Todd, Thanks for the info. I was going to upgrade after the testing, but now, it looks like I will have to do it earlier than expected.
I will do the upgrade, then resume. Cheers, Ben > On Jul 18, 2016, at 10:29 AM, Todd Lipcon <t...@cloudera.com> wrote: > > Hi Ben, > > Any chance that you are running Kudu 0.9.0 instead of 0.9.1? There's a known > serious bug in 0.9.0 which can cause this kind of corruption. > > Assuming that you are running with replication count 3 this time, you should > be able to move aside that tablet metadata file and start the server. It will > recreate a new repaired replica automatically. > > -Todd > > On Mon, Jul 18, 2016 at 10:28 AM, Benjamin Kim <bbuil...@gmail.com > <mailto:bbuil...@gmail.com>> wrote: > During my re-population of the Kudu table, I am getting this error trying to > restart a tablet server after it went down. The job that populates this table > has been running for over a week. > > [libprotobuf ERROR google/protobuf/message_lite.cc:123] Can't parse message > of type "kudu.tablet.TabletSuperBlockPB" because it is missing required > fields: rowsets[2324].columns[15].block > F0718 17:01:26.783571 468 tablet_server_main.cc:55] Check failed: _s.ok() > Bad status: IO error: Could not init Tablet Manager: Failed to open tablet > metadata for tablet: 24637ee6f3e5440181ce3f20b1b298ba: Failed to load tablet > metadata for tablet id 24637ee6f3e5440181ce3f20b1b298ba: Could not load > tablet metadata from > /mnt/data1/kudu/data/tablet-meta/24637ee6f3e5440181ce3f20b1b298ba: Unable to > parse PB from path: > /mnt/data1/kudu/data/tablet-meta/24637ee6f3e5440181ce3f20b1b298ba > *** Check failure stack trace: *** > @ 0x7d794d google::LogMessage::Fail() > @ 0x7d984d google::LogMessage::SendToLog() > @ 0x7d7489 google::LogMessage::Flush() > @ 0x7da2ef google::LogMessageFatal::~LogMessageFatal() > @ 0x78172b (unknown) > @ 0x344d41ed5d (unknown) > @ 0x7811d1 (unknown) > > Does anyone know what this means? > > Thanks, > Ben > > >> On Jul 11, 2016, at 10:47 AM, Todd Lipcon <t...@cloudera.com >> <mailto:t...@cloudera.com>> wrote: >> >> On Mon, Jul 11, 2016 at 10:40 AM, Benjamin Kim <bbuil...@gmail.com >> <mailto:bbuil...@gmail.com>> wrote: >> Todd, >> >> I had it at one replica. Do I have to recreate? >> >> We don't currently have the ability to "accept data loss" on a tablet (or >> set of tablets). If the machine is gone for good, then currently the only >> easy way to recover is to recreate the table. If this sounds really painful, >> though, maybe we can work up some kind of tool you could use to just >> recreate the missing tablets (with those rows lost). >> >> -Todd >> >>> On Jul 11, 2016, at 10:37 AM, Todd Lipcon <t...@cloudera.com >>> <mailto:t...@cloudera.com>> wrote: >>> >>> Hey Ben, >>> >>> Is the table that you're querying replicated? Or was it created with only >>> one replica per tablet? >>> >>> -Todd >>> >>> On Mon, Jul 11, 2016 at 10:35 AM, Benjamin Kim <b...@amobee.com >>> <mailto:b...@amobee.com>> wrote: >>> Over the weekend, a tablet server went down. It’s not coming back up. So, I >>> decommissioned it and removed it from the cluster. Then, I restarted Kudu >>> because I was getting a timeout exception trying to do counts on the >>> table. Now, when I try again. I get the same error. >>> >>> 16/07/11 17:32:36 WARN scheduler.TaskSetManager: Lost task 468.3 in stage >>> 0.0 (TID 603, prod-dc1-datanode167.pdc1i.gradientx.com >>> <http://prod-dc1-datanode167.pdc1i.gradientx.com/>): >>> com.stumbleupon.async.TimeoutException: Timed out after 30000ms when >>> joining Deferred@712342716(state=PAUSED, result=Deferred@1765902299, >>> callback=passthrough -> scanner