Data Aggregator¶
Class: DataAggregatorBlockV1
Source: inference.core.workflows.core_steps.analytics.data_aggregator.v1.DataAggregatorBlockV1
The Data Aggregator block collects and processes data from Workflows to generate time-based statistical summaries. It allows users to define custom aggregation strategies over specified intervals, making it suitable for creating analytics on data streams.
The block enables:
-
feeding it with data from other Workflow blocks and applying in-place operations (for instance to extract desired values out of model predictions)
-
using multiple aggregation modes, including
sum
,avg
,max
,min
,count
and others -
specifying aggregation interval flexibly
Feeding Data Aggregator¶
You can specify the data to aggregate by referencing input sources using the data
field. Optionally,
for each specified data
input you can apply chain of UQL operations with data_operations
property.
For example, the following configuration:
data = {
"predictions_model_a": "$steps.model_a.predictions",
"predictions_model_b": "$steps.model_b.predictions",
}
data_operations = {
"predictions_model_a": [
{"type": "DetectionsPropertyExtract", "property_name": "class_name"}
],
"predictions_model_b": [{"type": "SequenceLength"}]
}
on each step run will at first take predictions_model_a
to extract list of detected classes
and calculate the number of predicted bounding boxes for predictions_model_b
.
Specifying data aggregations¶
For each input data referenced by data
property you can specify list of aggregation operations, that
include:
-
sum
: Taking the sum of values (requires data to be numeric) -
avg
: Taking the average of values (requires data to be numeric) -
max
: Taking the max of values (requires data to be numeric) -
min
: Taking the min of values (requires data to be numeric) -
count
: Counting the values - if provided value is list - operation will add length of the list into aggregated state -
distinct
: deduplication of encountered values - providing list of unique values in the output. If aggregation data is list - operation will add each element of the list into aggregated state. -
count_distinct
: counting occurrences of distinct values - providing number of different values that were encountered. If aggregation data is list - operation will add each element of the list into aggregated state. -
count_distinct
: counting distinct values - providing number of different values that were encountered. If aggregation data is list - operation will add each element of the list into aggregated state. -
values_counts
: counting occurrences of each distinct value - providing dictionary mapping each unique value encountered into the number of observations. If aggregation data is list - operation will add each element of the list into aggregated state. -
values_difference
: calculates the difference between max and min observed value (requires data to be numeric)
If we take the data
and data_operations
from the example above and specify aggregation_mode
in the following way:
aggregation_mode = {
"predictions_model_a": ["distinct", "count_distinct"],
"predictions_model_b": ["avg"],
}
Our aggregation report will contain the following values:
{
"predictions_model_a_distinct": ["car", "person", "dog"],
"predictions_model_a_count_distinct": {"car": 378, "person": 128, "dog": 37},
"predictions_model_b_avg": 7.35,
}
where:
-
predictions_model_a_distinct
provides distinct classes predicted by model A in aggregation window -
predictions_model_a_count_distinct
provides number of classes instances predicted by model A in aggregation window -
predictions_model_b_avg
provides average number of bounding boxes predicted by model B in aggregation window
Interval nature of the block¶
Block behaviour is dictated by internal 'clock'
Behaviour of this block differs from other, more classical blocks which output the data for each input. Data Aggregator block maintains its internal state that dictates when the data will be produced, flushing internal aggregation state of the block.
You can expect that most of the times, once fed with data, the block will produce empty outputs, effectively terminating downstream processing:
--- input_batch[0] ----> ┌───────────────────────┐ ----> <Empty>
--- input_batch[1] ----> │ │ ----> <Empty>
... │ Data Aggregator │ ----> <Empty>
... │ │ ----> <Empty>
--- input_batch[n] ----> └───────────────────────┘ ----> <Empty>
But once for a while, the block will yield aggregated data and flush its internal state:
--- input_batch[0] ----> ┌───────────────────────┐ ----> <Empty>
--- input_batch[1] ----> │ │ ----> <Empty>
... │ Data Aggregator │ ----> {<aggregated_report>}
... │ │ ----> <Empty> # first datapoint added to new state
--- input_batch[n] ----> └───────────────────────┘ ----> <Empty>
Setting the aggregation interval is possible with interval
and interval_unit
property.
interval
specifies the length of aggregation window and interval_unit
bounds the interval
value
into units. You can specify the interval based on:
-
time elapse: using
["seconds", "minutes", "hours"]
asinterval_unit
will make the Data Aggregator to yield the aggregated report based on time that elapsed since last report was released - this setting is relevant for processing of video streams. -
number of runs: using
runs
asinterval_unit
- this setting is relevant for processing of video files, as in this context wall-clock time elapse is not the proper way of getting meaningful reports.
