:Version: 0.10.1 :Download: http://pypi.python.org/pypi/kairos :Source: https://github.com/agoragames/kairos :Keywords: python, redis, mongo, sql, mysql, sqlite, postgresql, cassandra, timeseries, rrd, gevent, statistics
.. contents:: :local:
.. _kairos-overview:
Kairos provides time series storage using Redis, Mongo, SQL or Cassandra
backends. Kairos is intended to replace RRD and Whisper in situations where
the scale and flexibility of other data stores is required. It works with
gevent <http://www.gevent.org/> and is the library on which
torus <https://github.com/agoragames/torus> is built.
Recommended for python 2.7 and later, it can work with previous versions if you
install OrderedDict <https://pypi.python.org/pypi/ordereddict>_.
Kairos provides a consistent API for a variety of timeseries types and the
storage engines they're implemented in. Each timestamp is resolved to a
consistent bucket identifier ("interval") based on the number of whole seconds
since epoch, or a number corresponding to the Gregorian date associated with
the relative intervals [daily, weekly, monthly, yearly] (e.g 19991231).
Within that, data can optionally be stored at resolutions (e.g. "daily,
in 1 hour increments"). Multiple intervals can be tracked within a timeseries,
each with its own resolution and optional TTL (e.g. 60 days of daily data,
lifetime monthly data).
In data stores that support it, TTLs can be set for automatically deleting
data after a set number of intervals; other data stores expose an expire()
method for deleting data programmatically.
Within each interval (or resolution), data is stored according to the type of the timeseries and what each backend supports. The values tracked in each timeseries can be loosely typed for backends that support it, else the type will be whatever is set in the timeseries constructor. Even when loosely typed, it should be assumed that the value should be a string or number.
series All data will be stored in the order in which it arrives. Uses data store list types where supported, else it will be timestamped records that come as close as possible to the order in which they were written. Queries will return list objects.
histogram A hash of unique values to the number of its occurrences within an interval. Uses data store dictionaries where supported, else it will be separate records for each unique value and timestamp. Queries will return dictionary objects. Built-in transforms assume that keys are real numbers.
count A simple counter will be maintained for each interval, starting at 0 for an interval. Queries will return an integer.
gauge Stores the last-written value for each interval. Queries will return whatever the value type was.
set Stores all the unique values within an interval. Uses data store sets where supported, else it will be separate records for each unique value. Queries will return set objects.
Kairos supports all storage engines using the same API. The constructor will return a Timeseries instance tailored for the type of data and the storage engine, and the API for updating and querying the timeseries is consistent for all combinations of data type and storage engine.
The first argument is a handle to a supported storage engine or a URL (see below), and the rest of the keyword arguments configure the timeseries. The keyword arguments supported by all storage engines are:
type
Required, defines the type of the timeseries. Raises a NotImplementedError
if the backend does not support the type.
read_func
Optional, is a function applied to all values read back from the
database. Without it, values will be strings for Redis, whatever
write_func defined for Mongo. Must accept a string value for Redis
(empty string for no data) and can return anything.
write_func 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 saved according to the rules of the storage engine.
intervals Required, a dictionary of interval configurations in the form of: ::
{
# interval name, used in keys and should conform to best
# practices according to the storage engine.
minute: {
# Required. The number of seconds that the interval will cover,
# or a supported Gregorian interval.
step: 60,
# Optional. The maximum number of intervals to maintain. If supplied,
# will use Redis and Mongo expiration to delete old intervals, else
# intervals exist in perpetuity. If the storage engine doesn't support
# expiry, will be used to implement the expire() call.
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 a Gregorian interval.
resolution: 60,
}
}In addition to specifying step and resolution in terms of seconds,
kairos also supports a simplified format for larger time intervals. For
hours (h), days (d), weeks (w), months (m) and years (y), you can use
the format 30d to represent 30 days, for example.
