skip to navigation
skip to content

Not Logged In

torus 0.6.3

Carbon and Graphite replacement using Kairos for timeseries storage

Latest Version: 0.7.1

A service implementing the Carbon protocol to store time series data using kairos and an HTTP server to query and analyze the data.

Motivation

Kairos, an RRD-inspired timeseries library, provides an improved storage engine and many more features than most other systems backing statsd. Compared to traditional disk stores such as RRD and Whisper, Torus adds:

  • simple runtime for ease in development and deployment
  • abstraction on top of kairos for histograms
  • compact storage for sparse data points
  • scaling with per-schema hosting and sharding
  • non-buffering semantics for aggregate processing
  • consistent hashing of timestamps for ease in interleaving and interpolation
  • programmatic interface to data processing

StatsD Quick Start

A configuration file that tracks hourly, daily and monthly data in SQLite is available in examples/statsd.py. The default will create a temp directory for the current user to store the databases (e.g. /tmp/torus.user on Unix). Change STORAGE_DIR at the top of the file to set a permanent location.

If you have installed torus in a virtual env, you can use foreman to start both karbon and torus. If you're running torus out of the repository, then you can use foreman start -f Procfile.dev

Carbon Server

The karbon application runs the Carbon-compatible stat collection application. It is a drop-in replacement for the Carbon backend of statsd. It takes the following arguments:

usage: karbon [-h] [--tcp TCP] [--config CONFIG]

Karbon, a Carbon-replacement data collection server

optional arguments:
  -h, --help       show this help message and exit
  --tcp TCP        TCP binding, in the form of "host:port", ":port", or
                   "port". Defaults to "localhost:2003".
  --config CONFIG  Configuration file to load. Can be called multiple times
                   for multiple configuration files.

The configuration is documented below. To reload the configuration(s), send a SIGHUP to the karbon process.

Query Server

The torus application is a replacement for Graphite. It is not API compatible with Graphite though it does aim to be familiar to Graphite users and provides a graphite-compatible JSON format for ease in integrating with existing toolchains.

usage: torus [-h] [--tcp TCP] [--config CONFIG]

Torus, a web server for mining data out of kairos

optional arguments:
  -h, --help       show this help message and exit
  --tcp TCP        TCP binding, in the form of "host:port", ":port", or
                   "port". Defaults to "localhost:8080".
  --config CONFIG  Configuration file to load. Can be called multiple times
                   for multiple configuration files.

For most use cases it can share a configuration with karbon. However, one could use Chef, puppet or a similar tool to templatize the configuration, and replace strings such as the host definition, so as to target a specific set of resources at reading the data.

To reload the configuration(s), send a SIGHUP to the torus process.

torus will respond to http://$tcp/$command?$parameters for the following commands, where $parameters is a standard URL encoded parameter list.

Commands

/series

DEPRECATED: formerly /data

Parameters

Fetches data for one or more statistics and returns a list of objects for each statistic. Returns data from the first schema that matches a statistic.

  • stat

    The name of the statistic to fetch. Each instance of the stat parameter is interpreted as a separate statistic. The statistic can either be in the form of $stat_name or $func($stat_name), where $func can be one of:

    • avg - the average of each datapoints in each time slice.
    • min - the minimum value of datapoints in each time slice.
    • max - the maximum value of datapoints in each time slice.
    • sum - the sum of datapoints in each time slice.
    • count - the number of datapoints in each time slice.

    Additionally, $func can be either a transform or a macro defined in the configuration. The $func can be anything that matches the pattern [a-zA-Z0-9_].

  • format

    One of [graphite, json], where graphite is a Graphite-compatible json format and json offers more nuanced representation of kairos' data structures.

  • condense

    One of [true, false], if kairos resolutions are configured for a schema, determines whether resolutions are flattened or returned as-is. Forced to true for graphite format.

  • collapse

    One of [true, false], if true then all of the data for each time interval will be collapsed into a single value. This is useful for calculating aggregates across a range (e.g. "all hits in last 5 days").

  • schema

    In cases where multiple schemas match a stat name, force a particular schema to be used.

  • interval

    The interval to choose, one of the intervals available in whatever schema matches stat. Must apply to all stat arguments.

  • start

    An optional timestamp for the beginning of the return interval. Can be in the form of a unix timestamp, a strftime-formatted string, or a human-readable relative value such as "today", "5 days ago", "last week", etc.

  • end

    An optional timestamp for the end of the return interval. Can accept the same values as start. With no arguments, this is implicitely the time at which the query is made.

  • steps

    Given either a start or end timestamp, this parameter defines the number of intervals (inclusive) after or before (respectively) to return. So if start is "last week" and steps=7, the result data will end with yesterday's data. If no timestamps are given, this is the number of intervals before the current time (inclusive).

Returns

A json structure.

[{
  'function': 'avg',
  'interval': 'hour',
  'schema': 'calls',
  'stat': 'avg(calls.system)',
  'stat_name' : 'calls.system',
  'target': 'calls.system',
  'datapoints': [[0.0391, 1362153600], [0, 1362157200]],

 },
 ...
]

The stat field will be the full name of the corresponding parameter, including the function (if any). The stat_name field will be just the name of the statistic that was matched to the schema, and target will be a copy of the same for clients which are expecting data in graphite format.

