Re: Multiple servers in a Ignite Cluster

[Tracyl]

Hi Vlad,

The ideal work flow for my use case is: I host two clusters, one is
computation cluster that run Spark jobs, the other is data cluster that host
Ignite node and cache hot data. Then at the run time, multiple Spark jobs
share this data cluster and query it. The problem I have is, I am
pre-loading Ignite cache using a Spark job. Once IgniteContext got
instantiated, it will launch Ignite Node with same number of Spark executors
I allocated. Then distributed cache will distribute data on those nodes
within my computation cluster as well, which I don't want because partial
data hosted by these nodes won't be there once my pre-load job dies.
Currently I force these node to be client mode so that the cache only
distributed to data cluster when I execute my pre-load job. Are there better
way to solve this? 

Thanks,
Tracy



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Re: Multiple servers in a Ignite Cluster

[Tracyl]

Thanks. Works fine now. 



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Re: Multiple servers in a Ignite Cluster

[Tracyl]

Thanks. In that case, my question is how to define the scope of cluster(Or
how to specify the cluster a server belongs to)? I assume if someone else
start a ignite node, would my ignite server auto-discover it as well?



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Multiple servers in a Ignite Cluster

[Tracyl]

I have following ignite config:

def initializeIgniteConfig() = {
val ipFinder = new TcpDiscoveryVmIpFinder()

val HOST = "xx.xx.xx.xx:47500..47509"
ipFinder.setAddresses(Collections.singletonList(HOST))

val discoverySpi = new TcpDiscoverySpi()
discoverySpi.setIpFinder(ipFinder)

val igniteConfig = new IgniteConfiguration()
igniteConfig.setDiscoverySpi(discoverySpi)

//Ignite uses work directory as a relative directory for internal
write activities, for example, logging.
// Every Ignite node (server or client) has it's own work directory
independently of other nodes.

igniteConfig.setWorkDirectory("/tmp")
igniteConfig
}

My use case is: I would like to have multiple ignite servers, each server
cache a subset of the data and I send distributed closures to each node to
do local computation. In this case, can I start multiple servers on one
single machine by just passing multiple ip and port? Or I will need to start
each server on each machine separately? Thanks in advance!



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Re: What's the difference between EntryProcessor and distributed closure?

[Tracyl]

Thanks Alexey.

By predicate/projection pushdown, I mean: currently I am storing a native
Spark Row object as value format of IgniteCache. If I retrieve it as an
IgniteRDD, I only want certain column of that Row object rather than
returning entire Row and do filter/projection at Spark level. Do you have
better recommendations to achieve this?

Tracy



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What's the difference between EntryProcessor and distributed closure?

[Tracyl]

What I would like to do is to achieve predicate/column project push-down to
the ignite cache layer. I guess this two options could do it, isn't it? If
so, what's the difference? Are there any other options to achieve predicate
push-down? Thanks in advance!



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Is it possible to enable both REPLICATED and PARTITIONED?

[Tracyl]

As subject shows.



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Re: Fail to cache rdd: java.lang.NoSuchMethodError: scala.Predef$.$conforms()Lscala/Predef$$less$colon$less;

[Tracyl]

Hi Denis,

This is really helpful. Yes, I need the original dataframe for other API.

Now I am using RDD[String, Row] as type and caching dataframe using:
val rdd = df.map(row => (row.getAs[String]("KEY"), row))
igniteRDD.savePairs(rdd)

It works perfectly fine. Also I was able to reconstruct the dataframe using
values collection and schema of rdd.  

Tracy



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Re: Fail to cache rdd: java.lang.NoSuchMethodError: scala.Predef$.$conforms()Lscala/Predef$$less$colon$less;

[Tracyl]

Thanks for prompt reply. So if I want to cache dataframe in IgniteCache, I
have to do define a custom data model class(e.g.
https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/model/Person.java
) as a schema of dataframe, then construct objects and declare cache to be
data model type? In other words, I have to do DataFrame => Custom class
object => IgniteRDD. And when I retrieve it, I have to do IgniteRDD =>
Custom class object => DataFrame, right?

Do I have other options for caching dataframe?



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Fail to cache rdd: java.lang.NoSuchMethodError: scala.Predef$.$conforms()Lscala/Predef$$less$colon$less;

[Tracyl]

Hi team, 

I was trying to cache dataframe in Ignite Cache. I was able to cache generic
type data elements(RDD). However each time when I use
igniteRDDF.saveValues() to cache a non-generic data type(e.g. RDD), it
will trigger the noSuchMethod for saveValues as following shows. I am using
scala 2.10, Spark 1.6.1 and Ignite 1.6.0. I did find the ignite-spark was
pulling in spark-core_2.11. After excluding native spark from ignite-spark,
I still got the same error. Any suggestions? Thanks in advance! 

Code sample: 
igniteRDD.saveValues(df.rdd()); 

Exceptions I got: 
Driver stacktrace: 
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
 
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
 
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
 
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) 
at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) 
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) 
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
 
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
 
at scala.Option.foreach(Option.scala:236) 
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
 
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
 
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
 
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
 
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) 
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858) 
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929) 
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920) 
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918) 
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) 
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) 
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) 
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918) 
at org.apache.ignite.spark.IgniteRDD.saveValues(IgniteRDD.scala:138) 
at ignitecontext.IgniteRDDExample.run(IgniteRDDExample.java:81) 
at ignitecontext.IgniteRDDExample.main(IgniteRDDExample.java:35) 
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) 
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) 
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
 
at java.lang.reflect.Method.invoke(Method.java:498) 
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
 
at
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181) 
at
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206) 
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121) 
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) 
Caused by: java.lang.NoSuchMethodError:
scala.Predef$.$conforms()Lscala/Predef$$less$colon$less; 
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1$$anonfun$apply$1.apply(IgniteRDD.scala:151)
 
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1$$anonfun$apply$1.apply(IgniteRDD.scala:150)
 
at scala.collection.Iterator$class.foreach(Iterator.scala:727) 
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) 
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1.apply(IgniteRDD.scala:150)
 
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1.apply(IgniteRDD.scala:138)
 
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
 
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
 
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) 
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) 
at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) 
at 

Could IgniteCache be accessed thr Spark JDBC data source api?