As of 0.3.0, kairos also supports the Gregorian calendar for step
and resolution. Either or both parameters can use the terms [daily, weekly, monthly, yearly] to describe an interval. You can also mix these
terms between step and resolution (e.g. daily in
1h resolutions). The expiration time for Gregorian dates is still defined
in terms of seconds and may not match the varying month lengths, leap years,
etc. Gregorian dates are translated into strptime- and strftime-compatible
keys (as integers) and so may be easier to use in raw form or with any
external tools. The duration parameter to transforms run on gregorian
series will be seconds in whole number of days (where a day is 86400 seconds).
Each of the supported storage engines also supports a set of keyword arguments
to configure their behavior. When intializing with a URL, the keyword argument
client_config can optionally be a dictionary which will be passed as
keyword arguments to the constructor for the client associated with the URL.
If kairos implements any custom keyword arguments from client_config those
are documented below.
Redis (redis://)
An example timeseries stored in Redis: ::
from kairos import Timeseries import redis
client = redis.Redis('localhost', 6379) t = Timeseries(client, type='histogram', read_func=float, intervals={ 'minute':{ 'step':60, # 60 seconds 'steps':120, # last 2 hours } })
t.insert('example', 3.14159) t.insert('example', 2.71828) print t.get('example', 'minute')
Additional keyword arguments are: ::
prefix Optional, is a prefix for all keys in this timeseries. If supplied and it doesn't end with ":", it will be automatically appended.
Supported URL formats__: ::
redis://localhost redis://localhost/3
__ https://github.com/andymccurdy/redis-py/blob/master/redis/client.py#L332
All supported <https://github.com/andymccurdy/redis-py/blob/master/redis/client.py#L361>_ configuration options can be passed in client_config.
Mongo (mongodb://)
An example timeseries stored in Mongo: ::
from kairos import Timeseries import pymongo
client = pymongo.MongoClient('localhost') t = Timeseries(client, type='histogram', read_func=float, intervals={ 'minute':{ 'step':60, # 60 seconds 'steps':120, # last 2 hours } })
t.insert('example', 3.14159) t.insert('example', 2.71828) print t.get('example', 'minute')
Additional keyword arguments are: ::
escape_character Optional, defines the character used to escape periods. Defaults to the unicode character "U+FFFF".
Supported URL formats__: ::
mongodb://localhost mongodb://localhost:27018/timeseries mongodb://guest:host@localhost/authed_db
__ http://docs.mongodb.org/manual/reference/connection-string/
All supported__ configuration arguments can be passed in client_config, in addition to: ::
database The name of the database to use. Defaults to 'kairos'. Required if using an auth database. Overrides any database provided in the URL.
__ http://api.mongodb.org/python/current/api/pymongo/mongo_client.html
SQL (*sql*://)
An example timeseries stored in a SQLite memory store: ::
from kairos import Timeseries from sqlalchemy import create_engine
client = create_engine('sqlite:///:memory:') t = Timeseries(client, type='histogram', read_func=int, intervals={ 'minute':{ 'step':60, # 60 seconds 'steps':120, # last 2 hours } })
t.insert('example', 3.14159) t.insert('example', 2.71828) print t.get('example', 'minute')
Additional keyword arguments are: ::
string_length Optional, configures the length of strings (VARCHARs). Defaults to 255. All tables have at least 2 string columns, and the size of these columns may impact usability of the SQL storage engine.
text_length Optional, configures the length of TEXT and BLOB columns. Defaults to 32Kbytes. Only matters if value_type is a text or blob.
table_name Optional, overrides the default table name for a timeseries type.
value_type Optional, defines the type of value to be stored in the timeseries. Defaults to float. Can be a string, a Python type or a SQLAlchemy type or instance.
'blob'
'bool'
<type 'bool'>
'boolean'
'clob'
'date'
<type 'datetime.date'>
'datetime'
<type 'datetime.datetime'>
'decimal'
<class 'decimal.Decimal'>
'float'
<type 'float'>
'int'
'int64'
'integer'
<type 'int'>
'long'
<type 'long'>
'str'
'string'
<type 'str'>
'text'
'time'
<type 'datetime.time'>
'unicode'
<type 'unicode'>Supported URL formats <http://docs.sqlalchemy.org/en/rel_0_9/core/engines.html#database-urls>_ are many and varied. A few examples: ::
sqlite:///:memory: postgresql://scott:tiger@localhost/mydatabase mysql+mysqldb://scott:tiger@localhost/foo oracle://scott:tiger@127.0.0.1:1521/sidname
All supported__ constructor arguments can be used in client_config.
