Controlling the environment of an application is vital for it's functionality and stability. Especially in a distributed environment it is important for developers to have control over the version of dependencies. In such an scenario it's a critical task to ensure possible conflicting requirements of multiple applications are not disturbing each other. That is why frameworks like YARN ensure that each application is executed in a self-contained environment - typically in a Linux Container or Docker Container - that is controlled by the developer. In this post we show what this means for Python environments being used by Spark. YARN Application Deployment As mentioned earlier does YARN execute each application in a self-contained environment on each host. This ensures the execution in a controlled environment managed by individual developers. The way this works in a nutshell is that the dependency of an application are distributed to each node typically via HDFS. This figure simplifies the fact that HDFS is actually being used to distribute the application. See HDFS distributed cache for reference. The files are uploaded to a staging folder /user/${username}/.${application} of the submitting user in HDFS. Because of the distributed architecture of HDFS it is ensured that multiple nodes have local copies of the files. In fact to ensure that a large fraction of the cluster has a local copy of application files and does not need to download them over the network, the HDFS replication factor is set much higher for this files than 3. Often a number between 10 and 20 is chosen for the replication factor. During the preparation of the container on a node you will notice in logs similar commands to the below example are being executed: ln -sf "/hadoop/yarn/local/usercache/vagrant/filecache/72/pyspark.zip" "pyspark.zip" The folder /hadoop/yarn/local/ is the configured location on each node where YARN stores it's needed files and logs locally. Creating a symbolic link like this inside the container makes the content of the zip file available. It is being referenced as "pyspark.zip". Using Conda Env For application developers this means that they can package and ship their controlled environment with each application. Other solutions like NFS or Amazon EFS shares are not needed, especially since solutions like shared folders makes for a bad architecture that is not designed to scale very well making the development of application less agile. The following example demonstrate the use of conda env to transport a python environment with a PySpark application needed to be executed. This sample application uses the NLTK package with the additional requirement of making tokenizer and tagger resources available to the application as well. Our sample application: import os import sys from pyspark import SparkContext from pyspark import SparkConf conf = SparkConf() conf.setAppName("spark-ntlk-env") sc = SparkContext(conf=conf) data = sc.textFile('hdfs:///user/vagrant/1970-Nixon.txt') def word_tokenize(x): import nltk return nltk.word_tokenize(x) def pos_tag(x): import nltk return nltk.pos_tag([x]) words = data.flatMap(word_tokenize) words.saveAsTextFile('hdfs:///user/vagrant/nixon_tokens') pos_word = words.map(pos_tag) pos_word.saveAsTextFile('hdfs:///user/vagrant/nixon_token_pos') Preparing the sample input data For our example we are using the provided samples of NLTK (http://www.nltk.org/nltk_data/) and upload them to HDFS: (nltk_env)$ python -m nltk.downloader -d nltk_data all (nltk_env)$ hdfs dfs -put nltk_data/corpora/state_union/1970-Nixon.txt /user/vagrant/ No Hard (Absolute) Links! Before we actually go and create our environment lets first take a quick moment to recap on how an environment is typically being composed. On a machine the environment is made out of variables linking to different target folders containing executable or other resource files. So if you execute a command it is either referenced from your PATH, PYTHON_LIBRARY, or any other defined variable. These variables link to files in directories like /usr/bin, /usr/local/bin or any other referenced location. They are called hard links or absolute reference as they start from root /. Environments using hard links are not easily transportable as they make strict assumption about the the overall execution engine (your OS for example) they are being used in. Therefor it is necessary to use relative links in a transportable/relocatable environment. This is especially true for conda env as it creates hard links by default. By making the conda env relocatable it can be used in a application by referencing it from the application root . (current dir) instead of the overall root /. By using the --copy options during the creation of the environment packages are being copied instead of being linked. Creating our relocatable environment together with nltk and numpy: conda create -n nltk_env --copy -y -q python=3 nltk numpy Fetching package metadata ....... Solving package specifications: .......... Package plan for installation in environment /home/datalab_user01/anaconda2/envs/nltk_env: The following packages will be downloaded: package | build ---------------------------|----------------- python-3.5.2 | 0 17.2 MB nltk-3.2.1 | py35_0 1.8 MB numpy-1.11.1 | py35_0 6.1 MB setuptools-23.0.0 | py35_0 460 KB wheel-0.29.0 | py35_0 82 KB pip-8.1.2 | py35_0 1.6 MB ------------------------------------------------------------ Total: 27.2 MB