I would like to know if there is a way to retrieve the count of live master and worker nodes of spark programatically(java) in a system.
Please help me to clarify the same.
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I want to have a view of Spark in Kibana such as decommissioned nodes per application, shuffle read and write per application, and more.
I know I can get all this information about these metrics here.
But I don't know how to send them to elastic search or how to do it the correct way. I know I can do it with Prometheus but I don't think that helps me.
Is there a way of doing so?
I'm drawing on ideas of using a multi tenant Spark cluster. The cluster execute jobs on demand for a specific tenant.
Is it possible to "know" the specific resources used by a specific job (for payment reasons)? E.g. if a job requires that several nodes in kubernetes is automatically allocated is it then possible to track which Spark jobs (and tenant at the end) that initiated these resource allocations? Or, jobs are always evenly spread out on allocated resources?
Tried to find information at the Apache Spark site and else where on the internet without success.
See https://spark.apache.org/docs/latest/monitoring.html
You can save data from Spark History Server as json and then write your own resource calc stuff.
It is Spark App you mean.
I'm going build apache cassandra 3.11.X cluster with 44 nodes. Each application server will have one cluster node so that application do r/w locally.
I have couple of questions running in my mind kindly answer if possible.
1.How many server Ip's should mention in seednode parameter?
2.How HA works when all the mentioned seed node goes down?
3.What is the dis-advantage to mention all the serverIP's in seednode parameter?
4.How cassandra scales with respect to data other than(Primary key and Tunable consistency). As per my assumption replication factor can improve HA chances but not performances.
then how performance will increase by adding more nodes?
5.Is there any sharding mechanism in Cassandra.
Answers are in order:
It's recommended to point to at least to 2 nodes per DC
Seed/contact node is used only for initial bootstrap - when your program reaches any of listed nodes, it "learns" the topology of whole cluster, and then driver listens for nodes status change, and adjust a list of available hosts. So even if seed node(s) goes down after connection is already established, driver will able to reach other nodes
it's harder to maintain usually - you need to keep a configuration parameters for your driver & list of nodes in sync.
When you have RF > 1, Cassandra may read or write data from/to any replica. Consistency level regulates how many nodes should return answer for read or write operation. When you add the new node, the data is redistributed to new node, and if you have correctly selected partition key, then new node start to receive requests in parallel to old nodes
Partition key is responsible for selection of replica(s) that will hold data associated with it - you can see it as a shard. But you need to be careful with selection of partition key - it's easy to create too big partitions, or partitions that will be "hot" (receiving most of operations in cluster - for example, if you're using the date as partition key, and always writing reading data for today).
P.S. I would recommend to read DataStax Architecture guide - it contains a lot of information about Cassandra as well...
I am looking for directions:
I have a cassandra database with latitude & longitude data. I need to search for data within a radius or a box coordinates around a point. I am using golang(gocql) client to query Cassandra.
I need some understanding regarding Spark and Cassandra as this seams like the way to go.
Is the following assumptions correct; I have 2 Cassandra nodes(the data in a replica of 2).
Should I then install an extra node and install Spark on this and then connect it to the other two existing Cassandra nodes containing the data(With the Spark Connector from DataStax).
And do the two existing Cassandra nodes need to have Spark workers installed on them to work with Spark Master node?
When the Spark setup is in place, do you query(Scala) the existing data and then save the data onto the Spark node and then query this with the gaoling(gocql) client?
Any directions is welcome
Thanks in advance
Geospatial Searching is a pretty deep topic. If it's just doing searches that you're after (not batch/analytics), I can tell you that you probably don't want to use Spark. Spark isn't very good at 'searching' for data - even when it's geospatial. The main reason is that Spark doesn't index data for efficient searches and you'd have to create a job/context (unless using job server) every time you'd want to do a search. That takes forever when you're thinking in terms of user facing application time.
Solr, Elastic Search, and DataStax Enterprise Search (Disclaimer I work for DataStax) are all capable of box and radius searches on Cassandra data and do so in near real time.
To answer your original question though, if the bulk of your analytics in general come from Cassandra data, it may be good idea to run Spark on the same nodes as Cassandra for data locality. The nice thing is that Spark scales quite nicely, so if you find Spark taking too many resources from Cassandra, you can simply scale out (both Cassandra and Spark).
Should I then install an extra node and install Spark on this and then
connect it to the other two existing Cassandra nodes containing the
data(With the Spark Connector from DataStax).
Spark is a cluster compute engine so it needs a cluster of nodes to work well. You'll need to install it on all nodes if you want it to be as efficient as possible.
And do the two existing Cassandra nodes need to have Spark workers
installed on them to work with Spark Master node?
I don't think they 'have' to have them, but it's a good idea for locality. There's a really good video on academy.datastax.com that shows how the spark cassandra connector reads data from Cassandra to Spark. I think it will clear a lot of things up for you: https://academy.datastax.com/demos/how-spark-cassandra-connector-reads-data
When the Spark setup is in place, do you query(Scala) the existing
data and then save the data onto the Spark node and then query this
with the gaoling(gocql) client?
The Spark-Cassandra connector can communicate to both Cassandra and Spark. There are methods, saveToCassandra(), for example, that will write data back to Cassandra your jobs are processed. Then you can use your client as you normally would.
There are some really good free Spark + Cassandra tutorials at academy.datastax.com. This is also a good place to start: http://rustyrazorblade.com/2015/01/introduction-to-spark-cassandra/
What is the best way to share spark RDD data between two spark jobs.
I have a case where job 1: Spark Sliding window Streaming App, will be consuming data at regular intervals and creating RDD. This we do not want to persist to storage.
Job 2: Query job that will access the same RDD created in job 1 and generate reports.
I have seen few queries where they were suggesting SPARK Job Server, but as it is a open source not sure if it a possible solution, but any pointers will be of great help.
thankyou !
The short answer is you can't share RDD's between jobs. The only way you can share data is to write that data to HDFS and then pull it within the other job. If speed is an issue and you want to maintain a constant stream of data you can use HBase which will allow for very fast access and processing from the second job.
To get a better idea you should look here:
Serializing RDD
You can share RDDs across different applications using Apache Ignite.
Apache ignite provides an abstraction to share the RDDs through which applications can access the RDDs corresponding to different applications. In addition Ignite has the support for SQL indexes, where as native Spark doesn't.
Please refer https://ignite.apache.org/features/igniterdd.html for more details.
According to the official document describes：
Note that none of the modes currently provide memory sharing across applications. If you would like to share data this way, we recommend running a single server application that can serve multiple requests by querying the same RDDs.
http://spark.apache.org/docs/latest/job-scheduling.html
You can save to a temporary view. Table will be available to other sessions until the one that creates it is closed