opened -> wakeup thread Executor task >>> launch worker-2, errback=openScanner errback -> passthrough -> wakeup >>> thread Executor task launch worker-2) >>> at com.stumbleupon.async.Deferred.doJoin(Deferred.java:1177) >>> at com.stumbleupon.async.Deferred.join(Deferred.java:1045) >>> at org.kududb.client.KuduScanner.nextRows(KuduScanner.java:57) >>> at org.kududb.spark.kudu.RowResultIteratorScala.hasNext(KuduRDD.scala:99) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>> at >>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:88) >>> at >>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) >>> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) >>> at org.apache.spark.scheduler.Task.run(Task.scala:89) >>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>> at >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>> at >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>> at java.lang.Thread.run(Thread.java:745) >>> >>> Does anyone know how to recover from this? >>> >>> Thanks, >>> Benjamin Kim >>> Data Solutions Architect >>> >>> [a•mo•bee] (n.) the company defining digital marketing. >>> >>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900> >>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | >>> www.amobee.com <http://www.amobee.com/> >>>> On Jul 6, 2016, at 9:46 AM, Dan Burkert <d...@cloudera.com >>>> <mailto:d...@cloudera.com>> wrote: >>>> >>>> >>>> >>>> On Wed, Jul 6, 2016 at 7:05 AM, Benjamin Kim <bbuil...@gmail.com >>>> <mailto:bbuil...@gmail.com>> wrote: >>>> Over the weekend, the row count is up to <500M. I will give it another few >>>> days to get to 1B rows. I still get consistent times ~15s for doing row >>>> counts despite the amount of data growing. >>>> >>>> On another note, I got a solicitation email from SnappyData to evaluate >>>> their product. They claim to be the “Spark Data Store” with tight >>>> integration with Spark executors. It claims to be an OLTP and OLAP system >>>> with being an in-memory data store first then to disk. After going to >>>> several Spark events, it would seem that this is the new “hot” area for >>>> vendors. They all (MemSQL, Redis, Aerospike, Datastax, etc.) claim to be >>>> the best "Spark Data Store”. I’m wondering if Kudu will become this too? >>>> With the performance I’ve seen so far, it would seem that it can be a >>>> contender. All that is needed is a hardened Spark connector package, I >>>> would think. The next evaluation I will be conducting is to see if >>>> SnappyData’s claims are valid by doing my own tests. >>>> >>>> It's hard to compare Kudu against any other data store without a lot of >>>> analysis and thorough benchmarking, but it is certainly a goal of Kudu to >>>> be a great platform for ingesting and analyzing data through Spark. Up >>>> till this point most of the Spark work has been community driven, but more >>>> thorough integration testing of the Spark connector is going to be a focus >>>> going forward. >>>> >>>> - Dan >>>> >>>> >>>> Cheers, >>>> Ben >>>> >>>> >>>> >>>>> On Jun 15, 2016, at 12:47 AM, Todd Lipcon <t...@cloudera.com >>>>> <mailto:t...@cloudera.com>> wrote: >>>>> >>>>> Hi Benjamin, >>>>> >>>>> What workload are you using for benchmarks? Using spark or something more >>>>> custom? rdd or data frame or SQL, etc? Maybe you can share the schema and >>>>> some queries >>>>> >>>>> Todd >>>>> >>>>> Todd >>>>> >>>>> On Jun 15, 2016 8:10 AM, "Benjamin Kim" <bbuil...@gmail.com >>>>> <mailto:bbuil...