The following NEW packages will be INSTALLED: mkl: 11.3.3-0 (copy) nltk: 3.2.1-py35_0 (copy) numpy: 1.11.1-py35_0 (copy) openssl: 1.0.2h-1 (copy) pip: 8.1.2-py35_0 (copy) python: 3.5.2-0 (copy) readline: 6.2-2 (copy) setuptools: 23.0.0-py35_0 (copy) sqlite: 3.13.0-0 (copy) tk: 8.5.18-0 (copy) wheel: 0.29.0-py35_0 (copy) xz: 5.2.2-0 (copy) zlib: 1.2.8-3 (copy) # # To activate this environment, use: # $ source activate nltk_env # # To deactivate this environment, use: # $ source deactivate # This also works for different python version 3.x or 2.x! Zip it and Ship it! Now that we have our relocatable environment all set we are able to package it and ship it as part of our sample PySpark job. $ cd ~/anaconda2/envs/ $ zip -r nltk_env.zip nltk_env Making this available in during the execution of our application in a YARN container we have for one distribute the package and for second change the default environment of Spark for python to your location. The variable controlling the python environment for python applications in Spark is named PYSPARK_PYTHON. PYSPARK_PYTHON=./NLTK/nltk_env/bin/python spark-submit --conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./NLTK/nltk_env/bin/python --master yarn-cluster --archives nltk_env.zip#NLTK spark_nltk_sample.py Our virtual environment is linked by NLTK that is why the path in PYSPARK_PYTHON is pointing to ./NLTK/content/of/zip/... . The exact command being executed during container creation is something like this: ln -sf "/hadoop/yarn/local/usercache/vagrant/filecache/71/nltk_env.zip" "NLTK" Shipping additional resources with an application is controlled by the --files and --archives options as shown here. The options being used here are documented in Spark Yarn Configuration and Spark Environment Variables for reference. Packaging tokenizer and taggers Doing just the above will unfortunately fail, because using the NLTK parser in the way we are using it in the example program has some additional dependencies. If you have followed the above steps executing submitting it to your YARN cluster will result in the following exception at container level: Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last): ... raise LookupError(resource_not_found) LookupError: ********************************************************************** Resource 'taggers/averaged_perceptron_tagger/averaged_perceptron _tagger.pickle' not found. Please use the NLTK Downloader to obtain the resource: >>> nltk.download() Searched in: - '/home/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' ********************************************************************** at org.apache.spark.api.python.PythonRunner$anon$1.read(PythonRDD.scala:166) at org.apache.spark.api.python.PythonRunner$anon$1.<init>(PythonRDD.scala:207) at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125) at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) ... 1 more The problem is that NLTK expects the follwoing resource in the tokenizers/punkt/english.pickle to be available in either of the following locations: Searched in: - '/home/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' The good thing about this is, that we by now should now how we can ship the required dependency to our application. We can do it the same way we did with our python environment. Again it is imporant to reensure yourself how the resource is going to be referenced. NLTK expects it by default in the current location under tokenizers/punkt/english.pickle that is why we navigate into the folder for packaging and reference the zip file wiht tokenizer.zip#tokenizer. (nltk_env)$ cd nltk_data/tokenizers/ (nltk_env)$ zip -r ../../tokenizers.zip * (nltk_env)$ cd ../../ (nltk_env)$ cd nltk_data/taggers/ (nltk_env)$ zip -r ../../taggers.zip * (nltk_env)$ cd ../../ At a later point our program will expect a tagger in the same fashion already demonstrated in the above snippet. Using YARN Locations We can ship those zip resources the same way we shipped our conda env. In addition environment variable can be used to control resource discovery and allocation. For NLTK you can use the environment variable NLTK_DATA to control the path. Setting this in Spark can be done similar to the way we set PYSPARK_PYTHON: --conf spark.yarn.appMasterEnv.NLTK_DATA=./ Additionally YARN exposes the container path via the environment variable PWD. This can be used in NLTK as to add it to the search path as follows: def word_tokenize(x): import nltk nltk.data.path.append(os.environ.get('PWD')) return nltk.word_tokenize(x) The submission of your application becomes now: PYSPARK_PYTHON=./NLTK/nltk_env/bin/python spark-submit --conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./NLTK/nltk_env/bin/python --conf spark.yarn.appMasterEnv.NLTK_DATA=./ --master yarn-cluster --archives nltk_env.zip#NLTK,tokenizers.zip#tokenizers,taggers.zip#taggers spark_nltk_sample.py The expected result should be something like the following: (nltk_env)$ hdfs dfs -cat /user/datalab_user01/nixon_tokens/* | head -n 20 Annual Message to the Congress on the State of the Union . January 22 , 1970 Mr. Speaker , Mr. And: (nltk_env)$ hdfs dfs -cat /user/datalab_user01/nixon_token_pos/* | head -n 20 [(u'Annual', 'JJ')] [(u'Message', 'NN')] [(u'to', 'TO')] [(u'the', 'DT')] [(u'Congress', 'NNP')] [(u'on', 'IN')] [(u'the', 'DT')] [(u'State', 'NNP')] [(u'of', 'IN')] [(u'the', 'DT')] [(u'Union', 'NN')] [(u'.', '.')] [(u'January', 'NNP')] [(u'22', 'CD')] [(u',', ',')] [(u'1970', 'CD')] [(u'Mr.', 'NNP')] [(u'Speaker', 'NN')] [(u',', ',')] [(u'Mr.', 'NNP')] Further Readings PySpark Internals Spark NLTK Example NLTK Data Virtualenv in Hadoop Streaming Conda Intro Conda Env with Spark Python Env support in Spark (SPARK-13587) Post was first published here: http://henning.kropponline.de/2016/09/24/running-pyspark-with-conda-env/ ... View more

