We are running 2 nodes in a cluster - replication factor 1.
After writing a burst of data, we see the following via node tool status.
Node 1 - load 22G (owns 48.2)
Node 2 - load 17G (owns 51.8)
As the payload size per record is exactly equal - what could lead to a node showing higher load despite lower ownership?
Nodetool status uses the Owns column to indicate the effective percentage of the token range owned by the nodes. While GB is Size of your records
Dont see anything wrong here. Your data is almost evenly distributed around your two nodes which is exactly what you want for perfekt performance.
Related
I have 3 node cluster and upon checking the nodetool status; Load is just less than 100 GB on all three nodes. The replication factor is two and percentage own is 65-70% for all three.
However when I inspected the /data directory it is having index.db files for size more than 400 GB and the total size of the keyspace directory is more than 700GB.
Any idea on why a huge gap??
Let me know if any extra details are required :)
PS: nodetool listsnapshots command shows an empty list (No snapshots)
Analysis - we tried redeploying the setup but still the same results; tried researching this topic but no luck.
Expectation - I was expecting this difference in the load and the size of data directory to be negligible if not zero.
The YCSB Endpoint benchmark would have you believe that Cassandra is the golden child of Nosql databases. However, recreating the results on our own boxes (8 cores with hyperthreading, 60 GB memory, 2 500 GB SSD), we are having dismal read throughput for workload b (read mostly, aka 95% read, 5% update).
The cassandra.yaml settings are exactly the same as the Endpoint settings, barring the different ip addresses, and our disk configuration (1 SSD for data, 1 for a commit log). While their throughput is ~38,000 operations per second, ours is ~16,000 regardless (relatively) of the threads/number of client nodes. I.e. one worker node with 256 threads will report ~16,000 ops/sec, while 4 nodes will each report ~4,000 ops/sec
I've set the readahead value to 8KB for the SSD data drive. I'll put the custom workload file below.
When analyzing disk io & cpu usage with iostat, it seems that the reading throughput is consistently ~200,000 KB/s, which seems to suggest that the ycsb cluster throughput should be higher (records are 100 bytes). ~25-30% of cpu seems to be under %iowait, 10-25% in use by the user.
top and nload stats are not ostensibly bottlenecked (<50% memory usage, and 10-50 Mbits/sec for a 10 Gb/s link).
# The name of the workload class to use
workload=com.yahoo.ycsb.workloads.CoreWorkload
# There is no default setting for recordcount but it is
# required to be set.
# The number of records in the table to be inserted in
# the load phase or the number of records already in the
# table before the run phase.
recordcount=2000000000
# There is no default setting for operationcount but it is
# required to be set.
# The number of operations to use during the run phase.
operationcount=9000000
# The offset of the first insertion
insertstart=0
insertcount=500000000
core_workload_insertion_retry_limit = 10
core_workload_insertion_retry_interval = 1
# The number of fields in a record
fieldcount=10
# The size of each field (in bytes)
fieldlength=10
# Should read all fields
readallfields=true
# Should write all fields on update
writeallfields=false
fieldlengthdistribution=constant
readproportion=0.95
updateproportion=0.05
insertproportion=0
readmodifywriteproportion=0
scanproportion=0
maxscanlength=1000
scanlengthdistribution=uniform
insertorder=hashed
requestdistribution=zipfian
hotspotdatafraction=0.2
hotspotopnfraction=0.8
table=usertable
measurementtype=histogram
histogram.buckets=1000
timeseries.granularity=1000
The key was increasing native_transport_max_threads in the casssandra.yaml file.
Along with the increased settings in the comment (increasing connections in ycsb client as well as concurrent read/writes in cassandra), Cassandra jumped to ~80,000 ops/sec.
Setup:
We have 3 nodes Cassandra cluster having data of around 850G on each node, we have LVM setup for Cassandra data directory (currently consisting 3 drives 800G + 100G + 100G) and have separate volume (non LVM) for cassandra_logs
Versions:
Cassandra v2.0.14.425
DSE v4.6.6-1
Issue:
After adding 3rd (100G) volume in LVM on each of the node, all the nodes went very high in disk I/O and they go down quite often, servers also become inaccessible and we need to reboot the servers, servers don't get stable and we need to reboot after every 10 - 15 mins.