[Tracyl]

Hey team, 

I was able to use JDBC driver tool to access IgniteCache. Is it possible to
connect to Ignite thr Spark JDBC data source api? Below are the code and
exceptions I got. It seems like the connection is successful but there are
datatype mapping issues. Do I need to define some schema from the server
side? Any suggestion would be really helpful for me. Thanks in advance! 

Sample Code: 
val props = Map( 
  "url" -> "jdbc:ignite://ip:11211/TestCache", 
  "driver" -> "org.apache.ignite.IgniteJdbcDriver", 
  "dbtable" -> "Person" 
) 
val personDF = ctx.read.format("jdbc").options(props).load() 

Exceptions: 
Exception in thread "main" java.sql.SQLException: Unsupported type  
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.org$apache$spark$sql$execution$datasources$jdbc$JDBCRDD$$getCatalystType(JDBCRDD.scala:103)
 
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anonfun$1.apply(JDBCRDD.scala:140)
 
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anonfun$1.apply(JDBCRDD.scala:140)
 
at scala.Option.getOrElse(Option.scala:120) 
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:139)
 
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation.(JDBCRelation.scala:91)
 
at
org.apache.spark.sql.execution.datasources.jdbc.DefaultSource.createRelation(DefaultSource.scala:60)
 
at
org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:158)
 
at
org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:119) 
at
ignite.spark.DataFrameLoader$.personTest(DataFrameLoader.scala:131) 
at ignite.spark.DataFrameLoader.personTest(DataFrameLoader.scala) 
at jdbc.PersonLoader.executeTransaction(PersonLoader.java:175) 
at jdbc.PersonLoader.main(PersonLoader.java:115) 
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) 
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) 
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
 
at java.lang.reflect.Method.invoke(Method.java:498) 
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
 
at
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181) 
at
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206) 
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121) 
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) 



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test

[Tracyl]

test



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Does IgniteCache could be access thr Spark JDBC data source api?

[Tracyl]

Hey team,

I was able to use JDBC driver tool to access IgniteCache. Is it possible to
connect to Ignite thr Spark JDBC data source api? Below are the code and
exceptions I got. It seems like the connection is successful but there are
datatype mapping issues. Do I need to define some schema from the server
side? Any suggestion would be really helpful for me. Thanks in advance! 

Sample Code:
val props = Map(
  "url" -> "jdbc:ignite://ip:11211/TestCache",
  "driver" -> "org.apache.ignite.IgniteJdbcDriver",
  "dbtable" -> "Person"
) 
val personDF = ctx.read.format("jdbc").options(props).load()

Exceptions:
Exception in thread "main" java.sql.SQLException: Unsupported type 
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.org$apache$spark$sql$execution$datasources$jdbc$JDBCRDD$$getCatalystType(JDBCRDD.scala:103)
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anonfun$1.apply(JDBCRDD.scala:140)
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anonfun$1.apply(JDBCRDD.scala:140)
at scala.Option.getOrElse(Option.scala:120)
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:139)
at
org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation.(JDBCRelation.scala:91)
at
org.apache.spark.sql.execution.datasources.jdbc.DefaultSource.createRelation(DefaultSource.scala:60)
at
org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:158)
at
org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:119)
at
ignite.spark.DataFrameLoader$.personTest(DataFrameLoader.scala:131)
at ignite.spark.DataFrameLoader.personTest(DataFrameLoader.scala)
at jdbc.PersonLoader.executeTransaction(PersonLoader.java:175)
at jdbc.PersonLoader.main(PersonLoader.java:115)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
at
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)




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Fail to cache rdd: java.lang.NoSuchMethodError: scala.Predef$.$conforms()Lscala/Predef$$less$colon$less;

[Tracyl]

Hi team,

I was trying to cache dataframe in Ignite Cache. I was able to cache generic
type data elements(RDD). However each time when I use
igniteRDDF.saveValues() to cache a non-generic data type(e.g. RDD), it
will trigger the noSuchMethod for saveValues as following shows. I am using
scala 2.10, Spark 1.6.1 and Ignite 1.6.0. I did find the ignite-spark was
pulling in spark-core_2.11. After excluding native spark from ignite-spark,
I still got the same error. Any suggestions? Thanks in advance!

Code sample:
igniteRDD.saveValues(df.rdd());

Exceptions I got:
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
at org.apache.ignite.spark.IgniteRDD.saveValues(IgniteRDD.scala:138)
at ignitecontext.IgniteRDDExample.run(IgniteRDDExample.java:81)
at ignitecontext.IgniteRDDExample.main(IgniteRDDExample.java:35)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
at
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at
org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.NoSuchMethodError:
scala.Predef$.$conforms()Lscala/Predef$$less$colon$less;
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1$$anonfun$apply$1.apply(IgniteRDD.scala:151)
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1$$anonfun$apply$1.apply(IgniteRDD.scala:150)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1.apply(IgniteRDD.scala:150)
at
org.apache.ignite.spark.IgniteRDD$$anonfun$saveValues$1.apply(IgniteRDD.scala:138)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at