The most recent release of Kafka 0.9 with it's comprehensive security implementation has reached an important milestone. In his blog post Kafka Security 101 Ismael from Confluent describes the security features part of the release very well. As a part II of the here published post about Kafka Security with Kerberos this post discussed a sample implementation of a Java Kafka producer with authentication. It is part of a mini series of posts discussing secure HDP clients, connecting services to a secured cluster, and kerberizing the HDP Sandbox (Download HDP Sandbox). In this effort at the end of this post we will also create a Kafka Servlet to publish messages to a secured broker. Kafka provides SSL and Kerberos authentication. Only Kerberos is discussed here. Kafka from now on supports four different communication protocols between Consumers, Producers, and Brokers. Each protocol considers different security aspects, while PLAINTEXT is the old insecure communication protocol. PLAINTEXT (non-authenticated, non-encrypted) SSL (SSL authentication, encrypted) PLAINTEXT+SASL (authentication, non-encrypted) SSL+SASL (encrypted authentication, encrypted transport) A Kafka client needs to be configured to use the protocol of the corresponding broker. This tells the client to use authentication for communication with the broker: Properties props = new Properties(); props.put("security.protocol", "PLAINTEXTSASL"); Making use of Kerberos authentication in Java is provided by the Java Authentication and Authorization Service (JAAS) which is a pluggable authentication method similar to PAM supporting multiple authentication methods. In this case the authentication method being used is GSS-API for Kerberos. Demo Setup For JAAS a proper configuration of GSS would be needed in addition to being in possession of proper credentials, obviously. Some credentials can be created with MIT Kerberos like this: (as root) $ kadmin.local -q "addprinc -pw hadoop kafka-user" $ kadmin.local -q "xst -k /home/kafka-user/kafka-user.keytab kafka-user@MYCORP.NET" (Creating a keytab will make the existing password invalid. To change your password back to hadoop use as root:) $ kadmin.local -q "cpw -pw hadoop hdfs-user" The last line is not necessarily needed as it creates us a so called keytab - basically an encrypted password of the user - that can be used for password less authentication for example for automated services. We will make use of that here as well. First we need to prepare a test topic to publish messages with proper privileges for our kafka-user: # Become Kafka admin $ kinit -kt /etc/security/keytabs/kafka.service.keytab kafka/one.hdp@MYCORP.NET # Set privileges for kafka-user $ /usr/hdp/current/kafka-broker/bin/kafka-acls.sh --add --allow-principals user:kafka-user --operation ALL --topic test --authorizer-properties zookeeper.connect=one.hdp:2181 Adding following acls for resource: Topic:test user:kafka-user has Allow permission for operations: All from hosts: * Following is list of acls for resource: Topic:test user:kafka-user has Allow permission for operations: All from hosts: * As a sample producer we will use this: package hdp.sample; import java.util.Date; import java.util.Properties; import kafka.javaapi.producer.Producer; import kafka.producer.KeyedMessage; import kafka.producer.ProducerConfig; public class KafkaProducer { public static void main(String... args) { String topic = args[1]; Properties props = new Properties(); props.put("metadata.broker.list", args[0]); props.put("serializer.class", "kafka.serializer.StringEncoder"); props.put("request.required.acks", "1"); props.put("security.protocol", "PLAINTEXTSASL"); ProducerConfig config = new ProducerConfig(props); Producer producer = new Producer<String, String>(config); for (int i = 0; i < 10; i++){ producer.send(new KeyedMessage<String, String>(topic, "Test Date: " + new Date())); } } } With this setup we can go ahead demonstrating two ways to use a JAAS context to authenticate with the Kafka broker. At first we will configure a context to use the existing privileges possessed by the executing user. Next we use a so called keytab to demonstrate a password-less login for automated producer processes. At last we will look at a Servlet implementation provided here. Authentication with User Login To configure a JAAS config with userKeyTab set to false and useTicketCache to true, so that the privileges of the current users are being used. KafkaClient { com.sun.security.auth.module.Krb5LoginModule required useKeyTab=false useTicketCache=true serviceName="kafka"; }; We store this in a file under /home/kafka-user/kafka-jaas.conf and exeute the broker like this: # list current user context $ klist Ticket cache: FILE:/tmp/krb5cc_0 Default principal: kafka-user@MYCORP.NET Valid starting Expires Service principal 21.02.2016 16:13:13 22.02.2016 16:13:13 krbtgt/MYCORP.NET@MYCORP.NET # execute java producer $ java -Djava.security.auth.login.config=/home/kafka-user/kafka-jaas.conf -Djava.security.krb5.conf=/etc/krb5.conf -Djavax.security.auth.useSubjectCredsOnly=false -cp hdp-kafka-sample-1.0-SNAPSHOT.jar:/usr/hdp/current/kafka-broker/libs/* hdp.sample.KafkaProducer one.hdp:6667 test # consume sample messages for test $ /usr/hdp/current/kafka-broker/bin/kafka-simple-consumer-shell.sh --broker-list one.hdp:6667 --topic test --security-protocol PLAINTEXTSASL --partition 0 {metadata.broker.list=one.hdp:6667, request.timeout.ms=1000, client.id=SimpleConsumerShell, security.protocol=PLAINTEXTSASL} Test Date: Sun Feb 21 16:12:05 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Test Date: Sun Feb 21 16:12:06 UTC 2016 Using Keytab to Login Next we will configure the JAAS context to use a generated keytab file instead of the security context of the executing user. Before we can do this we need to create the keytab storing it also under /home/kafka-user/kafka-user.keytab. $ kadmin.local -q "xst -k /home/kafka-user/kafka-user.keytab kafka-user@MYCORP.NET" Authenticating as principal kafka-user/admin@MYCORP.NET with password. Entry for principal kafka-user@MYCORP.NET with kvno 2, encryption type aes256-cts-hmac-sha1-96 added to keytab WRFILE:/home/kafka-user/kafka-user.keytab. Entry for principal kafka-user@MYCORP.NET with kvno 2, encryption type aes128-cts-hmac-sha1-96 added to keytab WRFILE:/home/kafka-user/kafka-user.keytab. Entry for principal kafka-user@MYCORP.NET with kvno 2, encryption type des3-cbc-sha1 added to keytab WRFILE:/home/kafka-user/kafka-user.keytab. Entry for principal kafka-user@MYCORP.NET with kvno 2, encryption type arcfour-hmac added to keytab WRFILE:/home/kafka-user/kafka-user.keytab. Entry for principal kafka-user@MYCORP.NET with kvno 2, encryption type des-hmac-sha1 added to keytab WRFILE:/home/kafka-user/kafka-user.keytab. Entry for principal kafka-user@MYCORP.NET with kvno 2, encryption type des-cbc-md5 added to keytab WRFILE:/home/kafka-user/kafka-user.keytab. $ chown kafka-user. /home/kafka-user/kafka-user.keytab The JAAS configuration can now be changed to look like this: KafkaClient { com.sun.security.auth.module.Krb5LoginModule required doNotPrompt=true useTicketCache=true principal="kafka-user@MYCORP.NET" useKeyTab=true serviceName="kafka" keyTab="/home/kafka-user/kafka-user.keytab" client=true; }; This will use the keytab stored under /home/kafka-user/kafka-user.keytab while the user executing the producer must not be logged in to any security controller: $ klist klist: Credentials cache file '/tmp/krb5cc_0' not found $ java -Djava.security.auth.login.config=/home/kafka-user/kafka-jaas.conf -Djava.security.krb5.conf=/etc/krb5.conf -Djavax.security.auth.useSubjectCredsOnly=true -cp hdp-kafka-sample-1.0-SNAPSHOT.jar:/usr/hdp/current/kafka-broker/libs/* hdp.sample.KafkaProducer one.hdp:6667 test Kafka Producer Servlet In a last example we will add a Kafka Servlet to the hdp-web-sample project previously described in this post. Our Servlet will get the topic and message as a GET parameter. The Servlet looks as follwoing: package hdp.webapp; import java.io.IOException; import java.io.PrintWriter; import java.util.Properties; import javax.servlet.Servlet; import javax.servlet.ServletException; import javax.servlet.http.HttpServlet; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpServletResponse; import kafka.javaapi.producer.Producer; import kafka.producer.KeyedMessage; import kafka.producer.ProducerConfig; public class KafkaServlet extends HttpServlet implements Servlet { protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { String topic = request.getParameter("topic"); String msg = request.getParameter("msg"); Properties props = new Properties(); props.put("metadata.broker.list", "one.hdp:6667"); props.put("serializer.class", "kafka.serializer.StringEncoder"); props.put("request.required.acks", "1"); props.put("security.protocol", "PLAINTEXTSASL"); ProducerConfig config = new ProducerConfig(props); Producer producer = new Producer<String, String>(config); producer.send(new KeyedMessage<String, String>(topic, msg)); PrintWriter out = response.getWriter(); out.println("<html>"); out.println("<head><title>Write to topic: "+ topic +"</title></head>"); out.println("<body><h1>/"+ msg +"</h1>"); out.println("</html>"); out.close(); } } Again we are changing the JAAS config of the Tomcat service to be able to make use of the previously generated keytab. The jaas.conf of Tomcat will contain now this: KafkaClient { com.sun.security.auth.module.Krb5LoginModule required doNotPrompt=true useTicketCache=true principal="kafka-user@MYCORP.NET" useKeyTab=true serviceName="kafka" keyTab="/home/kafka-user/kafka-user.keytab" client=true; }; com.sun.security.jgss.krb5.initiate { com.sun.security.auth.module.Krb5LoginModule required doNotPrompt=true principal="tomcat/one.hdp@MYCORP.NET" useKeyTab=true keyTab="/etc/tomcat/tomcat.keytab" storeKey=true; }; After deploying the web app and restarting tomcat with this newly adapted JAAS config you should be able to publish message to a secured broker be triggering the following GET address from a browser http://one.hdp:8099/hdp-web/kafka?topic=test&msg=Test1 . The response should be a 200 OK like this: You might be having some issues and in particular seeing this Exception: SEVERE: Servlet.service() for servlet [KafkaServlet] in context with path [/hdp-web] threw exception [Servlet execution threw an exception] with root cause javax.security.auth.login.LoginException: Unable to obtain password from user at com.sun.security.auth.module.Krb5LoginModule.promptForPass(Krb5LoginModule.java:897) at