I have some general questions about fiware-cosmos, apologies if they are basic but Im trying to understand the architecture and use of cosmos.
I saw that you are planning to integrate Apache Spark into Cosmos ? Do you have a road map or date for that to happen ? What happens if I want to use Spark now ?
What Hadoop service sources can be used ? I think I read that Cosmos supports Cloudera CDH services and raw Hadoop server services ? What about HortonWorks or MapR ?
I know that non standard file systems can be used with Hadoop, for instance MapR-FS, are options like this possible with Cosmos ?
I also read that Cosmos "sits" on top of fiware and so Hadoop as a service (HaaS) can be used and Hadoop clusters generated using open stack ? However, I saw that people are referring to a shared fiware cloud ? Does fiware run as a remote cloud ? Can a local cloud be used on a customer site ?
Is cosmos the only Apache Hadoop/Spark solution on fiware.org ?
Finally, if Cloudera CDH can be used with Cosmos how does the Cloudera cluster manager fit into the mix ? Can it still be used ?
Sorry for all of the questions :)
Cosmos is the name of the Global Instance of the Big Data GE in FIWARE Lab. It is a shared Hadoop instance already deployed in the cloud, ready to be used by FIWARE users.
In fact, there are two instances: The "old" one, serving a pretty old version of the Hadoop stack and being cosmos.lab.fiware.org its entry point. And the "new" one, which is a pair of Hadoop clusters, one for data storage and another one for data analysis; the entry points are storing.cosmos.lab.fiware.org and computing.cosmos.lab.fiware.org.
Of course, you can deploy any other Hadoop (or even Spark) instance by your own in the FIWARE cloud (or any other cloud, such as Amazon one).
Regarding Spark, since it was initially in our plans to deploy it in FIWARE Lab (that's why it appears in the roadmap), it is not clear nowadays it is gonna be deployed.
Related
I have a task which requires me to create a Go program to read from an HBASE table.
HBASE is installed in a MapR cluster.
Every other application (Java) uses a MapR client to connect to the MapR cluster so as to retrieve the data.
However, I am unable to find a way to connect to HBASE with a Go application.
I have found HBASE package, but it does not support integration with MapR.
It would be great if anyone could guide me in this situation.
I also have seen that for MapR 6 and above has Go support through OJAI, but sadly, upgrading MapR is not an option.
Can someone advice me how to proceed in this situation?
If you are actually running HBase in MapR, then the Go package for HBase should work (assuming version match and such).
If you are actually using the MapR DB Binary tables (which are roughly HBase compatible) the likely best approach would be to use the Thrift API or REST.
The OJAI lightweight client should work well in Go since it uses gRPC to talk to the underlying table (and thus gains lots of portability). The problem in your case won't be so much that you need to upgrade the platform so much as the lightweight client only works with MapR DB JSON (the document oriented version of MapR DB).
Ping me directly if you would like more information.
Well, my company is considering to move from Hadoop to Kubernetes. We can find solutions in Kubernetes for tools such as cassandra, sparks, etc. So the last problem for us is how to store massive amount of files in Kubernetes, saying 1 PB. FYI, we DO NOT want to use online storage services such as S3.
As far as I know, HDFS is merely used in Kubernetes and there are a few replacement products such as Torus and Quobyte. So my question is, any recommendation for the filesystem on Kubernetes? Or any better solution?
Many thanks.
You can use a Hadoop Compatible FileSystem such as Ceph or Minio. Both of which offer S3-compatible REST APIs for reading and writing. In Kubernetes, Ceph can be deployed using the Rook project.
But overall, running HDFS in Kubernetes would require stateful services like the NameNode, and DataNodes with proper affinity and network rules in place. The Hadoop Ozone project is a realization that object storage is more common for microservice workloads than HDFS block storage as reasonably trying to analyze PB of data using distributed microservices wasn't feasible. (I'm only speculating)
The alternative is to use Docker support in Hadoop & YARN 3.x
I am trying to update HDP architecture so data residing in Hive tables can be accessed by REST APIs. What are the best approaches how to expose data from HDP to other services?
This is my initial idea:
I am storing data in Hive tables and I want to expose some of the information through REST API therefore I thought that using HCatalog/WebHCat would be the best solution. However, I found out that it allows only to query metadata.