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/data_aggregator@v1
to add the block as
as step in your workflow.
Properties¶
Name | Type | Description | Refs |
---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
data_operations |
Dict[str, List[Union[ClassificationPropertyExtract, ConvertDictionaryToJSON, ConvertImageToBase64, ConvertImageToJPEG, DetectionsFilter, DetectionsOffset, DetectionsPropertyExtract, DetectionsRename, DetectionsSelection, DetectionsShift, DetectionsToDictionary, Divide, ExtractDetectionProperty, ExtractFrameMetadata, ExtractImageProperty, LookupTable, Multiply, NumberRound, NumericSequenceAggregate, PickDetectionsByParentClass, RandomNumber, SequenceAggregate, SequenceApply, SequenceElementsCount, SequenceLength, SequenceMap, SortDetections, StringMatches, StringSubSequence, StringToLowerCase, StringToUpperCase, TimestampToISOFormat, ToBoolean, ToNumber, ToString]]] |
UQL definitions of operations to be performed on defined data w.r.t. element of the data. | ❌ |
aggregation_mode |
Dict[str, List[str]] |
Lists of aggregation operations to apply on each input data. | ❌ |
interval_unit |
str |
Unit to measure interval . |
❌ |
interval |
int |
Length of aggregation interval. | ❌ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow
runtime. See Bindings for more info.
Available Connections¶
Compatible Blocks
Check what blocks you can connect to Data Aggregator
in version v1
.
- inputs:
Anthropic Claude
,Triangle Visualization
,Distance Measurement
,Roboflow Dataset Upload
,Absolute Static Crop
,Segment Anything 2 Model
,QR Code Detection
,SIFT
,Identify Outliers
,Byte Tracker
,Detections Transformation
,Detections Stabilizer
,Keypoint Visualization
,VLM as Detector
,SIFT Comparison
,Image Convert Grayscale
,PTZ Tracking (ONVIF)
,Multi-Label Classification Model
,Model Comparison Visualization
,Perception Encoder Embedding Model
,Dynamic Zone
,Path Deviation
,JSON Parser
,Roboflow Custom Metadata
,Image Slicer
,Stitch OCR Detections
,Crop Visualization
,Corner Visualization
,Multi-Label Classification Model
,Pixelate Visualization
,Velocity
,Overlap Filter
,Mask Visualization
,Clip Comparison
,Continue If
,Keypoint Detection Model
,Data Aggregator
,Moondream2
,Bounding Rectangle
,Detections Filter
,Bounding Box Visualization
,Barcode Detection
,Camera Focus
,CogVLM
,Instance Segmentation Model
,Dynamic Crop
,Halo Visualization
,Cosine Similarity
,Florence-2 Model
,Time in Zone
,Florence-2 Model
,VLM as Detector
,LMM For Classification
,Ellipse Visualization
,First Non Empty Or Default
,Llama 3.2 Vision
,Line Counter
,Clip Comparison
,Single-Label Classification Model
,Stitch Images
,Environment Secrets Store
,OCR Model
,Property Definition
,Object Detection Model
,Byte Tracker
,Blur Visualization
,Dominant Color
,YOLO-World Model
,Trace Visualization
,LMM
,Label Visualization
,Model Monitoring Inference Aggregator
,Time in Zone
,Pixel Color Count
,Rate Limiter
,Image Preprocessing
,Relative Static Crop
,Byte Tracker
,Keypoint Detection Model
,Image Threshold
,Reference Path Visualization
,Slack Notification
,Stability AI Outpainting
,Instance Segmentation Model
,Roboflow Dataset Upload
,Detection Offset
,Google Vision OCR
,Dimension Collapse
,Stability AI Inpainting
,Background Color Visualization
,CSV Formatter
,Circle Visualization
,Image Blur
,Google Gemini
,OpenAI
,Line Counter Visualization
,Expression
,Line Counter
,VLM as Classifier
,Local File Sink
,Image Slicer
,Cache Set
,VLM as Classifier
,Color Visualization
,Qwen2.5-VL
,Polygon Visualization
,Email Notification
,Size Measurement
,Single-Label Classification Model
,Gaze Detection
,Perspective Correction
,Detections Consensus
,Path Deviation