__ http://docs.sqlalchemy.org/en/rel_0_9/core/engines.html#sqlalchemy.create_engine
Cassandra (cassandra://, cql://)
An example timeseries stored in Cassandra: ::
from kairos import Timeseries import cql
client = cql.connect('localhost', 9160, 'keyspace', cql_version='3.0.0') t = Timeseries(client, type='histogram', read_func=int, intervals={ 'minute':{ 'step':60, # 60 seconds 'steps':120, # last 2 hours } })
t.insert('example', 3.14159) t.insert('example', 2.71828) print t.get('example', 'minute')
Additional keyword arguments are: ::
table_name Optional, overrides the default table name for a timeseries type.
pool_size Optional, set a cap on the pool size. Defines the maximum number of connections to maintain in the pool. Defaults to 0 for no maximum.
value_type Optional, defines the type of value to be stored in the timeseries. Defaults to float. Can be a string or a Python type.
<type 'unicode'>
string
decimal
<type 'long'>
int
double
unicode
float
long
<type 'bool'>
<type 'float'>
boolean
int64
str
text
blob
clob
integer
bool
<type 'str'>
<type 'int'>
inetSupported URL formats are: ::
cql:// cassandra://localhost:9160 cassandra://localhost/database
All supported__ constructor arguments can be used in client_config.
__ https://code.google.com/a/apache-extras.org/p/cassandra-dbapi2/source/browse/cql/connection.py
kairos requires cql <https://pypi.python.org/pypi/cql> as it supports
CQL3 <https://cassandra.apache.org/doc/cql3/CQL.html> and gevent. This
requires that the keyspace be created before the connection, and the keyword
argument cql_version='3.0.0' must be used.
A notable downside of this library is that it does not support a list of endpoints to connect to, so is missing key High Availability features.
It is likely that future versions of kairos will require
cassandra-driver <https://github.com/datastax/python-driver>_ when it
is ready.
Cassandra counters can only store integers, and cannot be used for a running total of floating point numbers.
Kairos implements a connection pooling mechanism on top of cql. The pool
is a simple soft-cap on the number of connections maintained in the pool,
but not necessarily the total number of connections at a time. An optional
hard cap may be implemented in a future release.
There are two methods to insert data, Timeseries.insert and Timeseries.bulk_insert.
insert
(optional) The timestamp of the statistic, defaults to time.time() if not supplied(optional) The number of time intervals before (<0) or after (>0) timestamp to copy the data(optional) Any additional keyword arguments supported by a backend, see belowFor series and histogram timeseries types, value can be whatever
you'd like, optionally processed through the write_func method before being
written to storage. Depending on your needs, value (or the output of
write_func) does not have to be a number, and can be used to track such
things as unique occurances of a string or references to other objects, such
as MongoDB ObjectIds. Note that many of the aggregate functions in histogram
expect the data to be real numbers.
For the count type, value is optional and should be a float or integer
representing the amount by which to increment or decrement name; it defaults
to 1.
For the gauge type, value can be anything and it will be stored as-is.
For all timeseries types, if value is one of (list,tuple,set), will
call bulk_insert.
The intervals option allows the caller to simulate the value appearing in
time periods before or after the timestamp. This is useful for creating
fast trending (e.g. "count over last seven days"). It is important to note
that, because the time periods are simulated, resolution is lost for the
the simulated timestamps.
Redis
Redis supports an additional keyword argument, pipeline, to give the caller
control over batches of commands. If pipeline is supplied, the execute
method will not be called and it is up to the caller to do so.
bulk_insert
(optional) The number of time intervals before (<0) or after (>0) timestamp to copy the data(optional) Any additional keyword arguments supported by a backend, see belowThe inserts field must take the following form: ::
{
timestamp : {
name: [ value, ... ],
...
},
...
}The meaning of timestamp, name and value are identical to those
parameters in insert. The caller can insert any number of timestamps,
statistic names and values, and the backend will optimize the insert where
possible. See details on the different backends below. Where a backend does
not support an optimized bulk insert, the data structure will be processed
such that each value will be passed to insert.
