There are indeed many tuning points here. If the name nodes and journal
nodes can be larger, perhaps even bonding multiple 10gbyte nics, one can
easily scale. I did have one client where the file counts forced multiple
clusters. But we were able to differentiate by airframe types ... eg fixed
wing
Here is what I found on Horton website.
Namespace scalability
While HDFS cluster storage scales horizontally with the addition of datanodes,
the namespace does not. Currently the namespace can only be vertically scaled
on a single namenode. The namenode stores the entire file system
All
I am looking for a Hadoop engineer consultant or full time employee to start
right away.
We are a well funded startup with explosive growth.
Please send me you resume and availability.
Regards
Tim
Softiron.com
-
To
Hey
Namaskara~Nalama~Guten Tag~Bonjour
Sorry, I am new to Spark.
Spark claims it can do all that what MapReduce can do (and more!) but 10X
times faster on disk, and 100X faster in memory. Why would then I use
Mapreduce at all?
Thanks
Deepak
--
Keigu
Deepak
73500 12833
www.simtree.net,
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Hi team,
I'm working to run HDFS in kubernetes; all configuration is ready: kube-dns,
hdfs-site.xml and ssh. But when I create files in HDFS I got the following
exception. In exception, "10.0.1.126:50010" is the host's ip & port instead of
container; is there any configuration to ask
Hi,
I read some (old?) articles from Internet about Mapr-FS vs HDFS.
https://www.mapr.com/products/m5-features/no-namenode-architecture
It states that HDFS Federation has
a) "Multiple Single Points of Failure", is it really true?
Why MapR uses HDFS but not HDFS2 in its comparison as this would
Hi Ascot,
No, especially with the Block ID based datanode layout (
https://issues.apache.org/jira/browse/HDFS-6482) this should no longer be
true on HDFS.
If you do plan to have millions of files per datanode, you'd do well to
familiarize yourself with