com.sun.security.auth.module.Krb5LoginModule.attemptAuthentication(Krb5LoginModule.java:760) at com.sun.security.auth.module.Krb5LoginModule.login(Krb5LoginModule.java:617) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:497) at javax.security.auth.login.LoginContext.invoke(LoginContext.java:755) at javax.security.auth.login.LoginContext.access$000(LoginContext.java:195) at javax.security.auth.login.LoginContext$4.run(LoginContext.java:682) at javax.security.auth.login.LoginContext$4.run(LoginContext.java:680) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.login.LoginContext.invokePriv(LoginContext.java:680) at javax.security.auth.login.LoginContext.login(LoginContext.java:587) at org.apache.kafka.common.security.kerberos.Login.login(Login.java:298) at org.apache.kafka.common.security.kerberos.Login.<init>(Login.java:104) at kafka.common.security.LoginManager$.init(LoginManager.scala:36) at kafka.producer.Producer.<init>(Producer.scala:50) at kafka.producer.Producer.<init>(Producer.scala:73) at kafka.javaapi.producer.Producer.<init>(Producer.scala:26) at hdp.webapp.KafkaServlet.doGet(KafkaServlet.java:33) at javax.servlet.http.HttpServlet.service(HttpServlet.java:620) at javax.servlet.http.HttpServlet.service(HttpServlet.java:727) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:303) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.apache.tomcat.websocket.server.WsFilter.doFilter(WsFilter.java:52) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:220) at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:122) at org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:501) at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:171) at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:102) at org.apache.catalina.valves.AccessLogValve.invoke(AccessLogValve.java:950) at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:116) at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:408) at org.apache.coyote.http11.AbstractHttp11Processor.process(AbstractHttp11Processor.java:1040) at org.apache.coyote.AbstractProtocol$AbstractConnectionHandler.process(AbstractProtocol.java:607) at org.apache.tomcat.util.net.JIoEndpoint$SocketProcessor.run(JIoEndpoint.java:314) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61) at java.lang.Thread.run(Thread.java:745) If are seeing the message javax.security.auth.login.LoginException: Unable to obtain password from user it likely refers to your keytab file, as being the users password. So make sure that the tomcat user is able to read that file stored under /home/kafka-user/kafka-user.keytab for example. Further Readings Kafka Security 101 Kafka Security Kafka Sasl/Kerberos and SSL Implementation Oracle Doc: JAAS Authentication Krb5LoginModule Flume with kerberized KafkaJAAS Login Configuration File This article was first published under: http://henning.kropponline.de/2016/02/21/secure-kafka-java-producer-with-kerberos/ ... View more

Origin: http://henning.kropponline.de/2015/05/19/hivesink-for-flume/ With the most recent release of HDP (v2.2.4) Hive Streaming is shipped as technical preview. It can for example be used with Storm to ingest streaming data collected from Kafka as demonstrated here. But it also still has some serious limitations and in case of Storm a major bug. Nevertheless Hive Streaming is likely to become the tool of choice when it comes to streamline data ingestion to Hadoop. So it is worth to explore already today. Flume's upcoming release 1.6 will contain a HiveSink capable of leveraging Hive Streaming for data ingestion. In the following post we will use it as a replacement for the HDFS sink used in a previous post here. Other then replacing the HDFS sink with a HiveSink none of the previous setup will change, except for Hive table schema which needs to be adjusted as part of the requirements that currently exist around Hive Streaming. So let's get started by looking into these restrictions. Hive Streaming Limitations The only file format supported is ORC. So the original schema of the stocks table needs to be adjusted to reflect that: DROP TABLE IF EXISTS stocks; CREATE EXTERNAL TABLE stocks ( date STRING, open DOUBLE, high DOUBLE, low DOUBLE, close DOUBLE, volume BIGINT, adj_close DOUBLE) PARTITIONED BY(year STRING) CLUSTERED BY (date) into 3 buckets STORED AS ORC LOCATION '/ingest/stocks'; As you can see from the schema the table now also is bucketed, which is required by Hive Streaming. Further more we need to set the following: hive.txn.manager = org.apache.hadoop.hive.ql.lockmgr.DbTxnManager hive.compactor.initiator.on = true hive.compactor.worker.threads > 0 (eg. 5) Configuration diff as given by Ambari: Also important to know is that the current Streaming API only supports delimited input data (CSV, tab seperated) or JSON (strict syntax). Flume Hive Sink For the Flume Hive Sink the following configurations with their defaults can or must be configured: hive.metastore hive.database hive.table hive.partition hive.txnsPerBatchAsk = 100 batchSize = 15000 serializer (delimited | json) serializer.delimiter = , serializer.fieldnames batchSize = 15000 idleTimeout = 0 callTimeout = 10000 heartBeatInterval = 240 maxOpenConnections useLocalTimeStamp timeZone roundUnit round hour minute second roundValue With the previous example we can use the following Flume configuration. The batch size and the transactions per batch are not set very high which probably be different in a production setup, but is also dependent on the data stream to expect. flume-hive-ingest.sources = src1 flume-hive-ingest.channels = chan1 flume-hive-ingest.sinks = sink1 flume-hive-ingest.sources.src1.type = spooldir flume-hive-ingest.sources.src1.channels = chan1 flume-hive-ingest.sources.src1.spoolDir = /vagrant/flume_log flume-hive-ingest.sources.src1.interceptors = skipHeadI dateI flume-hive-ingest.sources.src1.interceptors.skipHeadI.type = regex_filter flume-hive-ingest.sources.src1.interceptors.skipHeadI.regex = ^Date.