About cassandra, we started using a cluster with a very high workload (4 hosts) The machines have 128Gb each and 64 CPU. My max_heap is with 48 GB and my new_heap is with 2 GB. What is considered in this case? Anyone?
There is no script or anything that analyzes this type of configuration.
The older servers running 60Gb and 16 procs, and thats ok.
Why this problem?
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I intend to create a cassandra cluster with 10 nodes (16v cpu + 32 Gb of RAM each).
However, for the generation of this cluster, I intend to use a high-end storage (SSD only) with 320k IOPS. These machines will be spread over 10 machines with VMWARE 6.7 installed. Any contraindications in this case? Even though it is a very performative architecture for any type of application / database?
It looks server side is quite okay but you need to consider other things like network, OS and data modelling part to opt good performance in Cassandra.
You can take a look datastax recommendation here :-
https://docs.datastax.com/en/dse/5.1/dse-admin/datastax_enterprise/config/configRecommendedSettings.html
We are running a Cassandra 5 nodes cluster (3.10) with 8 cores, 32 memory and 2TB disk each.
The cluster is running in k8s over google cloud.
Recently our disk size was increased from 400GB to ~ 800GB in each node, at that point we start suppering from many read/write timeouts.
when checking the usage of the node in their resources we notice that their CPU is at 1.5 - 2, ram is 17GB.
it seems like they are bound from some reason and the only observation we saw is that there a reverse correlation between disk size and used cpu, the higher disk usage the lower the cpu usage.
is there a way to see what's blocking the CPU and RAM from utilizing 100% of their resources?
We have setup Cassandra single node of 3.11 with JDK 1.8 on ec2 with instance type t2.large which has 2 CPU and 7 GB of RAM.
We facing the issue that Cassandra keeps reaching CPU 100% even we do not have that much load.
We have 7GB of RAM but Cassandra not utilizing that Memory.it only uses 1.7-1.8 GB of RAM.
What configuration needs to change to reduce CPU utilization to not reach to 100%.
what best configuration to get better performance out of Cassandra.
Right now we able to get only about 100-120 read and 50-100 write operation per sec.
Please, some one helps us to understand the issue and what ways to improve performance configuration.
I'm using Spark 1.3.1 on StandAlone mode in my cluster which has 7 machines. 2 of the machines are powerful and have 64 cores and 1024 GB memory, while the others have 40 cores and 256 GB memory. One of the powerful machines is set to be the master, and others are set to be the slaves. Each of the slave machine runs 4 workers.
When I'm running my driver program on one of the powerful machines, I see that it takes the cores only from the two powerful machines. Below is a part of the web UI of my spark master.
My configuration of this Spark driver program is as follows:
spark.scheduling.mode=FAIR
spark.default.parallelism=32
spark.cores.max=512
spark.executor.memory=256g
spark.logConf=true
Why spark does this? Is this a good thing or a bad thing? Thanks!
Consider lowering your executors memory from the 256GB that you have defined.
For the future, take in consideration assigning around 75% of available memory.
I have created a Spark cluster of 8 machines. Each machine have 104 GB of RAM and 16 virtual cores.
I seems that Spark only sees 42 GB of RAM per machine which is not correct. Do you know why Spark does not see all the RAM of the machines?
PS : I am using Apache Spark 1.2
Seems like a common misconception. What is displayed is the spark.storage.memoryFraction :
https://stackoverflow.com/a/28363743/4278362
Spark makes no attempt at guessing the available memory. Executors use as much memory as you specify with the spark.executor.memory setting. Looks like it's set to 42 GB.