Documentation
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Overview ¶
Package ts provides a high-level time series API on top of an underlying Cockroach datastore. Cockroach itself provides a single monolithic, sorted key-value map. The ts package collects time series data, consisting of timestamped data points, and efficiently stores it in the key-value store.
Storing time series data is a unique challenge for databases. Time series data is typically generated at an extremely high volume, and is queried by providing a range of time of arbitrary size, which can lead to an enormous amount of data being scanned for a query. Many specialized time series databases exist to meet these challenges.
Cockroach organizes time series data by series name and timestamp; when querying a given series over a time range, all data in that range is thus stored contiguously. However, Cockroach transforms the data in a few important ways in order to effectively query a large amount of data.
Downsampling ¶
The amount of data produced by time series sampling can be staggering; the naive solution, storing every incoming data point with perfect fidelity in a unique key, would commmand a tremendous amount of computing resources.
However, in most cases a perfect fidelity is not necessary or desired; the exact time a sample was taken is unimportant, with the overall trend of the data over time being far more important to analysis than the individual samples.
With this in mind, Cockroach downsamples data before storing it; the original timestamp for each data point in a series is not recorded. Cockroach instead divides time into contiguous slots of uniform length (e.g. 10 seconds); if multiple data points for a series fall in the same slot, they are aggregated together. Aggregated data for a slot records the count, sum, min and max of data points that were found in that slot. A single slot is referred to as a "sample", and the length of the slot is known as the "sample duration".
Cockroach also uses its own key space efficiently by storing data for multiple samples in a single key. This is done by again dividing time into contiguous slots, but with a longer duration; this is known as the "key duration". For example, Cockroach samples much of its internal data at a resolution of 10 seconds, but stores it with a "key duration" of 1 hour, meaning that all samples that fall in the same hour are stored at the same key. This strategy helps reduce the number of keys scanned during a query.
Finally, Cockroach can record the same series at multiple sample durations, in a process commonly known as "rollup". For example, a single series may be recorded with a sample size of 10 seconds, but also record the same data with a sample size of 1 hour. The 1 hour data will have much less information, but can be queried much faster; this is very useful when querying a series over a very long period of time (e.g. an entire month or year).
A specific sample duration in Cockroach is known as a Resolution. Cockroach supports a fixed set of Resolutions; each Resolution has a fixed sample duration and a key duration. For example, the resolution "Resolution10s" has a sample duration of 10 seconds and a key duration of 1 hour.
Source Keys ¶
Another dimension of time series queries is the aggregation of multiple series; for example, you may want to query the same data point across multiple machines on a cluster.
While Cockroach will support this, in some cases queries are almost *always* an aggregate of multiple source series; for example, we often want to query storage data for a Cockroach accounting prefix, but data is always collected from multiple stores; the information on a specific range is rarely relevant, as they can arbitrarily split and rebalance over time.
Unforunately, data from multiple sources cannot be safely aggregated in the same way as multiple data points from the same series can be downsampled; each series must be stored separately. However, Cockroach *can* optimize for this situation by altering the keyspace; Cockroach can store data from multiple sources contiguously in the key space, ensuring that the multiple series can be queried together more efficiently.
This is done by creating a "source key", an optional identifier that is separate from the series name itself. Source keys are appended to the key as a suffix, after the series name and timestamp; this means that data that is from the same series and time period, but is from different sources, will be stored adjacently in the key space. Data from all sources in a series can thus be queried in a single scan.
Example ¶
A hypothetical example from Cockroach: we want to record the size of all data stored in the cluster with a key prefix of "ExampleApplication". This will let us track data usage for a single application in a shared cluster.
The series name is: Cockroach.disksize.ExampleApplication
Data points for this series are automatically collected from any store that contains keys with a prefix of "ExampleApplication". When data points are written, they are recorded with a store key of: [store id]
There are 3 stores which contain data: 3, 9 and 10. These are arbitrary and may change over time, and are not often interested in the behavior of a specific store in this context.