* flume-hive-ingest.sources.src1.interceptors.skipHeadI.excludeEvents = true flume-hive-ingest.sources.src1.interceptors.dateI.type = regex_extractor flume-hive-ingest.sources.src1.interceptors.dateI.regex = ^(\d+)-.* flume-hive-ingest.sources.src1.interceptors.dateI.serializers = y flume-hive-ingest.sources.src1.interceptors.dateI.serializers.y.name = year flume-hive-ingest.channels.chan1.type = memory flume-hive-ingest.channels.chan1.capacity = 1000 flume-hive-ingest.channels.chan1.transactionCapacity = 100 flume-hive-ingest.sinks.sink1.type = hive flume-hive-ingest.sinks.sink1.channel = chan1 flume-hive-ingest.sinks.sink1.hive.metastore = thirft://one.hdp:9083 flume-hive-ingest.sinks.sink1.hive.database = default flume-hive-ingest.sinks.sink1.hive.table = stocks flume-hive-ingest.sinks.sink1.hive.partition = %{year} flume-hive-ingest.sinks.sink1.hive.txnsPerBatchAsk = 2 flume-hive-ingest.sinks.sink1.batchSize = 10 flume-hive-ingest.sinks.sink1.serializer = delimited flume-hive-ingest.sinks.sink1.serializer.delimiter = , flume-hive-ingest.sinks.sink1.serializer.fieldnames = date,open,high,low,close,volume,adj_close Before starting a Flume agent with this configuration you might need to set HIVE_HOME and HCAT_HOME as flume-ng will only put the required Hive jars into the classpath with this logic: add_hive_paths(){ if [ -d "${HIVE_HOME}/lib" ]; then info "Including Hive libraries found via ($HIVE_HOME) for Hive access" FLUME_CLASSPATH="$FLUME_CLASSPATH:$HIVE_HOME/lib/*" fi if [ -d "${HCAT_HOME}/share/hcatalog" ]; then info "Including HCatalog libraries found via ($HCAT_HOME) for Hive access" FLUME_CLASSPATH="$FLUME_CLASSPATH:${HCAT_HOME}/share/hcatalog/*" fi } Setting them in my case was pretty straight forward: export HIVE_HOME=/usr/hdp/current/hive-server2 export HCAT_HOME=/usr/hdp/current/hive-webhcat Now we can start the flume agent, obviously after we have created the stocks table: $ hcat -f data/stocks_schema.hive $ apache-flume-1.6.0-bin/bin/flume-ng agent -f data/flume-file-hive-ingest.conf -n flume-hive-ingest When working correctly you should be able to see output similar to this, once you copy the stocks data into the spooling directory: 16/05/15 15:19:18 INFO ql.Driver: OK 16/05/15 15:19:18 INFO log.PerfLogger: <PERFLOG method=releaseLocks from=org.apache.hadoop.hive.ql.Driver> 16/05/15 15:19:18 INFO log.PerfLogger: </PERFLOG method=releaseLocks start=1431703158539 end=1431703158543 duration=4 from=org.apache.hadoop.hive.ql.Driver> 16/05/15 15:19:18 INFO log.PerfLogger: </PERFLOG method=Driver.run start=1431703158452 end=1431703158543 duration=91 from=org.apache.hadoop.hive.ql.Driver> 16/05/15 15:19:20 INFO hive.metastore: Trying to connect to metastore with URI thirft://one.hdp:9083 16/05/15 15:19:20 INFO hive.metastore: Connected to metastore. 16/05/15 15:19:20 INFO hive.HiveWriter: Acquired Txn Batch TxnIds=[1743...1744] on endPoint = {metaStoreUri='thirft://one.hdp:9083', database='default', table='stocks', partitionVals=[1996] }. Switching to first txn 16/05/15 15:19:20 INFO hive.HiveWriter: Committing Txn 1742 on EndPoint: {metaStoreUri='thirft://one.hdp:9083', database='default', table='stocks', partitionVals=[1997] } 16/05/15 15:19:20 INFO hive.HiveWriter: Acquired Txn Batch TxnIds=[1745...1746] on endPoint = {metaStoreUri='thirft://one.hdp:9083', database='default', table='stocks', partitionVals=[1997] }. Switching to first txn Troubleshooting If something goes wrong, for example with failing connection to the metastore please: Check the requirements posted here or on the Hive wiki. Also check that your schema is bucketed and read the Exception message carefully. Increase the timeout for the HiveWriter to connect to the Metastore and again read the Exception message carefully. Make hdfs://tmp/hive and file:///tmp/hive writable (eg. chmod 777) A typical error message could look like this: 16/05/15 14:53:39 WARN hive.HiveSink: sink1 : Failed connecting to EndPoint {metaStoreUri='one.hdp:9083', database='default', table='stocks', partitionVals=[${year}] } org.apache.flume.sink.hive.HiveWriter$ConnectException: Failed connecting to EndPoint {metaStoreUri='one.hdp:9083', database='default', table='stocks', partitionVals=[${year}] } at org.apache.flume.sink.hive.HiveWriter.<init>(HiveWriter.java:98) at org.apache.flume.sink.hive.HiveSink.getOrCreateWriter(HiveSink.java:343) at org.apache.flume.sink.hive.HiveSink.drainOneBatch(HiveSink.java:296) at org.apache.flume.sink.hive.HiveSink.process(HiveSink.java:254) at org.apache.flume.sink.DefaultSinkProcessor.process(DefaultSinkProcessor.java:68) at org.apache.flume.SinkRunner$PollingRunner.run(SinkRunner.java:147) at java.lang.Thread.run(Thread.java:745) Caused by: org.apache.flume.sink.hive.HiveWriter$ConnectException: Failed connecting to EndPoint {metaStoreUri='one.hdp:9083', database='default', table='stocks', partitionVals=[${year}] } at org.apache.flume.sink.hive.HiveWriter.newConnection(HiveWriter.java:320) at org.apache.flume.sink.hive.HiveWriter.