The inserts structure can be a dict or OrderedDict. If you need
the insert order preserved, such as when inserting into a series or
gauge, you should use OrderedDict.
If timestamp is unknown, use None for the key and it will be set to
the current value of time.time(). Note that this may alter ordering if
inserts is an OrderedDict.
NOTE bulk inserts will increase memory usage of the client process.
Redis
Redis bulk inserts are implemented by using a single pipeline (without
transactions) and committing the pipeline after all bulk inserts have been
executed. The bulk insert also supports the pipeline argument, with the
same rules as insert.
Mongo
Mongo bulk inserts are implemented by joining all of the data together into
a condensed set of queries and updates. As the configuration of a timeseries
may result in multiple timestamps resolving to the same record (e.g. per-day
data), this could result in significant performance gains when the timeseries
is a count, histogram or gauge.
SQL
There is no optimization for bulk inserts in SQL due to the lack of native update-or-insert support. The generic SQL implementation requires an attempted update to be committed before kairos can determine if an insert is required. Future versions may have optimized implementations for specific SQL servers which support such a feature, at which time bulk inserts may be optimized for those specific backends.
Cassandra #########
The cql library has no support for transactions, grouping, etc.
There are two methods to query meta data about a Timeseries.
list
There are no arguments. Returns a list of all of the stat names stored in the Timeseries.
properties
Takes a single argument, the name of the timeseries. Returns a dictionary with the following fields: ::
{ interval : { 'first' : timestamp, 'last' : timestamp } }
interval will be the named interval, such as "minute". For each interval,
there is a dictionary of properties. first is the timestamp of the first
data point in the timeseries, and last is the last data point in the
timeseries.
There are three methods to read data, Timeseries.get, Timeseries.series
and Timeseries.iterate. get will return data from a single bucket,
and series will return data from several buckets. iterate will use
the Timeseries.properties method to determine the date range of the data,
and return a generator that calls get for every possible interval in
the date range.
get
Supports the following parameters. All optional parameters are keyword arguments.
(optional) The timestamp to read, defaults to time.time()(optional) DEPRECATED Use condense instead. Support for this will be removed entirely in a future release.(optional) Optionally process each row of data. Supports [mean, count, min, max, sum], or any callable that accepts datapoints according to the type of series (e.g histograms are dictionaries, counts are integers, etc). Transforms are called after read_func has cast the data type and after resolution data is optionally condensed. If transform is one of (list,tuple,set), will load the data once and run all the transforms on that data set. If transform is a dict of the form { transform_name : transform_func }, will run all of the transform functions on the data set. See Customized Reads_ for more on custom transforms.(optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads_.(optional) Can be a callable to implement Customized Reads_.(optional) If using resolutions, True will collapse the resolution data into a single row. Can be a callable to implement Customized Reads_.(optional) Can be a callable to implement Customized Reads_.Returns a dictionary of { timestamp : data }, where timestamp is a Unix timestamp
and data is a data structure corresponding to the type of series, or whatever
transform returns. If not using resolutions or condense=True, the length
of the dictionary is 1, else it will be the number of resolution buckets within
the interval that contained data. If transform is a list, data will be a
dictionary of { transform_func : transformed_data }. If transform is a dict,
data will be a dictionary of { transform_name : transformed_data }.
series
Almost identical to get, supports the following parameters. All optional parameters are keyword arguments.
