About bleonhardi

bleonhardi · ‎08-15-2016

Linkedin? There is only one Benjamin Leonhardi there

bleonhardi · ‎08-12-2016

@jovan karamacoski I think you might want to contact us for a services engagement. I strongly suspect that what you want to achieve and what you asking about are not compatible. On hadoop normally some files will be hot not specific blocks. And files will be per definition widely distributed across nodes. So moving specific "hot" drives will not make you happy. Also esp. If you write having some nodes with more network than others doesn't sound like a winning combination. Since slow nodes will be a bottleneck and it's all linked together. That's how hdfs works. If you want some files to be faster you might want to look at hdfs storage tiering. Using that you could put "hot" data on fast storage like ssds. You could also look at node labels to put specific applications on fast nodes with lots of cpu etc. But moving single drives ??? That will not make you happy. Per definitely hdfs will not care. One balancer later and all your careful planning is gone. Oh and lastly there is no online move of data nodes. You always need to stop a data node change the storage layout and start it again. It will send the updated block report to the Namenode.

bleonhardi · ‎08-12-2016

just use doAs=true make sure only hive can read the warehouse folder and you are done. Hive cli can start but not access anything

bleonhardi · ‎08-12-2016

"If it runs in the Appmaster, what exactly are "the computed input splits" that jobclient stores into HDFS while submitting the Job ??" InputSplits are simply the work assignments of a mapper. I.e. you have the inputfolder /in/file1 /in/file2 And assume file1 has 200MB and file2 100MB ( default block size 128MB ) So the InputFormat per default will generate 3 input splits ( on the appmaster its a function of InputFormat) InputSplit1: /in/file1:0:128000000 InputSplit2: /in/file1:128000001:200000000 InputSplit3:/in/file2:0:100000000 ( per default one split = 1 block but he COULD do whatever he wants. He does this for example for small files where he uses MultiFileInputSplits which span multiple files ) "And how map works if the split spans over data blocks in two different data nodes??" So the mapper comes up ( normally locally to the block ) and starts reading the file with the offset provided. HDFS by definition is global and if you read non local parts of a file he will read it over the network but local is obviously more efficient. But he COULD read anything. The HDFS API makes it transparent. So NORMALLY the InputSplit generation will be done in a way that this does not happen. So data can be read locally but its not a necessary precondition. Often maps are non local ( you can see that in the resource manager ) and then he can simply read the data over the network. The API call is identical. Reading an HDFS file in Java is the same as reading a local file. Its just an extension to the Java FileSystem API.

bleonhardi · ‎08-12-2016

Hello jovan, Yes you can simply move a folder. Data nodes are beautifully simple that way. We just did it on our cluster. Stop hdfs, copy the folder to a new location and change the location in the ambari configuration. just try it with a single drive on a single node ( using ambari groups) ( you can do an hadoop fsck / to check for under replicated blocks after the test). A single drive will not lead to inconsistencies in any case. In general data nodes do not care where the blocks are as long as they still find the files with the right block id in the data folders. You can theoretically do it on a running cluster but you need to use ambari groups do it one server at a time and make sure you do it quickly so Namenode doesn't start to schedule large number of replica additions because of the missing data node ( hdfs waits a biy before it fixes under replication in case a data node just reboots)

bleonhardi · ‎08-11-2016

Not sure what you mean. Do you want to know WHY blocks get under replicated? There are different possibilities for a block to vanish but by and large its simple: a) The block replica doesn't get written in the first place This happens during network or node failure during a write. HDFS will still return the write of a block as successful as long as one of the block replica writes was successful . So if for example the third designated datanode dies during the write process the write is still successful but the block will be under replicated. The write process doesn't care and they depend on the Namenode to schedule a copy later on. b) The block replicas get deleted later. That can have lots of different reasons. Node dies, drive dies, you delete a block file in the drive. Blocks after all are simple bog standard Linux files with a name blkxxxx which is the block id. They can also get corrupted ( HDFS does CRC checks regularly and blocks that are corrupted will be replaced with a healthy copy. And many more ... So perhaps you should be a bit more specific with your question?

bleonhardi · ‎08-11-2016

The namenode has a list of all the files blocks and block replicas in memory. A gigantic hashtable. Datanodes send block reports to it to give it an overview of all the blocks in the system. Periodically the namenode checks if all blocks have the desired replication level. If not it schedules either block deletion ( if the replication level is too high which can happen if a node crashed and was re added to the cluster ) or block copies.

bleonhardi · ‎08-09-2016

That's not the question. The point is where you run the scp command and as whom. If you run shell it can be executed on any datanode. So all datanodes need to be able to run the command you put into there successfully and have access to your remote machine. If you use ssh action you can force the execution of the scp command unto an edge node. Only that one then needs access to your remote server.