<init>(HiveWriter.java:86) ... 6 more Caused by: java.lang.NullPointerException at org.apache.thrift.transport.TSocket.open(TSocket.java:168) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.open(HiveMetaStoreClient.java:358) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:215) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:161) at org.apache.hive.hcatalog.streaming.HiveEndPoint$ConnectionImpl.getMetaStoreClient(HiveEndPoint.java:448) at org.apache.hive.hcatalog.streaming.HiveEndPoint$ConnectionImpl.<init>(HiveEndPoint.java:274) at org.apache.hive.hcatalog.streaming.HiveEndPoint$ConnectionImpl.<init>(HiveEndPoint.java:243) at org.apache.hive.hcatalog.streaming.HiveEndPoint.newConnectionImpl(HiveEndPoint.java:180) at org.apache.hive.hcatalog.streaming.HiveEndPoint.newConnection(HiveEndPoint.java:157) at org.apache.hive.hcatalog.streaming.HiveEndPoint.newConnection(HiveEndPoint.java:110) at org.apache.flume.sink.hive.HiveWriter$6.call(HiveWriter.java:316) at org.apache.flume.sink.hive.HiveWriter$6.call(HiveWriter.java:313) at org.apache.flume.sink.hive.HiveWriter$9.call(HiveWriter.java:366) at java.util.concurrent.FutureTask.run(FutureTask.java:262) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) ... 1 more 16/05/15 14:53:39 ERROR flume.SinkRunner: Unable to deliver event. Exception follows. Further Readings Hive Streaming Flume HiveSink HDP Flume Apache Flume: Distributed Log Collection for Hadoop - Second Edition (Amazon) Apache Hive Essentials (Amazon) ... View more

Origin: http://henning.kropponline.de/2015/09/27/storm-serialization-with-avro-using-kryo-serializer/ Working with complex data events can be a challenge designing Storm topologies for real-time data processing. In such cases emitting single values for multiple and varying event characteristics soon reveals it's limitations. For message serialization Storm leverages the Kryo serialization framework used by many other projects. Kryo keeps a registry of serializers being used for corresponding Class types. Mappings in that registry can be overridden or added making the framework extendable to diverse type serializations. On the other hand Avro is a very popular "data serialization system" that bridges between many different programming languages and tools. While the fact that data objects can be described in JSON makes it really easy to use, Avro is often being used for it's support of schema evolution. With support for schema evolution the same implementation (Storm topology) could be capable of reading different versions of the same data event without adaptation. This makes it a very good fit for Storm as a intermediator between data ingestion points and data storage in today's Enterprise Data Architectures. Storm Enterprise Data Architecture The example here does not provide complex event samples to illustrated that point, but it gives an end to end implementation of a Storm topology where events get send to a Kafka queue as Avro objects processesed natively by a real-time processing topology. The example can be found here. It's a simple Hive Streaming example where stock events are read from a CSV file and send to Kafka. Stock events are a flat, none complex data type as already mentioned, but we'll still use it to demo serialization with using Avro. Deserialization in Storm Before we look at the beginning, let's start with the end. When we have everything working properly we should be able to use our defined event object as such in any bolt part of the topology: Stock stock = (Stock) tuple.getValueByField("myobj_fieldname"); // OR by index // Stock stock = tuple.getValue(0); As demonstrated we should be able to cast our object simply from the tuple as it will already be present in serialized form inside the tuple. Storm will take care of the serialization for us. Remember Storm internally is using Kryo for Serialization as described here. It is using this for all data types in a tuple. To make this work with our object described in Avro we simply have to register a customer serializer with Storm's Kryo. The above snippet also concludes, that if we try to get retrieve the date in any other way, for example like this tuple.getBinary(0) we will receive an error. An Exception in such a case could look like this: 2015-09-23 10:52:57 s.AvroStockDataBolt [ERROR] java.lang.ClassCastException: storm_hive_streaming_example.model.Stock cannot be cast to [B java.lang.ClassCastException: storm_hive_streaming_example.model.Stock cannot be cast to [B at backtype.storm.tuple.TupleImpl.getBinary(TupleImpl.java:144) ~[storm-core-0.10.0.2.3.0.0-2557.jar:0.10.0.2.3.0.0-2557] at storm_hive_streaming_example.FieldEmitBolt.execute(FieldEmitBolt.java:34) ~[stormjar.jar:na] The sample error message clearly stats that our already serialized object simply can not be cast to a binary. So how do we set things up from the start? Spout Scheme Let's return to the beginning of all, the ingestion of events into a queue for example. The part being responsible for reading an event of a data source, like for example a message broker, is known as a Spout to Storm. Typically we have one spout for a specific data source other than having single purpose Spouts of a topology. Hence the a spout needs to be adaptive to the use case and events being issued. Storm uses so called "Scheme" to configure the data declaration of receiving and emitting events by the Spout. The Scheme