Configuration

The configuration for torus includes a definition for schemas, aggregates, custom functions that can be used in queries, and debugging settings. The schema for torus is an extension of the kairos schema; each of the key-value pairs in a schema definition will be passed to the timeseries constructor. The configuration files can include 1 or more of the following:

SCHEMAS = {

  # The name of the time series
  unique_counts : {

    # A dictionary similar to kairos with a few additions

    # One of (series, histogram, count, gauge). Optional, defaults to "count".
    type: 'histogram'

    # The database type, host and database identifier in which the
    # timeseries is stored. If this is not a string, assumed to be a
    # connection instance and will be used natively (e.g. for Redis
    # unix domain sockets). The full redis, mongo and SQLite URI schemes
    # are supported (requires redis 2.7.5).
    #
    # http://docs.mongodb.org/manual/reference/connection-string/
    # http://docs.sqlalchemy.org/en/rel_0_9/core/engines.html#database-urls
    #
    # Cassandra URLs are in the form "cassandra://host[:port]/[keyspace],
    # where the keyspace defaults to "torus". The host settings
    # [user,password,consistency_level] are supported.
    # https://code.google.com/a/apache-extras.org/p/cassandra-dbapi2/source/browse/cql/connection.py
    #
    # host: 'redis://localhost'
    # host: 'redis://localhost/3'
    # host: 'mongodb://localhost'
    # host: 'mongodb://localhost:27018/timeseries'
    # host: 'mongodb://guest:host@localhost/authed_db'
    host: 'redis://localhost:6379/0'

    # Optional, a dictionary of parameters to pass as keyword arguments to
    # the database handle constructor.
    #
    #   Redis:      passed to `Redis.from_url()`
    #   Mongo:      passed to `MongoClient()`
    #   SQL:        passed to `sqlalchemy.create_engine()`
    #   Cassandra:  passed to `cql.connect()`
    #
    # host_settings: {
    #   connection_pool=redis.connection.ConnectionPool(max_connections=50)
    # }

    # Patterns for any matching stats to store in this schema. If this is
    # a string, matches just one pattern, else if it's a list of strings,
    # matches any of the patterns. The pattern(s) will be used as-is in the
    # python regex library with no flags.
    match: [ 'application.hits.*',  ]

    # Defines how many intervals before (negative) or after (positive) that
    # a copy of data should be written to whenever data is inserted. The
    # extra storage size offsets much faster calculation of aggregates over
    # pre-determined date range.
    #
    # Example: for a schema storing daily values, will store a value as if
    # it occurred any time in the last 30 days.
    # rolling: -30

    # Optional, is a prefix for all keys in this histogram. If supplied
    # and it doesn't end with ":", it will be automatically appended.
    # prefix: 'application'

    # Optional, allows one to replace the stat name and value with another.
    # Takes two arguments and must return a tuple of two items (statistic,
    # value). If the statistic is None, will skip writing the statistic.
    # The value will be a string on input, and on output must be acceptable
    # to any write_func defined.
    # transform: lambda s,v: (None,None) if 0>long_or_float(v)>3.14 else (s,v)

    # Optional, is a function applied to all values read back from the
    # database. Without it, values will be strings. Must accept a string
    # value and can return anything. Defaults to long_or_float, which
    # tries to cast to a long and failing that, cast to a float.
    # long_or_float is available for all schemas to use.
    read_func: float

    # Optional, is a function applied to all values when writing. Can be
    # used for histogram resolution, converting an object into an id, etc.
    # Must accept whatever can be inserted into a timeseries and return an
    # object which can be cast to a string.  Defaults to long_or_float,
    # which tries to cast to a long and failing that, cast to a float.
    write_func: lambda v: '%0.3f'%(v)

    # Required, a dictionary of interval configurations in the form of:
    intervals: {
      # interval name, used in redis keys and should conform to best practices
      # and not include ":" or "."
      minute: {

        # Required. The number of seconds that the interval will cover,
        # or one of the Gregorian intervals "daily", "weekly", "monthly"
        # or "yearly"
        step: 60,

        # Optional. The maximum number of intervals to maintain. If supplied,
        # will use redis expiration to delete old intervals, else intervals
        # exist in perpetuity.
        steps: 240,

        # Optional. Defines the resolution of the data, i.e. the number of
        # seconds in which data is assumed to have occurred "at the same time".
        # So if you're tracking a month long time series, you may only need
        # resolution down to the day, or resolution=86400. Defaults to same
        # value as "step". Can also be one of the supported Gregorian intevals.
        resolution: 60,
        }
      }
    }
  },
  ...
}

# Similar to Carbon aggregator but without the time buffer. Matching stats
# will be processed through any matching schemas.  Is a list of tuples to
# support rolling up any number of dissimilar stats into a single one. At
# this time key names must be in the character set [a-zA-Z0-9_-]
AGGREGATES = [
  ('application.rollup', 'application.count.*'),
  ('application.result.<code>', 'application.http.status.<code>'),
]

