Dealing with 'smaller' data
I'm considering whether or not it is worth introducing Spark at my new company. The data is no-where near Hadoop size at this point (it sits in an RDS Postgres cluster). I'm wondering at which point it is worth the overhead of adding the Spark infrastructure (deployment scripts, monitoring, etc).
Re: Dealing with 'smaller' data
The honest answer is that it is unclear to me at this point. I guess what I am really wondering is if there are cases where one would find it beneficial to use Spark against one or more RDBs? On Thu, Feb 26, 2015 at 8:06 PM, Tobias Pfeiffer t...@preferred.jp wrote: Gary, On Fri, Feb 27, 2015 at 8:40 AM, Gary Malouf malouf.g...@gmail.com wrote: I'm considering whether or not it is worth introducing Spark at my new company. The data is no-where near Hadoop size at this point (it sits in an RDS Postgres cluster). Will it ever become Hadoop size? Looking at the overhead of running even a simple Hadoop setup (securely and with good performance, given about 1e6 configuration parameters), I think it makes sense to stay in non-Hadoop mode as long as possible. People may disagree ;-) Tobias PS. You may also want to have a look at http://aadrake.com/command-line-tools-can-be-235x-faster-than-your-hadoop-cluster.html
Re: Dealing with 'smaller' data
So when deciding whether to take on installing/configuring Spark, the size of the data does not automatically make that decision in your mind. Thanks, Gary On Thu, Feb 26, 2015 at 8:55 PM, Tobias Pfeiffer t...@preferred.jp wrote: Hi On Fri, Feb 27, 2015 at 10:50 AM, Gary Malouf malouf.g...@gmail.com wrote: The honest answer is that it is unclear to me at this point. I guess what I am really wondering is if there are cases where one would find it beneficial to use Spark against one or more RDBs? Well, RDBs are all about *storage*, while Spark is about *computation*. If you have a very expensive computation (that can be parallelized in some way), then you might want to use Spark, even though your data lives in an ordinary RDB. Think raytracing, where you do something for every pixel in the output image and you could get your scene description from a database, write the result to a database, but use Spark to do two minutes of calculation for every pixel in parallel (or so). Tobias
GraphX bug re-opened
We keep running into https://issues.apache.org/jira/browse/SPARK-2823 when trying to use GraphX. The cost of repartitioning the data is really high for us (lots of network traffic) which is killing the job performance. I understand the bug was reverted to stabilize unit tests, but frankly it makes it very hard to tune Spark applications with the limits this puts on someone. What is the process to get fixing this prioritized if we do not have the cycles to do it ourselves?
Shuffle Intensive Job: sendMessageReliably failed because ack was not received within 60 sec
Has anyone else received this type of error? We are not sure what the issue is nor how to correct it to get our job to complete...
Re: Sourcing data from RedShift
Hi guys, We ultimately needed to add 8 ec2 xl's to get better performance. As was suspected, we could not fit all the data into ram. This worked great with files sized around 100-350MB in size as our initial export task produced. Unfortunately, for the partition settings that we were able to get to work with GraphX (unable to change parallelism due to bug), we are unable to keep writing files at this size - our output ends up being closer to 1GB per file. As a result, our job seems to struggle working with a 100GB worth of these files. We are in a rough spot because upgrading Spark right now is not reasonable for us but this bug prevents solving the issue. On Fri, Nov 14, 2014 at 9:29 PM, Gary Malouf malouf.g...@gmail.com wrote: I'll try this out and follow up with what I find. On Fri, Nov 14, 2014 at 8:54 PM, Xiangrui Meng m...@databricks.com wrote: For each node, if the CSV reader is implemented efficiently, you should be able to hit at least half of the theoretical network bandwidth, which is about 60MB/second/node. So if you just do counting, the expect time should be within 3 minutes. Note that your cluster have 15GB * 12 = 180GB RAM in total. If you use the default spark.storage.memoryFraction, it can barely cache 100GB of data, not considering the overhead. So if your operation need to cache the data to be efficient, you may need a larger cluster or change the storage level to MEMORY_AND_DISK. -Xiangrui On Nov 14, 2014, at 5:32 PM, Gary Malouf malouf.g...@gmail.com wrote: Hmm, we actually read the CSV data in S3 now and were looking to avoid that. Unfortunately, we've experienced dreadful performance reading 100GB of text data for a job directly from S3 - our hope had been connecting directly to Redshift would provide some boost. We had been using 12 m3.xlarges, but increasing default parallelism (to 2x # of cpus across cluster) and increasing partitions during reading did not seem to help. On Fri, Nov 14, 2014 at 6:51 PM, Xiangrui Meng m...