Hi all! We are looking for a solution to replace MySQL as a single point of failure in our DB layer. Being a non-redundant component it causes us downtimes in cases of hardware failure and during upgrades.
We are an e-commerce company maintaining around 100 mln. items in more than 10 countries. That makes ~300 Gb of meta data per country. To cope with the volumes we use custom made sharding on MySQL therefore we need a solution providing high write and read performance. MySQL cluster does not suit us as it uses to transfer between nodes high volumes of data. Our infrastructure is made of around 100 nodes. We use Hadoop, non-relational information is stored in Hbase. - I would like to know if anybody here is using Phoenix for similar scale? What issues are you facing during regular maintenance and during the peak load periods? - What were the scaling impediments you had to overcome? Or do you scale up by simply adding nodes? - Do you have downtimes? What are the causes? - What about failover? Do you experience data losses? - Do the maintenance operations - like backups at runtime affect performance? - How is upgrading the systems organized? Is it seamless for the cluster? - Are you able to reclaim the freed space (to compact the files used by the DB)? Without shutting down a node? - What about performance during multiple write/read operations? Any locking issues? Pretty much need to get a feeling how painful it is to handle such volumes on Phoenix. Thank you! All the best, Aleksandr
