Performance
There are a couple optimizations that keep Instruments fast.
ets-backed-counters
ETS backed counters
Probe counters actually increment or decrement a value in an ETS table, every
fast_counter_report_interval milliseconds, the aggregated values are flushed to
statsd. Furthermore, the ETS tables are sharded across schedulers and can be written to
without any concurrency. Because of this, counters are effectively free and with a
conservative flush interval, will put little pressure on your statsd server.
iodata-metric-names
IOData metric names
Instruments uses macros to implement the metric names, and automatically converts interpolated strings into IOLists. This means you can have many generated names without increasing the amount of binary memory you're using. For example:
def increment_rpc(rpc_name),
do: Instruments.increment("my_module.rpc.#{rpc_name}")will be rewritten to the call:
def increment_rpc(rpc_name),
do: Instruments.increment(["my_module.rpc.", Kernel.to_string(rpc_name)])If you wish, you may pass any IOData as the name of a metric.
sample-rates
Sample Rates
For histograms, measure calls and timings, the default sample rate is pegged to 0.1.
This is so you don't accidentally overload your metrics collector. It can be
overridden by passing sample_rate: float_value to your metrics call in the
options.