Well, I have been working some with Spark and the biggest hurdle is that Spark does not allow me to run multiple jobs in parallel i.e. at the point of starting the job to taking the table of “Individuals” I will have to wait until all that processing is done before I can start an additional one so I will need to upon demand start various additional jobs where I get “Addresses”, “Invoices”, … and so on I know I could increase number of Workers/Executors and use Mesos for handling the scheduling and resource management but we have so far not been able to get it dynamic/flexible enough Although I admit that this could still be a way forward we have not evaluated it 100% yet, so I have not completely given up that thought
-Tobias From: Justin Cameron <jus...@instaclustr.com> Reply-To: "user@cassandra.apache.org" <user@cassandra.apache.org> Date: Thursday, 27 April 2017 at 01:36 To: "user@cassandra.apache.org" <user@cassandra.apache.org> Subject: Re: How can I efficiently export the content of my table to KAFKA You could probably save yourself a lot of hassle by just writing a Spark job that scans through the entire table, converts each row to JSON and dumps the output into a Kafka topic. It should be fairly straightforward to implement. Spark will manage the partitioning of "Producer" processes for you - no need for a "Coordinator" topic. On Thu, 27 Apr 2017 at 05:49 Tobias Eriksson <tobias.eriks...@qvantel.com<mailto:tobias.eriks...@qvantel.com>> wrote: Hi I would like to make a dump of the database, in JSON format, to KAFKA The database contains lots of data, millions and in some cases billions of “rows” I will provide the customer with an export of the data, where they can read it off of a KAFKA topic My thinking was to have it scalable such that I will distribute the token range of all available partition-keys to a number of (N) processes (JSON-Producers) First I will have a process which will read through the available tokens and then publish them on a KAFKA “Coordinator” Topic And then I can create 1, 10, 20 or N processes that will act as Producers to the real KAFKA topic, and pick available tokens/partition-keys off of the “Coordinator” Topic One by one until all the “rows” have been processed. So the JOSN-Producer will take e.g. a range of 1000 “rows” and convert them into my own JSON format and post to KAFKA And then after that take another 1000 “rows” and then …. And then another 1000 “rows” and so on, until it is done. I base my idea on how I believe Apache Spark Connector accomplishes data locality, i.e. being aware of where tokens reside and figured that since that is possible it should be possible to create a job-list in a KAFKA topic, and have each Producer pick jobs from there, and read up data from Cassandra based on the partition key (token) and then post the JSON on the export KAFKA topic. https://dzone.com/articles/data-locality-w-cassandra-how Would you consider this a good idea ? Would there in fact be a better idea, what would that be then ? -Tobias -- Justin Cameron Senior Software Engineer [https://cdn2.hubspot.net/hubfs/2549680/Instaclustr-Navy-logo-new.png]<https://www.instaclustr.com/> This email has been sent on behalf of Instaclustr Pty. Limited (Australia) and Instaclustr Inc (USA). This email and any attachments may contain confidential and legally privileged information. If you are not the intended recipient, do not copy or disclose its content, but please reply to this email immediately and highlight the error to the sender and then immediately delete the message.
