cassandra-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Benedict (JIRA)" <j...@apache.org>
Subject [jira] [Created] (CASSANDRA-6486) Latency Measurement
Date Sat, 14 Dec 2013 00:11:06 GMT
Benedict created CASSANDRA-6486:
-----------------------------------

             Summary: Latency Measurement
                 Key: CASSANDRA-6486
                 URL: https://issues.apache.org/jira/browse/CASSANDRA-6486
             Project: Cassandra
          Issue Type: Improvement
            Reporter: Benedict
            Assignee: Benedict


Latency measurement in Cassandra is currently suboptimal. Exactly what the latency measurements
tell you isn't intuitively clear due to their exponentially decaying, but amount to some view
of the latency per (unweighted) operation over the past, approximately, 10 minute period,
with greater weight given to more recent operations. This has some obvious flaws, the most
notable being that due to probabilistic sampling, large outlier events (e.g. GC) can easily
be lost over a multi-minute time horizon, and even when caught are unlikely to appear even
in the 99.9th percentile due to accounting for a tiny fraction of events numerically.

I'm generally thinking about how we might improve on this, and want to dump my ideas here
for discussion. I think the following things should be targeted:

1) Ability to see uniform latency measurements for different time horizons stretching back
from the present, e.g. last 1s, 1m, 1hr and 1day
2) Ability to bound the error margin of statistics for all of these intervals
3) Protect against losing outlier measurements
4) Possibly offer the ability to weight statistics, so that longer latencies are not underplayed
even if they are counted
5) Preferably non-blocking, memory efficient, and relatively garbage-free

(3) and (4) are the trickiest, as a theoretically sound and general approach isn't immediately
obvious. There are a number of possibilities that spring to mind:
1) ensure that we have enough sample points that we are probabilistically guaranteed to not
lose them, but over large time horizons this is problematic due to memory constraints, and
it doesn't address (4);
2) count large events multiple times (or sub-slices of the events), based on e.g. average
op-rate. I am not a fan of this idea because it makes possibly bad assumptions about behaviour
and doesn't seem very theoretically sound;
3) weight the probability of retaining an event by its length. the problem with this approach
is that it ties you into (4) without offering the current view of statistics (i.e. unweighted
operations), and it also doesn't lend itself to efficient implementation

I'm currently leaning towards a fourth approach, which attempts to hybridise uniform sampling
and histogram behaviour, by separating the sample space into ranges, each some multiple of
the last (say 2x the size). Each range has a uniform sample of events that occured in that
range, plus a count of total events. Ideally the size of the sample will be variable based
on the number of events occurring in any range, but that there will be a lower-bound calculated
to ensure we do not lose events.

This approach lends itself to all 5 goals above:
1) by maintaining the same structure for each time horizon, and uniformly sampling from all
of the directly lower order time horizons to maintain it;
2) by imposing minimum sample sizes for each range;
3) ditto (2);
4) by producing time/frequency-weighted statistics using the samples and counts from each
range;
5) with thread-local array-based timers that are synchronised with the global timer once every
minimum period, by the owning thread

This also extends reasonably nicely the timers I have already written for CASSANDRA-6199,
so some of the work is already done.

Thoughts / discussion would be welcome, especially if you think I've missed another obvious
approach.



--
This message was sent by Atlassian JIRA
(v6.1.4#6159)

Mime
View raw message