Other Info:
We have DSE recommended server settings (vm.max_map_count, file descriptor) configured on all nodes
RAM on each node : 24G
CPU on each node : 6 cores / 2600MHz
Disk on each node : 1000G (Data dir) / 8G (Logs)
As I suspected, you are having throughput problems on your disk. Here's what I looked at to give you background. The nodetool tpstats output from your three nodes had these lines:
Pool Name Active Pending Completed Blocked All time blocked
FlushWriter 0 0 22 0 8
FlushWriter 0 0 80 0 6
FlushWriter 0 0 38 0 9
The column I'm concerned about is the All Time Blocked. As a ratio to completed, you have a lot of blocking. The flushwriter is responsible for flushing memtables to the disk to keep the JVM from running out of memory or creating massive GC problems. The memtable is an in-memory representation of your tables. As your nodes take more writes, they start to fill and need to be flushed. That operation is a long sequential write to disk. Bookmark that. I'll come back to it.
When flushwriters are blocked, the heap starts to fill. If they stay blocked, you will see the requests starting to queue up and eventually the node will OOM.
Compaction might be running as well. Compaction is a long sequential read of SSTables into memory and then a long sequential flush of the merge sorted results. More sequential IO.
So all these operations on disk are sequential. Not random IOPs. If your disk is not able to handle simultaneous sequential read and write, IOWait shoots up, requests get blocked and then Cassandra has a really bad day.
You mentioned you are using Ceph. I haven't seen a successful deployment of Cassandra on Ceph yet. It will hold up for a while and then tip over on sequential load. Your easiest solution in the short term is to add more nodes to spread out the load. The medium term is to find some ways to optimize your stack for sequential disk loads, but that will eventually fail. Long term is get your data on real disks and off shared storage.
I have told this to consulting clients for years when using Cassandra "If your storage has an ethernet plug, you are doing it wrong" Good rule of thumb.
In my project, I use cassandra 2.0, and have 3 database servers.
2 of 3 servers has 2 TB of hard drive, the last has just 200 GB. So, I want the 2 servers response for higher load than the last one.
Cassandra: I use Murmur3Partitioner to partition the data.
My question is: how can I calculate the initial_token for each cassandra instance?
Thanks for your help :)
If you are using a somewhat recent version of Cassandra (2.x) then you can configure the number of tokens a node should hold relative to other nodes in the cluster. There is no need to specify token range boundaries via the initial_token any more. Instead you give a node a "weight" through the num_tokens parameter. As the capacity of your smaller node is roughly 1/10th of the big ones, adjust the weight of that node accordingly. The default weight is 256. So you could start with a weight of 25 for the smaller node and try and see whether it works OK that way.
Murmur3Partitioner : Uniformly distribute the data across the clusters based on the MurmurHash hash value.
Murmur3Partitioner uses a maximum possible range of hash values from -263 to +263-1. Here is the formula to calculate tokens:
python -c 'print [str(((264 / number_of_tokens) * i) - 263) for i in range(number_of_tokens)]'
For example, to generate tokens for 10 nodes:
python -c 'print [str(((264 / 10) * i) - 263) for i in range(10)]'
Been using a 6GB dataset with each source record being ~1KB in length when I accidentally added an index on a column that I am pretty sure has a 100% cardinality.
Tried dropping the index from cqlsh but by that point the two node cluster had gone into a run away death spiral with loadavg surpassing 20 on each node and cqlsh hung on the drop command for 30 minutes. Since this was just a test setup, I shut-down and destroyed the cluster and restarted.
This is a fairly disconcerting problem as it makes me fear a scenario where a junior developer is on a production cluster and they set an index on a similar high cardinality column. I scanned through the documentation and looked at the options in nodetool but there didn't seem to be anything along the lines of "abort job or abort building index".
Test environment:
2x m1.xlarge EC2 instances with 2 Raid 0 ephemeral disks
Dataset was 6GB, 1KB per record.
My question in summary: Is it possible to abort the process of building a secondary index AND or possible to stop/postpone running builds (indexing, compaction) for a later date.
nodetool -h node_address stop index_build
See: http://www.datastax.com/docs/1.2/references/nodetool#nodetool-stop