ package com.terradue.wps_hadoop.processes.ird.indicator; /** * @author "Francesco Cerasuolo (francesco.cerasuolo@terradue.com)" * */ public class SpeciesCodes { private static String csv[] = { "YFT,Thunnus albacares,Albacore,Rabil,Yellowfin tuna", "SKJ,Katsuwonus pelamis,Listao,Listado,Ocean skipjack", "BET,Thunnus obesus,Thon obese,Patudo,Bigeye tuna", "ALB,Thunnus alalunga,Germon,Atun blanco,Albacore", "BFT,Thunnus thynnus thynnus,Thon rouge,Atun rojo,Bluefin tuna", "SBF,Thunnus maccoyii,Thon rouge du sud,Atun rojo del sur,Southern bluefin tuna", "SFA,Istiophorus platypterus,Voilier Indo-Pacifique,Pez vela del Indo-Pacifico,Indo-Pacific sailfish", "BLM,Makaira indica,Makaire noir,Aguja negra,Black marlin", "BUM,Makaira nigricans,Makaire bleu Atlantique,Aguja azul,Atlantic blue marlin", "MLS,Tetrapturus audax,Marlin raye,Marlin rayado,Striped marlin", "BIL,Istiophoridae spp.,Poissons a rostre non classes,,Unclassified marlin", "SWO,Xiphias gladius,Espadon,Pez espada,Broadbill swordfish", "SSP,Tetrapturus angustirostris,Makaire a rostre court,Marlin trompa corta,short-billed spearfish", }; protected static String getSpeciesCode(String speciesName) { speciesName = speciesName.toUpperCase(); for (String csvRow: csv) { csvRow = csvRow.toUpperCase(); String[] words = csvRow.split(","); String code = words[0]; for (String word: words) if (word.contentEquals(speciesName)) return code; } throw new RuntimeException("Species not found."); } }
Constants.java
/** * */ package com.terradue.wps_hadoop.processes.ird.indicator; /** * @author "Francesco Cerasuolo (francesco.cerasuolo@terradue.com)" * */ public class Constants { protected static final String defaultWfsUrl = "http://mdst-macroes.ird.fr:8080/constellation/WS/wfs/tuna_atlas"; }
Build and package the sources
Now you can compile, build and package all. From ~/wps-hadoop-source directory, by using maven:
[{{host}} ~]$ cd wps-hadoop-source/ [{{host}} wps-hadoop-source]$ mvn clean package -P wps,linux-x86_64 [INFO] Scanning for projects... .... .... .... .... [INFO] Building tar : /home/imarine-wp10/wps-hadoop-source/target/wps-hadoop-1.2.0-SNAPSHOT-bin.tar.gz [INFO] ------------------------------------------------------------------------ [INFO] BUILD SUCCESS [INFO] ------------------------------------------------------------------------ [INFO] Total time: 13.225s [INFO] Finished at: Tue Apr 29 15:34:45 CEST 2014 [INFO] Final Memory: 24M/184M [INFO] ------------------------------------------------------------------------
After the mvn execution, you can see what’s generated into the target folder:
[{{host}} wps-hadoop-source]$ ll target/ total 22072 drwxr-xr-x 2 imarine-wp10 ciop 4096 Apr 29 15:36 archive-tmp drwxr-xr-x 4 imarine-wp10 ciop 4096 Apr 29 15:36 classes drwxr-xr-x 2 imarine-wp10 ciop 4096 Apr 29 15:36 maven-archiver drwxr-xr-x 2 imarine-wp10 ciop 4096 Apr 29 15:36 surefire -rw-r--r-- 1 imarine-wp10 ciop 22394573 Apr 29 15:36 wps-hadoop-1.2.0-SNAPSHOT-bin.tar.gz -rw-r--r-- 1 imarine-wp10 ciop 153429 Apr 29 15:36 wps-hadoop-1.2.0-SNAPSHOT.jar
The wps-hadoop-1.2.0-SNAPSHOT.jar library is all you need.
Configure the wps-hadoop web-app
Few more steps: copy the jar library obtained, update the wps_config.xml including this new process, and restart the web application.
