We are running a cassandra 2-node cluster .
The following is the latency stat for reads or writes when executed independently :
99% write avg write latency 99% read avg read latency GC time
545 .227 2816 1.793 2400
However,the total read time for the same batch set is almost 3 times worse when performing read and write in parallel(write latencies being almost unaffected).
99% read avg read latency GC time
4055 1.955 6851
There is not compaction recorded on the application keyspace - though we could see compaction on the system and system_schema tablespaces.
What may be causing the sizeable jump in read timings for the same sample set - when writes happen concurrent to read?
Another point to mention is that the bloom filter false positives is always 0 - which seems to indicate bloom filters are being used effectively.
Any pointers to investigate is appreciated.
Related
I'm getting the following error in the application logs that trying to connect to a Cassandra cluster with 6 nodes
com.datastax.driver.core.exceptions.OperationTimedOutException: [DB1:9042] Timed out waiting for server response
I have set java heap memory to 8GB (-Xms8G -Xmx8G), wondering if 8 GB is too much?
Below is time out configuration in cassandra.yaml
read_request_timeout_in_ms: 10000
range_request_timeout_in_ms: 20000
write_request_timeout_in_ms: 10000
request_timeout_in_ms: 20000
In the application there aren't heavy delete or update statements, so my question is what else may cause the long GC pause? the majority types of the GC pause that I can see in the log is G1 Evacuation Pause, what does it mean exactly?
The heap size heavily depends on the amount of data that you need to process. Usually for production workload minimum of 16Gb was recommended. Also, G1 isn't very effective on the small heaps (less than 12Gb) - it's better to use default ParNewGC for such heap sizes, but you may need to tune GC to get better performance. Look into this blog post that explains tuning of the GC.
Regarding your question on the "G1 Evacuation Pause" - look into this blog posts: 1 and 2. Here is quote from 2nd post:
Evacuation Pause (G1 collection) in which live objects are copied from one set of regions (young OR young+old) to another set. It is a stop-the-world activity and all
the application threads are stopped at a safepoint during this time.
For you this means that you're filling regions very fast and regions are big, so it requires significant amount of time to copy data.
My Cassandra application entails primarily counter writes and reads. As such, having a counter cache is important to performance. I increased the counter cache size in cassandra.yaml from 1000 to 3500 and did a cassandra service restart. The results were not what I expected. Disk use went way up, throughput went way down and it appears the counter cache is not being utilized at all based on what I'm seeing in nodetool info (see below). It's been almost two hours now and performance is still very bad.
I saw this same pattern yesterday when I increased the counter cache from 0 to 1000. It went quite awhile without using the counter cache at all and then for some reason it started using it. My question is whether there is something I need to do to activate counter cache utilization?
Here are my settings in cassandra.yaml for the counter cache:
counter_cache_size_in_mb: 3500
counter_cache_save_period: 7200
counter_cache_keys_to_save: (currently left unset)
Here's what I get out of nodetool info after about 90 minutes:
Gossip active : true
Thrift active : false
Native Transport active: false
Load : 1.64 TiB
Generation No : 1559914322
Uptime (seconds) : 6869
Heap Memory (MB) : 15796.00 / 20480.00
Off Heap Memory (MB) : 1265.64
Data Center : WDC07
Rack : R10
Exceptions : 0
Key Cache : entries 1345871, size 1.79 GiB, capacity 1.95 GiB, 67936405 hits, 83407954 requests, 0.815 recent hit rate, 14400 save period in seconds
Row Cache : entries 0, size 0 bytes, capacity 0 bytes, 0 hits, 0 requests, NaN recent hit rate, 0 save period in seconds
Counter Cache : entries 5294462, size 778.34 MiB, capacity 3.42 GiB, 0 hits, 0 requests, NaN recent hit rate, 7200 save period in seconds
Chunk Cache : entries 24064, size 1.47 GiB, capacity 1.47 GiB, 65602315 misses, 83689310 requests, 0.216 recent hit rate, 3968.677 microseconds miss latency
Percent Repaired : 8.561186035383143%
Token : (invoke with -T/--tokens to see all 256 tokens)
Here's a nodetool info on the Counter Cache prior to increasing the size:
Counter Cache : entries 6802239, size 1000 MiB, capacity 1000 MiB,
57154988 hits, 435820358 requests, 0.131 recent hit rate,
7200 save period in seconds
Update:
I've been running for several days now trying various values of the counter cache size on various nodes. It is consistent that the counter cache isn't enabled until it reaches capacity. That's just how it works as far as I can tell. If anybody knows a way to enable the cache before it is full let me know. I'm setting it very high because it seems optimal but that means that the cache is down for several hours while it fills up and while it's down my disks are absolutely maxed out with read requests...
