instrument.output
By default, metrics are printed to standard output using print_metric. You can provide your own metric recording funtion. It should take three arguments: count of items, elapsed time in seconds, and name, which can be None:
>>> def my_metric(name, count, elapsed): ... print("Iterable %s produced %d items in %d milliseconds"%(name, count, int(round(elapsed*1000)))) ... >>> _ = instrument.all(math_is_hard(5), metric=my_metric, name="bogomips") >>> list(_) Iterable bogomips produced 5 items in 5000 milliseconds [0, 1, 4, 9, 16]
Unless individually specified, metrics are reported using the global instrument.default_metric. To change the active default, simply assign another metric function to this attribute. In general, you should configure your metric functions at program startup, before recording any metrics. .make_multi_metric composes several metrics functions together, for simultaneous display to multiple outputs.
.logging writes metrics to a standard library logger, using the metric's name.
>>> from instrument.output.logging import log_metric >>> _ = instrument.all(math_is_hard(5), metric=log_metric, name="bogomips") >>> list(_) INFO:instrument.bogomips:5 items in 5.00 seconds [0, 1, 4, 9, 16]
.csv saves raw metrics as comma separated text files. This is useful for conducting external analysis. .csv is threadsafe; use under multiprocessing requires some care.
.CSVFileMetric saves all metrics to a single file with three columns: metric name, item count & elapsed time. Create an instance of this class and pass its .CSVFileMetric.metric method to measurement functions. The outfile parameter controls where to write data; an existing file will be overwritten.
>>> import tempfile, os.path >>> csv_filename = os.path.join(tempfile.gettempdir(), "my_metrics_file.csv") >>> from instrument.output.csv import CSVFileMetric >>> csvfm = CSVFileMetric(csv_filename) >>> _ = instrument.all(math_is_hard(5), metric=csvfm.metric, name="bogomips") >>> list(_) [0, 1, 4, 9, 16]
.CSVDirMetric saves metrics to multiple files, named after each metric, with two columns: item count & elapsed time. This class is global to your program; do not manually create instances. Instead, use the classmethod .CSVDirMetric.metric. Set the class variable outdir to a directory in which to store files. The contents of this directory will be deleted on startup.
Both classes support at dump_atexit flag, which will register a handler to write data when the interpreter finishes execution. Set to false to manage yourself.
.table reports aggregate statistics and .plot generates plots (graphs). These are useful for benchmarking or batch jobs; for live systems, statsd is a better choice. .table and .plot are threadsafe; use under multiprocessing requires some care.
.TableMetric and .PlotMetric are global to your program; do not manually create instances. Instead, use the classmethod .metric. The dump_atexit flag will register a handler to write data when the interpreter finishes execution. Set to false to manage yourself.
.TableMetric prints pretty tables of aggregate population statistics. Set the class variable outfile to a file-like object (defaults to stderr):
>>> from instrument.output.table import TableMetric >>> _ = instrument.all(math_is_hard(5), metric=TableMetric.metric, name="bogomips") >>> list(_) [0, 1, 4, 9, 16]
You'll get a nice table for output:
Name Count Mean Count Stddev Elapsed Mean Elapsed Stddev
alice 47.96 28.44 310.85 291.16
bob 50.08 28.84 333.98 297.11
charles 51.79 29.22 353.58 300.82.PlotMetric generates plots using matplotlib. Plots are saved to multiple files, named after each metric. Set the class variable outdir to a directory in which to store files. The contents of this directory will be deleted on startup.
For monitoring production systems, the .statsd_metric function can be used to record metrics to statsd and graphite. Each metric will generate two buckets: a count and a timing.