(optional) The timestamp which should be in the first interval of the returned data.(optional) The timestamp which should be in the last interval of the returned data. (optional) The number of steps in the interval to read, defaults to either steps in the configuration or 1. Ignored if both start and end are defined. If either start or end are defined, steps is inclusive of whatever interval that timestamp falls into.(optional) DEPRECATED Use condense instead. Support for this will be removed entirely in a future release.(optional) Optionally process each row of data. Supports [mean, count, min, max, sum], or any callable that accepts a list of datapoints according to the type of series (e.g histograms are dictionaries, counts are integers, etc). Transforms are called after read_func has cast the data type and after resolution data is optionally condensed. If transform is one of (list,tuple,set), will load the data once and run all the transforms on that data set. If transform is a dict of the form { transform_name : transform_func }, will run all of the transform functions on the data set. See Customized Reads_ for more on custom transforms.(optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads_.(optional) Can be a callable to implement Customized Reads_.(optional) If using resolutions, True will collapse the resolution data into a single row. Can be a callable to implement Customized Reads_.(optional) Can be a callable to implement Customized Reads_.(optional) True will collapse all of the data in the date range into a single result. Can be a callable to implement Customized Reads_.Returns an ordered dictionary of { interval_timestamp : { resolution_timestamp: data } },
where interval_timestamp and resolution_timestamp are Unix timestamps
and data is a data structure corresponding to the type of series, or whatever
transform returns. If not using resolutions or condense=True, the dictionary
will be of the form { interval_timestamp : data }.
All variations of transform and the resulting format of data are the same
as in get.
If both start and end are defined, the returned data will start and end
on intervals including those timestamps. If only start is defined, then the
return data will start with an interval that includes that timestamp, with the
total number of intervals returned defined by steps. If only end is
defined, then the return data will end with an interval that includes that
timestamp, with the total number of intervals preceeding it defined by steps.
It is important to note that the interval timestamps in the returned data will
not necessarily match start or end. This is because of the consistent
hashing scheme that kairos uses, such that start and end will be
translated into the bucket in which it can be found.
iterate
Almost identical to get except it does not accept a timestamp argument.
(optional) Optionally process each row of data. Supports [mean, count, min, max, sum], or any callable that accepts datapoints according to the type of series (e.g histograms are dictionaries, counts are integers, etc). Transforms are called after read_func has cast the data type and after resolution data is optionally condensed. If transform is one of (list,tuple,set), will load the data once and run all the transforms on that data set. If transform is a dict of the form { transform_name : transform_func }, will run all of the transform functions on the data set. See Customized Reads_ for more on custom transforms.(optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads_.(optional) Can be a callable to implement Customized Reads_.(optional) If using resolutions, True will collapse the resolution data into a single row. Can be a callable to implement Customized Reads_.(optional) Can be a callable to implement Customized Reads_.Returns a generator which iterates over ( timestamp, data ) tuples, where
timestamp is a Unix timestamp and data corresponds to the rules
documented in get. Yields a tuple for each potential timestamp in the
entire date range of the timeseries, even if there is no data.
ALPHA This feature is still being explored and the API may change significantly.
There are times when the data in a timeseries requires processing to be pushed onto the datastore.
There are times when one needs custom control over the reading and processing
of data in a timeseries. As there is no good way to do this generically,
the get and series API supports several keyword arguments to customize
access to the data. Common use cases are to handle large sets of data that
can be processed in the datastore, and situations where one wants to implement
cutom analysis of the dataset such as calculating variance.
General
The following functions can be overloaded with keyword parameters to get and
series (collapse being only used for a series).
fetch
A customized database read function. The usage varies depending on the backends which are described in detail below. IMPORTANT You are welcome to change the type of the return value, but be wary that transforms, condense and collapse functionality may not work properly with the changed data types.
process_row ###########
The function which handles the type casting of the data read from the backend
and also calling the read_func if it has been defined for the time series.
It is required that you define this function if you overload fetch such
that the returned data type is not the same as the time series' native format
(dict for histogram, list for series, etc).
The function must be in the form of process_row(data), where:
fetch implementation, not
including any time stamps.The function may return any data type, but if it's not the native format of the time series, additional downstream functions may have to be overloaded.
condense ########
If the condense argument is a callable, the caller can override how resolution
data is collapsed (reduced) into a single interval. The argument will always be
in the form of: ::
{
'resolution_t0' :
Where <data_tN> is the data returned from the native or fetch
implementation and passed through the native or custom process_row
implementation.
The function should return a single value, optionally in the same format as
<data_tN>, but this method could also be used for calculating such
things as rate of change or variance within a time interval.
join_rows #########
If the join_rows argument is a callable and the name parameter to get
or series is one of (list,tuple,set), this method will be called to join
the data from several named timeseries into a single result. The argument will
always be in the form of: ::
[