@databricks.com wrote: Michael is correct. Using direct connection to dump data would be slow because there is only a single connection. Please use UNLOAD with ESCAPE option to dump the table to S3. See instructions at http://docs.aws.amazon.com/redshift/latest/dg/r_UNLOAD.html And then load them back using the redshift input format we wrote: https://github.com/databricks/spark-redshift (we moved the implementation to github/databricks). Right now all columns are loaded as string columns, and you need to do type casting manually. We plan to add a parser that can translate Redshift table schema directly to Spark SQL schema, but no ETA yet. -Xiangrui On Nov 14, 2014, at 3:46 PM, Michael Armbrust mich...@databricks.com wrote: I'd guess that its an s3n://key:secret_key@bucket/path from the UNLOAD command used to produce the data. Xiangrui can correct me if I'm wrong though. On Fri, Nov 14, 2014 at 2:19 PM, Gary Malouf malouf.g...@gmail.com wrote: We have a bunch of data in RedShift tables that we'd like to pull in during job runs to Spark. What is the path/url format one uses to pull data from there? (This is in reference to using the https://github.com/mengxr/redshift-input-format)
Sourcing data from RedShift
We have a bunch of data in RedShift tables that we'd like to pull in during job runs to Spark. What is the path/url format one uses to pull data from there? (This is in reference to using the https://github.com/mengxr/redshift-input-format)
Re: Sourcing data from RedShift
Hmm, we actually read the CSV data in S3 now and were looking to avoid that. Unfortunately, we've experienced dreadful performance reading 100GB of text data for a job directly from S3 - our hope had been connecting directly to Redshift would provide some boost. We had been using 12 m3.xlarges, but increasing default parallelism (to 2x # of cpus across cluster) and increasing partitions during reading did not seem to help. On Fri, Nov 14, 2014 at 6:51 PM, Xiangrui Meng m...@databricks.com wrote: Michael is correct. Using direct connection to dump data would be slow because there is only a single connection. Please use UNLOAD with ESCAPE option to dump the table to S3. See instructions at http://docs.aws.amazon.com/redshift/latest/dg/r_UNLOAD.html And then load them back using the redshift input format we wrote: https://github.com/databricks/spark-redshift (we moved the implementation to github/databricks). Right now all columns are loaded as string columns, and you need to do type casting manually. We plan to add a parser that can translate Redshift table schema directly to Spark SQL schema, but no ETA yet. -Xiangrui On Nov 14, 2014, at 3:46 PM, Michael Armbrust mich...@databricks.com wrote: I'd guess that its an s3n://key:secret_key@bucket/path from the UNLOAD command used to produce the data. Xiangrui can correct me if I'm wrong though. On Fri, Nov 14, 2014 at 2:19 PM, Gary Malouf malouf.g...@gmail.com wrote: We have a bunch of data in RedShift tables that we'd like to pull in during job runs to Spark. What is the path/url format one uses to pull data from there? (This is in reference to using the https://github.com/mengxr/redshift-input-format)
Re: Sourcing data from RedShift
I'll try this out and follow up with what I find. On Fri, Nov 14, 2014 at 8:54 PM, Xiangrui Meng m...@databricks.com wrote: For each node, if the CSV reader is implemented efficiently, you should be able to hit at least half of the theoretical network bandwidth, which is about 60MB/second/node. So if you just do counting, the expect time should be within 3 minutes. Note that your cluster have 15GB * 12 = 180GB RAM in total. If you use the default spark.storage.memoryFraction, it can barely cache 100GB of data, not considering the overhead. So if your operation need to cache the data to be efficient, you may need a larger cluster or change the storage level to MEMORY_AND_DISK. -Xiangrui On Nov 14, 2014, at 5:32 PM, Gary Malouf malouf.g...@gmail.com wrote: Hmm, we actually read the CSV data in S3 now and were looking to avoid that. Unfortunately, we've experienced dreadful performance reading 100GB of text data for a job directly from S3 - our hope had been connecting directly to Redshift would provide some boost. We had been using 12 m3.xlarges, but increasing default parallelism (to 2x # of cpus across cluster) and increasing partitions during reading did not seem to help. On Fri, Nov 14, 2014 at 6:51 PM, Xiangrui Meng m...