API¶
DP³'s has HTTP API which you can use to post datapoints and to read data stored in DP³.
As the API is made using FastAPI, there is also an interactive documentation available at /docs
endpoint.
There are several API endpoints:
GET /
: check if API is running (just returnsIt works!
message)POST /datapoints
: insert datapoints into DP³GET /entity/<entity_type>
: list current snapshots of all entities of given typeGET /entity/<entity_type>/get
: get current snapshots of entities of entity typeGET /entity/<entity_type>/count
: get total document count for query of entity typeGET /entity/<entity_type>/<entity_id>
: get data of entity with given entity idGET /entity/<entity_type>/<entity_id>/get/<attr_id>
: get attribute valueGET /entity/<entity_type>/<entity_id>/set/<attr_id>
: set attribute valueGET /entity/<entity_type>/_/distinct/<attr_id>
: get distinct attribute values and their counts based on latest snapshotsDELETE /entity/<entity_type>/<entity_id>
: delete entity data for given idPOST /entity/<entity_type>/<entity_id>/ttl
: extend TTLs of the specified entityGET /entities
: list entity configurationGET /control/<action>
: send a pre-defined action into execution queue.GET /telemetry/sources_validity
: get information about the validity of the data sources
Index¶
Health check.
Request¶
GET /
Response¶
200 OK
:
{
"detail": "It works!"
}
Insert datapoints¶
Request¶
POST /datapoints
All data are written to DP³ in the form of datapoints. A datapoint sets a value of a given attribute of given entity.
It is a JSON-encoded object with the set of keys defined in the table below. Presence of some keys depends on the primary type of the attribute (plain/observations/timseries).
Payload to this endpoint is JSON array of datapoints. For example:
Key | Description | Data-type | Required? | Plain | Observations | Timeseries |
---|---|---|---|---|---|---|
type |
Entity type | string | mandatory | ✔ | ✔ | ✔ |
id |
Entity identification | string | mandatory | ✔ | ✔ | ✔ |
attr |
Attribute name | string | mandatory | ✔ | ✔ | ✔ |
v |
The value to set, depends on attr. type and data-type, see below | -- | mandatory | ✔ | ✔ | ✔ |
t1 |
Start time of the observation interval | string (RFC 3339 format) | mandatory | -- | ✔ | ✔ |
t2 |
End time of the observation interval | string (RFC 3339 format) | optional, default=t1 |
-- | ✔ | ✔ |
c |
Confidence | float (0.0-1.0) | optional, default=1.0 | -- | ✔ | ✔ |
src |
Identification of the information source | string | optional, default="" | ✔ | ✔ | ✔ |
More details depends on the particular type of the attribute.
Examples of datapoints¶
Plain¶
Observations¶
{
"type": "ip",
"id": "192.168.0.1",
"attr": "open_ports",
"v": [22, 80, 443],
"t1": "2022-08-01T12:00:00",
"t2": "2022-08-01T12:10:00",
"src": "open_ports_module"
}
Timeseries¶
regular
:
{
...
"t1": "2022-08-01T12:00:00",
"t2": "2022-08-01T12:20:00", // assuming time_step = 5 min
"v": {
"a": [1, 3, 0, 2]
}
}
irregular
: timestamps must always be present
{
...
"t1": "2022-08-01T12:00:00",
"t2": "2022-08-01T12:05:00",
"v": {
"time": ["2022-08-01T12:00:00", "2022-08-01T12:01:10", "2022-08-01T12:01:15", "2022-08-01T12:03:30"],
"x": [0.5, 0.8, 1.2, 0.7],
"y": [-1, 3, 0, 0]
}
}
irregular_interval
:
{
...
"t1": "2022-08-01T12:00:00",
"t2": "2022-08-01T12:05:00",
"v": {
"time_first": ["2022-08-01T12:00:00", "2022-08-01T12:01:10", "2022-08-01T12:01:15", "2022-08-01T12:03:30"],
"time_last": ["2022-08-01T12:01:00", "2022-08-01T12:01:15", "2022-08-01T12:03:00", "2022-08-01T12:03:40"],
"x": [0.5, 0.8, 1.2, 0.7],
"y": [-1, 3, 0, 0]
}
}
Relations¶
Can be represented using both plain attributes and observations. The difference will be only in time specification. Two examples using observations:
no data - link<mac>
: Sent as a dictionary with a single "eid"
key.
{
"type": "ip",
"id": "192.168.0.1",
"attr": "mac_addrs",
"v": {"eid": "AA:AA:AA:AA:AA"},
"t1": "2022-08-01T12:00:00",
"t2": "2022-08-01T12:10:00"
}
with additional data - link<ip, int>
: Sent as a dictionary with "eid"
and "data"
keys.
{
"type": "ip",
"id": "192.168.0.1",
"attr": "ip_dep",
"v": {"eid": "192.168.0.2", "data": 22},
"t1": "2022-08-01T12:00:00",
"t2": "2022-08-01T12:10:00"
}
Response¶
200 OK
:
400 Bad request
:
Returns some validation error message, for example:
1 validation error for DataPointObservations_some_field
v -> some_embedded_dict_field
field required (type=value_error.missing)
List entities¶
Deprecated
This endpoint is deprecated and will be removed in the future,
Use GET /entity/<entity_type>/get
to get paged documents and
GET /entity/<entity_type>/count
to get total document count for query.