interface declares the methods deserialize(byte[] pojoBytes) for deserializing the event collected. It returns a list of objects instead of just one object as one event could potentially be serialized into several data fields. Here the StockAvroScheme emits the complete Stock object in one field. The second method that needs to be implemented by the Scheme interface is the getOutputFields() method. This method is responsible for advertising the field definition to the receiving bolts. As by the implementation below the stock object gets send in one field. public class StockAvroScheme implements Scheme { private static final Logger LOG = LoggerFactory.getLogger(Stock.class); // deserializing the message recieved by the Spout public List<Object> deserialize(byte[] pojoBytes) { StockAvroSerializer serializer = new StockAvroSerializer(); // Kryo Serializer Stock stock = serializer.read(null, new Input(pojoBytes), Stock.class); List<Object> values = new ArrayList<>(); values.add(0, stock); return values; } // defining the output fields of the Spout public Fields getOutputFields() { return new Fields(new String[]{ FieldNames.STOCK_FIELD }); } } This Scheme can be as illustrated below by the YAML topology configuration using Storm Flux: components: # defines a scheme for the spout to emit a Stock.class object - id: "stockAvroScheme" className: "storm_hive_streaming_example.serializer.StockAvroScheme" # adding the defined stock scheme to the multi-scheme that can be assigned to the spout - id: "stockMultiScheme" className: "backtype.storm.spout.SchemeAsMultiScheme" constructorArgs: - ref: "stockAvroScheme" - id: "zkHosts" className: "storm.kafka.ZkHosts" constructorArgs: - "${hive-streaming-example.zk.hosts}" # configuring the spout to read bytes from Kafka and emit Stock.class - id: "stockSpoutConfig" className: "storm.kafka.SpoutConfig" constructorArgs: - ref: "zkHosts" # brokerHosts - "${hive-streaming-example.kafka.topic}" # topic - "${hive-streaming-example.kafka.zkRoot}" # zkRoot - "${hive-streaming-example.kafka.spoutId}" # id properties: - name: "scheme" ref: "stockMultiScheme" # use the stock scheme previously defined Last but not least we still need to register our customer serializer with Storm. Registering the Serializer Tuples are send to Spouts and Bolts running in a separate JVMs either on the same or on a remote host. In case of sending the tuple it needs to get serialized and deserialized prior to placing the tuple on the the output collector. For the serialization Storm uses Kryo Serializer. In order to use a custom Serializer implementation it needs to get registered with the Kryo instance being used by Strom. This can be done as part of the topology configuration. Here is the configuration definition using Storm Flux: name: "hive-streaming-example-avro-scheme" config: topology.workers: 1 # define serializers being used by tuples de-/serializing values. See http://storm.apache.org/documentation/Serialization.html topology.kryo.register: - storm_hive_streaming_example.model.Stock: storm_hive_streaming_example.serializer.StockAvroSerializer With this registration of the custom Kryo Serializer the AvroStockDataBolt can simply cast the Stock object from the tuple value emit it to the FieldEmitBolt, which decomposes the Stock instance into separate field being used by the HiveBolt. Having the AvroStockDataBolt and FieldEmitBolt would not make sense in a real implementation as the Scheme could obviously already be configured to do all that - deserialize and emit fields to the HiveBolt. Having these two extra bolts is just for demonstration purposes. Finally the custom Kryo Serializer which implements a write(Kryo kryo, Output output, Stock object) and read(Kryo kryo, Input input, Class<Stock> type). Having a general implementation of generic Avro types would be ideal. public class StockAvroSerializer extends Serializer<Stock> { private static final Logger LOG = LoggerFactory.getLogger(StockAvroSerializer.class); private Schema SCHEMA = Stock.getClassSchema(); public void write(Kryo kryo, Output output, Stock object) { DatumWriter<Stock> writer = new SpecificDatumWriter<>(SCHEMA); ByteArrayOutputStream out = new ByteArrayOutputStream(); BinaryEncoder encoder = EncoderFactory.get().binaryEncoder(out, null); try { writer.write(object, encoder); encoder.flush(); } catch (IOException e) { LOG.error(e.toString(), e); } IOUtils.closeQuietly(out); byte[] outBytes = out.toByteArray(); output.writeInt(outBytes.length, true); output.write(outBytes); } public Stock read(Kryo kryo, Input input, Class<Stock> type) { byte[] value = input.getBuffer(); SpecificDatumReader<Stock> reader = new SpecificDatumReader<>(SCHEMA); Stock record = null; try { record = reader.read(null, DecoderFactory.get().binaryDecoder(value, null)); } catch (IOException e) { LOG.error(e.toString(), e); } return record; } } Further Readings Storm Serialization Storm Hive Streaming Example (Github) Storm Flux Avro Specification Kafka Storm Starter (Github) Kryo Serializable http://www.confluent.io/blog/stream-data-platform-2/ Simple Example Using Kryo Storm Blueprints: Patterns for Distributed Realtime Computation (Amazon) Storm Applied: Strategies for Real-Time Event Processing (Amazon) ... 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Repo DescriptionRepo Info Github Repo URL https://github.com/hkropp/storm-hive-streaming-example Github account name hkropp Repo name storm-hive-streaming-example ... View more