@gmail.com>> wrote: >>>>> Hi Todd, >>>>> >>>>> Now that Kudu 0.9.0 is out. I have done some tests. Already, I am >>>>> impressed. Compared to HBase, read and write performance are better. >>>>> Write performance has the greatest improvement (> 4x), while read is > >>>>> 1.5x. Albeit, these are only preliminary tests. Do you know of a way to >>>>> really do some conclusive tests? I want to see if I can match your >>>>> results on my 50 node cluster. >>>>> >>>>> Thanks, >>>>> Ben >>>>> >>>>>> On May 30, 2016, at 10:33 AM, Todd Lipcon <t...@cloudera.com >>>>>> <mailto:t...@cloudera.com>> wrote: >>>>>> >>>>>> On Sat, May 28, 2016 at 7:12 AM, Benjamin Kim <bbuil...@gmail.com >>>>>> <mailto:bbuil...@gmail.com>> wrote: >>>>>> Todd, >>>>>> >>>>>> It sounds like Kudu can possibly top or match those numbers put out by >>>>>> Aerospike. Do you have any performance statistics published or any >>>>>> instructions as to measure them myself as good way to test? In addition, >>>>>> this will be a test using Spark, so should I wait for Kudu version 0.9.0 >>>>>> where support will be built in? >>>>>> >>>>>> We don't have a lot of benchmarks published yet, especially on the write >>>>>> side. I've found that thorough cross-system benchmarks are very >>>>>> difficult to do fairly and accurately, and often times users end up >>>>>> misguided if they pay too much attention to them :) So, given a finite >>>>>> number of developers working on Kudu, I think we've tended to spend more >>>>>> time on the project itself and less time focusing on "competition". I'm >>>>>> sure there are use cases where Kudu will beat out Aerospike, and >>>>>> probably use cases where Aerospike will beat Kudu as well. >>>>>> >>>>>> From my perspective, it would be great if you can share some details of >>>>>> your workload, especially if there are some areas you're finding Kudu >>>>>> lacking. Maybe we can spot some easy code changes we could make to >>>>>> improve performance, or suggest a tuning variable you could change. >>>>>> >>>>>> -Todd >>>>>> >>>>>> >>>>>>> On May 27, 2016, at 9:19 PM, Todd Lipcon <t...@cloudera.com >>>>>>> <mailto:t...@cloudera.com>> wrote: >>>>>>> >>>>>>> On Fri, May 27, 2016 at 8:20 PM, Benjamin Kim <bbuil...@gmail.com >>>>>>> <mailto:bbuil...@gmail.com>> wrote: >>>>>>> Hi Mike, >>>>>>> >>>>>>> First of all, thanks for the link. It looks like an interesting read. I >>>>>>> checked that Aerospike is currently at version 3.8.2.3, and in the >>>>>>> article, they are evaluating version 3.5.4. The main thing that >>>>>>> impressed me was their claim that they can beat Cassandra and HBase by >>>>>>> 8x for writing and 25x for reading. Their big claim to fame is that >>>>>>> Aerospike can write 1M records per second with only 50 nodes. I wanted >>>>>>> to see if this is real. >>>>>>> >>>>>>> 1M records per second on 50 nodes is pretty doable by Kudu as well, >>>>>>> depending on the size of your records and the insertion order. I've >>>>>>> been playing with a ~70 node cluster recently and seen 1M+ >>>>>>> writes/second sustained, and bursting above 4M. These are 1KB rows with >>>>>>> 11 columns, and with pretty old HDD-only nodes. I think newer >>>>>>> flash-based nodes could do better. >>>>>>> >>>>>>> >>>>>>> To answer your questions, we have a DMP with user profiles with many >>>>>>> attributes. We create segmentation information off of these attributes >>>>>>> to classify them. Then, we can target advertising appropriately for our >>>>>>> sales department. Much of the data processing is for applying models on >>>>>>> all or if not most of every profile’s attributes to find similarities >>>>>>> (nearest neighbor/clustering) over a large number of rows when batch >>>>>>> processing or a small subset of rows for quick online scoring. So, our >>>>>>> use case is a typical advanced analytics scenario. We