Todd, It’s no problem to start over again. But, a tool like that would be helpful. Gaps in data can be accommodated for by just back filling.
Thanks, Ben > On Jul 11, 2016, at 10:47 AM, Todd Lipcon <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