@databricks.com wrote: Michael is correct. Using direct connection to dump data would be slow because there is only a single connection. Please use UNLOAD with ESCAPE option to dump the table to S3. See instructions at http://docs.aws.amazon.com/redshift/latest/dg/r_UNLOAD.html And then load them back using the redshift input format we wrote: https://github.com/databricks/spark-redshift (we moved the implementation to github/databricks). Right now all columns are loaded as string columns, and you need to do type casting manually. We plan to add a parser that can translate Redshift table schema directly to Spark SQL schema, but no ETA yet. -Xiangrui On Nov 14, 2014, at 3:46 PM, Michael Armbrust mich...@databricks.com wrote: I'd guess that its an s3n://key:secret_key@bucket/path from the UNLOAD command used to produce the data. Xiangrui can correct me if I'm wrong though. On Fri, Nov 14, 2014 at 2:19 PM, Gary Malouf malouf.g...@gmail.com wrote: We have a bunch of data in RedShift tables that we'd like to pull in during job runs to Spark. What is the path/url format one uses to pull data from there? (This is in reference to using the https://github.com/mengxr/redshift-input-format)
Short Circuit Local Reads
Cloudera had a blog post about this in August 2013: http://blog.cloudera.com/blog/2013/08/how-improved-short-circuit-local-reads-bring-better-performance-and-security-to-hadoop/ Has anyone been using this in production - curious as to if it made a significant difference from a Spark perspective.
Dealing with Time Series Data
I have a use case for our data in HDFS that involves sorting chunks of data into time series format by a specific characteristic and doing computations from that. At large scale, what is the most efficient way to do this? Obviously, having the data sharded by that characteristic would make the performance significantly better, but are there good tools Spark can do to help us?
Dealing with Idle shells
We have our quantitative team using Spark as part of their daily work. One of the more common problems we run into is that people unintentionally leave their shells open throughout the day. This eats up memory in the cluster and causes others to have limited resources to run their jobs. With something like Hive or many client applications for SQL databases, this is not really an issue but with Spark it's a significant inconvenience to non-technical users. Someone ends up having to post throughout the day in chats to ensure people are using their shells or to 'get off the cluster'. Just wondering if anyone else has experienced this type of issue and how they are managing it. One idea we've had is to implement an 'idle timeout' monitor for the shell, though on the surface this appears quite challenging.
DistCP - Spark-based
We are probably still the minority, but our analytics platform based on Spark + HDFS does not have map/reduce installed. I'm wondering if there is a distcp equivalent that leverages Spark to do the work. Our team is trying to find the best way to do cross-datacenter replication of our HDFS data to minimize the impact of outages/dc failure.
Regarding tooling/performance vs RedShift
My company is leaning towards moving much of their analytics work from our own Spark/Mesos/HDFS/Cassandra set up to RedShift. To date, I have been the internal advocate for using Spark for analytics, but a number of good points have been brought up to me. The reasons being pushed are: - RedShift exposes a jdbc interface out of the box (no devops work there) and data looks and feels like it is in a normal sql database. They want this out of the box from Spark, no trying to figure out which version matches this version of Hive/Shark/SparkSQL etc. Yes, the next release theoretically supports this but there have been release issues our team has battled to date that erode the trust. - Complaints around challenges we have faced running a spark shell locally against a cluster in EC2. It is partly a devops issue of deploying the correct configurations to local machines, being able to kick a user off hogging RAM, etc. - I want to be able to run queries from my python shell against your sequence file data, roll it up and in the same shell leverage python graph tools. - I'm not very familiar with the Python setup, but I believe by being able to run locally AND somehow add custom libraries to be accessed from PySpark this could be done. - Joins will perform much better (in RedShift) because it says it sorts it's keys. We cannot pre-compute all joins away. Basically, their argument is two-fold: 1) We get tooling out of the box from RedShift (specifically, stable JDBC access) - Spark we often are waiting for devops to get the right combo of tools working or for libraries to support sequence files. 2) There is a belief that for many of our queries (assumed to often be joins) a columnar database will perform orders of magnitude better. Anyway, a test is being setup to compare the two on the performance side but from a tools perspective it's hard to counter the issues that are brought up.