Copy the jar library
You simply copy the jar from target folder inside wps-hadoop-source to lib directory of wps-hadoop web-app:
[{{host}} ~]$ cp wps-hadoop-source/target/wps-hadoop-1.2.0-SNAPSHOT.jar wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/webapps/wps/WEB-INF/lib/
Update the wps_config.xml
[{{host}} ~]$ vi wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/webapps/wps/config/wps_config.xml
You’ve to simply add a property
<Property name="Algorithm" active="true">com.terradue.wps_hadoop.processes.ird.indicator.IndicatorI1</Property> inside //AlgorithmRepositoryList/Repository[@name=“UploadedAlgorithmRepository] ... <AlgorithmRepositoryList> <Repository name="UploadedAlgorithmRepository" className="org.n52.wps.server.UploadedAlgorithmRepository" active="true"> <Property name="Algorithm" active="true">com.terradue.wps_hadoop.processes.ird.indicator.IndicatorI1</Property> </Repository> </AlgorithmRepositoryList> <RemoteRepositoryList/> <Server hostname="{{host}}" hostport="8888" includeDataInputsInResponse="false" computationTimeoutMilliSeconds="5" cacheCapabilites="false" webappPath="wps" repoReloadInterval="0"> <Database/> </Server> </WPSConfiguration>
Notice that the property inner text is com.terradue.wps_hadoop.processes.ird.indicator.IndicatorI1, exactly the package+className(without .java)
Restart the wps-hadoop web application
You ca do this simply by touch the web.xml:
[{{host}} ~]$ touch wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/webapps/wps/WEB-INF/web.xml
or by restarting the tomcat (hard reload):
[{{host}} ~]$ wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/shutdown.sh Using LD_LIBRARY_PATH: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/lib/natives Using CATALINA_BASE: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded Using CATALINA_HOME: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded Using CATALINA_TMPDIR: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/temp Using JRE_HOME: /usr Using CLASSPATH: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/bootstrap.jar:/home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/tomcat-juli.jar [{{host}} ~]$ wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/startup.sh Using LD_LIBRARY_PATH: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/lib/natives Using CATALINA_BASE: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded Using CATALINA_HOME: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded Using CATALINA_TMPDIR: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/temp Using JRE_HOME: /usr Using CLASSPATH: /home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/bootstrap.jar:/home/imarine-wp10/wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/bin/tomcat-juli.jar
Now the process is plugged inside the wps-hadoop system. Note: the restart can take some seconds; you see the progress by see in realtime the log (tail -f wps-hadoop-0.1-SNAPSHOT-tomcat-embedded/logs/catalina.out)
Run, Test and Debug
Run & Test
getCapabilities: http://Template:Host:8888/wps/WebProcessingService?Request=GetCapabilities&Service=WPS You should see the new process created:
<wps:Process wps:processVersion="1.0.0"> <ows:Identifier> com.terradue.wps_hadoop.processes.ird.indicator.IndicatorI1 </ows:Identifier> <ows:Title>IRD Tuna Atlas Indicator i1</ows:Title> </wps:Process>
describeProcess: http://Template:Host:8888/wps/WebProcessingService?Service=WPS&Version=1.0.0&Request=DescribeProcess&Identifier=com.terradue.wps_hadoop.processes.ird.indicator.IndicatorI1
You should see the process description xml.
executeProcess (example): async: http://Template:Host:8888/wps/WebProcessingService?service=wps&version=1.0.0&request=Execute &identifier=com.terradue.wps_hadoop.processes.ird.indicator.IndicatorI1 &dataInputs=species=BFT;species=BFT;&ResponseDocument=result sync: http://Template:Host:8888/wps/WebProcessingService?service=wps&version=1.0.0&request=Execute &identifier=com.terradue.wps_hadoop.processes.ird.indicator.IndicatorI1 &dataInputs=species=BFT;species=BFT;&ResponseDocument=result&storeExecuteResponse=true&status=true
The wps-hadoop web-app include a simple wps-webclient, you can access it by http://Template:Host:8888/client.html
Debug
During wps-hadoop process execution, you can check the catalina.out log: inside it it’s displayed the hadoop tracking url and the status. If you follow the url, you can see all map/reduce attempts with logs, in real time.
2014-04-29 16:19:24,008 [pool-21-thread-6] INFO org.apache.hadoop.streaming.StreamJob: (by wps-hadoop) JobId: job_201404161623_0007 2014-04-29 16:19:24,008 [pool-21-thread-6] INFO org.apache.hadoop.streaming.StreamJob: To kill this job, run: 2014-04-29 16:19:24,009 [pool-21-thread-6] INFO org.apache.hadoop.streaming.StreamJob: UNDEF/bin/hadoop job -Dmapred.job.tracker={{host}}:8021 -kill job_201404161623_0007 2014-04-29 16:19:24,037 [pool-21-thread-6] INFO org.apache.hadoop.streaming.StreamJob: Tracking URL: http://{{host}}:50030/jobdetails.jsp?jobid=job_201404161623_0007 2014-04-29 16:19:24,129 [pool-21-thread-4] INFO org.n52.wps.server.request.ExecuteRequest: Update received from Subject, state changed to : 73 2014-04-29 16:19:24,131 [pool-21-thread-4] INFO org.apache.hadoop.streaming.StreamJob: map 100% reduce 33% 2014-04-29 16:19:25,039 [pool-21-thread-6] INFO org.n52.wps.server.request.ExecuteRequest: Update received from Subject, state changed to : 0 2014-04-29 16:19:25,039 [pool-21-thread-6] INFO org.apache.hadoop.streaming.StreamJob: map 0% reduce 0% 2014-04-29 16:19:25,137 [pool-21-thread-4] INFO org.n52.wps.server.request.ExecuteRequest: Update received from Subject, state changed to : 100 2014-04-29 16:19:25,139 [pool-21-thread-4] INFO org.apache.hadoop.streaming.StreamJob: map 100% reduce 100% 2014-04-29 16:19:28,146 [pool-21-thread-4] INFO org.apache.hadoop.streaming.StreamJob: Job complete: job_201404161623_0005 2014-04-29 16:19:28,428 [pool-21-thread-4] INFO org.apache.hadoop.streaming.StreamJob: Output: /store/a5cc3063-b8d8-4422-ad32-0c7f387e07dd/output/
Note: it’s convenient to set streaming.setDebugMode(true) inside your java process class. In this way you can see the bash run file execution in debug mode, and into the tracking url you can see each bash statement execution.
