Todd, I had it at one replica. Do I have to recreate?
Thanks, Ben > On Jul 11, 2016, at 10:37 AM, Todd Lipcon <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