,Camera Calibration
,OpenAI
,Detections Merge
,Delta Filter
,Buffer
,SmolVLM2
,Detections Stitch
,Twilio SMS Notification
,OpenAI
,Detections Classes Replacement
,Depth Estimation
,Dot Visualization
,Template Matching
,Classification Label Visualization
,Webhook Sink
,SIFT Comparison
,Stability AI Image Generation
,Object Detection Model
,Image Contours
,Identify Changes
,Grid Visualization
,CLIP Embedding Model
,Cache Get
,Polygon Zone Visualization
- outputs:
Anthropic Claude
,Triangle Visualization
,Distance Measurement
,Roboflow Dataset Upload
,Absolute Static Crop
,Segment Anything 2 Model
,QR Code Detection
,SIFT
,Identify Outliers
,Byte Tracker
,Detections Transformation
,Detections Stabilizer
,Keypoint Visualization
,VLM as Detector
,SIFT Comparison
,Image Convert Grayscale
,PTZ Tracking (ONVIF)
,Multi-Label Classification Model
,Model Comparison Visualization
,Perception Encoder Embedding Model
,Dynamic Zone
,Path Deviation
,JSON Parser
,Roboflow Custom Metadata
,Image Slicer
,Stitch OCR Detections
,Crop Visualization
,Corner Visualization
,Multi-Label Classification Model
,Pixelate Visualization
,Overlap Filter
,Velocity
,Mask Visualization
,Clip Comparison
,Continue If
,Keypoint Detection Model
,Data Aggregator
,Moondream2
,Bounding Rectangle
,Detections Filter
,Bounding Box Visualization
,Barcode Detection
,Camera Focus
,CogVLM
,Instance Segmentation Model
,Dynamic Crop
,Halo Visualization
,Florence-2 Model
,Time in Zone
,Florence-2 Model
,VLM as Detector
,LMM For Classification
,Ellipse Visualization
,First Non Empty Or Default
,Llama 3.2 Vision
,Line Counter
,Clip Comparison
,Single-Label Classification Model
,Stitch Images
,OCR Model
,Byte Tracker
,Object Detection Model
,Property Definition
,Blur Visualization
,Dominant Color
,YOLO-World Model
,Trace Visualization
,Label Visualization
,LMM
,Model Monitoring Inference Aggregator
,Time in Zone
,Pixel Color Count
,Rate Limiter
,Image Preprocessing
,Keypoint Detection Model
,Byte Tracker
,Relative Static Crop
,Image Threshold
,Reference Path Visualization
,Slack Notification
,Stability AI Outpainting
,Instance Segmentation Model
,Roboflow Dataset Upload
,Detection Offset
,Google Vision OCR
,Dimension Collapse
,CLIP Embedding Model
,Stability AI Inpainting
,Background Color Visualization
,CSV Formatter
,Circle Visualization
,Image Blur
,Google Gemini
,OpenAI
,Line Counter Visualization
,Expression
,Line Counter
,VLM as Classifier
,Local File Sink
,Image Slicer
,Cache Set
,VLM as Classifier
,Color Visualization
,Qwen2.5-VL
,Polygon Visualization
,Email Notification
,Single-Label Classification Model
,Size Measurement
,Gaze Detection
,Perspective Correction
,Detections Consensus
,Path Deviation
,Camera Calibration
,OpenAI
,Detections Merge
,Delta Filter
,Buffer
,SmolVLM2
,Detections Stitch
,Twilio SMS Notification
,OpenAI
,Detections Classes Replacement
,Depth Estimation
,Dot Visualization
,Template Matching
,Classification Label Visualization
,Webhook Sink
,SIFT Comparison
,Stability AI Image Generation
,Object Detection Model
,Image Contours
,Identify Changes
,Grid Visualization
,Cosine Similarity
,Cache Get
,Polygon Zone Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Data Aggregator
in version v1
has.
Bindings
Example JSON definition of step Data Aggregator
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/data_aggregator@v1",
"data": {
"predictions": "$steps.model.predictions",
"reference": "$inputs.reference_class_names"
},
"data_operations": {
"predictions": [
{
"property_name": "class_name",
"type": "DetectionsPropertyExtract"
}
]
},
"aggregation_mode": {
"predictions": [
"distinct",
"count_distinct"
]
},
"interval_unit": "seconds",
"interval": 10
}