Re: Runnning a Spark Shell locally against EC2
This will be awesome - it's been one of the major issues for our analytics team as they hope to use their own python libraries. On Wed, Aug 6, 2014 at 2:40 PM, Andrew Or and...@databricks.com wrote: Hi Gary, This has indeed been a limitation of Spark, in that drivers and executors use random ephemeral ports to talk to each other. If you are submitting a Spark job from your local machine in client mode (meaning, the driver runs on your machine), you will need to open up all TCP ports from your worker machines, a requirement that is not super secure. However, a very recent commit changes this ( https://github.com/apache/spark/commit/09f7e4587bbdf74207d2629e8c1314f93d865999) in that you can now manually configure all ports and only open up the ones you configured. This will be available in Spark 1.1. -Andrew 2014-08-06 8:29 GMT-07:00 Gary Malouf malouf.g...@gmail.com: We have Spark 1.0.1 on Mesos deployed as a cluster in EC2. Our Devops lead tells me that Spark jobs can not be submitted from local machines due to the complexity of opening the right ports to the world etc. Are other people running the shell locally in a production environment?
Spark memory management
I have a few questions about managing Spark memory: 1) In a standalone setup, is their any cpu prioritization across users running jobs? If so, what is the behavior here? 2) With Spark 1.1, users will more easily be able to run drivers/shells from remote locations that do not cause firewall headaches. Is there a way to kill an individual user's job from the console without killing workers? We are in Mesos and are not aware of an easy way to handle this, but I imagine standalone mode may handle this.
Re: Regarding tooling/performance vs RedShift
Forgot to cc the mailing list :) On Wed, Aug 6, 2014 at 3:41 PM, Daniel, Ronald (ELS-SDG) r.dan...@elsevier.com wrote: Agreed. Being able to use SQL to make a table, pass it to a graph algorithm, pass that output to a machine learning algorithm, being able to invoke user defined python functions, … are capabilities that far exceed what we can do with Redshift. The total performance will be much better, and the programmer productivity will be much better, even if the SQL portion is not quite as fast. Mostly I was just objecting to Redshift does very well, but Shark is on par or better than it in most of the tests when that was not how I read the results, and Redshift was on HDDs. BTW – What are you doing w/ Spark? We have a lot of text and other content that we want to mine, and are shifting onto Spark so we have the greater capabilities mentioned above. Best regards, Ron Daniel, Jr. Director, Elsevier Labs r.dan...@elsevier.com mobile: +1 619 208 3064 *From:* Gary Malouf [mailto:malouf.g...@gmail.com] *Sent:* Wednesday, August 06, 2014 12:35 PM *To:* Daniel, Ronald (ELS-SDG) *Subject:* Re: Regarding tooling/performance vs RedShift Hi Ronald, In my opinion, the performance just has to be 'close' to make that piece irrelevant. I think the real issue comes down to tooling and the ease of connecting their various python tools from the office to results coming out of Spark/other solution in 'the cloud'. On Wed, Aug 6, 2014 at 1:43 PM, Daniel, Ronald (ELS-SDG) r.dan...@elsevier.com wrote: Just to point out that the benchmark you point to has Redshift running on HDD machines instead of SSD, and it is still faster than Shark in all but one case. Like Gary, I'm also interested in replacing something we have on Redshift with Spark SQL, as it will give me much greater capability to process things. I'm willing to sacrifice some performance for the greater capability. But it would be nice to see the benchmark updated with Spark SQL, and with a more competitive configuration of Redshift. Best regards, and keep up the great work! Ron *From:* Nicholas Chammas [mailto:nicholas.cham...