Time series data storage in Redis, Mongo, SQL and Cassandra

## Project description

Version: 0.10.1 http://pypi.python.org/pypi/kairos https://github.com/agoragames/kairos python, redis, mongo, sql, mysql, sqlite, postgresql, cassandra, timeseries, rrd, gevent, statistics

## 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 and is the library on which torus is built.

Recommended for python 2.7 and later, it can work with previous versions if you install 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.

## Usage

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.

### Constructor

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.
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).

### Storage Engines

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=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')


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


All supported 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')


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


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.


#### 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')


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 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.

#### 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')


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'>
inet


Supported URL formats are:

cql://
cassandra://localhost:9160
cassandra://localhost/database


All supported constructor arguments can be used in client_config.

kairos requires cql as it supports CQL3 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 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.

### Inserting Data

There are two methods to insert data, Timeseries.insert and Timeseries.bulk_insert.

#### insert

• name The name of the statistic
• value The value of the statistic (optional for count timeseries), or a list of values
• timestamp (optional) The timestamp of the statistic, defaults to time.time() if not supplied
• intervals (optional) The number of time intervals before (<0) or after (>0) timestamp to copy the data
• **kwargs (optional) Any additional keyword arguments supported by a backend, see below

For 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

• inserts The structure of inserts (see below)
• intervals (optional) The number of time intervals before (<0) or after (>0) timestamp to copy the data
• **kwargs (optional) Any additional keyword arguments supported by a backend, see below

The 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.

### Meta Data

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.

• name The name of the statistic, or a list of names whose data will be joined together.
• interval The named interval to read from
• timestamp (optional) The timestamp to read, defaults to time.time()
• condensed (optional) DEPRECATED Use condense instead. Support for this will be removed entirely in a future release.
• transform (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.
• fetch (optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads.
• process_row (optional) Can be a callable to implement Customized Reads.
• condense (optional) If using resolutions, True will collapse the resolution data into a single row. Can be a callable to implement Customized Reads.
• join_rows (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.

• name The name of the statistic, or a list of names whose data will be joined together.
• interval The named interval to read from
• start (optional) The timestamp which should be in the first interval of the returned data.
• end (optional) The timestamp which should be in the last interval of the returned data.
• steps (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.
• condensed (optional) DEPRECATED Use condense instead. Support for this will be removed entirely in a future release.
• transform (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.
• fetch (optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads.
• process_row (optional) Can be a callable to implement Customized Reads.
• condense (optional) If using resolutions, True will collapse the resolution data into a single row. Can be a callable to implement Customized Reads.
• join_rows (optional) Can be a callable to implement Customized Reads.
• collapse (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.

• name The name of the statistic, or a list of names whose data will be joined together.
• interval The named interval to read from
• transform (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.
• fetch (optional) Function to use instead of the built-in implementations for fetching data. See Customized Reads.
• process_row (optional) Can be a callable to implement Customized Reads.
• condense (optional) If using resolutions, True will collapse the resolution data into a single row. Can be a callable to implement Customized Reads.
• join_rows (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:

• data The row data generated by the native or 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' : <data_t0>,
'resolution_t1' : <data_t1>,
...
'resolution_tN' : <data_tN>,
}


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:

[
<data_series0>,
<data_series1>,
...
<data_seriesN>
]


Where <data_series0> will be the data within a single timestamp window in the series’ native format or whatever was generated by custom implementations of fetch, process_row and/or condense. It is important to note that not every series will contain data points within a given time interval.

In addition to reducing multiple time series’ worth of data within an interval into a single result, this method could be used to implement cross-series analytics such as unions, intersections and differentials.

##### collapse

If the collapse argument is a callable, the caller can override how interval data is collapsed (reduced) into a single result. The native implementation is to call the condense function implemented by a time series. The arguments are the same as a custom condense function, as-is the expected return value.

It’s important to note that if collapse is defined, the series will automatically be condensed as well, so if fetch is overloaded to return a custom data type, then condense must also be defined. If collapse is True, the custom condense function will be used if defined.

In addition to collapsing the result of a time series into a single data set, this method could also be used to calculate data across a time series, such as variance.

##### transform

As noted previously, transform can be any callable, list of names or callables, or a named map of transform names or callables. The transforms will be processed after all previous native or custom read functions, including collapse.

Transforms must accept 2 parameters, the data and the time interval in seconds over which that data was captured. The data parameter will be of a type corresponding to the timeseries type, or whatever was generated by a custom fetch. For example:

def custom_rate_for_counts(data, duration):
return float(data) / float(duration)


For gregorian timeseries, duration will seconds in terms of the whole number of days over which the data was captured, where a day is 86400 seconds.