bleonhardi · ‎08-09-2016

No the expunge should happen immediately, although HDFS may take a bit till the datanodes actually get around to delete the files but it shouldn't take long. So expunge doesn't help? Weird 🙂

bleonhardi · ‎08-09-2016

You see that line: 16/08/0909:16:13 INFO fs.TrashPolicyDefault:Namenode trash configuration:Deletion interval =360 minutes,Emptier interval =0 minutes. Per default HDFS uses a trash. You can bypass this with rm -skipTrash or just delete the trash with hadoop fs -expunge

bleonhardi · ‎08-09-2016

May I ask why you cannot simply run the oozie workflow as user test. yarn will use the linuxtaskcontroller to execute a script as the user who runs the oozie workflow. The shell action can be run on any datanode node so I don't assume you made sudo -u work passwordless on all datanodes. Alternatively you can use the SSH action to execute a program on a specific node. ( then you need to allow the oozie user password less SSH to the user on the node who is running the workflow. ) In that case everything is completely as if you run the command locally for example on an edge node.

bleonhardi · ‎08-08-2016

Gopal and me gave a couple of tips in here to increase the parallelity ( since Hive is normally not tuned for cartesian joins and creates too few mappers ). https://community.hortonworks.com/questions/44749/hive-query-running-on-tez-contains-a-mapper-that-h.html#comment-45388 Apart from that my second point still holds you should create some pre-filtering to reduce the amount of points you need to compare. There are a ton of different ways to do this: https://en.wikipedia.org/wiki/Spatial_database#Spatial_index You can put points in grids and make sure that a data point in one grid entry cannot be closer to any point of the other grid entry than your max distance for example.

bleonhardi · ‎08-06-2016

So the folder where you have your workflow.xml in ( in HDFS ) can optionally contain a sub directory lib. Any jar that is in that lib folder will be taken by oozie and added to the CLASSPATH of the application ( and any file will be added to the execution folder so you can access it ). But I have never seen an error when the folder does not exist. How does you workflow look like? Also as said it should not be necessary but you could try creating a lib folder there to see if you get a more useful error message after.

bleonhardi · ‎08-06-2016

Yeah you might be able to give the calling action name through as a parameter and then use a Switch to go to the correct action but it sounds like almost more work.

bleonhardi · ‎08-04-2016

Hmmm not sure there is a way to do that unless you have one error handling action for each action. I don't think there is a dynamic to based on inputs in oozie. So I think you would need to copy and paste your error handling action: action1 ->action1.1 -> error1 -> action1.1 action2 -> action2.1 -> error2 -> action 2.1 and so on and so forth. Not very elegant I suppose though.

bleonhardi · ‎08-04-2016

Why don't you want a hive external table? It is just a temporary entry without any significant overhead. You can also use OrcStorage in pig to write orc files directly. http://pig.apache.org/docs/r0.15.0/func.html#OrcStorage Similar functions are available for spark Or you might be able to write a custom Mapreduce function using an ORC outputformat.

bleonhardi · ‎08-04-2016

Puh I think you need to make that decision yourself. A single big application will always be more efficient i.e. faster. You can also modularize a Spark project so working on a single task doesn't change the code from the others. However it becomes complex and you as said need to stop/start the application whenever you make a change to any part of it. Also if you use something CPU heavy as Tika the overhead of a separate topic in the middle doesn't sound too big anymore. So I think I would also strive for something like Input sources -> Parsing, Tika, normalization -> Kafka with a normalized document format ( JSON? ) -> Analytical application. But its hard to make an intelligent answer from a mile away 🙂 .

bleonhardi · ‎08-04-2016

Yeah see above. I think you just have to have a client like Hive that opens a TezClient, creates an Application master and then submits more DAGs to it. Specifically in Hive you have per default one Tez session per jdbc connection. So if you run multiple queries over the same jdbc connection they use the same Tez client, same Tez session and as long as the timeout is not reached the same application master. Yes I think it sounds a bit more magical than it is, the reuse is just the session mode where the client can send multiple DAGs to the same Tez AM. As said in LLAP you will have shared long running processes that can be discovered so its a bit different. http://hortonworks.com/blog/introducing-tez-sessions/

bleonhardi · ‎08-04-2016

Yeah I have to say I didn't look into the hive code so I am not sure if you can actually "find" running Tez applications and attach to them. I think its just the TezClient being kept open in hive server/ pig whatever and then submitting more DAGs to the existing AM. But there might be ways for discovery. But basically Tez doesn't take over much of what yarn does. This will be a bit different with LLAP. Which is like a big yarn container running multiple tez tasks. That one will have some workload management, scheduling etc. https://tez.apache.org/releases/0.7.1/tez-api-javadocs/org/apache/tez/client/TezClient.html

bleonhardi · ‎08-03-2016

So he is definitely correct if you have a single application when you purely look at performance. You essentially get rid of the second kafka overhead. However there are situations where a second set of queues can be helpful: You decouple parsing and cleansing from analysis which has big advantages: - So you can have one parser app and then multiple analytical applications you can start and stop as you please without impacting the other parser and analytical apps. - You can write simple analytical applications that take a parsed, cleaned subset of data they are interested in so people can consume data they actually want and don't have to worry about the initial parsing/cleansing etc.

