Is it possible to create a Hadoop cluster on Dataproc with no or very minimal HDFS space by setting dfs.datanode.du.reserved to about 95% or 100% of the total node size? The plan is to use GCS for all persistent storage while the local file system will primarily be used for Spark's shuffle data. Some of the Hive queries may still need the scratch on HDFS which explains the need for minimal HDFS.
I did create a cluster with a 10-90 split and did not notice any issues with my test jobs.
Could there be stability issues with Dataproc if this approach is taken?
Also, are there concerns with deleting the Data Node daemon from Dataproc's worker nodes, thereby using the Primary workers as compute only nodes. The rationale is that Dataproc currently doesn't allow a mix of preemptible and non preemptible secondary workers. So want to check if we can repurpose primary workers as compute only non-PVM nodes while the other secondary workers can be compute only PVM nodes.
I am starting a GCP project and am well-versed enough in AZURE and AWS less so, but know enough there having done a DDD setup.
What you describe is similar to AWS setup and I looked recently here: https://jayendrapatil.com/google-cloud-dataproc/
My impression is you can run without HDFS here as well - 0%. The key point is that performance with a suite of jobs will - like also for AWS & AZURE - benefit from writing to and reading from ephemeral HDFS, as it is faster than Google Cloud Storage. I cannot see stability issues; I can use Spark now without HDFS if I really want.
On the 2nd question, stick to what they have engineered. Why try and force things? On AWS we lived with the limitations on scaling down with Spark.
Original Spark distributive supports several cluster managers like YARN, Mesos, Spark Standalone, K8s.
I can't find what is under the hood in Databricks Spark, which cluster manager it is using, and is it possible to change?
What's Databricks Spark architecture?
Thanks.
You can't check the cluster manager in Databricks, and you really don't need that because this part is managed for you. You can think about it as a kind of standalone cluster, but there are differences. General Databricks architecture is shown here.
You can change the cluster configuration by different means - init scripts, configuration parameters, etc. See documentation for more details.
I a newbie to spark. I am using Azure Databricks and I am writing python code with PySpark. There is one particular topic which is confusing me:
Do nodes have separate storage memory (I don't mean RAM/cache)? Or they all share the same storage? If they share the same storage, then can two different applications running in different Spark Context exchange data accordingly?
I don't understand why sometime we refer to the storage by dbfs:/tmp/..., and other times we refer to it by /dbfs/tmp/... Example: If I am using the dbutils package from databricks, we use something like: dbfs:/tmp/... to refer to a directory in the file system. However, if I'm using regular python code, I say /dbfs/tmp/.
Your help is much appreciated!!
Each node has separate RAM memory and caching. For example, if you have a cluster with 4GB and 3 nodes. When you deploy your spark application, it will run worker processes depending on cluster configuration and query requirements and it will create virtual machines on separate nodes or on same node. These node memories are not shared between each other during the life of the application.
This is more about Hadoop resource sharing question and can find more information from YARN resource management. This is very brief overview
https://databricks.com/session/resource-management-and-spark-as-a-first-class-data-processing-framework-on-hadoop
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 new to Apache Spark, and I just learned that Spark supports three types of cluster:
Standalone - meaning Spark will manage its own cluster
YARN - using Hadoop's YARN resource manager
Mesos - Apache's dedicated resource manager project
I think I should try Standalone first. In the future, I need to build a large cluster (hundreds of instances).
Which cluster type should I choose?
Spark Standalone Manager : A simple cluster manager included with Spark that makes it easy to set up a cluster. By default, each application uses all the available nodes in the cluster.
A few benefits of YARN over Standalone & Mesos:
YARN allows you to dynamically share and centrally configure the same pool of cluster resources between all frameworks that run on YARN.
You can take advantage of all the features of YARN schedulers for categorizing, isolating, and prioritizing workloads.
The Spark standalone mode requires each application to run an executor on every node in the cluster; whereas with YARN, you choose the number of executors to use
YARN directly handles rack and machine locality in your requests, which is convenient.
The resource request model is, oddly, backwards in Mesos. In YARN, you (the framework) request containers with a given specification and give locality preferences. In Mesos you get resource "offers" and choose to accept or reject those based on your own scheduling policy. The Mesos model is a arguably more flexible, but seemingly more work for the person implementing the framework.
If you have a big Hadoop cluster already in place, YARN is better choice.
The Standalone manager requires the user configure each of the nodes with the shared secret. Mesos’ default authentication module, Cyrus SASL, can be replaced with a custom module. YARN has security for authentication, service level authorization, authentication for Web consoles and data confidentiality. Hadoop authentication uses Kerberos to verify that each user and service is authenticated by Kerberos.
High availability is offered by all three cluster managers but Hadoop YARN doesn’t need to run a separate ZooKeeper Failover Controller.
Useful links:
spark documentation page
agildata article
I think the best to answer that are those who work on Spark. So, from Learning Spark
Start with a standalone cluster if this is a new deployment.
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.
If you would like to run Spark alongside other applications, or to use
richer resource scheduling capabilities (e.g. queues), both YARN and
Mesos provide these features. Of these, YARN will likely be
preinstalled in many Hadoop distributions.
One advantage of Mesos over both YARN and standalone mode is its
fine-grained sharing option, which lets interactive applications such
as the Spark shell scale down their CPU allocation between commands.
This makes it attractive in environments where multiple users are
running interactive shells.
In all cases, it is best to run Spark on the same nodes as HDFS for
fast access to storage. You can install Mesos or the standalone
cluster manager on the same nodes manually, or most Hadoop
distributions already install YARN and HDFS together.
Standalone is pretty clear as other mentioned it should be used only when you have spark only workload.
Between yarn and mesos, One thing to consider is the fact that unlike mapreduce, spark job grabs executors and hold it for entire lifetime of a job. where in mapreduce a job can get and release mappers and reducers over lifetime.
if you have long running spark jobs which during the lifetime of a job doesn't fully utilize all the resources it got in beginning, you may want to share those resources to other app and that you can only do either via Mesos or Spark dynamic scheduling. https://spark.apache.org/docs/2.0.2/job-scheduling.html#scheduling-across-applications
So with yarn, only way have dynamic allocation for spark is by using spark provided dynamic allocation. Yarn won't interfere in that while Mesos will. Again this whole point is only important if you have a long running spark application and you would like to scale it up and down dynamically.
In this case and similar dilemmas in data engineering, there are many side questions to be answered before choosing one distribution method over another.
For example, if you are not running your processing engine on more than 3 nodes, you usually are not facing too big of a problem to handle so your margin of performance tuning between YARN and SparkStandalone (based on experience) will not clarify your decision. Because usually you will try to make your pipeline simple, specially when your services are not self-managed by cloud and bugs and failures happen often.
I choose standalone for relatively small or not-complex pipelines but if I'm feeling alright and have a Hadoop cluster already in place, I prefer to take advantage of all the extra configs that Hadoop(Yarn) can give me.
Mesos has more sophisticated scheduling design, allowing applications like Spark to negotiate with it. It's more suitable for the diversity of applications today. I found this site really insightful:
https://www.oreilly.com/ideas/a-tale-of-two-clusters-mesos-and-yarn
"... YARN is optimized for scheduling Hadoop jobs, which are historically (and still typically) batch jobs with long run times. This means that YARN was not designed for long-running services, nor for short-lived interactive queries (like small and fast Spark jobs), and while it’s possible to have it schedule other kinds of workloads, this is not an ideal model. The resource demands, execution model, and architectural demands of MapReduce are very different from those of long-running services, such as web servers or SOA applications, or real-time workloads like those of Spark or Storm..."