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gauged 0.1.0

A fast, append-only storage layer for numeric data that changes over time

Latest Version: 0.3.0

## Gauged

A fast, append-only storage layer for gauges, counters, timers and other numeric data types that change over time.


- Cache-aware data structures and algorithms for speed and memory-efficiency.
- Efficient range queries and roll-ups of any size down to the configurable resolution of 1 second.
- Use either `MySQL`, `PostgreSQL` or `SQLite` as a backend.

## Installation

The library can be installed with `easy_install` or `pip`

$ pip install gauged

Python 2.7.x (CPython or PyPy) is required.

## Backends

You can use either `MySQL`, `PostgreSQL` or `SQLite` as a backend. If no URL is specified than a SQLite-based in-memory database will be used

from gauged import Gauged

# any one of:
gauged = Gauged('mysql://root@localhost/gauged')
gauged = Gauged('postgresql://postgres@localhost/gauged')
gauged = Gauged('sqlite:////tmp/gauged.db')
gauged = Gauged()

On first run you'll need to create the schema


## Writing data

The library has no concept of data types when writing. Rather, you store your counter, gauge and timer data in the same way, as a `(key, float_value)` pair, and then use the appropriate method when reading the data back

with gauged.writer as writer:
    writer.add({ 'requests': 1, 'response_time': 0.45, 'memory_usage': 145.6 }, timestamp=1389747759902)
    writer.add({ 'requests': 1, 'response_time': 0.25, 'memory_usage': 148.3 }, timestamp=1389747760456)

The timestamp can be omitted (it defaults to now). Note that Gauged is append-only; data must be written in chronological order.

You can also write data to separate namespaces

with gauged.writer as writer:
    writer.add('requests', 1, namespace=1)
    writer.add('requests', 1, namespace=2)

For more information, see the [technical overview][technical-overview].

## Reading data

##### gauged.aggregate(key, aggregate, start=None, end=None, namespace=None, percentile=None)

Fetch all values associated with the key during the specified date range (`[start, end)`), and then aggregate them using one of `Gauged.MIN`, `Gauged.MAX`, `Gauged.SUM`, `Gauged.COUNT`, `Gauged.MEAN`, `Gauged.MEDIAN`, `Gauged.STDDEV` or `Gauged.PERCENTILE`.

The `start` and `end` parameters can be either a timestamp in milliseconds, a `datetime` instance or a negative timestamp in milliseconds which is interpreted as relative to now. If omitted, both parameters default to the boundaries of all data that exists in the `namespace`. Note that the `Gauged.SECOND`, `Gauged.MINUTE`, `Gauged.HOUR`, `Gauged.DAY`, `GAUGED.WEEK` and `Gauged.NOW` constants can also be used.

Here's some examples

# Count the total number of requests
requests = gauged.aggregate('requests', Gauged.SUM)

# Count the number of requests between 2014/01/01 and 2014/01/08
requests = gauged.aggregate('requests', Gauged.SUM, start=datetime(2014, 1, 1),
    end=datetime(2014, 1, 8))

# Get the 95th percentile response time from the past week
response_time = gauged.aggregate('response_time', Gauged.PERCENTILE,
    percentile=95, start=-Gauged.WEEK)

##### gauged.aggregate_series(key, aggregate, interval=Gauged.DAY, **kwargs)

The time series variant of `aggregate()`. This method takes the same kwargs as `aggregate()` and also accepts an `interval` in milliseconds.

This method returns a `TimeSeries` instance. See [gauged/results/][] for the result API.

The method is approximately equal to

from gauged import TimeSeries
points = []
for timestamp in xrange(start, end, interval):
    aggregate = gauged.aggregate(key, aggregate, start=timestamp, end=timestamp+interval)
    points.append(( timestamp, aggregate ))
result = TimeSeries(points)

##### gauged.value(key, timestamp=None, namespace=None)

Read the value of a key at the specified time (defaults to now).