bleonhardi · ‎08-03-2016

yeah but without the error we cannot really help. I suppose you mean a classnotfound exception? So your udf uses a lot of exotic imports?

bleonhardi · ‎08-03-2016

Hi Shiva, Its a Tez client API call you would need to do to find already existing Application Masters of your user in the cluster. You can then hook up with them. The main user at the moment is Hive. Which utilizes it to reduce the startup cost of a query. Essentially each JDBC connection of hive session ( if enabled ) map to one application master in yarn. So when you run a query hive will check if an application master already exists ( using the tez client api calls ) and uses that AM. Or creates a new one otherwise.

bleonhardi · ‎08-01-2016

Have a look at the article: https://community.hortonworks.com/articles/25726/spark-streaming-explained-kafka-to-phoenix.html You could change the parser function to trim strings and remove quotes. Apart from that it would work very similarily. Instead of using the manual Phoenix connector there are now also out of the box phoenix connectors. So you can choose but the parser is a pretty good general purpose example of how to work with a CSV and add custom code.

bleonhardi · ‎08-01-2016

Hive was essentially a java library that kicks off MapReduce jobs. So the hive cli for example runs a full "server" in its client. If you have multiple clients all of them do their own SQL parsing/optimization etc. In Hive1 there was a thrift server which was like a proxy server for this. So a thrift ( data serialization framework ) client could connect to it instead of doing all the computations locally. All of that is not relevant anymore since Hiveserver2 has been the default for many years in all distributions and is a proper database server with concurrency/security/logging/workload management... You still have the hive client available but this will be deprecated soon in favor of beeline which is a command line client that connects to hiveserver2 through jdbc. This is desirable since the hive cli punches a lot of holes into central hive administration. So forget about hiveserver1 thrift server and thrift client.

bleonhardi · ‎08-01-2016

@Steven Hirsch I think you can try it for one application, one possibility is to simply switch off ATS for a bit that helped me once but not a second time ( Tez still tries to log to it ) So if you really want to switch it off completely you can add the following settings: tez.history.logging.service.class = org.apache.tez.dag.history.logging.impl.SimpleHistoryLoggingService and to see the logs: tez.simple.history.logging.dir=/mylogdir Also removing the following ATSHooks hive.exec.pre.hooks= hive.exec.post.hooks= Also potentially reduce log levels hive.tez.log.level=ERROR And see if it makes things faster. Again if you don't see a difference you may have other issues. But its worth to rule out. ATS 1.5 has been enabled in HDP2.4. Also ATS1.0 has some tuning options. If that is really your bottleneck Hortonworks support may be able to help.

bleonhardi · ‎08-01-2016

Again see below the block size doesn't really change anything apart from the number of total tasks. You still have the same number of tasks running at the same time which are defined by the executor-cores. Can you do a top on the machines while the job is runnoing? If your CPU utilization is low you want to increase the executor-cores if they are close to max. reducing it a bit might be working ( to reduce task switching ) . However if that doesn't change anything. you might just have to live with the prformance. Or buy more nodes.

bleonhardi · ‎08-01-2016

Running UDFs in pig is what pig is for. You should fix that problem. Have you registered your jars? http://pig.apache.org/docs/r0.16.0/udf.html#udf-java There are other possibilities as well, Spark comes to mind esp. with python it can be relatively easy to setup ( although it also has its problems like python versions ) And there are some ETL tools that can utilize hadoop. But by and large pig with java udfs is a very straight forward way to do custom data cleaning on data in hadoop. There is no reason you shouldn't get it to work.

bleonhardi · ‎08-01-2016

Just want to support that answer. a repartition is not bad in any case since I have seen some interesting characteristics with kafka producers. If you use round robin for them they send data to a random partition and switch them every 10 min or so. So it is possible that a single Partition in Kafka will randomly get ALL the data and blow your spark application up ( Flume kafka connector was my example ). A repartition after the KafkaStream fixed that. You can parametrize this based on the number of executors etc.

bleonhardi · ‎08-01-2016

How about you just try it? I am pretty sure it will be the same but just make two CTAS tables and test it quickly.

bleonhardi · ‎07-31-2016

The number of cores are the number of tasks "slots" in the executor. This is the reference to "you want 2-3x of physical cores". You want to make sure that at the same time spark runs more tasks than cores of the cpu ( there is hyper threading and some overhead). So assuming you have 15000 tasks and 100 executor cores in total spark will run them in 150 "waves". Think of it like a yarn in yarn. Now you also have vcores in yarn and the executor cores are translated into vcore requests but normally they are not switched on and purely ornamental. I.e. Yarn only uses memory for assignment )

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