List latest snapshots of all ids present in database under entity type,
filtered by generic_filter
and fulltext_filters
.
Contains only the latest snapshot per entity.
Counts all results for given query.
Request¶
GET /entity/<entity_type>
Optional query parameters:
- skip: how many entities to skip (default: 0)
- limit: how many entities to return (default: 20)
- fulltext_filters: dictionary of fulltext filters (default: no filters)
- generic_filter: dictionary of generic filters (default: no filters)
Response¶
Get entities¶
Get a list of latest snapshots of all ids present in database under entity type,
filtered by generic_filter
and fulltext_filters
.
Contains only the latest snapshot per entity.
Uses pagination, default limit is 20, setting to 0 will return all results.
Fulltext filters are interpreted as regular expressions.
Only string values may be filtered this way. There's no validation that queried attribute
can be fulltext filtered.
Only plain and observation attributes with string-based data types can be queried.
Array and set data types are supported as well as long as they are not multi value
at the same time.
If you need to filter EIDs, use attribute eid
.
Generic filter allows filtering using generic MongoDB query (including $and
, $or
,$lt
, etc.).
For querying non-JSON-native types, you can use the following magic strings,
as are defined by the search & replace magic
module.
There are no attribute name checks (may be added in the future).
Generic and fulltext filters are merged - fulltext overrides conflicting keys.
Request¶
GET /entity/<entity_type>
Optional query parameters:
- skip: how many entities to skip (default: 0)
- limit: how many entities to return (default: 20)
- fulltext_filters: dictionary of fulltext filters (default: no filters)
- generic_filter: dictionary of generic filters (default: no filters)
Response¶
Count entities¶
Count latest snapshots of all ids present in database under entity type,
filtered by generic_filter
and fulltext_filters
.
See GET /entity/<entity_type>/get
for details on filter format.
Request¶
GET /entity/<entity_type>/count
Optional query parameters:
- fulltext_filters: dictionary of fulltext filters (default: no filters)
- generic_filter: dictionary of generic filters (default: no filters)
Response¶
Get Eid data¶
Get data of entity type's eid.
Contains all snapshots and master record. Snapshots are ordered by ascending creation time.
Request¶
GET /entity/<entity_type>/<entity_id>
Optional query parameters:
- date_from: date-time string
- date_to: date-time string
Response¶
Get attr value¶
Get attribute value
Value is either of:
- current value: in case of plain attribute
- current value and history: in case of observation attribute
- history: in case of timeseries attribute
Request¶
GET /entity/<entity_type>/<entity_id>/get/<attr_id>
Optional query parameters:
- date_from: date-time string
- date_to: date-time string
Response¶
Set attr value¶
Set current value of attribute
Internally just creates datapoint for specified attribute and value.
This endpoint is meant for editable
plain attributes -- for direct user edit on DP3 web UI.
Request¶
POST /entity/<entity_type>/<entity_id>/set/<attr_id>
Required request body:
Response¶
Get distinct values¶
Gets distinct attribute values and their counts based on latest snapshots
Useful for displaying <select>
enumeration fields.
Works for all plain and observation data types except dict
and json
.
Request¶
GET /entity/<entity_type>/_/distinct/<attr_id>
Response¶
Delete Eid data¶
Delete master record and snapshots with the specified etype
and eid
.
Raw datapoints are not deleted,
and the entity can be restored by sending new datapoints with the same etype
and eid
.
Request¶
DELETE /entity/<entity_type>/<entity_id>
Response¶
Extend TTLs¶
Extend TTLs of the specified entity.
Raw datapoints are not deleted,
and the entity can be restored by sending new datapoints with the same etype
and eid
.
Request¶
POST /entity/<entity_type>/<entity_id>/ttl
The request body must be a dictionary of TTLs to extend, with string keys to identify the type of TTL. The values must be UTC timestamps, for example:
TTLs of the same name will be extended, and you add as many TTL names as you want.
Response¶
Entities¶
List entity types
Returns dictionary containing all entity types configured -- their simplified configuration and current state information.
Request¶
GET /entities
Response¶
{
"<entity_id>": {
"id": "<entity_id>",
"id_data_type": "<entity_spec.id_data_type>",
"name": "<entity_spec.name>",
"attribs": "<MODEL_SPEC.attribs(e_id)>",
"eid_estimate_count": "<DB.estimate_count_eids(e_id)>"
},
...
}
Control¶
Execute Action - Sends the given action into execution queue.
You can see the enabled actions in /config/control.yml
, available are:
make_snapshots
- Makes an out-of-order snapshot of all entitiesrefresh_on_entity_creation
- Re-runs theon_entity_creation
callback for selectedetype
refresh_module_config
- Re-runs theload_config
for selected module and will refresh the values derived by the module when configured to do so
You can learn more about the actions in the Actions section of the Control
configuration documentation.
Request¶
GET /control/<action>
Response¶
Telemetry¶
Returns information about the validity of the data sources, i.e. when the last datapoint was received from each source.
Request¶
GET /telemetry/sources_validity