Michael Burman created CASSANDRA-14281:
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Summary: LatencyMetrics performance
Key: CASSANDRA-14281
URL: https://issues.apache.org/jira/browse/CASSANDRA-14281
Project: Cassandra
Issue Type: Improvement
Components: Core
Reporter: Michael Burman
Currently for each write/read/rangequery/CAS touching the CFS we write a
latency metric which takes a lot of processing time (up to 66% of the total
processing time if the update was empty).
The way latencies are recorded is to use both a dropwizard "Timer" as well as
"Counter". Latter is used for totalLatency and the previous is decaying metric
for rates and certain percentile metrics. We then replicate all of these CFS
writes to the KeyspaceMetrics and globalWriteLatencies.
For example, for each CFS write we do first write to the CFS's metrics and then
to Keyspace's metrics and finally globalMetrics. The way Timer is built is to
maintain a Histogram and a Meter and update both when Timer is updated. The
Meter then updates 4 different values (1 minute rate, 5 minute rate, 15 minutes
rate and a counter).
So for each CFS write we actually do 15 different counter updates. And then of
course maintain their states at the same time while writing. These operations
are very slow when combined.
A small JMH benchmark doing an update against a single LatencyMetrics with 4
threads gives us around 5.2M updates / second. With the current writeLatency
metric (having 2 parents) we get only 1.6M updates / second.
I'm proposing to update this to use a small circular buffer HdrHistogram
implementation. We would maintain a rolling buffer with last 15 minutes of
histograms (30 seconds per histogram) and update the correct bucket each time.
When requesting metrics we would then merge requested amount of buckets to a
new histogram and parse results from it. This moves some of the load from
writing of the metrics to reading them (which is much more infrequent
operation), including the parent metrics. It also allows us to maintain the
current metrics structure - if we wish to do so.
My prototype with this approach improves the performance to around 13.8M
updates/second, thus almost 9 times faster than the current approach. We also
maintain HdrHistogram already in the Cassandra's lib so there's no new
dependencies to add (java-driver also uses it).
FUTURE:
This opens up some possibilities, such as replacing all dropwizard
Histograms/Meters with the new approach (to reduce overhead elsewhere in the
codebase). It would also allow us to supply downloadable histograms directly
from the Cassandra or store them to the disk each time a bucket is filled if
user wishes to monitor latency history or graph all percentiles.
HdrHistogram also provides the ability to "fix" these histograms with pause
tracking, such as GC pauses which we currently can't do (as dropwizard
histograms can't be merged).
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