Apache Sedona + Iceberg GEO meetup in San Francisco

rates seamlessly into existing workflows and formats like Iceberg and Parquet. Spatial data can be used just like any other data type, unlocking powerful insights for business intelligence, analytics, and more. Speakers: Jia Yu – Co-Founder & Chief Architect, Wherobots (https://wherobots.com/) Mat

[ANNOUNCE] Apache Sedona 1.7.1 released

Dear all, We are happy to report that we have released Apache Sedona 1.7.1. Thank you again for your help. Apache Sedona is a cluster computing system for processing large-scale spatial data on top of Apache Spark, Flink and Snowflake. Vote thread (Permalink from https://lists.apache.org/list.ht

[ANNOUNCE] Apache Sedona 1.7.0 released

Dear all, We are happy to report that we have released Apache Sedona 1.7.0. Thank you again for your help. Apache Sedona is a cluster computing system for processing large-scale spatial data. Vote thread (Permalink from https://lists.apache.org/list.html): https://lists.apache.org/thread/5hvcr80

Fwd: [ANNOUNCE] Apache Sedona 1.6.1 released

Dear all, We are happy to report that we have released Apache Sedona 1.6.1. Apache Sedona is a cluster computing system for processing large-scale spatial data. Website: http://sedona.apache.org/ Release notes: https://github.com/apache/sedona/blob/sedona-1.6.1/docs/setup/release-notes.md Down

Re: how to use spark.mesos.constraints

Hi, I am also trying to use the spark.mesos.constraints but it gives me the same error: job has not be accepted by any resources. I am doubting that I should start some additional service like ./sbin/start-mesos-shuffle-service.sh. Am I correct? Thanks, Jia On Tue, Dec 1, 2015 at 5:14 PM, rared

Re: What are the likely causes of org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle?

Hi Peng, I got exactly same error! My shuffle data is also very large. Have you figured out a method to solve that? Thanks, Jia On Fri, Apr 24, 2015 at 7:59 AM, Peng Cheng wrote: > I'm deploying a Spark data processing job on an EC2 cluster, the job is > small > for the cluster (16 cores with

Help!!!Map or join one large datasets then suddenly remote Akka client disassociated

Hi folks, Help me! I met a very weird problem. I really need some help!! Here is my situation: Case: Assign keys to two datasets (one is 96GB with 2.7 billion records and one 1.5GB with 30k records) via MapPartitions first, and j

Cannot change the memory of workers

Hi guys, Currently I am running Spark program on Amazon EC2. Each worker has around (less than but near to )2 gb memory. By default, I can see each worker is allocated 976 mb memory as the table shows below on Spark WEB UI. I know this value is from (Total memory minus 1 GB). But I want more than