Data is recorded for January 1st, 2016 between 10:05 pm and 11:05 pm. The data is recorded at a 10 second resolution.
The data is recorded into keys structurally similar to the following:
tsd.cockroach.disksize.ExampleApplication.10s.403234.3 tsd.cockroach.disksize.ExampleApplication.10s.403234.9 tsd.cockroach.disksize.ExampleApplication.10s.403234.10 tsd.cockroach.disksize.ExampleApplication.10s.403235.3 tsd.cockroach.disksize.ExampleApplication.10s.403235.9 tsd.cockroach.disksize.ExampleApplication.10s.403235.10
Data for each source is stored in two keys: one for the 10 pm hour, and one for the 11pm hour. Each key contains the tsd prefix, the series name, the resolution (10s), a timestamp representing the hour, and finally the series key. The keys will appear in the data store in the order shown above.
(Note that the keys will NOT be exactly as pictured above; they will be encoded in a way that is more efficient, but is not readily human readable.)
TODO(mrtracy): The ts package is a work in progress, and will initially only service queries for Cockroach's own internally generated time series data.
Index ¶
Constants ¶
const ( // URLPrefix is the prefix for all time series endpoints hosted by the // server. URLPrefix = "/ts/" // URLQuery is the relative URL which should accept query requests. URLQuery = URLPrefix + "query" )
Variables ¶
This section is empty.
Functions ¶
func MakeDataKey ¶
MakeDataKey creates a time series data key for the given series name, source, Resolution and timestamp. The timestamp is expressed in nanoseconds since the epoch; it will be truncated to an exact multiple of the supplied Resolution's KeyDuration.
Types ¶
type DB ¶
type DB struct {
// contains filtered or unexported fields
}
DB provides Cockroach's Time Series API.
func (*DB) PollSource ¶
func (db *DB) PollSource(source DataSource, frequency time.Duration, r Resolution, stopper *stop.Stopper)
PollSource begins a Goroutine which periodically queries the supplied DataSource for time series data, storing the returned data in the server. Stored data will be sampled using the provided Resolution. The polling process will continue until the provided stop.Stopper is stopped.
func (*DB) Query ¶
func (db *DB) Query(query proto.TimeSeriesQueryRequest_Query, r Resolution, startNanos, endNanos int64) ([]*proto.TimeSeriesDatapoint, []string, error)
Query returns datapoints for the named time series during the supplied time span. Data is returned as a series of consecutive data points.
Data is queried only at the Resolution supplied: if data for the named time series is not stored at the given resolution, an empty result will be returned.
All data stored on the server is downsampled to some degree; the data points returned represent the average value within a sample period. Each datapoint's timestamp falls in the middle of the sample period it represents.
If data for the named time series was collected from multiple sources, each returned datapoint will represent the sum of datapoints from all sources at the same time. The returned string slices contains a list of all sources for the metric which were aggregated to produce the result.
func (*DB) StoreData ¶
func (db *DB) StoreData(r Resolution, data []proto.TimeSeriesData) error
StoreData writes the supplied time series data to the cockroach server. Stored data will be sampled at the supplied resolution.
type DataSource ¶
type DataSource interface {
GetTimeSeriesData() []proto.TimeSeriesData
}
A DataSource can be queryied for a slice of time series data.
type Resolution ¶
type Resolution int64
Resolution is used to enumerate the different resolution values supported by Cockroach.
const ( // Resolution10s stores data with a sample resolution of 10 seconds. Resolution10s Resolution = 1 )
Resolution enumeration values are directly serialized and persisted into system keys; these values must never be altered or reordered.
func DecodeDataKey ¶
DecodeDataKey decodes a time series key into its components.
func (Resolution) KeyDuration ¶
func (r Resolution) KeyDuration() int64
KeyDuration returns the sample duration corresponding to this resolution value, expressed in nanoseconds. The key duration determines how many samples are stored at a single Cockroach key.
func (Resolution) SampleDuration ¶
func (r Resolution) SampleDuration() int64
SampleDuration returns the sample duration corresponding to this resolution value, expressed in nanoseconds.
Source Files