Facts & Figures

This article specifies the current default settings and limits in Anodot. 

Data and Anomalies Retentions 
First Baseline Creation Time 
Seasonality Detection Time 
Time to Catch First Anomalies 
Time to Close an Anomaly 
Time to Resume Learning  
Alerts Simulation Limits
Other Defaults

DATA AND ANOMALIES RETENTION RATES

Anodot distinguishes between two different types of retention rates:

Data retention rate - For how long Anodot keeps raw data information. 
Anomalies retention rate - For how long Anodot keeps anomalies data information.

The default retention rates for data and anomalies are not the same. To change these rates, contact Anodot’s CSM.
Note Certain retention rate changes will require an upgrade to Anodot's premium package. 
The data and anomalies retention rates by time scale are: 

Time Scale

Data Retention

Anomalies Retention

1 minute

7 days

3 days

5 minutes

30 days

7 days

1 hour

6 months

30 days

1 day

1 year

30 days

1 week

5 years

1 year

FIRST BASELINE CREATION TIME

The first baseline creation time is the minimum time it takes for a new baseline to initialize for a new metric. The baseline creation times by time scale are:

Time Scale

Baseline Creation Time

1 minute

1 hour

5 minutes

5 hours

1 hour

40 hours

1 day

10 days

1 week

6 weeks

Example
Learning_process_metric_weekly_seasonality.png

This example shows how after a few hours the initial baseline in created. For the first few days it is very wide; after several days the daily pattern is detected and the baseline becomes narrower. After 4 weeks the weekly seasonal pattern is detected and the baseline becomes very tight.

SEASONALITY DETECTION TIME

  • Seasonal patterns are detected with a minimum time of 4X the season length.
  • Daily seasonality requires at least 4 days for Anodot to calculate the daily seasonality of a metric.
  • Weekly seasonality requires at least 4 weeks for Anodot to calculate the weekly seasonality of a metric.

Note Not all signal types have seasonality. Seasonality detection is supported only for regular or near regular signals.

TIME TO DETECT FIRST ANOMALIES

The baseline must be created before Anodot can start detecting new metrics.  Anomalies in new metrics receive a lower significance score for the first few days. The times to detect anomalies in a new metric by time scale are:

Time scale

Times to detect first anomalies (in new metrics)

1 minute

1-2 days

5 minutes

2 days

1 hour

4 days

1 day

14 days

1 week

8 weeks

TIME TO CLOSE AN ANOMALY

An anomaly will start on the first data point that is outside the baseline. To close an anomaly, Anodot needs to have several data points within the baseline. The time it takes to close anomalies by time scale are:

Time Scale

Number of Data Points Required Within the Baseline to Close an Anomaly

1 minute

3

5 minutes

3

1 hour

2

1 day

2

1 week

1

Note - Open alerts that do not receive data for 4 days, are closed on the fourth day.

TIME TO RESUME LEARNING

Whenever Anodot detects an anomaly, the AI stops learning normal behavior. This to prevent learning an anomaly's behavior too soon as this may affect the baseline too early, thus generating false positive alerts later on. The time it takes Anodot to resume learning depends on the time scale.

The learning resume-times by time scale are:

Time Scale

Time to Resume Learning

1 minute

Resume learning after 2.5 hours since the anomaly started

5 minutes

Resume learning after 3 hours since the anomaly started

1 hour

Resume learning after 5 hours since the anomaly started

1 day

Resume learning after 72 hours since the anomaly started

1 week

Resume learning after 3 weeks since the anomaly started

ALERTS SIMULATION LIMITS

The minimum amount of data points required to run simulations. The maximum number of points over which a simulation can be run is 5 million data points.

Time Scale

Data Points 

Time Period

1 minute*

7200[1] 

5 days[1]

5 minutes

1440[2]

5 days[2]

1 hour

96

4 days

1 day

4

4 days

1 week

4

4 weeks

[1] For 1 minute interval, the number of data points may be reduced to 3600 data points for periods
longer than 10 days to allow for simulation when metrics are sparse.
[2]  For 5 minutes interval, the number of data points may be reduced to 760 data points for periods
longer than 10 days to allow for simulation when metrics are sparse.

OTHER DEFAULTS

  • Metrics limit -  The number of live, unique metrics are based on a customer’s contract.
    Anodot will ignore data points if this limit is breached. Either reduce the number of metrics or increase your contract metrics quota.
  • EPS limit - The number of data points, calculated over a period of 1 minute.
    For example: the limit of 6K EPS is 360,000 points in one minute.
    Customer-default is 2K EPS; this will be increased based on the number of unique metrics package. 
    The same applies to the events API limit.
  • Samples in a single request - A single API request may contain up to 10,000 samples. Larger requests should be divided in to multiple, smaller requests. Anodot recommends sending up to 1000 data points per request. If more than 1000 data points are needed (up to 10,000), the request must meet the EPS limit.
  • # of Concurrent Data Connections - the maximum between 100 and the # of metrics/1,000 (i.e. for 1M metrics, 1,000 connections).
  • Tags limit - Tags can be associated to up to 500K metrics in one API call.
  • Composite limits - Composite can compose by default up to 80K unique metrics. This can be increased up to 100K unique metrics in a composite.
  • Number of metrics in a single alert - The number of unique metrics in a single alert setting (without composite) is 150K unique metrics.
  • Number of configured alerts - the total number of alerts must not exceed 1000 alerts.
  • Max correlated alerts in one Anomaly - 2000 metrics can be correlated in one Anomaly.
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