#### Redis

The function must be in the form of fetch(handle, key), where:

• handle Either a Redis client or pipeline instance
• key The key for the timeseries data

The return value should correspond to the data type of timeseries, e.g. dict for a histogram. One should always assume that handle is both a pipeline and a client, and fetch should return the result of, e.g. handle.hlen(...), but that it cannot be used to return a literal, such as lambda: h,k: { 'foo' : h.hlen(k) }

#### Mongo

The function must be in the form of fetch(handle, **kwargs), where:

• handle A PyMongo Collection
• spec The (suggested) query specification
• sort The (suggested) sort definition for the query
• method The suggested method to use on the handle

The required return value depends on the value of method.

• find_one Should return a hash in the form { value : <data> }, where <data> should correspond to the data type of the timeseries, e.g. list for a series. May directly return a result from pymongo.collection.find_one.
• find Should return an iterable in the form [ { value: <data> }, ... ], where <data> follows the same rules as find_one.

Re-implementing the default functionality would look like:

def mongo_fetch(handle, spec, sort, method):
if method=='find':
return handle.find( spec=spec, sort=sort )
elif method=='find_one':
return handle.find_one( spec )


#### SQL

The function must be in the form fetch(connection, table, name, interval, i_start, i_end), where:

• connection A SQLAlchemy Connection
• table A SQLAlchemy Table
• name The name of the stat to fetch
• interval The interval of the stat to fetch
• i_start The interval timestamp (starting) key
• i_end (optional) For a series, the ending timestamp key

The return value should be in the form of

{
'interval_t0' : {
'resolution_t0t0' : <data_t0t0>,
'resolution_t0t1' : <data_t0t1>,
...
'resolution_t0tN' : <data_t0tN>
},
'interval_t1' : { ... },
...
'interval_tN' : { ... },
}


If the series doesn’t use resolutions, then resolution_tNtN should be None, and so each interval will be in the form { 'interval_tN: { None : <data_tN> } }. This is inherent in the way that data is stored within the tables.

If i_end is supplied, the query should be over the range i_time >= i_start AND i_time <= i_end, else the query should be for the interval i_time = i_start.

#### Cassandra

The function must be in the form fetch(connection, table, name, interval, i_start, i_end), where:

• cursor A cql Connection
• table The name of the table
• name The name of the stat to fetch
• interval The interval of the stat to fetch
• intervals The list of interval timestamps

The return value should be in the form of

{
'interval_t0' : {
'resolution_t0t0' : <data_t0t0>,
'resolution_t0t1' : <data_t0t1>,
...
'resolution_t0tN' : <data_t0tN>
},
'interval_t1' : { ... },
...
'interval_tN' : { ... },
}


If the series doesn’t use resolutions, then resolution_tNtN should be None, and so each interval will be in the form { 'interval_tN: { None : <data_tN> } } and can be determined when a row has an r_time of -1.

If intervals is a list of 1, it’s effectively a get query where i_time = intervals[0], else it’s i_time >= intervals[0] AND i_time <= intervals[-1]. The full list of intervals is supplied to workaround Cassandra’s lack of grouping support in situations where an aggregate per i_time is desired.

### Deleting Data

#### delete

Takes a single argument, the name of the timeseries. Will delete all data for that timeseries in all intervals.

#### delete_all

Deletes every timeseries for all intervals. This method may be fast in data stores that support optimized deletes, else it will have to delete for each timeseries returned in list.

#### expire

Takes a single argument, the name of the timeseries. For storage engines that do not support expiry, such as SQL, will delete expired data from intervals for which steps is defined. All other storage engines will raise the NotImplementedError exception.

### Dragons!

Kairos achieves its efficiency by using TTLs and data structures in combination with a key naming scheme that generates consistent keys based on any timestamp relative to epoch. However, just like RRDtool, changing any attribute of the timeseries means that new data will be stored differently than old data. For this reason it’s best to completely delete all data in an old time series before creating or querying using a new configuration.

If you want to migrate data, there are tools in torus that can help.

## Installation

Kairos is available on pypi and can be installed using pip

pip install kairos


If installing from source:

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

pip install -r development.pip

• without development requirements

pip install -r requirements.pip


Note that kairos does not install packages for any of the supported backends, and that you must do this yourself.

## Tests

Use nose to run the test suite.

\$ nosetests


The test suite can be controlled through several environment variables, all defaulting to true.

• TEST_REDIS true
• TEST_MONGO true
• TEST_SQL true
• TEST_CASSANDRA true
• TEST_SERIES true
• TEST_HISTOGRAM true
• TEST_COUNT true
• TEST_GAUGE true
• TEST_SET true
• SQL_HOST sqlite:///:memory:
• CASSANDRA_KEYSPACE kairos

• Round-robbin intervals for datastores without TTLs
• Round-robbin databases: memcache (and compatible, e.g. ElastiCache), Riak, DynamoDB, SimpleDB, GDBM, Berkeley DB, and more
• Redis optimizations
• Capped collection support for mongo
• Python 3 support
• InfluxDB support
• Bloom filters
• Joined series populate a data structure at query time
• Joined series support concurrency “runner”

## Project details

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