@gmail.com] *Sent:* Wednesday, August 06, 2014 9:30 AM *To:* Gary Malouf *Cc:* user *Subject:* Re: Regarding tooling/performance vs RedShift 1) We get tooling out of the box from RedShift (specifically, stable JDBC access) - Spark we often are waiting for devops to get the right combo of tools working or for libraries to support sequence files. The arguments about JDBC access and simpler setup definitely make sense. My first non-trivial Spark application was actually an ETL process that sliced and diced JSON + tabular data and then loaded it into Redshift. From there on you got all the benefits of your average C-store database, plus the added benefit of Amazon managing many annoying setup and admin details for your Redshift cluster. One area I'm looking forward to seeing Spark SQL excel at is offering fast JDBC access to raw data--i.e. directly against S3 / HDFS; no ETL required. For easy and flexible data exploration, I don't think you can beat that with a C-store that you have to ETL stuff into. 2) There is a belief that for many of our queries (assumed to often be joins) a columnar database will perform orders of magnitude better. This is definitely a it depends statement, but there is a detailed benchmark here https://amplab.cs.berkeley.edu/benchmark/ comparing Shark, Redshift, and other systems. Have you seen it? Redshift does very well, but Shark is on par or better than it in most of the tests. Of course, going forward we'll want to see Spark SQL match this kind of performance, and that remains to be seen. Nick On Wed, Aug 6, 2014 at 12:06 PM, Gary Malouf malouf.g...@gmail.com wrote: My company is leaning towards moving much of their analytics work from our own Spark/Mesos/HDFS/Cassandra set up to RedShift. To date, I have been the internal advocate for using Spark for analytics, but a number of good points have been brought up to me. The reasons being pushed are: - RedShift exposes a jdbc interface out of the box (no devops work there) and data looks and feels like it is in a normal sql database. They want this out of the box from Spark, no trying to figure out which version matches this version of Hive/Shark/SparkSQL etc. Yes, the next release theoretically supports this but there have been release issues our team has battled to date that erode the trust. - Complaints around challenges we have faced running a spark shell locally against a cluster in EC2. It is partly a devops issue of deploying the correct configurations to local machines, being able to kick a user off hogging RAM, etc. - I want to be able to run queries from my python shell against your sequence file data, roll it up and in the same shell leverage python graph tools. - I'm not very
Re: Regarding tooling/performance vs RedShift
Also, regarding something like redshift not having MLlib built in, much of that could be done on the derived results. On Aug 6, 2014 4:07 PM, Nicholas Chammas nicholas.cham...@gmail.com wrote: On Wed, Aug 6, 2014 at 3:41 PM, Daniel, Ronald (ELS-SDG) r.dan...@elsevier.com wrote: Mostly I was just objecting to Redshift does very well, but Shark is on par or better than it in most of the tests when that was not how I read the results, and Redshift was on HDDs. My bad. You are correct; the only test Shark (mem) does better on is test #1 Scan Query. And indeed, it would be good to see an updated benchmark with Redshift running on SSDs. Nick
Re: Kryo Issue on Spark 1.0.1, Mesos 0.18.2
Maybe this is me misunderstanding the Spark system property behavior, but I'm not clear why the class being loaded ends up having '/' rather than '.' in it's fully qualified name. When I tested this out locally, the '/' were preventing the class from being loaded. On Fri, Jul 25, 2014 at 2:27 PM, Gary Malouf malouf.g...@gmail.com wrote: After upgrading to Spark 1.0.1 from 0.9.1 everything seemed to be going well. Looking at the Mesos slave logs, I noticed: ERROR KryoSerializer: Failed to run spark.kryo.registrator java.lang.ClassNotFoundException: com/mediacrossing/verrazano/kryo/MxDataRegistrator My spark-env.sh has the following when I run the Spark Shell: export MESOS_NATIVE_LIBRARY=/usr/local/lib/libmesos.so export MASTER=mesos://zk://n-01:2181,n-02:2181,n-03:2181/masters export ADD_JARS=/opt/spark/mx-lib/verrazano-assembly.jar # -XX:+UseCompressedOops must be disabled to use more than 32GB RAM SPARK_JAVA_OPTS=-Xss2m -XX:+UseCompressedOops -Dspark.local.dir=/opt/mesos-tmp -Dspark.executor.memory=4g -Dspark.serializer=org.apache.spark.serializer.KryoSerializer -Dspark.kryo.registrator=com.mediacrossing.verrazano.kryo.MxDataRegistrator -Dspark.kryoserializer.buffer.mb=16 -Dspark.akka.askTimeout=30 I was able to verify that our custom jar was being copied to each worker, but for some reason it is not finding my registrator class. Is anyone else struggling with Kryo on 1.0.x branch?