Another update:
Further running shows that occasionally the counter cache does kick in before it fills up. I really don't know why that is. I don't see a pattern yet. I would love to know the criteria for when this does and does not work.
One last update:
While the counter cache is filling up native transport is disabled for the node as well. Setting the counter to 3.5 GB I'm now going 24 hours with the node in this low performance state with native transport disabled.
I have found out a way to 100% of the time avoid the counter cache not being enabled and native transport mode disabled. This approach avoids the serious performance problems I encountered waiting for the counter cache to enable (sometimes for hours in my case since I want a large counter cache):
1. Prior to starting Cassandra, set cassandra.yaml file field counter_cache_size_in_mb to 0
2. After starting cassandra and getting it up and running use node tool commands to set the cache sizes:
Example command:
nodetool setcachecapacity 2000 0 1000
In this example, the first value of 2000 sets the key cache size, the second value of 0 is the row cache size and the third value of 1000 is the counter cache size.
Take measurements and decide if those are the optimal values. If not, you can repeat step two without restarting Cassandra with new values as needed
Further details:
Some things that don't work:
Setting the counter_cache_size_in_mb value if the counter cache is not yet enabled. This is the case where you started Cassandra with a non-zero value in counter_cache_size_in_mb in Cassandra.yaml and you have not yet reached that size threshold. If you do this, the counter cache will never enabled. Just don't do this. I would call this a defect but it is the way things currently work.
Testing that I did:
I tested this on five separate nodes multiple times with multiple values. Both initially when Cassandra is just coming up and after some period of time. This method I have described worked in every case. I guess I should have saved some screenshots of nodetool info to show results.
One last thing: If Cassandra developers are watching could they please consider tweaking the code so that this workaround isn't necessary?
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.
I notice a severe degradation in Cassandra write performance with continuous writes over time.
I am inserting time series data with time stamp (T) as the column name in a wide column that stores 24 hours worth of data in a single row.
Streaming data is written from data generator (4 instances, each with 256 threads) inserting data into multiple rows in parallel.
Additionally, data is also inserted into a column family that has indexes over DateType and UUIDType.
CF1:
Col1 | Col2 | Col3(DateType) | Col(UUIDType4) |
RowKey1
RowKey2
:
:
CF2 (Wide column family):
RowKey1 (T1, V1) (T2, V3) (T4, V4) ......
RowKey2 (T1, V1) (T3, V3) .....
:
:
The no. of data points inserted/sec decreases over time until no further inserts are possible. The initial performance is of the order of 60000 ops/sec for ~6-8 hours and then it gradually tapers down to 0 ops/sec. Restarting the DataStax_Cassandra_Community_Server on all nodes helps restore the original throughput, but the behaviour is observed again after a few hours.
OS: Windows Server 2008
No.of nodes: 5
Cassandra version: DataStax Community 1.2.3
RAM: 8GB
HeapSize: 3GB
Garbage collector: default settings [ParNewGC]
I also notice a phenomenal increase in the no. of Pending write requests as reported by the OpsCenter (~of magnitude 200,000) when the performance begins to degrade.
I fail to understand what is preventing the write operations to be completed and why do they pile up over time? I do not see anything suspicious in the Cassandra logs.
Has the OS settings got anything to do with this?
Any suggestions to probe this issue further?
Do you see an increase in pending compactions (nodetool compactionstats)? Or are you seeing blocked flush writers (nodetool tpstats)? I'm guessing you're writing data to Cassandra faster than it can be consumed.
Cassandra won't block on writes, but that doesn't mean that you won't see an increase in the amount of heap used. Pending writes have overhead, as do blocked memtables. In addition, each SSTable has some memory overhead. If compactions fall behind this is magnified. At some point you probably don't have enough headroom in your heap to allocate the objects required for a single write, and you end up spending all your time waiting for an allocation that the GC can't provide.
With increased total capacity, or more IO on the machines consuming the data you would be able to sustain this write rate, but everything indicates you don't have enough capacity to sustain that load over time.
Bringing your write timeout in line with the new default in 2.0 (of 2s instead of 10s) will help with your write backlog by allowing load shedding to kick in faster: https://issues.apache.org/jira/browse/CASSANDRA-6059
I've got pretty unusual latency patterns in my production setup:
the whole cluster (3 machines: 48 gig ram, 7500 rpm disk, 6 cores) shows latency spikes every 10 minutes, all machines at the same time.
See this screenshot.
I checked the logfiles and it seems as there are no compactions taking place at that time.
I've got 2k reads and 5k reads/sec. No optimizations have been made so far.
Caching is set to "ALL", hit rate for row cache is at ~0,7.
Any ideas? Is tuning memtable size an option?
Best,
Tobias