# A named map of functions which can be used in requests to torus
TRANSFORMS = {
  # Returns the number of elements
  'size' : lambda row: len(row)
}

# A named map of configuration options so that "foo(stat)" will result in
# a fixed set of options passed to kairos. This is especially useful for
# using the customized read feature of kairos. This example assumes a
# histogram stored in redis. A more complicated macro might use server-side
# scripting. All custom read functions exposed in kairos can be defined here.
# All fields of the query string, other than 'stat', can be set in the
# macro definition and will override those query parameters if they're
# provided. To use a transform in a macro, set the 'transform' field to
# either a string or a callable. Macros can make use of transforms defined
# in TRANSFORMS.
MACROS = {
  'unique' : {
    'fetch' : lambda handle,key: handle.hlen(key)
    'condense' : lambda data: sum(data.values()),
    'process_row' : lambda data: data,
    'join_rows' : lambda rows: sum(rows),
  }
}

Debugging

Debugging a schema or set of schemas can pose a challenge. Torus ships with schema_debug, a tool for testing any number of input strings against any number of schemas. It will output which rules match the input string, which database that match will be stored in, any aggregates that will be generated from the input rule, and then recursively any schemas and aggregates that match each aggregate.

usage: schema_debug [-h] [--config CONFIG] strings [strings ...]

Debugging tool for schemas

positional arguments:
  strings          One or more input strings to test against the scheams

optional arguments:
  -h, --help       show this help message and exit
  --config CONFIG  Configuration file to load. Can be called multiple times
                   for multiple configuration files.

Torus also supports the DEBUG flag which can be defined in any of the configuration files and which will cause karbon to print to stdout. If it is 0, or not defined, no output will be generated. If it is 1, karbon will log when it stores a raw value (STOR) or aggregate (AGRT), and statistics on the quantity and duration of processing (DONE). If DEBUG==2, karbon will also log every line it recieves (RECV) and lines that it skips (SKIP).

To use the debugging flag, you can change the value in one of the configuration files loaded by karbon, and then signal the process to reload with the command kill -SIGHUP `pidof karbon`.

Series Types

TODO: discuss different series types and their features.

Hosts

Intervals

Aggregates

Migration

There will be times that you need to migrate data from one schema to another. Torus ships with migrate to facilitate that.

usage: migrate [-h] --config CONFIG --source SOURCE --destination DESTINATION
               --interval INTERVAL [--start START] [--end END]
               [--concurrency CONCURRENCY] [--stat STAT] [--match MATCH]
               [--dry-run] [--verbose]

A tool to migrate data from one schema to another

optional arguments:
  -h, --help            show this help message and exit
  --config CONFIG       Configuration file to load. Can be called multiple
                        times for multiple configuration files.
  --source SOURCE       The name of the source schema [required]
  --destination DESTINATION
                        The name of the destination schema [required]
  --interval INTERVAL   The name of the interval from which to read data
                        [required]
  --start START         Only copy stats occurring on or after this date. Same
                        format as web parameter. [optional]
  --end END             Only copy stats occurring on or before this date. Same
                        format as web parameter. [optional]
  --concurrency CONCURRENCY
                        Set the concurrency on the schema target writing.
                        Defaults to 10.
  --stat STAT           The name of the stat to copy. Can be called multiple
                        times for a list of stats. If not provided, all stats
                        will be copied. [optional]
  --match MATCH         Pattern match to migrate a subset of the data.
                        [optional]
  --dry-run             Print out status but do not save results in the
                        destination schema. [optional]
  --verbose             Print out even more information during the migration
                        [optional]

Installation

Torus is available on pypi and can be installed using pip

pip install torus

If installing from source:

  • with development requirements (e.g. testing frameworks)

    pip install -r development.pip
    
  • without development requirements

    pip install -r requirements.pip
    

SQL

Torus installs SQLAlchemy to support SQL. To use your dialect of choice, you will likely have to install additional packages. Refer to the documentation for more details.

Tests

Use nose to run the test suite.

$ nosetests

Roadmap

  • Record metrics on karbon and torus usage
  • Add "dead letter" support for tracking stats that don't match any schema
  • Add stat delete endpoint to torus
  • Command line tools for querying data and optionally plotting using bashplotlib
  • Add tools for generating tasseo configurations (https://github.com/obfuscurity/tasseo)
  • Add ability to set transaction-commit intervals for Redis and SQLite backends
  • Investigate faster regular expression engines. pyre2 is currently in the running.
  • Expand supported stat naming (unicode, symbols, etc)
  • A relay host type for forwarding karbon data to another Carbon-compatible host
  • Schema migration tools
  • log and stdout for torus and karbon
 
File Type Py Version Uploaded on Size
torus-0.6.3.tar.gz (md5) Source 2014-02-04 21KB
  • Downloads (All Versions):
  • 6 downloads in the last day
  • 319 downloads in the last week
  • 1797 downloads in the last month