have tried HBase, >>>>>>> but it doesn’t work well for these types of analytics. >>>>>>> >>>>>>> I read, that Aerospike in the release notes, they did do many >>>>>>> improvements for batch and scan operations. >>>>>>> >>>>>>> I wonder what your thoughts are for using Kudu for this. >>>>>>> >>>>>>> Sounds like a good Kudu use case to me. I've heard great things about >>>>>>> Aerospike for the low latency random access portion, but I've also >>>>>>> heard that it's _very_ expensive, and not particularly suited to the >>>>>>> columnar scan workload. Lastly, I think the Apache license of Kudu is >>>>>>> much more appealing than the AGPL3 used by Aerospike. But, that's not >>>>>>> really a direct answer to the performance question :) >>>>>>> >>>>>>> >>>>>>> Thanks, >>>>>>> Ben >>>>>>> >>>>>>> >>>>>>>> On May 27, 2016, at 6:21 PM, Mike Percy <mpe...@cloudera.com >>>>>>>> <mailto:mpe...@cloudera.com>> wrote: >>>>>>>> >>>>>>>> Have you considered whether you have a scan heavy or a random access >>>>>>>> heavy workload? Have you considered whether you always access / update >>>>>>>> a whole row vs only a partial row? Kudu is a column store so has some >>>>>>>> awesome performance characteristics when you are doing a lot of >>>>>>>> scanning of just a couple of columns. >>>>>>>> >>>>>>>> I don't know the answer to your question but if your concern is >>>>>>>> performance then I would be interested in seeing comparisons from a >>>>>>>> perf perspective on certain workloads. >>>>>>>> >>>>>>>> Finally, a year ago Aerospike did quite poorly in a Jepsen test: >>>>>>>> https://aphyr.com/posts/324-jepsen-aerospike >>>>>>>> <https://aphyr.com/posts/324-jepsen-aerospike> >>>>>>>> >>>>>>>> I wonder if they have addressed any of those issues. >>>>>>>> >>>>>>>> Mike >>>>>>>> >>>>>>>> On Friday, May 27, 2016, Benjamin Kim <bbuil...@gmail.com >>>>>>>> <mailto:bbuil...@gmail.com>> wrote: >>>>>>>> I am just curious. How will Kudu compare with Aerospike >>>>>>>> (http://www.aerospike.com <http://www.aerospike.com/>)? I went to a >>>>>>>> Spark Roadshow and found out about this piece of software. It appears >>>>>>>> to fit our use case perfectly since we are an ad-tech company trying >>>>>>>> to leverage our user profiles data. Plus, it already has a Spark >>>>>>>> connector and has a SQL-like client. The tables can be accessed using >>>>>>>> Spark SQL DataFrames and, also, made into SQL tables for direct use >>>>>>>> with Spark SQL ODBC/JDBC Thriftserver. I see from the work done here >>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ >>>>>>>> <http://gerrit.cloudera.org:8080/#/c/2992/> that the Spark integration >>>>>>>> is well underway and, from the looks of it lately, almost complete. I >>>>>>>> would prefer to use Kudu since we are already a Cloudera shop, and >>>>>>>> Kudu is easy to deploy and configure using Cloudera Manager. I also >>>>>>>> hope that some of Aerospike’s speed optimization techniques can make >>>>>>>> it into Kudu in the future, if they have not been already thought of >>>>>>>> or included. >>>>>>>> >>>>>>>> Just some thoughts… >>>>>>>> >>>>>>>> Cheers, >>>>>>>> Ben >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> -- >>>>>>>> Mike Percy >>>>>>>> Software Engineer, Cloudera >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Todd Lipcon >>>>>>> Software Engineer, Cloudera >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Todd Lipcon >>>>>> Software Engineer, Cloudera >>>>> >>>> >>>> >>> >>> >>> >>> >>> -- >>> Todd Lipcon >>> Software Engineer, Cloudera >> >> >> >> >> -- >> Todd Lipcon >> Software Engineer, Cloudera > > > > > -- > Todd Lipcon > Software Engineer, Cloudera