Workarounds for accessing sequence file data via PySpark?
I am aware that today PySpark can not load sequence files directly. Are there work-arounds people are using (short of duplicating all the data to text files) for accessing this data?
SparkSQL with sequence file RDDs
Has anyone reported issues using SparkSQL with sequence files (all of our data is in this format within HDFS)? We are considering whether to burn the time upgrading to Spark 1.0 from 0.9 now and this is a main decision point for us.
Re: Spark Summit 2014 (Hotel suggestions)
Go to expedia/orbitz and look for hotels in the union square neighborhood. In my humble opinion having visited San Francisco, it is worth any extra cost to be as close as possible to the conference vs having to travel from other parts of the city. On Tue, May 27, 2014 at 9:36 AM, Gerard Maas gerard.m...@gmail.com wrote: +1 On Tue, May 27, 2014 at 3:22 PM, Pierre B pierre.borckm...@realimpactanalytics.com wrote: Hi everyone! Any recommendation anyone? Pierre -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Summit-2014-Hotel-suggestions-tp5457p6424.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
Re: is Mesos falling out of favor?
For what it is worth, our team here at MediaCrossinghttp://mediacrossing.com has been using the Spark/Mesos combination since last summer with much success (low operations overhead, high developer performance). IMO, Hadoop is overcomplicated from both a development and operations perspective so I am looking to lower our dependencies on it, not increase them. Our stack currently includes: - Spark 0.9.1 - Mesos 0.17 - Chronos - HDFS (CDH 5.0-mr1) - Flume 1.4.0 - ZooKeeper - Cassandra 2.0 (key-value store alternative to HBase) - Storm 0.9 (we prefer today to Spark Streaming) We've used Shark in the past as well, but since most of us prefer the Spark Shell we have not been maintaining it. Using Mesos to run Spark allows for us to optimize our available resources (CPU + RAM currently ) between Spark, Chronos and a number of other services. I see YARN as being heavily focused on MR2, but the reality is we are using Spark in large part because writing MapReduce jobs is verbose, hard to maintain and not performant (against Spark). We have the advantage of not having any real legacy Map/Reduce jobs to maintain, so that consideration does not come into play. Finally, I am a believer that for the long term direction of our company, the Berkeley stack https://amplab.cs.berkeley.edu/software/ will serve us best. Leveraging Mesos and Spark from the onset paves the way for this. On Sun, May 11, 2014 at 1:28 PM, Paco Nathan cet...@gmail.com wrote: That's FUD. Tracking the Mesos and Spark use cases, there are very large production deployments of these together. Some are rather private but others are being surfaced. IMHO, one of the most amazing case studies is from Christina Delimitrou http://youtu.be/YpmElyi94AA For a tutorial, use the following but upgrade it to latest production for Spark. There was a related O'Reilly webcast and Strata tutorial as well: http://mesosphere.io/learn/run-spark-on-mesos/ FWIW, I teach Intro to Spark with sections on CM4, YARN, Mesos, etc. Based on lots of student experiences, Mesos is clearly the shortest path to deploying a Spark cluster if you want to leverage the robustness, multi-tenancy for mixed workloads, less ops overhead, etc., that show up repeatedly in the use case analyses. My opinion only and not that of any of my clients: Don't believe the FUD from YHOO unless you really want to be stuck in 2009. On Wed, May 7, 2014 at 8:30 AM, deric barton.to...@gmail.com wrote: I'm also using right now SPARK_EXECUTOR_URI, though I would prefer distributing Spark as a binary package. For running examples with `./bin/run-example ...` it works fine, however tasks from spark-shell are getting lost. Error: Could not find or load main class org.apache.spark.executor.MesosExecutorBackend which looks more like problem with sbin/spark-executor and missing paths to jar. Anyone encountered this error before? I guess Yahoo invested quite a lot of effort into YARN and Spark integration (moreover when Mahout is migrating to Spark there's much more interest in Hadoop and Spark integration). If there would be some Mesos company working on Spark - Mesos integration it could be at least on the same level. I don't see any other reason why would be YARN better than Mesos, personally I like the latter, however I haven't checked YARN for a while, maybe they've made a significant progress. I think Mesos is more universal and flexible than YARN. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/is-Mesos-falling-out-of-favor-tp5444p5481.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