##### gauged.value_series(key, start=None, end=None, interval=Gauged.DAY, namespace=None)

The time series variant of `value()` which reads the value of a key at each `interval` steps in the range `[start, end)`.

##### gauged.keys(prefix=None, limit=None, offset=None, namespace=None)

Get a list of keys, optionally filtered by namespace or prefix.

##### gauged.namespaces()

Get a list of namespaces.

##### gauged.statistics(start=None, end=None, namespace=None)

Get write statistics for the specified namespace during the specified date range. The statistics include number of data points and the number of bytes they consume. See [gauged/results/][] for the result API.

## Plotting

The data can be plotted easily with [matplotlib][matplotlib]

import pylab

series = gauged.aggregate_series('requests', gauged.SUM, interval=gauged.DAY,
pylab.plot(series.dates, series.values, label='Requests per day for the past week')

## Configuration

Configuration should be passed to the constructor

gauged = Gauged('mysql://root@localhost/gauged', **config)

Configuration keys

- **key_whitelist** - a list of allowed keys. Default is `None`, i.e. allow all keys.
- **flush_seconds** - whether to periodically flush data when writing, e.g. `10` would cause a flush every 10 seconds. Default is `0` (don't flush).
- **namespace** - the default namespace to read and write to. Defaults to `0`.
- **key_overflow** - what to do when the key size is greater than the backend allows, either `Gauged.ERROR` (default) or `Gauged.IGNORE`.
- **gauge_nan** - what to do when attempting to write a `NaN` value, either `Gauged.ERROR` (default) or `Gauged.IGNORE`.
- **append_only_violation** - what to do when writes aren't done in chronological order, either `Gauged.ERROR` (default), `Gauged.IGNORE`, or `Gauged.REWRITE` which rewrites out-of-order timestamps in order to maintain the chronological constraint.
- **max_look_behind** - how far a `value(key, timestamp)` call will traverse when looking for the nearest measurement before `timestamp`. Default is `Gauged.WEEK`.
- **min_cache_interval** - time series calls with intervals smaller than this will not be cached. Default is `Gauged.HOUR`.
- **max_interval_steps** - throw an error if the number of interval steps is greater than this. Default is `31 * 24`.
- **block_size** - see the [technical overview][technical-overview]. Defaults to `Gauged.DAY`.
- **resolution** - see the [technical overview][technical-overview]. Defaults to `Gauged.SECOND`.

## Tests

You can run a subset of the test suite using only an in-memory driver with `make check-quick`.

To run the full suite, first edit the configuration in `test_drivers.cfg` so that PostgreSQL and Mysql both point to existing (and empty) databases, then run

$ make check

You can run coverage analysis with `make coverage` and run a lint tool `make lint`.

## Benchmarks

Use `python benchmark [OPTIONS]`

$ python --number 1000000 --days 365
Writing to sqlite:// (block_size=86400000, resolution=1000)
Spreading 1M measurements to key "foobar" over 365 days
Wrote 1M measurements in 4.912 seconds (203.6K/s) (rss: 12.4MB)
Gauge data uses 7.6MB (8B per measurement)
min() in 0.022s (read 45.2M measurements/s) (rss: 12.5MB)
max() in 0.022s (read 44.8M measurements/s) (rss: 12.6MB)
sum() in 0.023s (read 43.1M measurements/s) (rss: 12.6MB)
count() in 0.023s (read 43.2M measurements/s) (rss: 12.6MB)
mean() in 0.029s (read 35M measurements/s) (rss: 12.6MB)
stddev() in 0.044s (read 22.8M measurements/s) (rss: 23.6MB)
median() in 0.069s (read 14.6M measurements/s) (rss: 31.9MB)

You can also run `make cbenchmark` to run C benchmarks.

## License


File Type Py Version Uploaded on Size
gauged-0.1.0.tar.gz (md5) Source 2014-01-15 47KB
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