I was trying to get Apache spark run on Azure HDinsight by following the steps from http://azure.microsoft.com/en-us/documentation/articles/hdinsight-hadoop-spark-install/
I was wondering if I have to manage the master/slave failure recovery myself, or will HDinsight take care of it.
I'm also working on Spark Streaming applications on Azure HDInsight. Inside the Spark job, Spark and Yarn can provide some Fault-Tolerance for Master and Slave.
But sometimes, the driver and worker will also crash by the user-code error, spark internal issues, and Azure HDInsight issues. So, we need to make our own monitoring/daemon process, and maintain the recovery.
For Streaming Scenarios, it's even harder. As the Spark Streaming Job which need keep 7*24 running, there's the concern that how to keep the job recovery for the machine reboot and reimage.
Related
Does anyone know if there's any documentation about how to handle Structured Streaming in Databricks and cluster maintenance updates (runtime, OS, etc)?
I would like to know more about how it works, and if we (user) need to handle that manually or if there's some sort of mechanism that handles that internally.
See recovery from failures under streaming.
For any infrastructure problem that could occur:
Hence, to make your queries fault tolerant, you must enable query checkpointing and configure Databricks jobs to restart your queries automatically after a failure.
I have to design a setup to read incoming data from twitter (streaming). I have decided to use Apache Kafka with Spark streaming for real time processing. It is required to show analytics in a dashboard.
Now, being a newbie is this domain, My assumed data rate will be 10 Mb/sec maximum. I have decided to use 1 machine for Kafka of 12 cores and 16 GB memory. *Zookeeper will also be on same machine. Now, I am confused about Spark, it will have to perform streaming job analysis only. Later, analyzed data output is pushed to DB and dashboard.
Confused list:
Should I run Spark on Hadoop cluster or local file system ?
Is standalone mode of Spark can fulfill my requirements ?
Is my approach is appropriate or what should be best in this case ?
Try answer:
Should I run Spark on Hadoop cluster or local file system ?
recommend use hdfs,it can can save more data, ensure High availability.
Is standalone mode of Spark can fulfill my requirements ?
Standalone mode is the easiest to set up and will provide almost all the same features as the other cluster managers if you are only running Spark.
YARN allows you to dynamically share and centrally configure the same pool of cluster resources between all frameworks that run on YARN.
YARN doesn’t need to run a separate ZooKeeper Failover Controller.
YARN will likely be preinstalled in many Hadoop distributions.such as CDH HADOOP.
so recommend use
YARN doesn’t need to run a separate ZooKeeper Failover Controller.
so recommend yarn
Useful links:
spark yarn doc
spark standalone doc
other wonderful answer
Is my approach is appropriate or what should be best in this case ?
If you data not more than 10 million ,I think can use use local cluster to do it.
local mode avoid many nodes shuffle. shuffles between processes are faster than shuffles between nodes.
else recommend use greater than or equal 3 nodes,That is real Hadoop cluster.
As a spark elementary players,this is my understand. I hope ace corrects me.
I use Apache Hive 2.1.1-cdh6.2.1 (Cloudera distribution) with MR as execution engine and YARN's Resource Manager using Capacity scheduler.
I'd like to try Spark as an execution engine for Hive. While going through the docs, I found a strange limitation:
Instead of the capacity scheduler, the fair scheduler is required. This fairly distributes an equal share of resources for jobs in the YARN cluster.
Having all the queues set up properly, that's very undesirable for me.
Is it possible to run Hive on Spark with YARN capacity scheduler? If not, why?
I'm not sure you can execute Hive using spark Engines. I highly recommend you configure Hive to use Tez https://cwiki.apache.org/confluence/display/Hive/Hive+on+Tez which is faster than MR and it's pretty similar to Spark due to it uses DAG as the task execution engine.
We are running it at work using the command on Beeline as described https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started just writing it at the beginning of the sql file to run
set hive.execution.engine=spark;
select ... from table....
We are not using capacity scheduler because there are hundreds of jobs run per yarn queue, and when jobs are resource avid, we have other queues to let them run. That also allows designing a configuration based on job consumption per queue more realistic based on the actual need of the group of jobs
Hope this helps
I have an analytics node running, with Spark Sql Thriftserver running on it. Now I can't run another Spark Application with spark-submit.
It says it doesn't have resources. How to configure the dse node, to be able to run both?
The SparkSqlThriftServer is a Spark application like any other. This means it requests and reserves all resources in the cluster by default.
There are two options if you want to run multiple applications at the same time:
Allocate only part of your resources to each application.
This is done by setting spark.cores.max to a smaller value than the max resources in your cluster.
See Spark Docs
Dynamic Allocation
Which allows applications to change the amount of resources they use depending on how much work they are trying to do.
See Spark Docs
I am attempting to leverage a Hadoop Spark Cluster in order to batch load a graph into Titan using the SparkGraphComputer and BulkLoaderVertex program, as specified here. This requires setting the spark configuration in a properties file, telling Titan where Spark is located, where to read the graph input from, where to store its output, etc.
The problem is that all of the examples seem to specify a local spark cluster through the option:
spark.master=local[*]
I, however, want to run this job on a remote Spark cluster which is on the same VNet as the VM where the titan instance is hosted. From what I have read, it seems that this can be accomplished by setting
spark.master=<spark_master_IP>:7077
This is giving me the error that all Spark masters are unresponsive, which disallows me from sending the job to the spark cluster to distribute the batch loading computations.
For reference, I am using Titan 1.0.0 and a Spark 1.6.4 cluster, which are both hosted on the same VNet. Spark is being managed by yarn, which also may be contributing to this difficulty.
Any sort of help/reference would be appreciated. I am sure that I have the correct IP for the spark master, and that I am using the right gremlin commands to accomplish bulk loading through the SparkGraphComputer. What I am not sure about is how to properly configure the Hadoop properties file in order to get Titan to communicate with a remote Spark cluster over a VNet.