What are the options that I have here?
Thank you
You can very well use WebHDFS which is basically a REST Service over Hadoop.
Please see documentation below:
https://hadoop.apache.org/docs/r1.0.4/webhdfs.html
The REST API gateway for the Apache Hadoop Ecosystem is called KNOX
I would check it before explore any other options. In other words, Do you have any reason to avoid using KNOX?
What version of HDP are you running?
The Knox component has been available for quite a while and manageable via Ambari.
Can you get an instance of HiveServer2 running in HTTP mode?
This would give you SQL access through J/ODBC drivers without requiring Hadoop config and binaries (other than those required for the drivers) on the client machines.
We are currently using Apache Hadoop (Vanilla Version) in our org. We are planning to migrate to AWS EMR. I'm trying to understand how AWS EMR Hadoop works internally (not how to use it), I'm mainly interested in Hadoop administration steps and how master and slave communicates and various configuration configurations. I already checked the AWS EMR documentation but I don't see detailed comparison.
Can someone recommend me a link/tutorial for migrating to AWS EMR from an Apache Hadoop.
During EMR cluster creation, it will ask you to specify Master and Node. a default settings will provision 1 master and two nodes for you. You can also specify what all applications you want to be in the cluster (e.g.: hadoop, hive, spark, zeppelin, hue, etc.).
Once the cluster is created, it will provision all the services. you can click on these services and access them via web, or using ssh into the master. For e.g: to access the ambari interface, go to the service within EMR and click it. a new window will be launched with the ambari monitoring service interface.
Installing these applications is very easy. all you have to do is specify all the services while cluster creation.
Amazon Elastic MapReduce uses a mostly standard implementation of Hadoop and associated tools.
See: AMI Versions Supported in Amazon EMR
The benefits of using EMR are in the automated deployment of instances. For example, launching a cluster with an appropriate AMI means that software is already loaded on each instance and HDFS is configured across the core nodes.
The Master and Slave (Core/Task) nodes communicate in exactly the normal way that they communicate in any Hadoop cluster. However, only one Master is supported (with no backup Master).
When migrating to EMR, check that you are using compatible versions of software (eg Hadoop, Hive, Pig, Impala, etc). Also consider using Amazon S3 for storage of data instead of HDFS, especially for storing source data, since data on S3 persists even after the EMR cluster is terminated.
Technically, Hadoop provided with EMR, can be few releases back. You should check EMR release notes for detailed application provided with each version. EMR takes care application provisioning, setup and configuration. Based on EC2 instance type, Hadoop (and other application configuration) will change. You can override default settings using configure application.
Other than this Hadoop you have on premises and EMR should be the same.
When running Hadoop in EC2, I seem to have two options:
A: Manage the cluster myself, using the EC2-specific shell scripts that come with Hadoop.
B: Use Elastic MapReduce, and pay a little extra for the convenience.
I'm leaning towards B, but I'd appreciate some advice from people with more experience. Here are my questions:
Are there any tasks that can be done with one of these methods but not the other?
Are there other options besides these two that I'm overlooking?
If I choose B, how easy would it be to go back to A? That is, what's the danger of vendor lock-in?
Third option:
You can use apache whirr to set up an hadoop cluster on ec2 (rackspace is also supported)
I have been told by people close to the Amazon Elastic MapReduce (EMR) development team that there are at least two other advantages to using EMR: a) Amazon is actively applying bug fixes and performance enhancements to the Hadoop code base used on EMR, and b) Amazon employs a high performance network between EMR servers and S3 servers that may not be available between EC2 servers and S3 servers.
UPDATE: See #mat's comments that refute the rumored advantages of using EMR.
Disclaimer: I'm the founder of Axemblr.com
There are also commercial alternatives you can use. Axemblr Tool for Cloudera CDH3 is a tool we are building that can deploy a cluster in just a few minutes with all you need (including Cloudera Hue, Mahout & Pig).
We are also building an alternative to EMR that's fully compatible from an API perspective, targeted at private clouds.
If you are wondering why it makes sense to run CDH on EC2 rather than EMR see:
http://www.quora.com/What-are-the-advantages-disadvantages-running-Clouderas-distribution-for-Hadoop-on-EC2-instances-rather-than-using-Amazons-Elastic-Map-Reduce-Service